research article hybrid swarm intelligence energy...

Research ArticleHybrid Swarm Intelligence Energy Efficient ClusteredRouting Algorithm for Wireless Sensor Networks

Rajeev Kumar1 and Dilip Kumar2

1Punjab Technical University Jalandhar 144001 India2Department of Electronics and Communication Engineering SLIET Longowal 148106 India

Correspondence should be addressed to Dilip Kumar dilipk78gmailcom

Received 3 July 2015 Revised 19 August 2015 Accepted 26 August 2015

Academic Editor Pietro Siciliano

Copyright copy 2016 R Kumar and D Kumar This is an open access article distributed under the Creative Commons AttributionLicense which permits unrestricted use distribution and reproduction in any medium provided the original work is properlycited

Currently wireless sensor networks (WSNs) are used inmany applications namely environmentmonitoring disastermanagementindustrial automation and medical electronics Sensor nodes carry many limitations like low battery life small memory spaceand limited computing capability To create a wireless sensor network more energy efficient swarm intelligence technique hasbeen applied to resolve many optimization issues in WSNs In many existing clustering techniques an artificial bee colony (ABC)algorithm is utilized to collect information from the field periodically Nevertheless in the event based applications an ant colonyoptimization (ACO) is a good solution to enhance the network lifespan In this paper we combine both algorithms (ie ABC andACO) and propose a new hybrid ABCACO algorithm to solve a Nondeterministic Polynomial (NP) hard and finite problem ofWSNs ABCACO algorithm is divided into three main parts (i) selection of optimal number of subregions and further subregionparts (ii) cluster head selection using ABC algorithm and (iii) efficient data transmission using ACO algorithm We use ahierarchical clustering technique for data transmission the data is transmitted frommember nodes to the subcluster heads and thenfrom subcluster heads to the elected cluster heads based on some threshold value Cluster heads use an ACO algorithm to discoverthe best route for data transmission to the base station (BS) The proposed approach is very useful in designing the framework forforest fire detection and monitoring The simulation results show that the ABCACO algorithm enhances the stability period by60 and also improves the goodput by 31 against LEACH and WSNCABC respectively

1 Introduction

Wireless sensor networks (WSNs) consist of various sensornodes which have the capability to sense process computeand communicate [1] A large number of sensor nodes andtheir different applications induce various hindrances duringimplementation in sensor networks Other than implemen-tation certain areas namely accessing of information datacollection and storage are still a dilemma Various specifi-cations of sensor nodes like their size computational abilitycost hardware constraint energy efficiency and other designparameters make the implementation of the sensor networka difficult task In present scenario optimization issuesbecome more tedious An energy specification of sensornetworks becomes intolerable in a huge environment whichin turn demands a concern related to various optimization

parameters That is why we formulated to assess the potencyof various evolutionary algorithms to enhance the networklifespan with energy as a constraint duly concentrating on thevarious transmission strategies [2] During a couple of yearsa number of evolutionary algorithms have been introducedfor evaluation of problems to get the best possible result outof the various solutions Many of the proposed algorithmsconcentrate on the principle of the population based heuristicsearch methods for solving general optimization problemsA swarm intelligent algorithm works on the principle behav-ior of flying birds in order to get to the resources beingimplemented in LEACH [3] protocol In these protocols theswarm intelligent algorithm is implemented to form variousclusters which in turn help in defining their respective CHswhile designing a WSN with the same conditions as in realtime networkThe swarmbased algorithmproduces desirable

Hindawi Publishing CorporationJournal of SensorsVolume 2016 Article ID 5836913 19 pageshttpdxdoiorg10115520165836913

2 Journal of Sensors

results Network lifespan can be further enhanced by imple-menting various best possible route detection techniquesArtificial bee colony (ABC) algorithm is a new upcomingapproach whose working totally resembles that of swarmbased optimization techniques while monitoring the clustersin WSN

The most unavoidable hindrances faced in the sensornetworks are about getting the best possible routes for datatransmission up to the BS In general WSN node operatesin a multinode pattern immense algorithms have beenintroduced for routing data to reach the destination In [4]somenotable routing techniques inWSNdesire to use ant as amoving agent while discovering the route in order to discoverthe best possible path for data transmission in WSN In thisresearch we prioritize the multiple nodes grounded in theirrespective power competency and there approaching natureto route the data through the best path In recent years a newpicture by applying routing based protocols on themovementof the swarms in order to depict the best possible path fordelivering the acquired data through the selected path wasintroduced Algorithm working with nodes should be moreenergy saver as well as fast enough to acquire and transmitthe data Ant colony optimization (ACO) designed in sucha way so that ants behaviors for finding food sources canbe depicted easily acts as a valuable solution for differentroute detection although this technique is not favourable ingoverning applications which needs timely data transfer [5]

For upgrading the execution of the network clusteringmechanisms have been connected to networks with hierar-chical structures while minimizing the fundamental energyutilization [6 7] Clustering is a technique to groupnumber ofnodes in areas in a densely employed wide-scale network [8]One of the nodes will be elected as a cluster head (CH) amongother nodes called clustermembers of their respective clusterIn clustering randomly deployed nodes join in carrying outthe job Here the signal to the nodes is advertised withnodes Signals are received by all sensor nodes from distinctCHs and nodes give response with their node-id to CH withstrength and this formation of clusters takes place WithoutCH for intracluster routing a proactive strategy is utilizedand for intercluster routing a reactive strategy is utilizedClustering has various challenges in deployment such asensuring connectivity between nodes determining optimalcluster sizes [9 10] and optimizing clustering structuresdynamically on the basis of the status of cluster members Fora hierarchical routing or network administration conventioncan be better executed with CHs In [11 12] the authors havestudied the influence of cluster based hierarchical routingthat is a quite famous approach with some benefits related towell organized communication and scalability For attainingpower efficient routing the concept of hierarchical routingis used Using two-tier clusters for sending information toBS has the profit of shorter transmit separations for nodesOnly some nodes are essential to send data to BS at fardistances and it is valid in those networks where there is ahigh density of nods Through hierarchical level cluster cancommunicate with other clusters or with external network atthe same time [13] In this paper we have introduced three-level hierarchy The first level is known as CH the second

level is known as subcluster head (SCH) and the third levelis denoted by member node Partial local computation isintroduced in each and every SCH at the second stage andthe local computation at CH where information is sent toBS directly This approach is used as regions and subregionsand significant progress is obtained The objectives of thesubregions and subregion parts are formulated as in

Maximum (119879SSC) = Minimum sum119894119895isinTee

119864Tee (1)

where 119879SSC is the work time of the square shaped cluster 119864Teeis the total energy expenditure in a cluster

The rest of thework is done in seven sections In Section 2we discuss the related work In Section 3 we define the radiomodel for wireless sensor network Section 4 describes thesquared optimization problem for CH and SCH Section 5exhibits the details of ABC ACO and the proposed approachABCACO In Section 6 the performances of ABCACOapproach are evaluated via a number of experiments andwe compare its simulation results with both WSNCABC andLEACH Finally Section 7 concludes the paper and suggeststhe further work to be carried out

2 Related Works

In literature various protocols have been considered withthe idea of enhancing the lifespan of sensor network bythe appropriation of cluster based sensor network structuralengineering A well-known protocol known as LEACH (LowEnergy Adaptive Clustering Hierarchy) [3] is based on dis-tributed clustering approach It prolongs the network lifespanby reducing the power dissipation No wastage of powerfor electing a node as a CH is the advantage of LEACHprotocol The CH selection process is primarily based uponthe inducted energy level of each sensor node that is thedisadvantage of LEACH So a sensor node that has low powerhas more chances of becoming a CH than the one thathas more power which leads to the dead state of the entirecluster The second disadvantage is the presence of moreoverheads in the development of the dynamic cluster In [9]the results have demonstrated that LEACH-C outperformsLEACH because of the improvement in cluster formation bythe BS Furthermore the fraction of CHs in each iteration ofLEACH-C is equivalent to the craved optimum number Anadditional clustering algorithm that enhances the lifespan ofnetwork has been presented in [14] known as HEED (HybridEnergy Efficient Distributed Clustering) The CH electionprocess in HEED is intentionally based upon the parametersthat is residual energy of the sensor nodes and in additionto this cost of communication within the clusters is alsoconsidered by the authors for increasing power efficiencyand further enhancing lifespan of network This protocolmaintains the CH for a fixed number of cycles Relatedto LEACH significant overhead is imposed by the sensornetwork by clustering in each round Noticeable power dis-sipation is imposed by this overhead that leads to decrease inlifespan of the network As HEED requires various iterationsfor the formation of clusters it suffers from a subsequent

Journal of Sensors 3

overhead as at each iteration the broadcasting of a lot ofpackets takes place Power Efficient Gathering in SensorInformation Systems (PEGASIS) has been developed in [15]for prolonging the network lifespan PEGASIS outperformsLEACH in terms of power dissipation for extending thenetwork lifespan It is not a clustering approach It is achain based protocol due to which all nodes interact withone another and can transmit data to a CH Sensor nodesdie in arbitrary locations in PEGASIS as a selection of CHshas been done without concerning the overall lifespan ofeach sensor node Delay in transmitting time bounds dataover a chain of sensor nodes is the main disadvantage ofPEGASIS Optimal routing may not be present as it is agreedy algorithm that selects the shortest path rather thanthe optimal one and there is just simply CH that may turnout to be a bottleneck when a lot of data is gathered atthis sensor node In [4] it is suggested in the literaturethat various power conserving methods are presented thattend to prolong the lifespan of the network The authorexplained a converged power efficient cluster based protocolthat is considered to prolong the lifespan of the networkusing a swarm intelligence mechanism called artificial beecolony (ABC) In [16] the authors introduce the EEABRprotocol which is in view of the ACO heuristic centred onthe standardWSN requirementsThey propose two redesignsin the AntNet routing algorithm for reducing the memoryutilized as a piece of sensor nodes as well as dealing withthe energy constraints of the routes discovered by the mobileagents In this paper the author has presented the enactedenergy level of sensor nodes and their routing distancewith the ACO probability equation by adjusting the routingconventions inWSN Likewise the algorithm considered dataaggregation yet did not consider the adjusted utilization ofenergy in the entire networks The protocol exhibited in [17]introduced improved information driven routing protocol ofWSN based upon the ant including the search ant so as toprovide past data to the neighbourhood ants It describeda ldquoretryrdquo principle so that the protocol does not come to ahalt In this the communication cost known as the Euclideandistance between the two vertexes is considered such thatthey can correspond with one another specifically Takinginto account this expense it builds a weighted graph byconsuming three types of ants namely chasing ants foragersand trailing ants Chasing ants are specifically used to makethe forward ants searching the sink (the destination node) ina scalablemanner In the proposed protocol the source nodeswere only 25 of the total nodes No framework lifespanutilizing this algorithm was as a part of the paper In [4] theauthors have met the prerequisite of WSN by adjusting antcolony and presenting information on a chip based routingcomponent for streamlining proficiency however it has beenconnected to the planar routing In [18] the authors areconcerned with the problem of multiagent path planning indiscrete-time pursuit-evasion games (PEGs) They proposedthe improved strategy that is I-ACO algorithms whichinclude the designed direction factor blocking rule andsmoothening rule Other papers for example [19 20] haveused ACO techniques to handle various routing issues inWSN but none of them have outperformedwhile considering

cluster based ad hoc sensor networks In this paper we haveapplied the ABC and ACO to propose a cluster based routingplan for WSNs

The fundamental thought in the proposed algorithm(Algorithm 1) is the choice of a CH and SCH that can reducethe distance between themselves and their neighbours withina cluster Each node is installed in such way that it hasequal distance from each of its neighbouring nodes and thisdistance is the same for every node of the networkThe squareshaped sensing field is divided into subregions and thensubregions parts based on optimal clustering mechanismFinally among the divided regions the CH is selected usingthe ABC algorithm and then CHs use ACO algorithmwhich is an organically propelled standard for improvedmethodology to discover a best route to the BS

3 Radio Energy Model

Our energy model for the WSN is taking into considerationthe first-order radio model as reported in [9] In this first-order radio model a radio energy disseminated model isproposed in which transmitter chunks the energy whileoperating the radio electronics and power amplifier on theother hand recipient disseminates the energy while initiatingthe radio electronics The first-order model uses both freespace and multipath fading models considering the distancebetween the sender and the receiver If the considereddistance between the transmitter and receiver is less than1198890 then the free space model is implemented else multipath

fading will be applicableWith a specific end goal to send l-bit message over the

network both the transmitting and receiving energies will becalculated by the following equations respectively

119864Trans (119897 119889) = 119864Trans-elec (119897) + 119864Trans-amp (119897 119889)

=

119897119864elec + 119897119864fs1198892 119889 le 119889

0

119897119864elec + 119897119864mp1198894 119889 gt 119889

0

(2)

119864Rece (119897 119889) = 119864Rece-elec (119897) = 119897119864elec (3)

The CHs of the cluster expend their energy at three levelsthat is receiving energy to get the signals from their membernodes aggregation energy to aggregate the signals andtransmitting energy to transmit the signals to other CHsAdditionally we accept that distance between transmitterand recipient is more prominent than threshold So energyutilization of CH for a cycle is given by

119864CHcycle = 119897119864Rece-elec (119873

119896minus 1) +

119873

119896119897119864DataAgr

+ 119897119864Trans-elec + 119897120598amp119889119899

toBS

(4)

And member nodes of cluster transmit their acquired signalsto their respective CHs So the energy utilization by themember node is given by

119864mem-node = 119897119864elec + 119897120598fs119889119899

toCH (5)


Assumptions(i)119873 nodes are uniformly dispersed within a square field(ii) Each node has unique ID Nodes located in the event area are grouped into one cluster(iii) Nodes are location-aware and quasi-stationary(iv) A sensor can compute the approximate distance based on the received signal strength (RSS)

and the radio power can be controlled(v) A fixed base station can be located inside or outside the network sensor fields(vi) All nodes are capable of operating in cluster head mode and sensing mode(vii) Data fusion is used to reduce the total data message sentVariables(i) D is the dimension of the squared region(ii) Nodes are the number of nodes in the (119863 times 119863) region(iii) 119864

0is the Initial energy of the nodes

(iv) Alive Nodes is the number of nodes left after the successful execution of previous round(v) 119889 is the distance between the nodes(vi) 119889

0is the threshold distance value between the free space and multipath fading model

(vii) Mmp is the amplification energy(viii) Mfs is the free space energy consumption(ix) N is the set of sensor nodes(x) Nodes C is the nodes in sub-region which is under consideration for CH selection(xi) Energy is the residual energy of the nodes(xii) V

119894119895is list of possible nodes positions to become a sub-region CH computed using ABC algorithm

(xiii) CH s is the list of CHs in the roundSet up phaseStep 1 Take a region of119863 times 119863 (m2) with119873 number of nodesStep 2 Initialize the Nodes (119873) with initial Energy (119864

0)

Step 3 For each round Until Alive Nodes gt 0 thenRepeat Step 4 to Step 16function Alive Nodes (Nodes)

Total Alive = 0for each node 119894 = 1 to length (Nodes) do

if Energy [Nodes[119894]] gt 0 thenNODE STATUS[119894] = 1Total Alive = Total Alive + 1

ElseNODE STATUS[119894] = 0

end ifend forreturn Total Alive Returns Alive node countendfunction

Step 4 Compute the distance of each node from other nodes and the BS by using Euclidian distance formuladist = radic(119909

1minus 1199092)2 + (119910

1minus 1199102)2

where 1199091and 119909

2are 119909-coordinates of nodes and 119910

1and 119910

2are 119910-coordinates of the nodes

Step 5 Compute119870opt for optimal number of sub regions that can be formed in given region119863 times 119863 (m2)and divide the given region119863 times 119863 (m2) into 119896 sub regions based on 119870opt valuefunction Region Optimum Value NODES

119870opt = Nullif BSlocation is ldquoOutsiderdquo thenif (119889 le 119889

0) then

119870opt = radic4119873

5 + 12119870119863 + 1211987021198632

else

119870opt = radic60119873120598fs

(71198632 + 1201198712 + 18011987141198632) 120598mpendif

endifreturn 119870opt

endfunction

Algorithm 1 Continued


Step 6 For each sub region repeat Steps 7 and 8Step 7 Select CH for each sub region using ABC algorithm using function ABC Cluster HeadEnergy Nodes CWhich accepts two arguments (1) Nodes residual Energy and (2) Nodes in that particular sub regionfunction ABC Cluster HeadEnergy Nodes C

Fitness to be CH = Nullfor each node 119894 = 1 to length(Nodes C) doFitness to be CH = Fitness(Nodes C[119894] times ID BS)end for

Threshold Energy = NULLResidual Energy = NULL

for each node 119894 = 1 to length(Nodes C) doResidual Energy = Residual Energy +

Energy(Nodes C[119894])end for

Threshhold Energy =Residual Energylength(Nodes C)

for each node 119894 = 1 to length(Nodes C) doProbability Nodes to CH[119894] =

Fitness to be CH[119894]

sumlength(Nodes C)119895=1

Fitness to be CH[119895]end for

Threshold Probability = 1length(Nodes C) New Position Calculatingfor each node 119894 = 1 to length(Nodes C) do

if Probability Nodes to CH[119894] gtThreshold Probability then

rand ABC = Upper Bound ABC +(Lower Bound ABC-Upper Bound ABC) times randV119894119895= Fitness Node CH(Prev CH Node) minus rand ABC times (Fitness Node CH(Prev CH Node) minus

Fitness Node CH (Nodes C[119894]))end ifend forNode CH Id = Nodes C max(V

119894119895)

endfunctionStep 8 For each sub region repeat Steps 9 and 10Step 9 Divide the sub region into sub region parts based on 119870opt value for this sub regionfunction Sub Region Optimum ValueNODES

