enhancing the lifetime of network using hybrid artificial ... paper/volume 54/issue...

International Journal of Engineering, Applied and Management Sciences Paradigms (IJEAM)

Volume 54 Issue 3 June 2019 364 ISSN 2320-6608

Enhancing the lifetime of network using Hybrid

Artificial Bee Colony – Nelder Mead (HABC-

NM) in coverage connected node

placement problem of target based WSN

Poonguzhali1, P.Bhavani

2, R.Ramachandiran

3

1,2,3Department of Computer Science and Engineering,

Sri Manakula Vinayagar Engineering College, Puducherry, India.

Abstract- Major concern in wireless sensor networks is to maximize network lifetime (in terms of rounds) while

maintaining a high quality of services (QoS) at each round such as target coverage and network connectivity. Due to the

power scarcity of sensors, a mechanism that can efficiently utilize energy has a great impact on extending network

lifetime. Most existing works concentrate on scheduling sensors between sleep and active modes to maximize network

lifetime while maintaining target/area coverage and network connectivity is a great challenge.To meet the challenges of

the existing work an effective Nelder-Mead (NM) mathematical model has been incorporated with Ant Bee Colony (ABC)

algorithm. The performance of proposed method will be tested in the test bed 3. Finally the obtained results are discussed

and the results will show the significance of proposed method on comparing existing algorithms.

Keywords- QoS(Quality of Service); ABC(Ant Bee Colony); NM (Nelder- Mead)

I. INTRODUCTION

A Wireless Sensor Network (WSN) can be defined as a collection of limited power based sensors with the ability to

cover an area/ a single point target and communicate the collected data to sink node (base station) either via single-

hop or through multi-hop [2, 3]. WSN is used for monitoring the physical and environmental changes such as

weather, wind, clouds, natural calamities etc. WSN has been considered or appreciated for its ability to cross the

domain which can also be stated as cross-multidisciplinary, highly cohesive with respect to the addressed problem,

which holds the recent trend with cutting edge knowledge regarding research field. It enables the user to access the

region where a human interference is difficult or sometimes which is impossible. This gives an API kind technology

by which the future generation shall process the information in more effective manner than now. This technology

has been addressed as most predominating technology of 21st century.

WSN has been considered as a most prominent network in wireless technology where sensing and collection of data

are more effective and also reliable in a wireless based system. This agility and ability of processing and

transformation of data in WSN leads the technology to get adapted and used in cross domain fields too. This type of

structure reduces the human effort in monitoring a region or an environment. The key domains of application of

WSN are discussed below. Figure 1 demonstrates the overview of WSN which consists of sensor nodes, sink nodes,

data transformation paths, relay nodes, etc.

Figure1. Overview of Wireless Sensor Network



Military applications: In military applications WSN has been used as an artificial based application. WSN is used to

track the battlefield, which can be also termed for communication, controlling, etc.

1.1 Target Based WSN

In target based WSN, the sensor nodes are deployed in the region that are to be sensed either in ad-hoc or

predetermined manner. Usually, ad-hoc type of deployment happens in the regions such as deep forest, volcano, etc.

These sensor nodes expect localization of nodes first before data aggregation. In predetermined deployment of

sensor nodes, the deployment position will be computed in prior to actual deployment. During the computation to

search for better or optimal positions with maximum coverage and less number of sensor nodes usage is an

optimization task to be handled. The existing challenges in deployment of sensor nodes are 1) each sensor nodes

holds limited amount of transmission range. 2) The sensors are equipped with limited power resources on which

once the power gets drained the node dies and data transmission is under debate. 3) Hazardous external properties

can damage the sensor nodes where an alternate sensor may give a backup and keep the network to be alive. This

states that the issue of covering target nodes and maintaining connectivity between sensors are important in target

based WSN.

1.2. coverage WSN

Coverage represents at least number of sensor nodes required for monitoring every single target in WSN. A

sensor node may reside in another cover set but each target should be covered at least by sensors. One among the

methodology is to subdivide the given region into parts and place sensors in each region. This method

completes the coverage issue but addressing the concept of minimal sensor usage is a missing factor in it.