119870opt = Nullif CHlocation is at ldquoCentrerdquo then

if (119889 le 1198890) then

119870opt = radic2119873else

119870opt = radic60119873120598fs71198632120598mp

endifelse if CHlocation is at ldquoCornerrdquo then

if (119889 le 1198890) then

119870opt = radic119873

2else

119870opt = radic15119873120598fs281198632120598mp

endifelse if CHlocation is at ldquomid-point of the bottom siderdquo then

if (119889 le 1198890) then

119870opt = radic4119873

5



else

119870opt = radic240119873120598fs1931198632120598mp

endifend ifEndfunctionStep 10 Select the sub cluster head (SCH) for each sub region parts based on given fitness formula(ie residual energy and distance to sub region CH)[Each SCH is responsible to communicate with CH of that sub region]Step 11 Select optimal path for CH of all sub regions for data transmit to BS using ACO algorithmfunction ACO Optimal PathCH s BS

for each node 119894 = 1 to length(CH s) doFitness = Fitness(CH s[119894] BS)

end forfor each node 119894 = 1 to length(CH s) dofor each node 119895 = 1 to length(CH s) do

if 119894 = 119895 then

Prob119896(119901 119902) =

[120591 (119901 119902)]120572

[120578 (119901 119902)]120573

sum119902isin119877119902

[120591 (119901 119902)]120572

[120578 (119901 119902)]120573 if 119902 notin 119872119896

end ifend for

end forendfunction Steady state phaseStep 12 Nodes wake up and sensed dataStep 13 Node forwards sensed data to SCH using TDMA with (2)Step 14 SCH receives sensed data from nodes by using (3) and transmits aggregated data to CH using CDMA with (2)Step 15 CH aggregates the data receivsed from all SCHs and send to BS using CDMA through a next hop receiver(ie CH of other sub region) with (2)Step 16 For each region

if Node Energy lt 119864th then go to Step 3

Algorithm 1 Pseudocode of the proposed algorithm

where 119889toCH is the average length of themember node to theirCH

4 Squared Optimization Problem

In [3] the authors show that if the cluster formation is notdone in a scalable manner then the total energy cost of theentire network is maximized at an exponential rate eitherthe number of clusters is more as compared to the optimalnumber (119870opt) of clusters or the number of clusters is lessthan 119870opt If there are a smaller number of clusters then theconsiderable number of CHs has to send their informationfar which leads to a higher global energy cost in the systemFurthermore if there are a bigger number of clusters then lessdata aggregation is done locally and for monitoring the entiresensor field a large number of transmissions are neededWhen the uniform distribution of sensor nodes over thesensing field is done the optimal probability of a sensor nodebeing selected as aCHas a function of spatial density has beendiscussed either by simulation [8 9] or analytically [21 22]This is a feasible clustering in the sense that well distributionof energy utilization is done by overall nodes and the totalenergy utilization is less This optimal clustering highly relies

on the energy model Similar energy model and analysisare used for the purpose of the study as presented in [14]and determination of the optimal value of being 119870opt doneanalytically Cluster arrangement algorithm guarantees thatthe acknowledged number of clusters per cycle is equivalentto 119870opt Here sensor area is supposed to be a square shapedwith an area of A It is required by the optimization problemto calculate the predicted squared distance between membernodes and their CH [9 23] For extracting a closed-formexpression for119870opt accordingly various special cases are wellthought out for obtaining an explicit parametric formula [24]

Suppose the sensing region is a squared shape area Aover which 119873 number of nodes are uniformly deployed If119870opt clusters are present then on an average119873119870opt node percluster having single CH and rest cluster member nodes willexist While receiving signals from the sensors aggregatingsignals and sending aggregated data to the BS there issome dissipation of energy by each and every CH [9] Thusthe energy dissipated in the CH during a cycle with thesupposition that each and every cycle has one frame is (4) andthe energy utilized in a cluster member node is equivalent to(5) The area of each cluster is around 1198632119870opt Like in [25]the expected distances are computed from a cluster member


node to its CH and from a CH to the BS Because of theuniform distribution of CHs in a119863times119863 (m2) sensor area theexpected squared region enclosed by each cluster with theCHpositioned at (119909CH 119910CH) can be computed as given below

radic1198632

119870opttimes radic

1198632

119870opt (6)

where 119870opt is the optimum number of CHs The membernodes are also deployed regularly and independently in eachcluster where we have

119864 [119909mem-nodes] = 119864 [119909CH] = 119864 [119910mem-nodes]

= 119864 [119910CH] =1

2(radic

1198632

119870opt)

119864 [(119909mem-nodes)2] = 119864 [(119909CH)

2] = 119864 [(119910mem-nodes)

2]

= 119864 [(119910CH)2] =

1198632

3119870opt

(7)

In this manner the predicted squared length from membernodes to their CH within a cluster can be computed as

119864 [1198892toCH] = 119864 [(119909mem-nodes minus 119909CH)2]

+ 119864 [(119910mem-nodes minus 119910CH)2]

= 119864 [(119909mem-nodes)2]

minus 119864 [119909mem-nodes] 119864 [119909CH] + 119864 [(119909CH)2]

+ 119864 [(119910mem-nodes)2]


=1198632

3119870opt

(8)

By substituting the value of 1198892toCH from (8) in (5) the energyused by each member node per cycle is given by

119864mem-nodes = 119897119864Trans-elec +119897120598fs1198632

3119870opt (9)

The energy utilization in the whole cluster during a singlecycle can be communicated by using

119864cluster = 119864CHcycle + (119873

119870optminus 1)119864mem-nodes (10)

Hence 119864cycle can be computed as

119864cycle = 119870opt119864cluster = 119870opt ((119897119864Rece-elec (119873

119870optminus 1)

+ 119897119864DataAgr119873

119870opt+ 119864Trans-elec + 119897120598amp119889

119899

toBS)

+ ((119873

119870optminus 1)(119897119864Trans-elec +

119897120598fs1198632

3119870opt)))

= 119873119897119864Rece-elec minus 119870opt119897119864Rece-elec + 119873119897119864dataAgr

+ 119870opt119897119864Trans-elec + 119870opt119897120598amp119889119899

toBS + 119873119897119864Trans-elec

minus 119870opt119897119864Trans-elec +119873119897120598fs119863

2

3119870optminus119897120598fs1198632

3= 119873119897119864Rece-elec

minus 119870opt119897119864Rece-elec + 119873119897119864DataAgr + 119870opt119897120598amp119889119899

toBS

+ 119873119897119864Trans-elec +119873

119897120598fs11986323119870opt minus 3

119897

120598fs1198632

(11)

By selecting the feasible solution to the first derivative of (11)that is 119870opt the total consumed energy per cycle that is119864cycle can be minimized The first and second derivative of119864cycle with respect to 119870opt are

120597119864cycle

120597119870opt= 119897120598amp119889

119899

toBS minus 119897119864Rece-elec minus119873119897120598fs119863

2

31198702opt

1205972119864cycle

1205972119870opt=

2119873119897120598fs1198632

31198703optgt 0

(12)

By setting the first derivative with respect to optimal number119870opt to zero the optimum number of CHs that is 119870opt canbe calculated as

0 = 119897120598amp119889119899


2

31198702opt

119897120598amp119889119899

toBS = 119897119864Rece-elec +119873119897120598fs119863

2

31198702opt

31198702opt119897120598amp119889119899

toBS = 31198702opt119897119864Rece-elec + 119873119897120598fs1198632

31198702opt (119897120598amp119889119899

toBS minus 119897119864Rece-elec) = 119873119897120598fs1198632

1198702opt =119873119897120598fs119863

2

3119897 (120598amp119889119899

toBS minus 119864Rece-elec)

119870opt = radic119873119897120598fs119863

2

3 (120598amp119889119899


(13)

We have shown that a feasible number of clusters dependupon the following parameters total number of nodes say119873sensing field dimensions (119863) average length between sensor


Table 1 Closed-form expressions for squared shaped sensing field119863 times 119863 [24]

Position of BS Radio model used Threshold value 1198890= radic120598fs120598mp Predicted value of 1198892toBS 119889

4

toBS

Centre

Free space 119889 le 1198890

1198632

6

Corner 21198632

3

Sidersquos mid-point 51198632

12

Outside 51198632

12+ 119863119870 + 1198702

Centre

Multipath 119889 gt 1198890

71198634

180

Corner 281198634

45


720

Outside 71198632

180+211986321198712

3+ 1198714

nodes and BS (119889119899toBS) receiving total energy consumed bysensor nodes (119864Rece-elce) and transmitter amplifier energyconsumption (120598amp) Some of the protocols [26ndash28] neglectthe receiving energy dissipation of sensor nodes in exploringthe routes and only consider the energy of the transmitterSo we also suppose that receiving circuitry to be verysmall If receiving circuitry is large it will be in the formof a constant overhead that can predominantly make theclustering schemes questionable [24] The upper and lowerrange can be attained by putting the least and biggest values of119889119899toBS in (13) and can compute the optimal value of 119870opt Alsothe solution of the second derivation of (11) is positive whichrepresents that the value of 119870opt defined in (13) results in theleast possible total energy consumption in WSNs with theregular node distribution Once the optimal number of CHsis obtained their locations are found by the ABC algorithmamong uniformly distributed sensors In [24] the authorshave derived the expected value of different powers of com-munication path between the nodes and the BS throughoutthe sensing field (ie 1198892toBS 119889

4

toBS) as shown in Table 1 If 119889 le1198890(ie for free space model) the expected value of 1198892toBS is

computed Also for 119889 gt 1198890(ie for two-ray model) the

expected value of 1198894toBS is computed Figures 1ndash3 show thesample plot of the network for different cases of experimentsWe have considered one case with BS position (ie outsidethe sensor field) to divide the region into subregions and threecases with CH position (ie centre corner and mid-point ofbottom side) to divide the subregions into subregion partsWe have used different values of 1198892toBS 119889

4

toBS from Table 1 andsubmit these values in (13) to calculate the values of 119870opt forall these cases The different values of 119870opt from (14) (15) areused to divide the region into subregions and the values of119870opt from (16)ndash(21) are used to divide the subregions intosubregion parts The simulation of the network environmentis executed and the nature of sensor nodes for every roundof experiments is visualized in Figures 1ndash3 These figuresshow the location of sensor nodes and BS The green colour

95

90

85

80

75

70

65

60

55

50

45

40

35

30

25

20

15

10

10 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95

5

5 5

CH at center

Figure 1 Square shaped network field where CH is located at thecentre

indicates the normal sensor nodes blue colour indicates theSCH and red colour indicates theCHThe square box symboloutside the 119910-axis indicates the BS node

Case 1 BS is placed at outside of the squared network field(a) If 119889 le 119889

0 then 119870opt from (13) can be calculated as

119870opt = radic119873120598fs119863

2

3 (120598amp1198892


119870opt = radic119873120598fs119863

2

3120598fs (5119863212 + 119863119870 + 1198702)


10 5

CH at corner

10 15 20 25 30 35 40 45 85807565 90 95605550 705

5

10

15

20

25

30

35

40

45

50

55

60

65

70

75

80

85

90

95

Figure 2 Square shaped network field where CH is located at thecorner

95

90

85

80

75

70

65

60

55

50

45

40

35

30

25

20

15

10

10 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95

5

5 5

CH at mid-pointof bottom-side

Figure 3 Square shaped network field where CH is located at themid-point of bottom side

119870opt = radic4119873

5 + 12119870119863 + 1211987021198632

(14)

(b) If 119889 gt 1198890 then119870opt from (13) can be calculated as

119870opt = radic119873120598fs119863

2

3 (120598amp1198894


119870opt = radic119873120598fs119863

2

3120598mp (71198634180 + 2119863211987123 + 1198714)

119870opt = radic60119873120598fs

(71198632 + 1201198712 + 18011987141198632) 120598mp

(15)

Case 2 CH is located at the centre of the squared networkfield as shown in Figure 1

(a) If 119889 le 1198890 then 119870opt from (13) can be calculated as

119870opt = radic119873120598fs119863

2

3 (120598amp1198892


119870opt = radic119873120598fs119863

2

3120598fs (11986326)

119870opt = radic2119873

(16)

(b) If 119889 gt 1198890 then 119870opt from (13) can be calculated as

119870opt = radic119873120598fs119863

2

3 (120598amp1198894


119870opt = radic119873120598fs119863

2

3120598mp (71198634180)

119870opt = radic60119873120598fs71198632120598mp

(17)

Case 3 CH is located at the corner of the squared networkfield as shown in Figure 2

(a) If 119889 le 1198890119870opt from (13) can be calculated as

119870opt = radic119873120598fs119863

2

3 (120598amp1198892


119870opt = radic119873120598fs119863

2

3120598fs (211986323)

119870opt = radic119873

2

(18)


(b) If 119889 gt 1198890 119870opt from (13) can be calculated as

119870opt = radic119873120598fs119863

2

3 (120598amp1198894


119870opt = radic119873120598fs119863

2

3120598mp (28119863445)

119870opt = radic15119873120598fs281198632120598mp

(19)

Case 4 CH is positioned at the mid-point of the bottom sideof the squared network field with the origin remaining in thebottom left corner as shown in Figure 3

(a) If 119889 le 1198890 then119870opt from (13) can be calculated as

119870opt = radic119873120598fs119863

2

3 (120598amp1198892


119870opt = radic119873120598fs119863

2

3120598fs (5119863212)

119870opt = radic4119873

5

(20)


119870opt = radic119873120598fs119863

2

3 (120598amp1198894


119870opt = radic119873120598fs119863

2

3120598mp (1931198634720)

119870opt = radic240119873120598fs1931198632120598mp

(21)

5 Protocol Description

51 Artificial Bee Colony Approach A recent swarm intelli-gent approachmotivated by the giftedmetaheuristic behaviorof honey bees [6] is known as artificial bee colony algorithmthat is ABC which was invented by Karaboga et al ABC isa swarm intelligence approach which is being attracted bythe foregoing nature of real time bees Bee colony optimiza-tion consumes three kinds of bees namely employed beesonlookers and scout bees The basic behavior of these bees isas follows

Scout Bees These bees randomly move throughout thesearching space and try to explore new food resources andcalculate their fitness function

Employed Bees Employed bees are currently employing andexploring a specific food resource and calculate the new

fitness value They communicate information about thisspecific food resource with onlooker bees waiting in thebeehive by performingwaggle danceThis information can beamount of nectar direction of food resource and its distancefrom the beehive

Onlooker Bees If the new fitness value is better than the valuecalculated by the scout bees then select the employed bee andrecruit onlookers to pursue the visited routes by employedbees and calculate fitness function Choose the onlooker beewith best fitness function

In artificial bee colony where the exploitation processis performed by an onlooker and employed bees in thesearching environment scout bees manage exploration offood resources

At the initial stage bee colony algorithm generates apopulation of these three types of bees along with their foodresource locations through a number of iterations startingfrom 119868 = 0 up to 119868max The food resources are flowerpetals and the food source positions are called solutionsthat have different amount of nectar which is known as thefitness function Each solution 119904

119894is a 119889-dimensional solution

vector where 119894 = 1 2 3 119911 119889 is number of optimizationvariables and 119911 denotes number of onlooker or employedbees which is equal to the food source locations Dependingupon the visual information of the food resource (ie itsshape colour and fragrance) the employed bees update thesolutions in their memory and evaluate the nectar amount ofthe newly generated food resource If the newly discoveredfood resource is better than the previous one in terms offitness value then bees keep the newly generated solutions intheir memory and neglect the previous solution or vice versaAt the second stage of iteration once all the employed beeshad discovered food resources bees communicate statisticsregarding the food resources with onlooker bees sitting inbeehive by performing a special dance called waggle danceThe food resource statistics can be quantity of nectar its direc-tion and distance from the beehive When onlooker bees getthe information about food resources by the employed beesthey choose the favourable food resource Depending uponthe fitness value the probability119875

119894[29] for an onlooker bee to

choose the food resource is evaluated by the given equation

119875119894=

fitness119894

sum119911119899=1

fitness119894

(22)

where fitness119894is the fitness function of the solution 119894 which

is proportional to the nectar quantity of food resource at theposition 119894 and 119911 is the number of food sources

To generate optimal food source location [29] bee colonyuses the given equation

1199091015840119894119895= 119909119894119895+ 119903119894119895(119909119894119895minus 119909119896119895) (23)

where 119895 isin 1 2 119863 are indexes that are arbitrarily selectedand 119896 isin 1 2 119898 where 119896 is determined arbitrarily that


must be different from 119894 where 119903119894119895is a random number that

lies within [minus1 1] It controls the generation of food sourcesin the neighbourhood of 119909

119894119895and also shows the comparison

of two food locations using bees Hence from (23) it isnoticed that if the difference in the 119909

119894119895and 119909

119895119896parameters

reduces variation towards the 119909119894119895solution also gets reduced

Therefore the moment searching process reaches the optimalsolution number of steps will be decreased accordingly If theparameter value generated by the searching process is greaterthan the maximum limit it will be sustainable and if foodsource location cannot be improved through a preordainednumber of iterations then that food resource will be replacedwith another food resource discovered by the scout beesThe predefined number of iterations is an important controlparameter of the artificial bee colony so called a limit forrejectionThe food resource whose nectar is neglected by thebees is replaced with a new food resource found by the scouts[29] using

119909119895119894= LB119895119894+ 119903 (0 1) (UB119895

119894minus LB119895119894)

forall119895 isin (1 2 119863) forall119894 isin (1 2 119873) (24)

Basically bee colony technique carries out various selectionprocedures

(i) Global Selection ProcedureThis process is followed byonlookers in order to discover favourable regionswithsome probability to choose the food resource by using(22)

(ii) Local Selection Procedure This process is executed byemployed bees and onlookers using the visual statusof food resources

(iii) Greedy Selection Procedure Onlooker and employedbees execute this procedure in which as long as thenectar quantity of the optimal food resource is largerthan the current food resource the bee skips thecurrent solution and retains the optimal solutiongenerated by using (23)

(iv) Random Selection Procedure This process is perfor-med by scouts as mentioned in (24)

52 Ant Colony Optimization Based Routing in WSN Antcolony optimization is a swarm intelligence approach thatis used to solve complex combinatorial problems [30] Thebest example for ACO application is AntNet algorithmwhichwas discovered by Di Caro and Dorigo [31] ACO considerssimulated ants as mobile agents that work collectively andcommunicate with each other via artificial pheromone trailsIn ACO based methodology each and every ant randomlytries to search a way in the search space using forward andbackward movements Ants are originated from a sourcenode at a regular time interval andwhile travelling through itsneighbouring nodes ant reaches its last destination so calledsink node say 119889 At whatever node the ant is the informationabout a node has to be interchanged with the destination (ieBS) as ants are self-propelling in nature therefore ants are set

in motion At each node 119901 the probability with which an antchooses the next node 119902 is given by

Prob119896(119901 119902)

=

[120591 (119901 119902)]120572sdot [120578 (119901 119902)]

120573

sum119902isin119877119902

[120591 (119901 119902)]120572sdot [120578 (119901 119902)]

120573 if 119902 notin 119872119896

0 otherwise

(25)

where 120591(119901 119902) are the pheromone generated by the backwardants and 120578(119901 119902) is the estimated heuristic function for energyand distance and 119877

119902is the recipient nodes For node 119901119872119896 is

the list of nodes previously visited by the ants 120572 and 120573 are twocontrol parameterswhich restrict the amount of pheromonesPheromone values are deposited along circular arcs such thateach arc (119901 119902) has a trail value 120591(119903 119904) isin [0 1] Since thedestination hop is a stable base station therefore the last nodeof the path for every ant journey is the same The higher theprobability Prob

119896(119901 119902) the higher the chances of the node 119902

being selected The heuristic value of the node 119901 is expressedby the following equation

120578 (119901 119902) =119899

sum119894=1

119864119894119888

119864119894119868

times 119863 (BS119911 119902119894) (26)

where 119864119894119888is the current energy of the 119894th node 119864119894

119868is the initial

energy of the 119894th node 119863(BS119911 119902119894) is minimum distance of

CH 119902119894from BS

119911 Henceforth an ant can choose the next node

on the basis of the amount of energy and distance level ofthe neighbouring nodes This inferred that if a node has alower energy level it means it has a lower probability of beingselected and vice versa The moment forward ant reachesits destination node a backward ant is originated and thememory of the foraging ant is exchanged to the trailing antand then the forward ant is releasedThe trailing antmoves inthe sameway as the forward ant except in backward directionWhilemoving hop to hop back to the source node the trailingant retains an amount of pheromone Δ120591119896 on every nodewhich is given by the following equation

Δ120591119896 = 119908 sdot (1

119871119896) (27)

where 119871119896 is the route length of the 119896th ant in terms ofnumber of visited hops and 119908 is the weighting coefficientThese pheromone qualities are retained in thememory for thefuture reference The quality of a node is determined by themeasurement of pheromones on the way to its neighbouringhops Therefore it is observed that the values of the variables119872119896 and 119871119896 have been confined in a memory stored for everyant in every node However this space is less than equal to afew bytes This information is placed onto the edge relatingto the foraging ant To control the quantity of pheromonespheromone evaporation operation is performed in order toavoid the immense amount of pheromone trails and to favourthe searching of new paths The evaporation mechanism isperformed by using the following equation

120591119901119902

= (1 minus 120588) 120591119901119902 (28)


where 0 lt 120588 le 1 is the abandonment of pheromone trail and120588 is the coefficient of stigmergy persistence

53 Proposed Hybrid (ABCACO) Algorithm The proposedalgorithm operates iteratively where each iteration initiateswith a setup phase followed by a steady-state phase Afteruniform or homogeneous deployment process of the net-work each node initially calculates distance to other nodesand from BS using the Euclidian formula At the startingof each setup phase all nodes transfer the informationregarding their energy and locations (calculated distances) tothe base stationThus using this information the base stationcomputes the average energy level of all hops and decideswhich node will be chosen as CH such that node must havesufficient energy and least distance to the BS After that thevalue of optimal clustering (119870opt) is calculated This value isbased on the position of BS meaning that BS is positionedoutside or centred of the square shaped sensing field On thebasis of119870opt and distance all nodes of the region are dividedinto square shaped clusters known as subregions Next BSselects CHs at each round by using theABC algorithm In thisalgorithm the CHs selection process is achieved using thefitness function given by (29) which is obtained analyticallyin which the communication energy and distance are thesignificant factors that are considered

fitness119894=119899

sum119894=1

119864119894119888

119864119894119868

times 119863 (BS119911 119896119894) (29)

where 119863(BS119911 119896) is the minimum distance of the node 119896

119894

from BS119911 Again 119870opt value is calculated and this value is

based on the position of CH that is CH is positioned atthe centre corner and mid-point of the bottom side of thesquare shaped sensing field On the basis of this 119870opt valueand distance all the nodes of subregions are further dividedinto subregions parts Based on a given fitness formula SCHof each CH is selected for each subregion part In steady-state phase all nodes are responsible for communicating withtheir SCH of that subregion part and each SCH is responsiblefor communicating with CH of that subregion Each SCHreceives data from its member nodes and aggregates thedata After aggregation SCH transfers data to the CH of thatsubregion Again the CH aggregates the data and sends itto the BS in a multihop fashion (ie CH to CH) using theoptimal path given by ACO algorithm In our case we haveconsidered that ants should always move towards the BS Sofor this purpose the number of hops is always less than orequal to 119870opt Depending upon the distance the optimizedhop count is calculated for each CH Next ants are startingfrom CHs to BS through the next possible hops and returnwith finding the optimized next hop From that optimizedhop the same process is repeated for the next optimized hoptowards BS and so on In ACO to accomplish a proficient andstrong routing process some key characteristics of distinctivesensor networks are considered Failure in communicationnodes is more feasible in WSNs than traditional networks asnodes are regularly positioned in unattended places and theyuse a restricted power supply So the network should not beaffected by a nodersquos breakdown and should be in an adaptive

structure to sustain the routing process A better solutionfor data transmission is achieved by packet switching In thepacket switched network message is broken up into packetsof fixed or variable size Each packet includes a header thatcontains the source address destination address and othercontrol information The size of a packet depends upon thetype of network and protocol used No resources are allocatedfor a packet in advance Resources are allocated on demandon a first-come first-served basisThe packets are routed overthe network hop to hop At each hop packet is stored brieflybefore being transmitted according to the information storedin its header These types of networks are called store andforward networks Individual packets may follow differentroutes to reach the destination In most protocols the trans-port layer is responsible for rearranging these packets beforerouting them on to the destination port numbers So whenfailure takes place in a trail the related data packet cannotreach the destination that is BSAcknowledgment signals areused to achieve guaranteed and reliable delivery So when anacknowledgment for a data packet is absent the source nodethat is CH retransmits that packet to an altered path There-fore routing becomesmore robust by using acknowledgmentrelated data transmission andmaintains different routes Alsoin this kind of network some routes would be shorter and setaside for minimum energy costs Communication on theseroutes should be made at regular intervals to decrease thetotal energy consumption cost using these paths That is toattain lower energy consumption more data packets shouldbe transmitted along shorter transmission routes

The setup and steady-state phase of the proposed algo-rithm are given below

Setup Phase In this phase the operation is divided into cyclesIn every cycle all nodes generate and transmit a message totheir SCH CH or base station The BS uses the proposedalgorithm to perform the following steps to (1) divide regioninto subregions and further subregion parts (2) select CHusing ABC algorithm and (3) compute the shortest multihoppath using ACO algorithm The process of the setup phase isshown in Figure 4

Steady-State Phase In this phase all nodes know their ownduty according to the notification message received in thesetup phase Member nodes sense the data and send to theirSCH Each SCH aggregates data received from its membernodes and sends to the CH of that subregion Further eachCH aggregates data received from SCHs and sends to thenext hop CH node on the shortest path determined insetup phase When the energy of any node is less than thethreshold energy then that node will not participate in theCH or SCH selection Otherwise the entire system continueswith the setup and steady-state phase The system continuesthese cycles until every nodersquos energy has been depleted Theprocess of the steady-state phase is shown in Figure 5

6 Simulation Results

In this section the simulation results of cluster based WSNclustering using artificial bee colony (WSNCABC) [32] have


No

Start

Nodes (N) are uniformly deployed in

value

Determine number of alive nodes

Compute distance of each node fromother nodes and from BS

into subregions

Select a CH for each region based onremaining energy and distance from BS using artificial bee colony algorithm

subregion into subregion parts basedon number of nodes in that particularsubregions

Select SCH for each subregion partbased on remaining energy anddistance from CH of that particularsubregion

Determine the shortest multihop pathfrom each CH to BS using ACO algorithm

Yes

Stop

No alive nodes

Dtimes D region

Initialize the nodes with energy (E0)

If N gt 0

Again compute Kopt to divide each

Compute Kopt to divide the region

Figure 4 Setup phase process

been compared with a well-known clustering based wirelesssensor protocol called ldquoLow Energy Adaptive ClusteringHierarchyrdquo (LEACH) [3] We used MATLAB 2012b andOracle JDeveloper 11 g for evaluation of this approach Thesimulations for 100 sensor nodes in a 100m times 100m sensingregion and for 225 sensor nodes in a 200m times 200m and300m times 300m region with equivalent initial energy of 05 Jand 10 J have been run Assume every node has a potential ofinteracting between base station and the sensor nodes In thesimulations LEACH andWSNCABC algorithm settings andassumptions are for consistent comparisons In experimentsa parallel model for MATLAB is used in providing periodicaldata transfers Free space and multipath radio propagationmodels are used in simulations For comparison LEACH andWSNCABC were used to verify the success of this approachA packet level simulation has been performed and utilizes theparameter values of the same physical layer as described in[9] To get the average results random topologies had beenconsidered through the simulations BS is located outsideof monitored region We consider various important aspects

as the number of alive nodes per round remaining energygoodput (ie total number of packets received at the BS)and the stability period of the entire network Simulation iscarried out up to 10 percent of the total number of alive nodesThe simulation parameters are mentioned in Table 2

After each round the number of live sensors goes downThe network remains alive till the last node Figures 6ndash11depict that the proposed approach has a large number ofalive nodes compared against LEACH and WSNCABC Auniform installation of clusters results in prolonged life ofnetwork While in another two approaches like WSNCABCand LEACH the clusters are installed randomly which maycause nodes to cover a longer distance and may cause poorperformance

Goodput Goodput is the number of packets delivered suc-cessfully per unit time [33]

Figure 12 shows the messages are transmitted more fre-quently as compared to WSNCABC and LEACH when BS islocated at (minus10 50) position The proposed approach depicts


BS receives the data fromnearest CH node

Each CH aggregates data receivedfrom SCHs and sends to the next hopCH node on the shortest pathdetermined in setup phase

Each SCH aggregates datareceived from nodes and sendsto the CH of that subregion

Nodes forward sensed data totheir SCH

Nodes wake up and sense data

Node will not participate in CH and SCH selection

No

Yes

Go to setup phase If N(E) lt threshold

Figure 5 Steady-state phase process

Table 2 Simulation parameters

Network parameter ValueField span Square 100 times 100 200 times 200 300 times 300Numbers of sensor nodes 100 225Location of BS (minus10 50)119864elec 50 nJbit119864fs 10 nJbitm2

119864mp 00013 pJbitm4

119864DA 50 nJbitsignalInitial energy 05 J 10 JChannel type Channelwireless channelRadio propagation model Free space multipathEnergy model BatteryRadio bit rate 1mbps

better results because the CHs are chosen very carefully afterconsidering the distance of each node from the BS The CHsare selected based on a high level of energy at least up toa predefined threshold value and the shortest distance fromthe BS The graphs indicate that data delivery rate of ourapproach is far better against WSNCABC and LEACH bya factor of 35 The other two approaches do not considerthese important parameters during CHs selection which isthe reason why they show poor performance

Stability Period Stability period is definedwhen the first nodeis dead The stability is a very important parameter in WSNsbecause after the death of the first node other nodes die

0 100 200 300 400 500 600 700 800 900 1000

ABCACOWSNCABC

LEACH

Rounds

0102030405060708090

100

Aliv

e nod

es

Figure 6 Number of alive nodes versus rounds with grid size 100 times100 and energy = 05 J

gradually which decreases the network lifetime ABCACOincreases the stability period of the network by 48 againstWSNCABC and by 60 against LEACH respectively asshown in Figure 13

Residual Energy The residual energy is calculated as followsresidual energy = initial energy minus current energy

Figures 14ndash19 demonstrate the residual energy of thenetwork versus number of rounds It clearly indicates thatABCACO algorithm enhances the lifespan of the networkover the other two algorithms by consuming less energy


010

020

030

040

050

060

070

080

090

010

0011

0012

0013

0014

0015

0016

0017

0018

0019

0020

0021

00

RoundsABCACOWSNCABC

LEACH

0102030405060708090

100

Aliv

e nod

es


0 100 200 300 400 500Rounds

ABCACOWSNCABC

LEACH

0255075

100125150175200225

Aliv

e nod

es


7 Application

This paper explains how the wireless sensor network technol-ogy helps with fire detection in real time One of the mostappealing features of WSN is environment auditing Wirelesssensor nodes are installed in various parts of the forestwhich measure temperature humidity and biometric dataand deliver this collected data to the BS However the successof forest fire detection system that is based upon the wirelesssensor nodes is restricted due to constrained energy resourcesof the sensor nodes and the rough environmental settingsImmediate response to fire plays a crucial role in this wholescenario to have the least amount of permanent destructionin the forest This requires constant surveillance of the areaWe decided to study WSN after considering the deficienciesof satellite and camera system Our proposed architecture notonly aims to detect the forest fire effectively and quickly butalso was designed considering the very limitations of sensornodes

In our system sensor nodes work under regular dayconditions exempting the period of forest fire such that thewireless sensor nodeswill be quite potential under the normalweather conditions and no fire A distributed (cluster based)

ABCACOWSNCABC

LEACH

0 100 200 300 400 500 600 700 800Rounds

0

50

100

150

200

250

Aliv

e nod

es


ABCACOWSNCABC

LEACH

0 25 50 75 100 125 150 175 200 225Rounds

0

50

100

150

200

250

Aliv

e nod

es

Figure 10 Number of alive nodes versus rounds with grid size 300times 300 and energy = 05 J

approach is used in each sensor node to detect fire threatcautiously in case of abnormally high temperatures to informthe BS about the possibility or occurrence of fire rapidly

From the literature [34ndash39] we study that single aspectof environmental monitoring is handled However the pro-posed system takes into account the densely deployed wire-less sensor nodes shortened transmission paths between thesensor nodes enable local computations (ie at SCH andCH)and used hybrid swarm intelligent approach with energy anddistance parameters for early detection of fire

Wireless sensor nodes and networks have unique featureswith many pros and cons of their application in forest firedetection and monitoring An effective forest fire detectionvia use of wireless sensor nodes should consider designgoals for example (a) energy efficiency (b) earliest forest firedetection (c) weather resistant structure and (d) predictingspread and direction of forest fire

From the abovementioned four design goals of firedetection we have proposed different strategies (i) a uni-form sensor deployment scheme (ii) a hierarchical clusterednetwork architecture where CH is elected by ABC algorithmwith parameters energy and distance from BS and SCH iselected by parameters energy anddistance from theirCH (iii)an intracluster communication with aggregation of data onSCH and (iv) intercluster communication (with aggregationof data at CH) by using ACO algorithm


0 50 100 150 200 250 300 350 400Rounds

ABCACOWSNCABC

LEACH

0

50

100

150

200

250

Aliv

e nod

es

Figure 11 Number of alive nodes versus rounds with grid size 300 times 300 and energy = 1 J

ABCACOWSNCABC

LEACH

Number of packets

68936

138131

102145

84794

59789

43983

32947

118263

85789

75742

30055

23240

16495

60690

42906

3226452096

49588

Grid 100 times 100Nodes 100Energy 05






Figure 12 Number of successfully received packets at BS located at (minus10 50)

ABCACOWSNCABC

LEACH

Round

631

425

157

1303

924

311

158

27 25

375

164

71 16 6 1

151

9 25







Figure 13 Comparison of protocols on the basis of the first node dead


0 100 200 300 400 500 600 700 800Rounds

ABCACOWSNCABC

LEACH

0

10

20

30

40

50

60

Ener

gy (J

)

Figure 14 Residual energy with grid size 100 times 100 and energy =05 J over rounds

010

020

030

040

050

060

070

080

090

010

0011

0012

0013

0014

0015

0016

0017

0018

00

RoundsABCACOWSNCABC

LEACH

020406080

100120

Ener

gy (J

)

Figure 15 Residual energy with grid size 100 times 100 and energy = 1 Jover rounds

0 50 100 150 200 250 300 350Rounds

ABCACOWSNCABC

LEACH

020406080

100120

Ener

gy (J

)


A sample illustration of a forest fire detection and thecommunication between the nodes is shown in Figure 20

8 Conclusion and Future Scope

In this paper we use ABC because of its simple usage andquick convergence Additionally we implement an ACOtechnique to solve the routing problem that exists in WSNsWe implement both periodical (ABC) and event based

0 100 200 300 400 500 600 700 800Rounds

ABCACOWSNCABC

LEACH

0

50

100

150

200

250

Ener

gy (J

)


0 25 50 75 100 125 150 175 200 225Rounds

ABCACOWSNCABC

LEACH

0

20

40

60

80

100

120

Ener

gy (J

)


0 100 200 300 400Rounds

ABCACOWSNCABC

LEACH

0

50

100

150

200

250

Ener

gy (J

)


(ACO) approaches to develop a new hybrid swarm intel-ligent energy efficient routing algorithm called ABCACOIn ABCACO the entire sensing field is divided into anoptimal number of subregions and subregion parts based on119870opt The proposed methodology provides a better clusteringapproach by selecting the CHs uniformly throughout thenetwork area Since we use hierarchical structure at thefirst level we implement ABC approach for the selection


SCH

5101520253035404550556065707580859095

10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 9552030 1525 10 5

CHBSNN

Figure 20 Detection of fire event and message forwarding amongthe nodes

of CHs and on the second level a better routing path isevaluated for data transmission by using ACO approachABCACO decreases the communication distance by placingthe CH node in the perfect position that provides maximumlifetime of the network The simulation results show thatABCACO outperforms network performance in terms oflifetime stability and goodput as compared to existing algo-rithmsThis paper leads to one step ahead to interdisciplinaryresearch within the biological systems to come up with betterbioinspired solutions for real world WSN such as neuralswarm system swarm fuzzy system and neural fuzzy systemThe bioinspired hybrid algorithms open new perspective inthe optimization solutions of WSNs

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

References

[1] I F Akyildiz W Su Y Sankarasubramaniam and E CayircildquoWireless sensor networks a surveyrdquo Computer Networks vol38 no 4 pp 393ndash422 2002

[2] D Caputo F Grimaccia M Mussetta and R E Zich ldquoAnenhanced GSO technique for wireless sensor networks opti-mizationrdquo in Proceedings of the IEEE Congress on EvolutionaryComputation (CEC rsquo08) pp 4074ndash4079 Hong Kong ChinaJune 2008

[3] W R Heinzelman A Chandrakasan and H BalakrishnanldquoEnergy-efficient communication protocol for wireless micro-sensor networksrdquo in Proceedings of the 33rd Annual HawaiiInternational Conference on System Sciences p 10 IEEE January2000

[4] S Okdem and D Karaboga ldquoRouting in wireless sensor net-works using an Ant Colony optimization (ACO) router chiprdquoSensors vol 9 no 2 pp 909ndash921 2009

[5] L Qing T Zhi Y Yuejun and L Yue ldquoMonitoring in industrialsystems using wireless sensor network with dynamic powermanagementrdquo IEEE Sensors Journal vol 9 pp 1596ndash1604 2009

[6] D Karaboga S Okdem and C Ozturk ldquoCluster based wirelesssensor network routing using artificial bee colony algorithmrdquoWireless Networks vol 18 no 7 pp 847ndash860 2012

[7] D Kumar T C Aseri and R B Patel ldquoDistributed clusterhead election (DCHE) scheme for improving lifetime of het-erogeneous sensor networksrdquo Tamkang Journal of Science andEngineering vol 13 no 3 pp 337ndash348 2010

[8] S Dhar Energy aware topology control protocols for wirelesssensor networks [PhD thesis] 2005

[9] W B Heinzelman A P Chandrakasan and H Balakrish-nan ldquoAn application-specific protocol architecture for wirelessmicrosensor networksrdquo IEEE Transactions onWireless Commu-nications vol 1 no 4 pp 660ndash670 2002