1.3 Coverage Connected sensor nodes

Wireless Sensor Networks has an immense amount of research aspects since its application range is vast. In

applications such as monitoring the surroundings, military border monitoring, warning of disasters, tracking the

targets etc. In these applications the targets nodes are to be covered and the gathered information should be

transformed to the base station for further processing either via single or multi-hop connection.

The conventional approaches fixes one sensor node to cover a target and the information are collected from the

sensor nodes. This become a successful task until the sensor nodes drain the battery source. Once the sensor node

fails to communicate the sensed data to base station due to insufficient power source, not only that particular target

data will be missed out but also the communication path where this drained sensor node is used for communication

purpose also will get affected and the entire network fails at this stage. This issue can be solved when more than one

number of sensor nodes are used for covering the targets and more number of sensor nodes are used for

communication between sensor nodes to base station. Thus in coverage, represents the number of sensor nodes

that are used to cover a target and in connected represents the number of sensor nodes that covers other sensor

node for recovery from node failure.

II. RELATED WORK

Liu and Zheng proposed theCoverage of target nodes in WSN, guarantees the sensing data to be covered accurately

without any loss in it. Coverage are even classified into point based coverage and region based coverage.

Connectivity between sensor nodes deals with the adequacy on which the communication between sources to

destination occurs. In this chapter, the research contributions of researchers on WSN coverage and connectivity

problems and its solving methodologies. This literature study has been done in two folds.

Carde et.al., extended their work where the sensors participate in one individual can also be used to cover other

targets in the same region. Hence this approach addresses multiple coverage uses using sensors.They proposed two

different methodologies for efficiently handling the coverage issue in WSN based on residual energy and the

accessible nodes for covering the targets.

Hongwu, Zhang et. al., proposed a heuristic greedy optimum coverage algorithm (HG-OCA). In HG-OCA, we first

design a network model in which power supplies of sensor nodes follow a normal distribution. Next, we analyze

energy model of target coverage, educe three rules to reduce network scale, present the concept of the key target and

the prior coverage of key target. Moreover, we choose sensor with most energy utility as active sensor. In the end,

we present HG-OCA to extend network lifetime, based on minimizing energy consumption of key target and

maximizing energy efficiency of sensor node. Measurement results show that the new algorithm could increase 80%

longer network lifetime and achieve more adaptability and stability.



Gu et. al., discussed the two important contributions. The first contribution is to have two lifetime upper bounds,

which could be used to justify performance of previously proposed heuristic algorithms. One upper bound is based

on the relaxation and reformulation technique while the other is derived by relaxing coverage constraints. We study

the interesting connection between those two bounds and thus endow them with physical meanings. The second

contribution is proposing a column generation based (CG) approach. The objective is to find an optimal schedule,

defined as a time table specifying from what time up to what time which sensor watches which targets while the

maximum lifetime has been obtained. We also offer an in-depth theoretic analysis as well as several novel

techniques to further optimize the approach. Numerical results not only demonstrate that the lifetime upper bounds

are very tight, but also verify that the proposed CG based approach constantly yields the optimal or near optimal

solution.

Zhao and Gurusamy proposed an algorithm CWGC which states the connected target coverage problem. This paper

consists of the theoretical analysis of the target coverage problem and then this problem has been modelled as

maximum cover tree problem. The theoretical analysis has been done in order to prove that the handled problem is

NP complete. They proposed two concepts namely greedy based method and approximation algorithm for

effectively solving target coverage problem. The considered factors of this paper are connectivity between sensors,

coverage of target nodes and real-time energy consumption model which is based on the distance between nodes.

Greedy method has been used to select the edges or the paths where the residual energy is high at each instance.

However, on applying this method the algorithm expects a recompilation of weights between the nodes when a new

cover set is generated.

Lu, Mingming et. al., this paper generalizes the sleep/active mode by adjusting sensing range to maximize total

number of rounds and presents a distributed heuristic to address this problem.

This paper is organized as follows. Section 1 describes introduction about the wireless sensor network, k coverage

connected nodes. Section 2 explains the literature review. Section 3 describes the proposed work. Experimental

results and discussion are described in the section 4. Finally, section 5 concludes the paper.

III. PROPOSED WORK

3.1. Nelder-Mead Method

Nelder-Mead (NM) algorithm also known as simplex search algorithm which was introduced in the year 1965 for

solving multidimensional optimization problem without taking the derivatives of them. Derivatives gives precise

results for optimization problems but the computational cost is expensive. NM Method was originally developed for

soling unconstrained optimization problems. Since it does not use derivative forms this method can be easily

adaptable for non-smooth functions.

NM method consists of four steps for achieving best solution from the given points or coordinates. The four steps

includes reflection, contraction, expansion a shrinkage. At initial stage a simplex transformation method is followed

in NM method in which the four steps which are discussed above will be a subcategory.

Ordering

Calculate the best ( ), second best ( ) and the worst points ( ) from the given position in the search space using

simplex method. For a maximization problem the computation will be as follows.

; ;

3.2. Centroid

Compute the centroid between and



3.3. Transformation

The transformation from one region in the search space to the other best position has been carried out in four steps

as it is discussed above. Initially the worst point will be replaced any of the best points using the above-mentioned

methods. The four steps are controlled by four different parameters namely using respectively. the control

parametric values are .

3.4 Reflection

Determine the reflection point using the calculated centroid and best point as follows

Compute the fitness value of point using the fitness function such that

The computed will be replaced by the worst solution if it satisfies the below Equation

3.5 Expansion

Determine the expansion point using the calculated centroid and reference point as follows


The computed will be replaced by the reference point if it satisfies the below Equation

3.6 Contraction

If the computed reference point is lesser than the second-best position ( ) determine the contraction point

using and

Outside: If then determine


The computed will be replaced by the referenced point if it satisfies the below Equation

Or else the shrink transformation will be called on for carrying the transformation process.



Inside: If then determine


The computed will be replaced by the best points if it satisfies the below Equation

Or else the shrink transformation will be called on for carrying the transformation process.

Shrink

Calculate new points using random points between best and neighborhood points


3.7 .ABC-NM for Coverage Connected Problem

ABC-NM pseudocode shows the hybrid version of ABC algorithm and mathematical Nelder Mead method for

solving connected coverage problem. The objectives of the connected coverage problem will be stated in 4.3.1. the

proposed concept will be given in Algorithm 4.1.



IV. EXPERIMENTAL RESULTS AND DISCUSSION

The proposed algorithm has been implemented in MATLAB v9 in a system with Intel core i7 processor with 3.2

GHz clock speed with 4GB RAM and 1TB HDD. The problem of coverage and connected node placement has

been designed and implemented in MATLAB using three different grid scenarios under different region: WSN1 -

300 300 meters, WSN2 - 500 500 meters and WSN3 - 700 700 meters. The sink nodes positions for WSN1,

WSN2 and WSN3 are (300, 150), (500,250) and (700,350) respectively.

Table 1. Parameter Settings

Parameters For 100 Targets For 200 Targets

Grid Size

300X300,

500X500,

700X700

300X300,

500X500,

700X700

Available Positions 100, 150, 200, 250 200, 250, 300, 350

Communication Range 50 meters 25 meters

Sensing Range 40 meters 20 meters

Population Size 50 50

Iteration 1000 1500

1, 2 1, 2

Table 2 shows the simulated values of F on 100 target nodes with different number of available potential positions.

The simulation has been carried out on six different algorithms with the same simulation parameters.