[10] D Kumar T C A Patel and R B Patel ldquoProlonging networklifetime and data accumulation in heterogeneous sensor net-worksrdquo International Arab Journal of Information Technologyvol 7 no 3 pp 302ndash309 2010

[11] D Kumar T C Aseri and R B Patel ldquoEEHC energy efficientheterogeneous clustered scheme for wireless sensor networksrdquoComputer Communications vol 32 no 4 pp 662ndash667 2009

[12] D Kumar T C Aseri and R B Patel ldquoEECDA energy efficientclustering and data aggregation protocol for heterogeneouswireless sensor networksrdquo International Journal of ComputersCommunications amp Control vol 6 no 1 pp 113ndash124 2011

[13] J Lloret M Garcia D Bri and J R Diaz ldquoA cluster-basedarchitecture to structure the topology of parallel wireless sensornetworksrdquo Sensors vol 9 no 12 pp 10513ndash10544 2009

[14] O Younis and S Fahmy ldquoHEED a hybrid energy-efficientdistributed clustering approach for ad hoc sensor networksrdquoIEEE Transactions on Mobile Computing vol 3 no 4 pp 366ndash379 2004

[15] S Lindsey C Raghavendra and K M Sivalingam ldquoDatagathering algorithms in sensor networks using energy metricsrdquoIEEE Transactions on Parallel and Distributed Systems vol 13no 9 pp 924ndash935 2002

[16] T Camilo C Carreto J S Silva and F Boavida ldquoAn energy-efficient ant-based routing algorithm for wireless sensor net-worksrdquo in Ant Colony Optimization and Swarm Intelligence5th International Workshop ANTS 2006 Brussels BelgiumSeptember 4ndash7 2006 Proceedings vol 4150 of Lecture Notes inComputer Science pp 49ndash59 Springer Berlin Germany 2006

[17] G Chen T-D Guo W-G Yang and T Zhao ldquoAn improvedant-based routing protocol in Wireless Sensor Networksrdquo inProceedings of the International Conference on CollaborativeComputing Networking Applications and Worksharing (Collab-orateCom rsquo06) pp 1ndash7 IEEEAtlanta GaUSANovember 2006

[18] R Du C L Chen X B Zhang and X P Guan ldquoPath planningand obstacle avoidance for PEGs in WSAN I-ACO basedalgorithms and implementationrdquo Ad-Hoc and Sensor WirelessNetworks vol 16 no 4 pp 323ndash345 2012

[19] L Juan S Chen and Z Chao ldquoAnt system based anycastrouting in wireless sensor networksrdquo in Proceedings of the Inter-national Conference on Wireless Communications Networkingand Mobile Computing (WiCom rsquo07) pp 2420ndash2423 ShanghaiChina September 2007

[20] Y Liu H Zhu K Xu and Y Jia ldquoA routing strategy based onant algorithm for WSNrdquo in Proceedings of the 3rd InternationalConference on Natural Computation (ICNC rsquo07) pp 685ndash689August 2007

[21] S Bandyopadhyay and E J Coyle ldquoAn energy efficient hier-archical clustering algorithm for wireless sensor networksrdquo in


Proceedings of the 22nd Annual Joint Conference of the IEEEComputer and Communications IEEE Societies (INFOCOM rsquo03)vol 3 pp 1713ndash1723 IEEE April 2003

[22] S Bandyopadhyay andE J Coyle ldquoMinimizing communicationcosts in hierarchically-clustered networks of wireless sensorsrdquoComputer Networks vol 44 no 1 pp 1ndash16 2004

[23] L Qing Q Zhu andMWang ldquoDesign of a distributed energy-efficient clustering algorithm for heterogeneous wireless sensornetworksrdquoComputer Communications vol 29 no 12 pp 2230ndash2237 2006

[24] N Amini A VahdatpourW XuM Gerla andM SarrafzadehldquoCluster size optimization in sensor networks with decentral-ized cluster-based protocolsrdquo Computer Communications vol35 no 2 pp 207ndash220 2012

[25] J Kim W Lee E Kim D-W Kim and H Kim ldquoCoverage-time optimized dynamic clustering of networked sensors forpervasive home networkingrdquo IEEE Transactions on ConsumerElectronics vol 53 no 2 pp 433ndash441 2007

[26] M Ettus ldquoSystem capacity latency and power consumption inmultihop-routed SS-CDMA wireless networksrdquo in Proceedingsof the IEEE Radio and Wireless Conference (RAWCON rsquo98) pp55ndash58 IEEE Colorado Springs Colo USA August 1998

[27] T H Meng and V Rodoplu ldquoDistributed network protocols forwireless communicationrdquo in Proceedings of the IEEE Interna-tional Symposium on Circuits and Systems (ISCAS rsquo98) vol 4pp 600ndash603 IEEE Monterey Calif USA May-June 1998

[28] T Shepard ldquoA channel access scheme for large dense packetradio networksrdquo in Proceedings of the Conference Proceedings onApplications Technologies Architectures and Protocols for Com-puter Communications (SIGCOMM rsquo96) pp 219ndash230 August1996

[29] D Karaboga and B Akay ldquoA comparative study of artificial Beecolony algorithmrdquo Applied Mathematics and Computation vol214 no 1 pp 108ndash132 2009

[30] M Dorigo and G Di Caro ldquoThe ant colony optimization meta-heuristicrdquo in New Ideas in Optimization D Corne M Dorigoand F Glover Eds pp 11ndash32 McGraw-Hill London UK 1999

[31] G Di Caro and M Dorigo ldquoAntNet distributed stigmergeticcontrol for communications networksrdquo Journal of ArtificialIntelligence Research vol 9 pp 317ndash365 1998

[32] D Karaboga S Okdem and C Ozturk ldquoCluster based wire-less sensor network routings using artificial bee colony algo-rithmrdquo in Proceedings of the IEEE International Conference onAutonomous and Intelligent Systems (AIS rsquo10) pp 1ndash5 IEEEPovoa de Varzim Portugal June 2010

[33] C Liu H Jiang Y Yang and G Xu ldquoOptimal parametersconfiguration for TCP goodput improvement in CR networksrdquoin Proceedings of the IEEE Spring Congress on Engineering andTechnology (S-CET rsquo12) pp 1ndash5 IEEE Xirsquoan China May 2012

[34] L Yu N Wang and X Meng ldquoReal-time forest fire detectionwith wireless sensor networksrdquo in Proceedings of the Interna-tional Conference onWireless Communications Networking andMobile Computing (WiMob rsquo05) vol 2 pp 1214ndash1217 IEEESeptember 2005

[35] B-L Wenning D Pesch A Timm-Giel and C Gorg ldquoEnvi-ronmental monitoring aware routing making environmentalsensor networks more robustrdquo Telecommunication Systems vol43 no 1-2 pp 3ndash11 2010

[36] D M Doolin and N Sitar ldquoWireless sensor nodes for wild firemonitoringrdquo in Symposium on Smart Structures and MaterialsProceedings of SPIE pp 477ndash484 San Diego Calif USA 2006

[37] C Hartung R Han C Seielstad and S Holbrook ldquoFireWxNeta multi-tiered portable wireless system for monitoring weatherconditions in wildland fire environmentsrdquo in Proceedings of the4th International Conference on Mobile Systems Applicationsand Services (MobiSys rsquo06) pp 28ndash41 June 2006

[38] J Lloret M Garcia D Bri and S Sendra ldquoA wireless sensornetwork deployment for rural and forest fire detection andverificationrdquo Sensors vol 9 no 11 pp 8722ndash8747 2009

[39] B Son and Y-S Her ldquoA design and implementation of forest-fires surveillance system based on wireless sensor networksfor South Korea mountainsrdquo International Journal of ComputerScience and Network Security vol 6 no 9 pp 124ndash130 2005

International Journal of

AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014


Active and Passive Electronic Components

Control Scienceand Engineering

Journal of



RotatingMachinery


Hindawi Publishing Corporation httpwwwhindawicom

Journal ofEngineeringVolume 2014

Submit your manuscripts athttpwwwhindawicom

VLSI Design



Shock and Vibration


Civil EngineeringAdvances in

Acoustics and VibrationAdvances in



Electrical and Computer Engineering

Journal of

Advances inOptoElectronics


Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

SensorsJournal of


Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014


Chemical EngineeringInternational Journal of Antennas and

Propagation




Navigation and Observation



DistributedSensor Networks



results Network lifespan can be further enhanced by imple-menting various best possible route detection techniquesArtificial bee colony (ABC) algorithm is a new upcomingapproach whose working totally resembles that of swarmbased optimization techniques while monitoring the clustersin WSN

The most unavoidable hindrances faced in the sensornetworks are about getting the best possible routes for datatransmission up to the BS In general WSN node operatesin a multinode pattern immense algorithms have beenintroduced for routing data to reach the destination In [4]somenotable routing techniques inWSNdesire to use ant as amoving agent while discovering the route in order to discoverthe best possible path for data transmission in WSN In thisresearch we prioritize the multiple nodes grounded in theirrespective power competency and there approaching natureto route the data through the best path In recent years a newpicture by applying routing based protocols on themovementof the swarms in order to depict the best possible path fordelivering the acquired data through the selected path wasintroduced Algorithm working with nodes should be moreenergy saver as well as fast enough to acquire and transmitthe data Ant colony optimization (ACO) designed in sucha way so that ants behaviors for finding food sources canbe depicted easily acts as a valuable solution for differentroute detection although this technique is not favourable ingoverning applications which needs timely data transfer [5]

For upgrading the execution of the network clusteringmechanisms have been connected to networks with hierar-chical structures while minimizing the fundamental energyutilization [6 7] Clustering is a technique to groupnumber ofnodes in areas in a densely employed wide-scale network [8]One of the nodes will be elected as a cluster head (CH) amongother nodes called clustermembers of their respective clusterIn clustering randomly deployed nodes join in carrying outthe job Here the signal to the nodes is advertised withnodes Signals are received by all sensor nodes from distinctCHs and nodes give response with their node-id to CH withstrength and this formation of clusters takes place WithoutCH for intracluster routing a proactive strategy is utilizedand for intercluster routing a reactive strategy is utilizedClustering has various challenges in deployment such asensuring connectivity between nodes determining optimalcluster sizes [9 10] and optimizing clustering structuresdynamically on the basis of the status of cluster members Fora hierarchical routing or network administration conventioncan be better executed with CHs In [11 12] the authors havestudied the influence of cluster based hierarchical routingthat is a quite famous approach with some benefits related towell organized communication and scalability For attainingpower efficient routing the concept of hierarchical routingis used Using two-tier clusters for sending information toBS has the profit of shorter transmit separations for nodesOnly some nodes are essential to send data to BS at fardistances and it is valid in those networks where there is ahigh density of nods Through hierarchical level cluster cancommunicate with other clusters or with external network atthe same time [13] In this paper we have introduced three-level hierarchy The first level is known as CH the second

level is known as subcluster head (SCH) and the third levelis denoted by member node Partial local computation isintroduced in each and every SCH at the second stage andthe local computation at CH where information is sent toBS directly This approach is used as regions and subregionsand significant progress is obtained The objectives of thesubregions and subregion parts are formulated as in

Maximum (119879SSC) = Minimum sum119894119895isinTee

119864Tee (1)

where 119879SSC is the work time of the square shaped cluster 119864Teeis the total energy expenditure in a cluster

The rest of thework is done in seven sections In Section 2we discuss the related work In Section 3 we define the radiomodel for wireless sensor network Section 4 describes thesquared optimization problem for CH and SCH Section 5exhibits the details of ABC ACO and the proposed approachABCACO In Section 6 the performances of ABCACOapproach are evaluated via a number of experiments andwe compare its simulation results with both WSNCABC andLEACH Finally Section 7 concludes the paper and suggeststhe further work to be carried out

2 Related Works

In literature various protocols have been considered withthe idea of enhancing the lifespan of sensor network bythe appropriation of cluster based sensor network structuralengineering A well-known protocol known as LEACH (LowEnergy Adaptive Clustering Hierarchy) [3] is based on dis-tributed clustering approach It prolongs the network lifespanby reducing the power dissipation No wastage of powerfor electing a node as a CH is the advantage of LEACHprotocol The CH selection process is primarily based uponthe inducted energy level of each sensor node that is thedisadvantage of LEACH So a sensor node that has low powerhas more chances of becoming a CH than the one thathas more power which leads to the dead state of the entirecluster The second disadvantage is the presence of moreoverheads in the development of the dynamic cluster In [9]the results have demonstrated that LEACH-C outperformsLEACH because of the improvement in cluster formation bythe BS Furthermore the fraction of CHs in each iteration ofLEACH-C is equivalent to the craved optimum number Anadditional clustering algorithm that enhances the lifespan ofnetwork has been presented in [14] known as HEED (HybridEnergy Efficient Distributed Clustering) The CH electionprocess in HEED is intentionally based upon the parametersthat is residual energy of the sensor nodes and in additionto this cost of communication within the clusters is alsoconsidered by the authors for increasing power efficiencyand further enhancing lifespan of network This protocolmaintains the CH for a fixed number of cycles Relatedto LEACH significant overhead is imposed by the sensornetwork by clustering in each round Noticeable power dis-sipation is imposed by this overhead that leads to decrease inlifespan of the network As HEED requires various iterationsfor the formation of clusters it suffers from a subsequent










=

119897119864elec + 119897119864fs1198892 119889 le 119889

0

119897119864elec + 119897119864mp1198894 119889 gt 119889

0

(2)




119896minus 1) +

119873

119896119897119864DataAgr

+ 119897119864Trans-elec + 119897120598amp119889119899

toBS

(4)


119864mem-node = 119897119864elec + 119897120598fs119889119899

toCH (5)










0)







1minus 1199092)2 + (119910

1minus 1199102)2

where 1199091and 119909


1and 119910




0) then

119870opt = radic4119873

5 + 12119870119863 + 1211987021198632

else

119870opt = radic60119873120598fs

(71198632 + 1201198712 + 18011987141198632) 120598mpendif


endfunction

















119894119895)



if (119889 le 1198890) then


119870opt = radic60119873120598fs71198632120598mp


if (119889 le 1198890) then

119870opt = radic119873

2else

119870opt = radic15119873120598fs281198632120598mp


if (119889 le 1198890) then

119870opt = radic4119873

5



else

119870opt = radic240119873120598fs1931198632120598mp




if 119894 = 119895 then

Prob119896(119901 119902) =

[120591 (119901 119902)]120572

[120578 (119901 119902)]120573

sum119902isin119877119902

[120591 (119901 119902)]120572

[120578 (119901 119902)]120573 if 119902 notin 119872119896

end ifend for











radic1198632


1198632

119870opt (6)



= 119864 [119910CH] =1

2(radic

1198632

119870opt)

119864 [(119909mem-nodes)2] = 119864 [(119909CH)

2] = 119864 [(119910mem-nodes)

2]

= 119864 [(119910CH)2] =

1198632

3119870opt

(7)




= 119864 [(119909mem-nodes)2]


+ 119864 [(119910mem-nodes)2]


=1198632

3119870opt

(8)



3119870opt (9)


119864cluster = 119864CHcycle + (119873




119870optminus 1)

+ 119897119864DataAgr119873


119899

toBS)

+ ((119873


119897120598fs1198632

3119870opt)))



toBS + 119873119897119864Trans-elec


2

3119870optminus119897120598fs1198632

3= 119873119897119864Rece-elec


toBS

+ 119873119897119864Trans-elec +119873

119897120598fs11986323119870opt minus 3

119897

120598fs1198632

(11)


120597119864cycle

120597119870opt= 119897120598amp119889

119899


2

31198702opt

1205972119864cycle

1205972119870opt=

2119873119897120598fs1198632

31198703optgt 0

(12)


0 = 119897120598amp119889119899


2

31198702opt

119897120598amp119889119899

toBS = 119897119864Rece-elec +119873119897120598fs119863

2

31198702opt

31198702opt119897120598amp119889119899


31198702opt (119897120598amp119889119899


1198702opt =119873119897120598fs119863

2

3119897 (120598amp119889119899


119870opt = radic119873119897120598fs119863

2

3 (120598amp119889119899


(13)





4

toBS

Centre

Free space 119889 le 1198890

1198632

6

Corner 21198632

3


12

Outside 51198632

12+ 119863119870 + 1198702

Centre


71198634

180

Corner 281198634

45


720

Outside 71198632

180+211986321198712

3+ 1198714


4




4


95

90

85

80

75

70

65

60

55

50

45

40

35

30

25

20

15

10

10 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95

5

5 5

CH at center





119870opt = radic119873120598fs119863

2

3 (120598amp1198892


119870opt = radic119873120598fs119863

2

3120598fs (5119863212 + 119863119870 + 1198702)


10 5

CH at corner

10 15 20 25 30 35 40 45 85807565 90 95605550 705

5

10

15

20

25

30

35

40

45

50

55

60

65

70

75

80

85

90

95


95

90

85

80

75

70

65

60

55

50

45

40

35

30

25

20

15

10

10 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95

5

5 5



119870opt = radic4119873

5 + 12119870119863 + 1211987021198632

(14)


119870opt = radic119873120598fs119863

2

3 (120598amp1198894


119870opt = radic119873120598fs119863

2

3120598mp (71198634180 + 2119863211987123 + 1198714)

119870opt = radic60119873120598fs

(71198632 + 1201198712 + 18011987141198632) 120598mp

(15)



119870opt = radic119873120598fs119863

2

3 (120598amp1198892


119870opt = radic119873120598fs119863

2

3120598fs (11986326)

119870opt = radic2119873

(16)


119870opt = radic119873120598fs119863

2

3 (120598amp1198894


119870opt = radic119873120598fs119863

2

3120598mp (71198634180)

119870opt = radic60119873120598fs71198632120598mp

(17)



119870opt = radic119873120598fs119863

2

3 (120598amp1198892


119870opt = radic119873120598fs119863

2

3120598fs (211986323)

119870opt = radic119873

2

(18)



119870opt = radic119873120598fs119863

2

3 (120598amp1198894


119870opt = radic119873120598fs119863

2

3120598mp (28119863445)

119870opt = radic15119873120598fs281198632120598mp

(19)



119870opt = radic119873120598fs119863

2

3 (120598amp1198892


119870opt = radic119873120598fs119863

2

3120598fs (5119863212)

119870opt = radic4119873

5

(20)


119870opt = radic119873120598fs119863

2

3 (120598amp1198894


119870opt = radic119873120598fs119863

2

3120598mp (1931198634720)

119870opt = radic240119873120598fs1931198632120598mp

(21)













119875119894=

fitness119894

sum119911119899=1

fitness119894

(22)




1199091015840119894119895= 119909119894119895+ 119903119894119895(119909119894119895minus 119909119896119895) (23)







119894119895and 119909




119909119895119894= LB119895119894+ 119903 (0 1) (UB119895

119894minus LB119895119894)









Prob119896(119901 119902)

=

[120591 (119901 119902)]120572sdot [120578 (119901 119902)]