Table 2. F-Value on 100 target nodes with varied #potential positions

Range Dimension PP Greedy Mini et

al GA-R GA-G HPG

ABC-

NM

k=1,

m=1

300X300

100 0.46 0.32 0.29 0.28 0.26 0.24

150 0.29 0.21 0.18 0.18 0.17 0.15

200 0.2 0.15 0.13 0.13 0.13 0.11

250 0.15 0.11 0.1 0.1 0.09 0.08

500X500

100 0.59 0.56 0.55 0.53 0.49 0.48

150 0.37 0.36 0.35 0.35 0.31 0.31

200 0.27 0.26 0.26 0.26 0.23 0.23

250 0.21 0.2 0.19 0.19 0.17 0.18

700X700

100 0.75 0.72 0.71 0.69 0.65 0.64

150 0.47 0.46 0.45 0.45 0.41 0.41

200 0.34 0.33 0.33 0.34 0.3 0.29

250 0.25 0.24 0.24 0.24 0.23 0.22

k=1,

m=2

300X300

100 0.49 0.37 0.34 0.31 0.34 0.26

150 0.32 0.22 0.2 0.19 0.21 0.17

200 0.23 0.15 0.12 0.14 0.13 0.13

250 0.17 0.11 0.09 0.1 0.1 0.09

500X500

100 0.6 0.59 0.58 0.55 0.53 0.5

150 0.4 0.39 0.38 0.33 0.34 0.33

200 0.29 0.29 0.27 0.23 0.25 0.25

250 0.22 0.22 0.21 0.18 0.18 0.18

700X700

100 0.75 0.74 0.73 0.7 0.68 0.65

150 0.47 0.46 0.45 0.4 0.41 0.41

200 0.34 0.34 0.32 0.28 0.3 0.29

250 0.25 0.25 0.24 0.21 0.23 0.22

k=2,

m=1 300X300

100 0.44 0.3 0.27 0.27 0.24 0.23

150 0.27 0.18 0.18 0.17 0.15 0.14

200 0.18 0.13 0.12 0.13 0.1 0.11

250 0.14 0.1 0.09 0.1 0.08 0.08



500X500

100 0.57 0.55 0.53 0.51 0.49 0.47

150 0.37 0.35 0.35 0.33 0.31 0.31

200 0.27 0.25 0.23 0.24 0.23 0.23

250 0.2 0.19 0.18 0.19 0.18 0.18

700X700

100 0.75 0.73 0.71 0.69 0.67 0.65

150 0.47 0.46 0.45 0.44 0.42 0.41

200 0.34 0.33 0.3 0.32 0.3 0.29

250 0.25 0.24 0.23 0.24 0.23 0.22

k=2,

m=2

300X300

100 0.42 0.29 0.27 0.26 0.24 0.23

150 0.27 0.19 0.17 0.15 0.16 0.15

200 0.19 0.13 0.13 0.11 0.1 0.11

250 0.13 0.1 0.1 0.08 0.08 0.09

500X500

100 0.55 0.53 0.5 0.47 0.46 0.43

150 0.34 0.33 0.33 0.31 0.29 0.29

200 0.25 0.25 0.25 0.23 0.29 0.21

250 0.2 0.19 0.2 0.18 0.17 0.16

700X700

100 0.75 0.73 0.7 0.67 0.66 0.63

150 0.47 0.47 0.47 0.45 0.43 0.41

200 0.34 0.34 0.34 0.32 0.3 0.29

250 0.25 0.24 0.25 0.23 0.23 0.22

Table 3 shows the simulated values of F on 200 target nodes with different number of available potential positions.