120573

sum119902isin119877119902

[120591 (119901 119902)]120572sdot [120578 (119901 119902)]

120573 if 119902 notin 119872119896

0 otherwise

(25)






120578 (119901 119902) =119899

sum119894=1

119864119894119888

119864119894119868

times 119863 (BS119911 119902119894) (26)




CH 119902119894from BS



Δ120591119896 = 119908 sdot (1

119871119896) (27)


120591119901119902

= (1 minus 120588) 120591119901119902 (28)




fitness119894=119899

sum119894=1

119864119894119888

119864119894119868

times 119863 (BS119911 119896119894) (29)


119894










No

Start


value



into subregions





Yes

Stop

No alive nodes

Dtimes D region


If N gt 0
















No

Yes





119864mp 00013 pJbitm4




0 100 200 300 400 500 600 700 800 900 1000

ABCACOWSNCABC

LEACH

Rounds

0102030405060708090

100

Aliv

e nod

es






010

020

030

040

050

060

070

080

090

010

0011

0012

0013

0014

0015

0016

0017

0018

0019

0020

0021

00

RoundsABCACOWSNCABC

LEACH

0102030405060708090

100

Aliv

e nod

es


0 100 200 300 400 500Rounds

ABCACOWSNCABC

LEACH

0255075

100125150175200225

Aliv

e nod

es


7 Application



ABCACOWSNCABC

LEACH

0 100 200 300 400 500 600 700 800Rounds

0

50

100

150

200

250

Aliv

e nod

es


ABCACOWSNCABC

LEACH

0 25 50 75 100 125 150 175 200 225Rounds

0

50

100

150

200

250

Aliv

e nod

es







0 50 100 150 200 250 300 350 400Rounds

ABCACOWSNCABC

LEACH

0

50

100

150

200

250

Aliv

e nod

es


ABCACOWSNCABC

LEACH

Number of packets

68936

138131

102145

84794

59789

43983

32947

118263

85789

75742

30055

23240

16495

60690

42906

3226452096

49588








ABCACOWSNCABC

LEACH

Round

631

425

157

1303

924

311

158

27 25

375

164

71 16 6 1

151

9 25









0 100 200 300 400 500 600 700 800Rounds

ABCACOWSNCABC

LEACH

0

10

20

30

40

50

60

Ener

gy (J

)


010

020

030

040

050

060

070

080

090

010

0011

0012

0013

0014

0015

0016

0017

0018

00

RoundsABCACOWSNCABC

LEACH

020406080

100120

Ener

gy (J

)


0 50 100 150 200 250 300 350Rounds

ABCACOWSNCABC

LEACH

020406080

100120

Ener

gy (J

)





0 100 200 300 400 500 600 700 800Rounds

ABCACOWSNCABC

LEACH

0

50

100

150

200

250

Ener

gy (J

)


0 25 50 75 100 125 150 175 200 225Rounds

ABCACOWSNCABC

LEACH

0

20

40

60

80

100

120

Ener

gy (J

)


0 100 200 300 400Rounds

ABCACOWSNCABC

LEACH

0

50

100

150

200

250

Ener

gy (J

)




SCH

5101520253035404550556065707580859095

10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 9552030 1525 10 5

CHBSNN





References












































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation


















=

119897119864elec + 119897119864fs1198892 119889 le 119889

0

119897119864elec + 119897119864mp1198894 119889 gt 119889

0

(2)




119896minus 1) +

119873

119896119897119864DataAgr

+ 119897119864Trans-elec + 119897120598amp119889119899

toBS

(4)


119864mem-node = 119897119864elec + 119897120598fs119889119899

toCH (5)










0)







1minus 1199092)2 + (119910

1minus 1199102)2

where 1199091and 119909


1and 119910




0) then

119870opt = radic4119873

5 + 12119870119863 + 1211987021198632

else

119870opt = radic60119873120598fs

(71198632 + 1201198712 + 18011987141198632) 120598mpendif


endfunction

















119894119895)



if (119889 le 1198890) then


119870opt = radic60119873120598fs71198632120598mp


if (119889 le 1198890) then

119870opt = radic119873

2else

119870opt = radic15119873120598fs281198632120598mp


if (119889 le 1198890) then

119870opt = radic4119873

5



else

119870opt = radic240119873120598fs1931198632120598mp




if 119894 = 119895 then

Prob119896(119901 119902) =

[120591 (119901 119902)]120572

[120578 (119901 119902)]120573

sum119902isin119877119902

[120591 (119901 119902)]120572

[120578 (119901 119902)]120573 if 119902 notin 119872119896

end ifend for











radic1198632


1198632

119870opt (6)



= 119864 [119910CH] =1

2(radic

1198632

119870opt)

119864 [(119909mem-nodes)2] = 119864 [(119909CH)

2] = 119864 [(119910mem-nodes)

2]

= 119864 [(119910CH)2] =

1198632

3119870opt

(7)




= 119864 [(119909mem-nodes)2]


+ 119864 [(119910mem-nodes)2]


=1198632

3119870opt

(8)



3119870opt (9)


119864cluster = 119864CHcycle + (119873




119870optminus 1)

+ 119897119864DataAgr119873


119899

toBS)

+ ((119873


119897120598fs1198632

3119870opt)))



toBS + 119873119897119864Trans-elec


2

3119870optminus119897120598fs1198632

3= 119873119897119864Rece-elec


toBS

+ 119873119897119864Trans-elec +119873

119897120598fs11986323119870opt minus 3

119897

120598fs1198632

(11)


120597119864cycle

120597119870opt= 119897120598amp119889

119899


2

31198702opt

1205972119864cycle

1205972119870opt=

2119873119897120598fs1198632

31198703optgt 0

(12)


0 = 119897120598amp119889119899


2

31198702opt

119897120598amp119889119899

toBS = 119897119864Rece-elec +119873119897120598fs119863

2

31198702opt

31198702opt119897120598amp119889119899


31198702opt (119897120598amp119889119899


1198702opt =119873119897120598fs119863

2

3119897 (120598amp119889119899


119870opt = radic119873119897120598fs119863

2

3 (120598amp119889119899


(13)





4

toBS

Centre

Free space 119889 le 1198890

1198632

6

Corner 21198632

3


12

Outside 51198632

12+ 119863119870 + 1198702

Centre


71198634

180

Corner 281198634

45


720

Outside 71198632

180+211986321198712

3+ 1198714


4




4


95

90

85

80

75

70

65

60

55

50

45

40

35

30

25

20

15

10

10 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95

5

5 5

CH at center





119870opt = radic119873120598fs119863

2

3 (120598amp1198892


119870opt = radic119873120598fs119863

2

3120598fs (5119863212 + 119863119870 + 1198702)


10 5

CH at corner

10 15 20 25 30 35 40 45 85807565 90 95605550 705

5

10

15

20

25

30

35

40

45

50

55

60

65

70

75

80

85

90

95


95

90

85

80

75

70

65

60

55

50

45

40

35

30

25

20

15

10

10 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95

5

5 5



119870opt = radic4119873

5 + 12119870119863 + 1211987021198632

(14)


119870opt = radic119873120598fs119863

2

3 (120598amp1198894


119870opt = radic119873120598fs119863

2

3120598mp (71198634180 + 2119863211987123 + 1198714)

119870opt = radic60119873120598fs

(71198632 + 1201198712 + 18011987141198632) 120598mp

(15)



119870opt = radic119873120598fs119863

2

3 (120598amp1198892


119870opt = radic119873120598fs119863

2

3120598fs (11986326)

119870opt = radic2119873

(16)


119870opt = radic119873120598fs119863

2

3 (120598amp1198894


119870opt = radic119873120598fs119863

2

3120598mp (71198634180)

119870opt = radic60119873120598fs71198632120598mp

(17)



119870opt = radic119873120598fs119863

2

3 (120598amp1198892


119870opt = radic119873120598fs119863

2

3120598fs (211986323)

119870opt = radic119873

2

(18)



119870opt = radic119873120598fs119863

2

3 (120598amp1198894


119870opt = radic119873120598fs119863

2

3120598mp (28119863445)

119870opt = radic15119873120598fs281198632120598mp

(19)



119870opt = radic119873120598fs119863

2

3 (120598amp1198892


119870opt = radic119873120598fs119863

2

3120598fs (5119863212)

119870opt = radic4119873

5

(20)


119870opt = radic119873120598fs119863

2

3 (120598amp1198894


119870opt = radic119873120598fs119863

2

3120598mp (1931198634720)

119870opt = radic240119873120598fs1931198632120598mp

(21)













119875119894=

fitness119894

sum119911119899=1

fitness119894

(22)




1199091015840119894119895= 119909119894119895+ 119903119894119895(119909119894119895minus 119909119896119895) (23)







119894119895and 119909




119909119895119894= LB119895119894+ 119903 (0 1) (UB119895

119894minus LB119895119894)









Prob119896(119901 119902)

=

[120591 (119901 119902)]120572sdot [120578 (119901 119902)]

120573

sum119902isin119877119902

[120591 (119901 119902)]120572sdot [120578 (119901 119902)]

120573 if 119902 notin 119872119896

0 otherwise

(25)






120578 (119901 119902) =119899

sum119894=1

119864119894119888

119864119894119868

times 119863 (BS119911 119902119894) (26)




CH 119902119894from BS



Δ120591119896 = 119908 sdot (1

119871119896) (27)


120591119901119902

= (1 minus 120588) 120591119901119902 (28)




fitness119894=119899

sum119894=1

119864119894119888

119864119894119868

times 119863 (BS119911 119896119894) (29)


119894










No

Start


value



into subregions





Yes

Stop

No alive nodes

Dtimes D region


If N gt 0
















No

Yes





119864mp 00013 pJbitm4




0 100 200 300 400 500 600 700 800 900 1000

ABCACOWSNCABC

LEACH

Rounds

0102030405060708090

100

Aliv

e nod

es






010

020

030

040

050

060

070

080

090

010

0011

0012

0013

0014

0015

0016

0017

0018

0019

0020

0021

00

RoundsABCACOWSNCABC

LEACH

0102030405060708090

100

Aliv

e nod

es


0 100 200 300 400 500Rounds

ABCACOWSNCABC

LEACH

0255075

100125150175200225

Aliv

e nod

es


7 Application



ABCACOWSNCABC

LEACH

0 100 200 300 400 500 600 700 800Rounds

0

50

100

150

200

250

Aliv

e nod

es


ABCACOWSNCABC

LEACH

0 25 50 75 100 125 150 175 200 225Rounds

0

50

100

150

200

250

Aliv

e nod

es







0 50 100 150 200 250 300 350 400Rounds

ABCACOWSNCABC

LEACH

0

50

100

150

200

250

Aliv

e nod

es


ABCACOWSNCABC

LEACH

Number of packets

68936

138131

102145

84794

59789

43983

32947

118263

85789

75742

30055

23240

16495

60690

42906

3226452096

49588








ABCACOWSNCABC

LEACH

Round

631

425

157

1303

924

311

158

27 25

375

164

71 16 6 1

151

9 25









0 100 200 300 400 500 600 700 800Rounds

ABCACOWSNCABC

LEACH

0

10

20

30

40

50

60

Ener

gy (J

)


010

020

030

040

050

060

070

080

090

010

0011

0012

0013

0014

0015

0016

0017

0018

00

RoundsABCACOWSNCABC

LEACH

020406080

100120

Ener

gy (J

)


0 50 100 150 200 250 300 350Rounds

ABCACOWSNCABC

LEACH

020406080

100120

Ener

gy (J

)





0 100 200 300 400 500 600 700 800Rounds

ABCACOWSNCABC

LEACH

0

50

100

150

200

250

Ener

gy (J

)


0 25 50 75 100 125 150 175 200 225Rounds

ABCACOWSNCABC

LEACH

0

20

40

60

80

100

120

Ener

gy (J

)


0 100 200 300 400Rounds

ABCACOWSNCABC

LEACH

0

50

100

150

200

250

Ener

gy (J

)




SCH

5101520253035404550556065707580859095

10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 9552030 1525 10 5

CHBSNN





References












































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation


















0)







1minus 1199092)2 + (119910

1minus 1199102)2

where 1199091and 119909


1and 119910




0) then

119870opt = radic4119873

5 + 12119870119863 + 1211987021198632

else

119870opt = radic60119873120598fs

(71198632 + 1201198712 + 18011987141198632) 120598mpendif


endfunction

















119894119895)



if (119889 le 1198890) then


119870opt = radic60119873120598fs71198632120598mp


if (119889 le 1198890) then

119870opt = radic119873

2else

119870opt = radic15119873120598fs281198632120598mp


if (119889 le 1198890) then

119870opt = radic4119873

5



else

119870opt = radic240119873120598fs1931198632120598mp




if 119894 = 119895 then

Prob119896(119901 119902) =

[120591 (119901 119902)]120572

[120578 (119901 119902)]120573

sum119902isin119877119902

[120591 (119901 119902)]120572

[120578 (119901 119902)]120573 if 119902 notin 119872119896

end ifend for











radic1198632


1198632

119870opt (6)



= 119864 [119910CH] =1

2(radic

1198632

119870opt)

119864 [(119909mem-nodes)2] = 119864 [(119909CH)

2] = 119864 [(119910mem-nodes)

2]

= 119864 [(119910CH)2] =

1198632

3119870opt

(7)




= 119864 [(119909mem-nodes)2]


+ 119864 [(119910mem-nodes)2]


=1198632

3119870opt

(8)



3119870opt (9)


119864cluster = 119864CHcycle + (119873




119870optminus 1)

+ 119897119864DataAgr119873


119899

toBS)

+ ((119873


119897120598fs1198632

3119870opt)))



toBS + 119873119897119864Trans-elec


2

3119870optminus119897120598fs1198632

3= 119873119897119864Rece-elec


toBS

+ 119873119897119864Trans-elec +119873

119897120598fs11986323119870opt minus 3

119897

120598fs1198632

(11)


120597119864cycle

120597119870opt= 119897120598amp119889

119899


2

31198702opt

1205972119864cycle

1205972119870opt=

2119873119897120598fs1198632

31198703optgt 0

(12)


0 = 119897120598amp119889119899


2

31198702opt

119897120598amp119889119899

toBS = 119897119864Rece-elec +119873119897120598fs119863

2

31198702opt

31198702opt119897120598amp119889119899


31198702opt (119897120598amp119889119899


1198702opt =119873119897120598fs119863

2

3119897 (120598amp119889119899


119870opt = radic119873119897120598fs119863

2

3 (120598amp119889119899


(13)





4

toBS

Centre

Free space 119889 le 1198890

1198632

6

Corner 21198632

3


12

Outside 51198632

12+ 119863119870 + 1198702

Centre


71198634

180

Corner 281198634

45


720

Outside 71198632

180+211986321198712

3+ 1198714


4




4


95

90

85

80

75

70

65

60

55

50

45

40

35

30

25

20

15

10

10 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95

5

5 5

CH at center





119870opt = radic119873120598fs119863

2

3 (120598amp1198892


119870opt = radic119873120598fs119863

2

3120598fs (5119863212 + 119863119870 + 1198702)


10 5

CH at corner

10 15 20 25 30 35 40 45 85807565 90 95605550 705

5

10

15

20

25

30

35

40

45

50

55

60

65

70

75

80

85

90

95


95

90

85

80

75

70

65

60

55

50

45

40

35

30

25

20

15

10

10 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95

5

5 5



119870opt = radic4119873

5 + 12119870119863 + 1211987021198632

(14)


119870opt = radic119873120598fs119863

2

3 (120598amp1198894


119870opt = radic119873120598fs119863

2

3120598mp (71198634180 + 2119863211987123 + 1198714)

119870opt = radic60119873120598fs

(71198632 + 1201198712 + 18011987141198632) 120598mp

(15)



119870opt = radic119873120598fs119863

2

3 (120598amp1198892


119870opt = radic119873120598fs119863

2

3120598fs (11986326)

119870opt = radic2119873

(16)


119870opt = radic119873120598fs119863

2

3 (120598amp1198894


119870opt = radic119873120598fs119863

2

3120598mp (71198634180)

119870opt = radic60119873120598fs71198632120598mp

(17)



119870opt = radic119873120598fs119863

2

3 (120598amp1198892


119870opt = radic119873120598fs119863

2

3120598fs (211986323)

119870opt = radic119873

2

(18)



119870opt = radic119873120598fs119863

2

3 (120598amp1198894


119870opt = radic119873120598fs119863

2

3120598mp (28119863445)

119870opt = radic15119873120598fs281198632120598mp

(19)



119870opt = radic119873120598fs119863

2

3 (120598amp1198892


119870opt = radic119873120598fs119863

2

3120598fs (5119863212)

119870opt = radic4119873

5

(20)


119870opt = radic119873120598fs119863

2

3 (120598amp1198894


119870opt = radic119873120598fs119863

2

3120598mp (1931198634720)

119870opt = radic240119873120598fs1931198632120598mp

(21)













119875119894=

fitness119894

sum119911119899=1

fitness119894

(22)




1199091015840119894119895= 119909119894119895+ 119903119894119895(119909119894119895minus 119909119896119895) (23)







119894119895and 119909




119909119895119894= LB119895119894+ 119903 (0 1) (UB119895

119894minus LB119895119894)









Prob119896(119901 119902)

=

[120591 (119901 119902)]120572sdot [120578 (119901 119902)]

120573

sum119902isin119877119902

[120591 (119901 119902)]120572sdot [120578 (119901 119902)]

120573 if 119902 notin 119872119896

0 otherwise

(25)






120578 (119901 119902) =119899

sum119894=1

119864119894119888

119864119894119868

times 119863 (BS119911 119902119894) (26)




CH 119902119894from BS



Δ120591119896 = 119908 sdot (1

119871119896) (27)


120591119901119902

= (1 minus 120588) 120591119901119902 (28)




fitness119894=119899

sum119894=1

119864119894119888

119864119894119868

times 119863 (BS119911 119896119894) (29)


119894










No

Start


value



into subregions





Yes

Stop

No alive nodes

Dtimes D region


If N gt 0
















No

Yes





119864mp 00013 pJbitm4




0 100 200 300 400 500 600 700 800 900 1000

ABCACOWSNCABC

LEACH

Rounds

0102030405060708090

100

Aliv

e nod

es






010

020

030

040

050

060

070

080

090

010

0011

0012

0013

0014

0015

0016

0017

0018

0019

0020

0021

00

RoundsABCACOWSNCABC

LEACH

0102030405060708090

100

Aliv

e nod

es


0 100 200 300 400 500Rounds

ABCACOWSNCABC

LEACH

0255075

100125150175200225

Aliv

e nod

es


7 Application



ABCACOWSNCABC

LEACH

0 100 200 300 400 500 600 700 800Rounds

0

50

100

150

200

250

Aliv

e nod

es


ABCACOWSNCABC

LEACH

0 25 50 75 100 125 150 175 200 225Rounds

0

50

100

150

200

250

Aliv

e nod

es







0 50 100 150 200 250 300 350 400Rounds

ABCACOWSNCABC

LEACH

0

50

100

150

200

250

Aliv

e nod

es


ABCACOWSNCABC

LEACH

Number of packets

68936

138131

102145

84794

59789

43983

32947

118263

85789

75742

30055

23240

16495

60690

42906

3226452096

49588








ABCACOWSNCABC

LEACH

Round

631

425

157

1303

924

311

158

27 25

375

164

71 16 6 1

151

9 25









0 100 200 300 400 500 600 700 800Rounds

ABCACOWSNCABC

LEACH

0

10

20

30

40

50

60

Ener

gy (J

)


010

020

030

040

050

060

070

080

090

010

0011

0012

0013

0014

0015

0016

0017

0018

00

RoundsABCACOWSNCABC

LEACH

020406080

100120

Ener

gy (J

)


0 50 100 150 200 250 300 350Rounds

ABCACOWSNCABC

LEACH

020406080

100120

Ener

gy (J

)





0 100 200 300 400 500 600 700 800Rounds

ABCACOWSNCABC

LEACH

0

50

100

150

200

250

Ener

gy (J

)


0 25 50 75 100 125 150 175 200 225Rounds

ABCACOWSNCABC

LEACH

0

20

40

60

80

100

120

Ener

gy (J

)


0 100 200 300 400Rounds

ABCACOWSNCABC

LEACH

0

50

100

150

200

250

Ener

gy (J

)




SCH

5101520253035404550556065707580859095

10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 9552030 1525 10 5

CHBSNN





References












































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation
























119894119895)



if (119889 le 1198890) then


119870opt = radic60119873120598fs71198632120598mp


if (119889 le 1198890) then

119870opt = radic119873

2else

119870opt = radic15119873120598fs281198632120598mp


if (119889 le 1198890) then

119870opt = radic4119873

5



else

119870opt = radic240119873120598fs1931198632120598mp




if 119894 = 119895 then

Prob119896(119901 119902) =

[120591 (119901 119902)]120572

[120578 (119901 119902)]120573

sum119902isin119877119902

[120591 (119901 119902)]120572

[120578 (119901 119902)]120573 if 119902 notin 119872119896

end ifend for











radic1198632


1198632

119870opt (6)



= 119864 [119910CH] =1

2(radic

1198632

119870opt)

119864 [(119909mem-nodes)2] = 119864 [(119909CH)

2] = 119864 [(119910mem-nodes)

2]

= 119864 [(119910CH)2] =

1198632

3119870opt

(7)




= 119864 [(119909mem-nodes)2]


+ 119864 [(119910mem-nodes)2]


=1198632

3119870opt

(8)



3119870opt (9)


119864cluster = 119864CHcycle + (119873




119870optminus 1)

+ 119897119864DataAgr119873


119899

toBS)

+ ((119873


119897120598fs1198632

3119870opt)))



toBS + 119873119897119864Trans-elec


2

3119870optminus119897120598fs1198632

3= 119873119897119864Rece-elec


toBS

+ 119873119897119864Trans-elec +119873

119897120598fs11986323119870opt minus 3

119897

120598fs1198632

(11)


120597119864cycle

120597119870opt= 119897120598amp119889

119899


2

31198702opt

1205972119864cycle

1205972119870opt=

2119873119897120598fs1198632

31198703optgt 0

(12)


0 = 119897120598amp119889119899


2

31198702opt

119897120598amp119889119899

toBS = 119897119864Rece-elec +119873119897120598fs119863

2

31198702opt

31198702opt119897120598amp119889119899


31198702opt (119897120598amp119889119899


1198702opt =119873119897120598fs119863

2

3119897 (120598amp119889119899


119870opt = radic119873119897120598fs119863

2

3 (120598amp119889119899


(13)





4

toBS

Centre

Free space 119889 le 1198890

1198632

6

Corner 21198632

3


12

Outside 51198632

12+ 119863119870 + 1198702

Centre


71198634

180

Corner 281198634

45


720

Outside 71198632

180+211986321198712

3+ 1198714


4




4


95

90

85

80

75

70

65

60

55

50

45

40

35

30

25

20

15

10

10 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95

5

5 5

CH at center





119870opt = radic119873120598fs119863

2

3 (120598amp1198892


119870opt = radic119873120598fs119863

2

3120598fs (5119863212 + 119863119870 + 1198702)


10 5

CH at corner

10 15 20 25 30 35 40 45 85807565 90 95605550 705

5

10

15

20

25

30

35

40

45

50

55

60

65

70

75

80

85

90

95


95

90

85

80

75

70

65

60

55

50

45

40

35

30

25

20

15

10

10 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95

5

5 5



119870opt = radic4119873

5 + 12119870119863 + 1211987021198632

(14)


119870opt = radic119873120598fs119863

2

3 (120598amp1198894


119870opt = radic119873120598fs119863

2

3120598mp (71198634180 + 2119863211987123 + 1198714)

119870opt = radic60119873120598fs

(71198632 + 1201198712 + 18011987141198632) 120598mp

(15)



119870opt = radic119873120598fs119863

2

3 (120598amp1198892


119870opt = radic119873120598fs119863

2

3120598fs (11986326)

119870opt = radic2119873

(16)


119870opt = radic119873120598fs119863

2

3 (120598amp1198894


119870opt = radic119873120598fs119863

2

3120598mp (71198634180)

119870opt = radic60119873120598fs71198632120598mp

(17)



119870opt = radic119873120598fs119863

2

3 (120598amp1198892


119870opt = radic119873120598fs119863

2

3120598fs (211986323)

119870opt = radic119873

2

(18)



119870opt = radic119873120598fs119863

2

3 (120598amp1198894


119870opt = radic119873120598fs119863

2

3120598mp (28119863445)

119870opt = radic15119873120598fs281198632120598mp

(19)



119870opt = radic119873120598fs119863

2

3 (120598amp1198892


119870opt = radic119873120598fs119863

2

3120598fs (5119863212)

119870opt = radic4119873

5

(20)


119870opt = radic119873120598fs119863

2

3 (120598amp1198894


119870opt = radic119873120598fs119863

2

3120598mp (1931198634720)

119870opt = radic240119873120598fs1931198632120598mp

(21)













119875119894=

fitness119894

sum119911119899=1

fitness119894

(22)




1199091015840119894119895= 119909119894119895+ 119903119894119895(119909119894119895minus 119909119896119895) (23)







119894119895and 119909




119909119895119894= LB119895119894+ 119903 (0 1) (UB119895

119894minus LB119895119894)









Prob119896(119901 119902)

=

[120591 (119901 119902)]120572sdot [120578 (119901 119902)]

120573

sum119902isin119877119902

[120591 (119901 119902)]120572sdot [120578 (119901 119902)]

120573 if 119902 notin 119872119896

0 otherwise

(25)






120578 (119901 119902) =119899

sum119894=1

119864119894119888

119864119894119868

times 119863 (BS119911 119902119894) (26)




CH 119902119894from BS



Δ120591119896 = 119908 sdot (1

119871119896) (27)


120591119901119902

= (1 minus 120588) 120591119901119902 (28)




fitness119894=119899

sum119894=1

119864119894119888

119864119894119868

times 119863 (BS119911 119896119894) (29)


119894










No

Start


value



into subregions





Yes

Stop

No alive nodes

Dtimes D region


If N gt 0
















No

Yes





119864mp 00013 pJbitm4




0 100 200 300 400 500 600 700 800 900 1000

ABCACOWSNCABC

LEACH

Rounds

0102030405060708090

100

Aliv

e nod

es






010

020

030

040

050

060

070

080

090

010

0011

0012

0013

0014

0015

0016

0017

0018

0019

0020

0021

00

RoundsABCACOWSNCABC

LEACH

0102030405060708090

100

Aliv

e nod

es


0 100 200 300 400 500Rounds

ABCACOWSNCABC

LEACH

0255075

100125150175200225

Aliv

e nod

es


7 Application



ABCACOWSNCABC

LEACH

0 100 200 300 400 500 600 700 800Rounds

0

50

100

150

200

250

Aliv

e nod

es


ABCACOWSNCABC

LEACH

0 25 50 75 100 125 150 175 200 225Rounds

0

50

100

150

200

250

Aliv

e nod

es







0 50 100 150 200 250 300 350 400Rounds

ABCACOWSNCABC

LEACH

0

50

100

150

200

250

Aliv

e nod

es


ABCACOWSNCABC

LEACH

Number of packets

68936

138131

102145

84794

59789

43983

32947

118263

85789

75742

30055

23240

16495

60690

42906

3226452096

49588








ABCACOWSNCABC

LEACH

Round

631

425

157

1303

924

311

158

27 25

375

164

71 16 6 1

151

9 25









0 100 200 300 400 500 600 700 800Rounds

ABCACOWSNCABC

LEACH

0

10

20

30

40

50

60

Ener

gy (J

)


010

020

030

040

050

060

070

080

090

010

0011

0012

0013

0014

0015

0016

0017

0018

00

RoundsABCACOWSNCABC

LEACH

020406080

100120

Ener

gy (J

)


0 50 100 150 200 250 300 350Rounds

ABCACOWSNCABC

LEACH

020406080

100120

Ener

gy (J

)





0 100 200 300 400 500 600 700 800Rounds

ABCACOWSNCABC

LEACH

0

50

100

150

200

250

Ener

gy (J

)


0 25 50 75 100 125 150 175 200 225Rounds

ABCACOWSNCABC

LEACH

0

20

40

60

80

100

120

Ener

gy (J

)


0 100 200 300 400Rounds

ABCACOWSNCABC

LEACH

0

50

100

150

200

250

Ener

gy (J

)




SCH

5101520253035404550556065707580859095

10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 9552030 1525 10 5

CHBSNN





References












































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










else

119870opt = radic240119873120598fs1931198632120598mp




if 119894 = 119895 then

Prob119896(119901 119902) =

[120591 (119901 119902)]120572

[120578 (119901 119902)]120573

sum119902isin119877119902

[120591 (119901 119902)]120572

[120578 (119901 119902)]120573 if 119902 notin 119872119896

end ifend for











radic1198632


1198632

119870opt (6)



= 119864 [119910CH] =1

2(radic

1198632

119870opt)

119864 [(119909mem-nodes)2] = 119864 [(119909CH)

2] = 119864 [(119910mem-nodes)

2]

= 119864 [(119910CH)2] =

1198632

3119870opt

(7)




= 119864 [(119909mem-nodes)2]


+ 119864 [(119910mem-nodes)2]


=1198632

3119870opt

(8)



3119870opt (9)


119864cluster = 119864CHcycle + (119873




119870optminus 1)

+ 119897119864DataAgr119873


119899

toBS)

+ ((119873


119897120598fs1198632

3119870opt)))



toBS + 119873119897119864Trans-elec


2

3119870optminus119897120598fs1198632

3= 119873119897119864Rece-elec


toBS

+ 119873119897119864Trans-elec +119873

119897120598fs11986323119870opt minus 3

119897

120598fs1198632

(11)


120597119864cycle

120597119870opt= 119897120598amp119889

119899


2

31198702opt

1205972119864cycle

1205972119870opt=

2119873119897120598fs1198632

31198703optgt 0

(12)


0 = 119897120598amp119889119899


2

31198702opt

119897120598amp119889119899

toBS = 119897119864Rece-elec +119873119897120598fs119863

2

31198702opt

31198702opt119897120598amp119889119899


31198702opt (119897120598amp119889119899


1198702opt =119873119897120598fs119863

2

3119897 (120598amp119889119899


119870opt = radic119873119897120598fs119863

2

3 (120598amp119889119899


(13)





4

toBS

Centre

Free space 119889 le 1198890

1198632

6

Corner 21198632

3


12

Outside 51198632

12+ 119863119870 + 1198702

Centre


71198634

180

Corner 281198634

45


720

Outside 71198632

180+211986321198712

3+ 1198714


4




4


95

90

85

80

75

70

65

60

55

50

45

40

35

30

25

20

15

10

10 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95

5

5 5

CH at center





119870opt = radic119873120598fs119863

2

3 (120598amp1198892


119870opt = radic119873120598fs119863

2

3120598fs (5119863212 + 119863119870 + 1198702)


10 5

CH at corner

10 15 20 25 30 35 40 45 85807565 90 95605550 705

5

10

15

20

25

30

35

40

45

50

55

60

65

70

75

80

85

90

95


95

90

85

80

75

70

65

60

55

50

45

40

35

30

25

20

15

10

10 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95

5

5 5



119870opt = radic4119873

5 + 12119870119863 + 1211987021198632

(14)


119870opt = radic119873120598fs119863

2

3 (120598amp1198894


119870opt = radic119873120598fs119863

2

3120598mp (71198634180 + 2119863211987123 + 1198714)

119870opt = radic60119873120598fs

(71198632 + 1201198712 + 18011987141198632) 120598mp

(15)



119870opt = radic119873120598fs119863

2

3 (120598amp1198892


119870opt = radic119873120598fs119863

2

3120598fs (11986326)

119870opt = radic2119873

(16)


119870opt = radic119873120598fs119863

2

3 (120598amp1198894


119870opt = radic119873120598fs119863

2

3120598mp (71198634180)

119870opt = radic60119873120598fs71198632120598mp

(17)



119870opt = radic119873120598fs119863

2

3 (120598amp1198892


119870opt = radic119873120598fs119863

2

3120598fs (211986323)

119870opt = radic119873

2

(18)



119870opt = radic119873120598fs119863

2

3 (120598amp1198894


119870opt = radic119873120598fs119863

2

3120598mp (28119863445)

119870opt = radic15119873120598fs281198632120598mp

(19)



119870opt = radic119873120598fs119863

2

3 (120598amp1198892


119870opt = radic119873120598fs119863

2

3120598fs (5119863212)

119870opt = radic4119873

5

(20)


119870opt = radic119873120598fs119863

2

3 (120598amp1198894


119870opt = radic119873120598fs119863

2

3120598mp (1931198634720)

119870opt = radic240119873120598fs1931198632120598mp

(21)













119875119894=

fitness119894

sum119911119899=1

fitness119894

(22)




1199091015840119894119895= 119909119894119895+ 119903119894119895(119909119894119895minus 119909119896119895) (23)







119894119895and 119909




119909119895119894= LB119895119894+ 119903 (0 1) (UB119895

119894minus LB119895119894)









Prob119896(119901 119902)

=

[120591 (119901 119902)]120572sdot [120578 (119901 119902)]

120573

sum119902isin119877119902

[120591 (119901 119902)]120572sdot [120578 (119901 119902)]

120573 if 119902 notin 119872119896

0 otherwise

(25)






120578 (119901 119902) =119899

sum119894=1

119864119894119888

119864119894119868

times 119863 (BS119911 119902119894) (26)




CH 119902119894from BS



Δ120591119896 = 119908 sdot (1

119871119896) (27)


120591119901119902

= (1 minus 120588) 120591119901119902 (28)




fitness119894=119899

sum119894=1

119864119894119888

119864119894119868

times 119863 (BS119911 119896119894) (29)


119894










No

Start


value



into subregions





Yes

Stop

No alive nodes

Dtimes D region


If N gt 0
















No

Yes





119864mp 00013 pJbitm4




0 100 200 300 400 500 600 700 800 900 1000

ABCACOWSNCABC

LEACH

Rounds

0102030405060708090

100

Aliv

e nod

es






010

020

030

040

050

060

070

080

090

010

0011

0012

0013

0014

0015

0016

0017

0018

0019

0020

0021

00

RoundsABCACOWSNCABC

LEACH

0102030405060708090

100

Aliv

e nod

es


0 100 200 300 400 500Rounds

ABCACOWSNCABC

LEACH

0255075

100125150175200225

Aliv

e nod

es


7 Application



ABCACOWSNCABC

LEACH

0 100 200 300 400 500 600 700 800Rounds

0

50

100

150

200

250

Aliv

e nod

es


ABCACOWSNCABC

LEACH

0 25 50 75 100 125 150 175 200 225Rounds

0

50

100

150

200

250

Aliv

e nod

es







0 50 100 150 200 250 300 350 400Rounds

ABCACOWSNCABC

LEACH

0

50

100

150

200

250

Aliv

e nod

es


ABCACOWSNCABC

LEACH

Number of packets

68936

138131

102145

84794

59789

43983

32947

118263

85789

75742

30055

23240

16495

60690

42906

3226452096

49588








ABCACOWSNCABC

LEACH

Round

631

425

157

1303

924

311

158

27 25

375

164

71 16 6 1

151

9 25









0 100 200 300 400 500 600 700 800Rounds

ABCACOWSNCABC

LEACH

0

10

20

30

40

50

60

Ener

gy (J

)


010

020

030

040

050

060

070

080

090

010

0011

0012

0013

0014

0015

0016

0017

0018

00

RoundsABCACOWSNCABC

LEACH

020406080

100120

Ener

gy (J

)


0 50 100 150 200 250 300 350Rounds

ABCACOWSNCABC

LEACH

020406080

100120

Ener

gy (J

)





0 100 200 300 400 500 600 700 800Rounds

ABCACOWSNCABC

LEACH

0

50

100

150

200

250

Ener

gy (J

)


0 25 50 75 100 125 150 175 200 225Rounds

ABCACOWSNCABC

LEACH

0

20

40

60

80

100

120

Ener

gy (J

)


0 100 200 300 400Rounds

ABCACOWSNCABC

LEACH

0

50

100

150

200

250

Ener

gy (J

)




SCH

5101520253035404550556065707580859095

10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 9552030 1525 10 5

CHBSNN





References












































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation











radic1198632


1198632

119870opt (6)



= 119864 [119910CH] =1

2(radic

1198632

119870opt)

119864 [(119909mem-nodes)2] = 119864 [(119909CH)

2] = 119864 [(119910mem-nodes)

2]

= 119864 [(119910CH)2] =

1198632

3119870opt

(7)




= 119864 [(119909mem-nodes)2]


+ 119864 [(119910mem-nodes)2]


=1198632

3119870opt

(8)



3119870opt (9)


119864cluster = 119864CHcycle + (119873




119870optminus 1)

+ 119897119864DataAgr119873


119899

toBS)

+ ((119873


119897120598fs1198632

3119870opt)))



toBS + 119873119897119864Trans-elec


2

3119870optminus119897120598fs1198632

3= 119873119897119864Rece-elec


toBS

+ 119873119897119864Trans-elec +119873

119897120598fs11986323119870opt minus 3

119897

120598fs1198632

(11)


120597119864cycle

120597119870opt= 119897120598amp119889

119899


2

31198702opt

1205972119864cycle

1205972119870opt=

2119873119897120598fs1198632

31198703optgt 0

(12)


0 = 119897120598amp119889119899


2

31198702opt

119897120598amp119889119899

toBS = 119897119864Rece-elec +119873119897120598fs119863

2

31198702opt

31198702opt119897120598amp119889119899


31198702opt (119897120598amp119889119899


1198702opt =119873119897120598fs119863

2

3119897 (120598amp119889119899


119870opt = radic119873119897120598fs119863

2

3 (120598amp119889119899


(13)





4

toBS

Centre

Free space 119889 le 1198890

1198632

6

Corner 21198632

3


12

Outside 51198632

12+ 119863119870 + 1198702

Centre


71198634

180

Corner 281198634

45


720

Outside 71198632

180+211986321198712

3+ 1198714


4




4


95

90

85

80

75

70

65

60

55

50

45

40

35

30

25

20

15

10

10 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95

5

5 5

CH at center





119870opt = radic119873120598fs119863

2

3 (120598amp1198892


119870opt = radic119873120598fs119863

2

3120598fs (5119863212 + 119863119870 + 1198702)


10 5

CH at corner

10 15 20 25 30 35 40 45 85807565 90 95605550 705

5

10

15

20

25

30

35

40

45

50

55

60

65

70

75

80

85

90

95


95

90

85

80

75

70

65

60

55

50

45

40

35

30

25

20

15

10

10 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95

5

5 5



119870opt = radic4119873

5 + 12119870119863 + 1211987021198632

(14)


119870opt = radic119873120598fs119863

2

3 (120598amp1198894


119870opt = radic119873120598fs119863

2

3120598mp (71198634180 + 2119863211987123 + 1198714)

119870opt = radic60119873120598fs

(71198632 + 1201198712 + 18011987141198632) 120598mp

(15)



119870opt = radic119873120598fs119863

2

3 (120598amp1198892


119870opt = radic119873120598fs119863

2

3120598fs (11986326)

119870opt = radic2119873

(16)


119870opt = radic119873120598fs119863

2

3 (120598amp1198894


119870opt = radic119873120598fs119863

2

3120598mp (71198634180)

119870opt = radic60119873120598fs71198632120598mp

(17)



119870opt = radic119873120598fs119863

2

3 (120598amp1198892


119870opt = radic119873120598fs119863

2

3120598fs (211986323)

119870opt = radic119873

2

(18)



119870opt = radic119873120598fs119863

2

3 (120598amp1198894


119870opt = radic119873120598fs119863

2

3120598mp (28119863445)

119870opt = radic15119873120598fs281198632120598mp

(19)



119870opt = radic119873120598fs119863

2

3 (120598amp1198892


119870opt = radic119873120598fs119863

2

3120598fs (5119863212)

119870opt = radic4119873

5

(20)


119870opt = radic119873120598fs119863

2

3 (120598amp1198894


119870opt = radic119873120598fs119863

2

3120598mp (1931198634720)

119870opt = radic240119873120598fs1931198632120598mp

(21)













119875119894=

fitness119894

sum119911119899=1

fitness119894

(22)




1199091015840119894119895= 119909119894119895+ 119903119894119895(119909119894119895minus 119909119896119895) (23)







119894119895and 119909




119909119895119894= LB119895119894+ 119903 (0 1) (UB119895

119894minus LB119895119894)









Prob119896(119901 119902)

=

[120591 (119901 119902)]120572sdot [120578 (119901 119902)]

120573

sum119902isin119877119902

[120591 (119901 119902)]120572sdot [120578 (119901 119902)]

120573 if 119902 notin 119872119896

0 otherwise

(25)






120578 (119901 119902) =119899

sum119894=1

119864119894119888

119864119894119868

times 119863 (BS119911 119902119894) (26)




CH 119902119894from BS



Δ120591119896 = 119908 sdot (1

119871119896) (27)


120591119901119902

= (1 minus 120588) 120591119901119902 (28)




fitness119894=119899

sum119894=1

119864119894119888

119864119894119868

times 119863 (BS119911 119896119894) (29)


119894










No

Start


value



into subregions





Yes

Stop

No alive nodes

Dtimes D region


If N gt 0
















No

Yes





119864mp 00013 pJbitm4




0 100 200 300 400 500 600 700 800 900 1000

ABCACOWSNCABC

LEACH

Rounds

0102030405060708090

100

Aliv

e nod

es






010

020

030

040

050

060

070

080

090

010

0011

0012

0013

0014

0015

0016

0017

0018

0019

0020

0021

00

RoundsABCACOWSNCABC

LEACH

0102030405060708090

100

Aliv

e nod

es


0 100 200 300 400 500Rounds

ABCACOWSNCABC

LEACH

0255075

100125150175200225

Aliv

e nod

es


7 Application



ABCACOWSNCABC

LEACH

0 100 200 300 400 500 600 700 800Rounds

0

50

100

150

200

250

Aliv

e nod

es


ABCACOWSNCABC

LEACH

0 25 50 75 100 125 150 175 200 225Rounds

0

50

100

150

200

250

Aliv

e nod

es







0 50 100 150 200 250 300 350 400Rounds

ABCACOWSNCABC

LEACH

0

50

100

150

200

250

Aliv

e nod

es


ABCACOWSNCABC

LEACH

Number of packets

68936

138131

102145

84794

59789

43983

32947

118263

85789

75742

30055

23240

16495

60690

42906

3226452096

49588








ABCACOWSNCABC

LEACH

Round

631

425

157

1303

924

311

158

27 25

375

164

71 16 6 1

151

9 25









0 100 200 300 400 500 600 700 800Rounds

ABCACOWSNCABC

LEACH

0

10

20

30

40

50

60

Ener

gy (J

)


010

020

030

040

050

060

070

080

090

010

0011

0012

0013

0014

0015

0016

0017

0018

00

RoundsABCACOWSNCABC

LEACH

020406080

100120

Ener

gy (J

)


0 50 100 150 200 250 300 350Rounds

ABCACOWSNCABC

LEACH

020406080

100120

Ener

gy (J

)





0 100 200 300 400 500 600 700 800Rounds

ABCACOWSNCABC

LEACH

0

50

100

150

200

250

Ener

gy (J

)


0 25 50 75 100 125 150 175 200 225Rounds

ABCACOWSNCABC

LEACH

0

20

40

60

80

100

120

Ener

gy (J

)


0 100 200 300 400Rounds

ABCACOWSNCABC

LEACH

0

50

100

150

200

250

Ener

gy (J

)




SCH

5101520253035404550556065707580859095

10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 9552030 1525 10 5

CHBSNN





References












































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation












4

toBS

Centre

Free space 119889 le 1198890

1198632

6

Corner 21198632

3


12

Outside 51198632

12+ 119863119870 + 1198702

Centre


71198634

180

Corner 281198634

45


720

Outside 71198632

180+211986321198712

3+ 1198714


4




4


95

90

85

80

75

70

65

60

55

50

45

40

35

30

25

20

15

10

10 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95

5

5 5

CH at center





119870opt = radic119873120598fs119863

2

3 (120598amp1198892


119870opt = radic119873120598fs119863

2

3120598fs (5119863212 + 119863119870 + 1198702)


10 5

CH at corner

10 15 20 25 30 35 40 45 85807565 90 95605550 705

5

10

15

20

25

30

35

40

45

50

55

60

65

70

75

80

85

90

95


95

90

85

80

75

70

65

60

55

50

45

40

35

30

25

20

15

10

10 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95

5

5 5



119870opt = radic4119873

5 + 12119870119863 + 1211987021198632

(14)


119870opt = radic119873120598fs119863

2

3 (120598amp1198894


119870opt = radic119873120598fs119863

2

3120598mp (71198634180 + 2119863211987123 + 1198714)

119870opt = radic60119873120598fs

(71198632 + 1201198712 + 18011987141198632) 120598mp

(15)



119870opt = radic119873120598fs119863

2

3 (120598amp1198892


119870opt = radic119873120598fs119863

2

3120598fs (11986326)

119870opt = radic2119873

(16)


119870opt = radic119873120598fs119863

2

3 (120598amp1198894


119870opt = radic119873120598fs119863

2

3120598mp (71198634180)

119870opt = radic60119873120598fs71198632120598mp

(17)



119870opt = radic119873120598fs119863

2

3 (120598amp1198892


119870opt = radic119873120598fs119863

2

3120598fs (211986323)

119870opt = radic119873

2

(18)



119870opt = radic119873120598fs119863

2

3 (120598amp1198894


119870opt = radic119873120598fs119863

2

3120598mp (28119863445)

119870opt = radic15119873120598fs281198632120598mp

(19)



119870opt = radic119873120598fs119863

2

3 (120598amp1198892


119870opt = radic119873120598fs119863

2

3120598fs (5119863212)

119870opt = radic4119873

5

(20)


119870opt = radic119873120598fs119863

2

3 (120598amp1198894


119870opt = radic119873120598fs119863

2

3120598mp (1931198634720)

119870opt = radic240119873120598fs1931198632120598mp

(21)













119875119894=

fitness119894

sum119911119899=1

fitness119894

(22)




1199091015840119894119895= 119909119894119895+ 119903119894119895(119909119894119895minus 119909119896119895) (23)







119894119895and 119909




119909119895119894= LB119895119894+ 119903 (0 1) (UB119895

119894minus LB119895119894)









Prob119896(119901 119902)

=

[120591 (119901 119902)]120572sdot [120578 (119901 119902)]

120573

sum119902isin119877119902

[120591 (119901 119902)]120572sdot [120578 (119901 119902)]

120573 if 119902 notin 119872119896

0 otherwise

(25)






120578 (119901 119902) =119899

sum119894=1

119864119894119888

119864119894119868

times 119863 (BS119911 119902119894) (26)




CH 119902119894from BS



Δ120591119896 = 119908 sdot (1

119871119896) (27)


120591119901119902

= (1 minus 120588) 120591119901119902 (28)




fitness119894=119899

sum119894=1

119864119894119888

119864119894119868

times 119863 (BS119911 119896119894) (29)


119894










No

Start


value



into subregions





Yes

Stop

No alive nodes

Dtimes D region


If N gt 0
















No

Yes





119864mp 00013 pJbitm4




0 100 200 300 400 500 600 700 800 900 1000

ABCACOWSNCABC

LEACH

Rounds

0102030405060708090

100

Aliv

e nod

es






010

020

030

040

050

060

070

080

090

010

0011

0012

0013

0014

0015

0016

0017

0018

0019

0020

0021

00

RoundsABCACOWSNCABC

LEACH

0102030405060708090

100

Aliv

e nod

es


0 100 200 300 400 500Rounds

ABCACOWSNCABC

LEACH

0255075

100125150175200225

Aliv

e nod

es


7 Application



ABCACOWSNCABC

LEACH

0 100 200 300 400 500 600 700 800Rounds

0

50

100

150

200

250

Aliv

e nod

es


ABCACOWSNCABC

LEACH

0 25 50 75 100 125 150 175 200 225Rounds

0

50

100

150

200

250

Aliv

e nod

es







0 50 100 150 200 250 300 350 400Rounds

ABCACOWSNCABC

LEACH

0

50

100

150

200

250

Aliv

e nod

es


ABCACOWSNCABC

LEACH

Number of packets

68936

138131

102145

84794

59789

43983

32947

118263

85789

75742

30055

23240

16495

60690

42906

3226452096

49588








ABCACOWSNCABC

LEACH

Round

631

425

157

1303

924

311

158

27 25

375

164

71 16 6 1

151

9 25









0 100 200 300 400 500 600 700 800Rounds

ABCACOWSNCABC

LEACH

0

10

20

30

40

50

60

Ener

gy (J

)


010

020

030

040

050

060

070

080

090

010

0011

0012

0013

0014

0015

0016

0017

0018

00

RoundsABCACOWSNCABC

LEACH

020406080

100120

Ener

gy (J

)


0 50 100 150 200 250 300 350Rounds

ABCACOWSNCABC

LEACH

020406080

100120

Ener

gy (J

)





0 100 200 300 400 500 600 700 800Rounds

ABCACOWSNCABC

LEACH

0

50

100

150

200

250

Ener

gy (J

)


0 25 50 75 100 125 150 175 200 225Rounds

ABCACOWSNCABC

LEACH

0

20

40

60

80

100

120

Ener

gy (J

)


0 100 200 300 400Rounds

ABCACOWSNCABC

LEACH

0

50

100

150

200

250

Ener

gy (J

)




SCH

5101520253035404550556065707580859095

10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 9552030 1525 10 5

CHBSNN





References












































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










10 5

CH at corner

10 15 20 25 30 35 40 45 85807565 90 95605550 705

5

10

15

20

25

30

35

40

45

50

55

60

65

70

75

80

85

90

95


95

90

85

80

75

70

65

60

55

50

45

40

35

30

25

20

15

10

10 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95

5

5 5



119870opt = radic4119873

5 + 12119870119863 + 1211987021198632

(14)


119870opt = radic119873120598fs119863

2

3 (120598amp1198894


119870opt = radic119873120598fs119863

2

3120598mp (71198634180 + 2119863211987123 + 1198714)

119870opt = radic60119873120598fs

(71198632 + 1201198712 + 18011987141198632) 120598mp

(15)



119870opt = radic119873120598fs119863

2

3 (120598amp1198892


119870opt = radic119873120598fs119863

2

3120598fs (11986326)

119870opt = radic2119873

(16)


119870opt = radic119873120598fs119863

2

3 (120598amp1198894


119870opt = radic119873120598fs119863

2

3120598mp (71198634180)

119870opt = radic60119873120598fs71198632120598mp

(17)



119870opt = radic119873120598fs119863

2

3 (120598amp1198892


119870opt = radic119873120598fs119863

2

3120598fs (211986323)

119870opt = radic119873

2

(18)



119870opt = radic119873120598fs119863

2

3 (120598amp1198894


119870opt = radic119873120598fs119863

2

3120598mp (28119863445)

119870opt = radic15119873120598fs281198632120598mp

(19)



119870opt = radic119873120598fs119863

2

3 (120598amp1198892


119870opt = radic119873120598fs119863

2

3120598fs (5119863212)

119870opt = radic4119873

5

(20)


119870opt = radic119873120598fs119863

2

3 (120598amp1198894


119870opt = radic119873120598fs119863

2

3120598mp (1931198634720)

119870opt = radic240119873120598fs1931198632120598mp

(21)













119875119894=

fitness119894

sum119911119899=1

fitness119894

(22)




1199091015840119894119895= 119909119894119895+ 119903119894119895(119909119894119895minus 119909119896119895) (23)







119894119895and 119909




119909119895119894= LB119895119894+ 119903 (0 1) (UB119895

119894minus LB119895119894)









Prob119896(119901 119902)

=

[120591 (119901 119902)]120572sdot [120578 (119901 119902)]

120573

sum119902isin119877119902

[120591 (119901 119902)]120572sdot [120578 (119901 119902)]

120573 if 119902 notin 119872119896

0 otherwise

(25)






120578 (119901 119902) =119899

sum119894=1

119864119894119888

119864119894119868

times 119863 (BS119911 119902119894) (26)




CH 119902119894from BS



Δ120591119896 = 119908 sdot (1

119871119896) (27)


120591119901119902

= (1 minus 120588) 120591119901119902 (28)




fitness119894=119899

sum119894=1

119864119894119888

119864119894119868

times 119863 (BS119911 119896119894) (29)


119894










No

Start


value



into subregions





Yes

Stop

No alive nodes

Dtimes D region


If N gt 0
















No

Yes





119864mp 00013 pJbitm4




0 100 200 300 400 500 600 700 800 900 1000

ABCACOWSNCABC

LEACH

Rounds

0102030405060708090

100

Aliv

e nod

es






010

020

030

040

050

060

070

080

090

010

0011

0012

0013

0014

0015

0016

0017

0018

0019

0020

0021

00

RoundsABCACOWSNCABC

LEACH

0102030405060708090

100

Aliv

e nod

es


0 100 200 300 400 500Rounds

ABCACOWSNCABC

LEACH

0255075

100125150175200225

Aliv

e nod

es


7 Application



ABCACOWSNCABC

LEACH

0 100 200 300 400 500 600 700 800Rounds

0

50

100

150

200

250

Aliv

e nod

es


ABCACOWSNCABC

LEACH

0 25 50 75 100 125 150 175 200 225Rounds

0

50

100

150

200

250

Aliv

e nod

es







0 50 100 150 200 250 300 350 400Rounds

ABCACOWSNCABC

LEACH

0

50

100

150

200

250

Aliv

e nod

es


ABCACOWSNCABC

LEACH

Number of packets

68936

138131

102145

84794

59789

43983

32947

118263

85789

75742

30055

23240

16495

60690

42906

3226452096

49588








ABCACOWSNCABC

LEACH

Round

631

425

157

1303

924

311

158

27 25

375

164

71 16 6 1

151

9 25









0 100 200 300 400 500 600 700 800Rounds

ABCACOWSNCABC

LEACH

0

10

20

30

40

50

60

Ener

gy (J

)


010

020

030

040

050

060

070

080

090

010

0011

0012

0013

0014

0015

0016

0017

0018

00

RoundsABCACOWSNCABC

LEACH

020406080

100120

Ener

gy (J

)


0 50 100 150 200 250 300 350Rounds

ABCACOWSNCABC

LEACH

020406080

100120

Ener

gy (J

)





0 100 200 300 400 500 600 700 800Rounds

ABCACOWSNCABC

LEACH

0

50

100

150

200

250

Ener

gy (J

)


0 25 50 75 100 125 150 175 200 225Rounds

ABCACOWSNCABC

LEACH

0

20

40

60

80

100

120

Ener

gy (J

)


0 100 200 300 400Rounds

ABCACOWSNCABC

LEACH

0

50

100

150

200

250

Ener

gy (J

)




SCH

5101520253035404550556065707580859095

10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 9552030 1525 10 5

CHBSNN





References












































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation











119870opt = radic119873120598fs119863

2

3 (120598amp1198894


119870opt = radic119873120598fs119863

2

3120598mp (28119863445)

119870opt = radic15119873120598fs281198632120598mp

(19)



119870opt = radic119873120598fs119863

2

3 (120598amp1198892


119870opt = radic119873120598fs119863

2

3120598fs (5119863212)

119870opt = radic4119873

5

(20)


119870opt = radic119873120598fs119863

2

3 (120598amp1198894


119870opt = radic119873120598fs119863

2

3120598mp (1931198634720)

119870opt = radic240119873120598fs1931198632120598mp

(21)













119875119894=

fitness119894

sum119911119899=1

fitness119894

(22)




1199091015840119894119895= 119909119894119895+ 119903119894119895(119909119894119895minus 119909119896119895) (23)







119894119895and 119909




119909119895119894= LB119895119894+ 119903 (0 1) (UB119895

119894minus LB119895119894)









Prob119896(119901 119902)

=

[120591 (119901 119902)]120572sdot [120578 (119901 119902)]

120573

sum119902isin119877119902

[120591 (119901 119902)]120572sdot [120578 (119901 119902)]

120573 if 119902 notin 119872119896

0 otherwise

(25)






120578 (119901 119902) =119899

sum119894=1

119864119894119888

119864119894119868

times 119863 (BS119911 119902119894) (26)




CH 119902119894from BS



Δ120591119896 = 119908 sdot (1

119871119896) (27)


120591119901119902

= (1 minus 120588) 120591119901119902 (28)




fitness119894=119899

sum119894=1

119864119894119888

119864119894119868

times 119863 (BS119911 119896119894) (29)


119894










No

Start


value



into subregions





Yes

Stop

No alive nodes

Dtimes D region


If N gt 0
















No

Yes





119864mp 00013 pJbitm4




0 100 200 300 400 500 600 700 800 900 1000

ABCACOWSNCABC

LEACH

Rounds

0102030405060708090

100

Aliv

e nod

es






010

020

030

040

050

060

070

080

090

010

0011

0012

0013

0014

0015

0016

0017

0018

0019

0020

0021

00

RoundsABCACOWSNCABC

LEACH

0102030405060708090

100

Aliv

e nod

es


0 100 200 300 400 500Rounds

ABCACOWSNCABC

LEACH

0255075

100125150175200225

Aliv

e nod

es


7 Application



ABCACOWSNCABC

LEACH

0 100 200 300 400 500 600 700 800Rounds

0

50

100

150

200

250

Aliv

e nod

es


ABCACOWSNCABC

LEACH

0 25 50 75 100 125 150 175 200 225Rounds

0

50

100

150

200

250

Aliv

e nod

es







0 50 100 150 200 250 300 350 400Rounds

ABCACOWSNCABC

LEACH

0

50

100

150

200

250

Aliv

e nod

es


ABCACOWSNCABC

LEACH

Number of packets

68936

138131

102145

84794

59789

43983

32947

118263

85789

75742

30055

23240

16495

60690

42906

3226452096

49588








ABCACOWSNCABC

LEACH

Round

631

425

157

1303

924

311

158

27 25

375

164

71 16 6 1

151

9 25









0 100 200 300 400 500 600 700 800Rounds

ABCACOWSNCABC

LEACH

0

10

20

30

40

50

60

Ener

gy (J

)


010

020

030

040

050

060

070

080

090

010

0011

0012

0013

0014

0015

0016

0017

0018

00

RoundsABCACOWSNCABC

LEACH

020406080

100120

Ener

gy (J

)


0 50 100 150 200 250 300 350Rounds

ABCACOWSNCABC

LEACH

020406080

100120

Ener

gy (J

)





0 100 200 300 400 500 600 700 800Rounds

ABCACOWSNCABC

LEACH

0

50

100

150

200

250

Ener

gy (J

)


0 25 50 75 100 125 150 175 200 225Rounds

ABCACOWSNCABC

LEACH

0

20

40

60

80

100

120

Ener

gy (J

)


0 100 200 300 400Rounds

ABCACOWSNCABC

LEACH

0

50

100

150

200

250

Ener

gy (J

)




SCH

5101520253035404550556065707580859095

10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 9552030 1525 10 5

CHBSNN





References












































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation














119894119895and 119909




119909119895119894= LB119895119894+ 119903 (0 1) (UB119895

119894minus LB119895119894)









Prob119896(119901 119902)

=

[120591 (119901 119902)]120572sdot [120578 (119901 119902)]

120573

sum119902isin119877119902

[120591 (119901 119902)]120572sdot [120578 (119901 119902)]

120573 if 119902 notin 119872119896

0 otherwise

(25)






120578 (119901 119902) =119899

sum119894=1

119864119894119888

119864119894119868

times 119863 (BS119911 119902119894) (26)




CH 119902119894from BS



Δ120591119896 = 119908 sdot (1

119871119896) (27)


120591119901119902

= (1 minus 120588) 120591119901119902 (28)




fitness119894=119899

sum119894=1

119864119894119888

119864119894119868

times 119863 (BS119911 119896119894) (29)


119894










No

Start


value



into subregions





Yes

Stop

No alive nodes

Dtimes D region


If N gt 0
















No

Yes





119864mp 00013 pJbitm4




0 100 200 300 400 500 600 700 800 900 1000

ABCACOWSNCABC

LEACH

Rounds

0102030405060708090

100

Aliv

e nod

es






010

020

030

040

050

060

070

080

090

010

0011

0012

0013

0014

0015

0016

0017

0018

0019

0020

0021

00

RoundsABCACOWSNCABC

LEACH

0102030405060708090

100

Aliv

e nod

es


0 100 200 300 400 500Rounds

ABCACOWSNCABC

LEACH

0255075

100125150175200225

Aliv

e nod

es


7 Application



ABCACOWSNCABC

LEACH

0 100 200 300 400 500 600 700 800Rounds

0

50

100

150

200

250

Aliv

e nod

es


ABCACOWSNCABC

LEACH

0 25 50 75 100 125 150 175 200 225Rounds

0

50

100

150

200

250

Aliv

e nod

es







0 50 100 150 200 250 300 350 400Rounds

ABCACOWSNCABC

LEACH

0

50

100

150

200

250

Aliv

e nod

es


ABCACOWSNCABC

LEACH

Number of packets

68936

138131

102145

84794

59789

43983

32947

118263

85789

75742

30055

23240

16495

60690

42906

3226452096

49588








ABCACOWSNCABC

LEACH

Round

631

425

157

1303

924

311

158

27 25

375

164

71 16 6 1

151

9 25









0 100 200 300 400 500 600 700 800Rounds

ABCACOWSNCABC

LEACH

0

10

20

30

40

50

60

Ener

gy (J

)


010

020

030

040

050

060

070

080

090

010

0011

0012

0013

0014

0015

0016

0017

0018

00

RoundsABCACOWSNCABC

LEACH

020406080

100120

Ener

gy (J

)


0 50 100 150 200 250 300 350Rounds

ABCACOWSNCABC

LEACH

020406080

100120

Ener

gy (J

)





0 100 200 300 400 500 600 700 800Rounds

ABCACOWSNCABC

LEACH

0

50

100

150

200

250

Ener

gy (J

)


0 25 50 75 100 125 150 175 200 225Rounds

ABCACOWSNCABC

LEACH

0

20

40

60

80

100

120

Ener

gy (J

)


0 100 200 300 400Rounds

ABCACOWSNCABC

LEACH

0

50

100

150

200

250

Ener

gy (J

)




SCH

5101520253035404550556065707580859095

10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 9552030 1525 10 5

CHBSNN





References












































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation












fitness119894=119899

sum119894=1

119864119894119888

119864119894119868

times 119863 (BS119911 119896119894) (29)


119894










No

Start


value



into subregions





Yes

Stop

No alive nodes

Dtimes D region


If N gt 0
















No

Yes





119864mp 00013 pJbitm4




0 100 200 300 400 500 600 700 800 900 1000

ABCACOWSNCABC

LEACH

Rounds

0102030405060708090

100

Aliv

e nod

es






010

020

030

040

050

060

070

080

090

010

0011

0012

0013

0014

0015

0016

0017

0018

0019

0020

0021

00

RoundsABCACOWSNCABC

LEACH

0102030405060708090

100

Aliv

e nod

es


0 100 200 300 400 500Rounds

ABCACOWSNCABC

LEACH

0255075

100125150175200225

Aliv

e nod

es


7 Application



ABCACOWSNCABC

LEACH

0 100 200 300 400 500 600 700 800Rounds

0

50

100

150

200

250

Aliv

e nod

es


ABCACOWSNCABC

LEACH

0 25 50 75 100 125 150 175 200 225Rounds

0

50

100

150

200

250

Aliv

e nod

es







0 50 100 150 200 250 300 350 400Rounds

ABCACOWSNCABC

LEACH

0

50

100

150

200

250

Aliv

e nod

es


ABCACOWSNCABC

LEACH

Number of packets

68936

138131

102145

84794

59789

43983

32947

118263

85789

75742

30055

23240

16495

60690

42906

3226452096

49588








ABCACOWSNCABC

LEACH

Round

631

425

157

1303

924

311

158

27 25

375

164

71 16 6 1

151

9 25









0 100 200 300 400 500 600 700 800Rounds

ABCACOWSNCABC

LEACH

0

10

20

30

40

50

60

Ener

gy (J

)


010

020

030

040

050

060

070

080

090

010

0011

0012

0013

0014

0015

0016

0017

0018

00

RoundsABCACOWSNCABC

LEACH

020406080

100120

Ener

gy (J

)


0 50 100 150 200 250 300 350Rounds

ABCACOWSNCABC

LEACH

020406080

100120

Ener

gy (J

)





0 100 200 300 400 500 600 700 800Rounds

ABCACOWSNCABC

LEACH

0

50

100

150

200

250

Ener

gy (J

)


0 25 50 75 100 125 150 175 200 225Rounds

ABCACOWSNCABC

LEACH

0

20

40

60

80

100

120

Ener

gy (J

)


0 100 200 300 400Rounds

ABCACOWSNCABC

LEACH

0

50

100

150

200

250

Ener

gy (J

)




SCH

5101520253035404550556065707580859095

10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 9552030 1525 10 5

CHBSNN





References












































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










No

Start


value



into subregions





Yes

Stop

No alive nodes

Dtimes D region


If N gt 0
















No

Yes





119864mp 00013 pJbitm4




0 100 200 300 400 500 600 700 800 900 1000

ABCACOWSNCABC

LEACH

Rounds

0102030405060708090

100

Aliv

e nod

es






010

020

030

040

050

060

070

080

090

010

0011

0012

0013

0014

0015

0016

0017

0018

0019

0020

0021

00

RoundsABCACOWSNCABC

LEACH

0102030405060708090

100

Aliv

e nod

es


0 100 200 300 400 500Rounds

ABCACOWSNCABC

LEACH

0255075

100125150175200225

Aliv

e nod

es


7 Application



ABCACOWSNCABC

LEACH

0 100 200 300 400 500 600 700 800Rounds

0

50

100

150

200

250

Aliv

e nod

es


ABCACOWSNCABC

LEACH

0 25 50 75 100 125 150 175 200 225Rounds

0

50

100

150

200

250

Aliv

e nod

es







0 50 100 150 200 250 300 350 400Rounds

ABCACOWSNCABC

LEACH

0

50

100

150

200

250

Aliv

e nod

es


ABCACOWSNCABC

LEACH

Number of packets

68936

138131

102145

84794

59789

43983

32947

118263

85789

75742

30055

23240

16495

60690

42906

3226452096

49588








ABCACOWSNCABC

LEACH

Round

631

425

157

1303

924

311

158

27 25

375

164

71 16 6 1

151

9 25









0 100 200 300 400 500 600 700 800Rounds

ABCACOWSNCABC

LEACH

0

10

20

30

40

50

60

Ener

gy (J

)


010

020

030

040

050

060

070

080

090

010

0011

0012

0013

0014

0015

0016

0017

0018

00

RoundsABCACOWSNCABC

LEACH

020406080

100120

Ener

gy (J

)


0 50 100 150 200 250 300 350Rounds

ABCACOWSNCABC

LEACH

020406080

100120

Ener

gy (J

)





0 100 200 300 400 500 600 700 800Rounds

ABCACOWSNCABC

LEACH

0

50

100

150

200

250

Ener

gy (J

)


0 25 50 75 100 125 150 175 200 225Rounds

ABCACOWSNCABC

LEACH

0

20

40

60

80

100

120

Ener

gy (J

)


0 100 200 300 400Rounds

ABCACOWSNCABC

LEACH

0

50

100

150

200

250

Ener

gy (J

)




SCH

5101520253035404550556065707580859095

10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 9552030 1525 10 5

CHBSNN





References












































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation
















No

Yes





119864mp 00013 pJbitm4




0 100 200 300 400 500 600 700 800 900 1000

ABCACOWSNCABC

LEACH

Rounds

0102030405060708090

100

Aliv

e nod

es






010

020

030

040

050

060

070

080

090

010

0011

0012

0013

0014

0015

0016

0017

0018

0019

0020

0021

00

RoundsABCACOWSNCABC

LEACH

0102030405060708090

100

Aliv

e nod

es


0 100 200 300 400 500Rounds

ABCACOWSNCABC

LEACH

0255075

100125150175200225

Aliv

e nod

es


7 Application



ABCACOWSNCABC

LEACH

0 100 200 300 400 500 600 700 800Rounds

0

50

100

150

200

250

Aliv

e nod

es


ABCACOWSNCABC

LEACH

0 25 50 75 100 125 150 175 200 225Rounds

0

50

100

150

200

250

Aliv

e nod

es







0 50 100 150 200 250 300 350 400Rounds

ABCACOWSNCABC

LEACH

0

50

100

150

200

250

Aliv

e nod

es


ABCACOWSNCABC

LEACH

Number of packets

68936

138131

102145

84794

59789

43983

32947

118263

85789

75742

30055

23240

16495

60690

42906

3226452096

49588








ABCACOWSNCABC

LEACH

Round

631

425

157

1303

924

311

158

27 25

375

164

71 16 6 1

151

9 25









0 100 200 300 400 500 600 700 800Rounds

ABCACOWSNCABC

LEACH

0

10

20

30

40

50

60

Ener

gy (J

)


010

020

030

040

050

060

070

080

090

010

0011

0012

0013

0014

0015

0016

0017

0018

00

RoundsABCACOWSNCABC

LEACH

020406080

100120

Ener

gy (J

)


0 50 100 150 200 250 300 350Rounds

ABCACOWSNCABC

LEACH

020406080

100120

Ener

gy (J

)





0 100 200 300 400 500 600 700 800Rounds

ABCACOWSNCABC

LEACH

0

50

100

150

200

250

Ener

gy (J

)


0 25 50 75 100 125 150 175 200 225Rounds

ABCACOWSNCABC

LEACH

0

20

40

60

80

100

120

Ener

gy (J

)


0 100 200 300 400Rounds

ABCACOWSNCABC

LEACH

0

50

100

150

200

250

Ener

gy (J

)




SCH

5101520253035404550556065707580859095

10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 9552030 1525 10 5

CHBSNN





References












































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










010

020

030

040

050

060

070

080

090

010

0011

0012

0013

0014

0015

0016

0017

0018

0019

0020

0021

00

RoundsABCACOWSNCABC

LEACH

0102030405060708090

100

Aliv

e nod

es


0 100 200 300 400 500Rounds

ABCACOWSNCABC

LEACH

0255075

100125150175200225

Aliv

e nod

es


7 Application



ABCACOWSNCABC

LEACH

0 100 200 300 400 500 600 700 800Rounds

0

50

100

150

200

250

Aliv

e nod

es


ABCACOWSNCABC

LEACH

0 25 50 75 100 125 150 175 200 225Rounds

0

50

100

150

200

250

Aliv

e nod

es







0 50 100 150 200 250 300 350 400Rounds

ABCACOWSNCABC

LEACH

0

50

100

150

200

250

Aliv

e nod

es


ABCACOWSNCABC

LEACH

Number of packets

68936

138131

102145

84794

59789

43983

32947

118263

85789

75742

30055

23240

16495

60690

42906

3226452096

49588








ABCACOWSNCABC

LEACH

Round

631

425

157

1303

924

311

158

27 25

375

164

71 16 6 1

151

9 25









0 100 200 300 400 500 600 700 800Rounds

ABCACOWSNCABC

LEACH

0

10

20

30

40

50

60

Ener

gy (J

)


010

020

030

040

050

060

070

080

090

010

0011

0012

0013

0014

0015

0016

0017

0018

00

RoundsABCACOWSNCABC

LEACH

020406080

100120

Ener

gy (J

)


0 50 100 150 200 250 300 350Rounds

ABCACOWSNCABC

LEACH

020406080

100120

Ener

gy (J

)





0 100 200 300 400 500 600 700 800Rounds

ABCACOWSNCABC

LEACH

0

50

100

150

200

250

Ener

gy (J

)


0 25 50 75 100 125 150 175 200 225Rounds

ABCACOWSNCABC

LEACH

0

20

40

60

80

100

120

Ener

gy (J

)


0 100 200 300 400Rounds

ABCACOWSNCABC

LEACH

0

50

100

150

200

250

Ener

gy (J

)




SCH

5101520253035404550556065707580859095

10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 9552030 1525 10 5

CHBSNN





References












































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










0 50 100 150 200 250 300 350 400Rounds

ABCACOWSNCABC

LEACH

0

50

100

150

200

250

Aliv

e nod

es


ABCACOWSNCABC

LEACH

Number of packets

68936

138131

102145

84794

59789

43983

32947

118263

85789

75742

30055

23240

16495

60690

42906

3226452096

49588








ABCACOWSNCABC

LEACH

Round

631

425

157

1303

924

311

158

27 25

375

164

71 16 6 1

151

9 25









0 100 200 300 400 500 600 700 800Rounds

ABCACOWSNCABC

LEACH

0

10

20

30

40

50

60

Ener

gy (J

)


010

020

030

040

050

060

070

080

090

010

0011

0012

0013

0014

0015

0016

0017

0018

00

RoundsABCACOWSNCABC

LEACH

020406080

100120

Ener

gy (J

)


0 50 100 150 200 250 300 350Rounds

ABCACOWSNCABC

LEACH

020406080

100120

Ener

gy (J

)





0 100 200 300 400 500 600 700 800Rounds

ABCACOWSNCABC

LEACH

0

50

100

150

200

250

Ener

gy (J

)


0 25 50 75 100 125 150 175 200 225Rounds

ABCACOWSNCABC

LEACH

0

20

40

60

80

100

120

Ener

gy (J

)


0 100 200 300 400Rounds

ABCACOWSNCABC

LEACH

0

50

100

150

200

250

Ener

gy (J

)




SCH

5101520253035404550556065707580859095

10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 9552030 1525 10 5

CHBSNN





References












































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










0 100 200 300 400 500 600 700 800Rounds

ABCACOWSNCABC

LEACH

0

10

20

30

40

50

60

Ener

gy (J

)


010

020

030

040

050

060

070

080

090

010

0011

0012

0013

0014

0015

0016

0017

0018

00

RoundsABCACOWSNCABC

LEACH

020406080

100120

Ener

gy (J

)


0 50 100 150 200 250 300 350Rounds

ABCACOWSNCABC

LEACH

020406080

100120

Ener

gy (J

)





0 100 200 300 400 500 600 700 800Rounds

ABCACOWSNCABC

LEACH

0

50

100

150

200

250

Ener

gy (J

)


0 25 50 75 100 125 150 175 200 225Rounds

ABCACOWSNCABC

LEACH

0

20

40

60

80

100

120

Ener

gy (J

)


0 100 200 300 400Rounds

ABCACOWSNCABC

LEACH

0

50

100

150

200

250

Ener

gy (J

)




SCH

5101520253035404550556065707580859095

10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 9552030 1525 10 5

CHBSNN





References












































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










SCH

5101520253035404550556065707580859095

10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 9552030 1525 10 5

CHBSNN





References












































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation









research article hybrid swarm intelligence energy...

Documents