Table 3. F-Value on 200 target nodes with varied #potential positions


al GA-R GA-G HPG

ABC-

NM

k=1,

m=1

300X300

200 0.46 0.32 0.29 0.28 0.26 0.12

250 0.22 0.16 0.14 0.14 0.13 0.09

300 0.13 0.1 0.09 0.08 0.08 0.07

350 0.09 0.07 0.06 0.06 0.06 0.06

500X500

200 0.59 0.56 0.55 0.53 0.49 0.24

250 0.28 0.27 0.27 0.26 0.23 0.18

300 0.18 0.17 0.17 0.17 0.15 0.15

350 0.13 0.12 0.12 0.12 0.11 0.13

700X700

200 0.38 0.35 0.34 0.32 0.28 0.32

250 0.28 0.27 0.27 0.26 0.23 0.25

300 0.23 0.22 0.22 0.22 0.2 0.19

350 0.18 0.17 0.17 0.17 0.16 0.16

k=1,

m=2

300X300

200 0.49 0.37 0.34 0.31 0.34 0.13

250 0.24 0.17 0.15 0.15 0.16 0.1

300 0.15 0.1 0.08 0.09 0.09 0.08

350 0.11 0.07 0.06 0.07 0.06 0.07

500X500

200 0.61 0.59 0.58 0.55 0.53 0.25

250 0.3 0.29 0.29 0.25 0.26 0.2

300 0.19 0.19 0.18 0.15 0.16 0.16

350 0.14 0.14 0.13 0.11 0.12 0.13

700X700

200 0.38 0.36 0.35 0.32 0.3 0.33

250 0.28 0.27 0.27 0.23 0.24 0.25

300 0.23 0.22 0.21 0.19 0.2 0.19

350 0.18 0.18 0.17 0.15 0.16 0.16

k=2,

m=1 300X300

200 0.44 0.3 0.27 0.27 0.24 0.12

250 0.2 0.14 0.14 0.13 0.12 0.08



300 0.12 0.09 0.08 0.09 0.07 0.07

350 0.09 0.07 0.06 0.06 0.05 0.06

500X500

200 0.57 0.55 0.53 0.51 0.49 0.24

250 0.28 0.27 0.26 0.25 0.24 0.18

300 0.18 0.17 0.15 0.16 0.15 0.15

350 0.13 0.12 0.11 0.12 0.11 0.13

700X700

200 0.38 0.36 0.34 0.32 0.3 0.33

250 0.28 0.27 0.27 0.26 0.24 0.25

300 0.23 0.22 0.2 0.21 0.2 0.19

350 0.18 0.17 0.17 0.18 0.16 0.16

k=2,

m=2

300X300

200 0.42 0.29 0.27 0.26 0.24 0.12

250 0.21 0.14 0.13 0.11 0.12 0.09

300 0.13 0.09 0.09 0.07 0.07 0.07

350 0.08 0.06 0.06 0.05 0.05 0.06

500X500

200 0.55 0.53 0.5 0.47 0.46 0.22

250 0.26 0.25 0.25 0.24 0.22 0.17

300 0.17 0.16 0.16 0.15 0.14 0.14

350 0.13 0.12 0.12 0.11 0.11 0.11

700X700

200 0.38 0.36 0.33 0.3 0.29 0.32

250 0.28 0.28 0.28 0.26 0.25 0.25

300 0.23 0.22 0.22 0.21 0.2 0.19

350 0.18 0.18 0.18 0.17 0.16 0.16

On comparing the mean of F-values for 100 target nodes our proposed ABC-NM outperforms existing algorithms

with 84% against Greedy, 29% against Mini, 17% against GA-R, 14% against GA-G and 9% against HPG on

300X300 grid size. On 500X500 grid ABC-NM outperforms existing algorithms with 20% against Greedy, 16%

against Mini, 13% against GA-R, 7% against GA-G and 2% against HPG. On 700X700 grid ABC-NM outperforms

existing algorithms with 16% against Greedy, 13% against Mini, 11% against GA-R, 7% against GA-G and 3%

against HPG.




against Mini, 57% against GA-R, 49% against GA-G and 43% against HPG. On 700X700 grid ABC-NM

outperforms existing algorithms with 16% against Greedy, 11% against Mini, 8% against GA-R, 3% against GA-G.

However proposed ABC-NM performance has been degraded by 4% against HPG.

Table 4 shows the computational time on 100 target nodes with different number of available potential positions.


Table 4.Computational Time on 100 target nodes with varied #potential positions

bb Dimension PP Greedy Mini et

al GA-R GA-G HPG

ABC-

NM

k=1,

m=1

300X300

100 5.71 5.94 4.19 6.48 4.64 5.27

150 6.04 5.41 6.49 6.02 6.76 4.09

200 6.42 5.08 5.33 5.03 4.65 5.65

250 4.82 5.08 5.4 5.02 5.22 5.48

500X500

100 10.23 8.11 8.93 9.34 8.67 8.9

150 9.15 11.36 9.84 9.85 8.55 8.1

200 9.09 11.04 11.52 8.72 10.26 7.83

250 8.29 8.3 9.09 8.33 10.71 9.93

700X700

100 13.93 13.08 13.29 12.72 13.16 11.83

150 11.37 11.63 12.92 14.72 12.72 11.2

200 14.74 11.74 12.7 13.45 12.25 14.89

250 14.93 14.33 11.36 14.69 14.74 12.15

k=1, 300X300 100 4.29 5.34 5.11 6.5 5.76 6.95



m=2 150 6.65 5.68 4.86 5.54 4.75 6.62

200 6.65 5.48 6.63 6.91 5.18 4.05

250 4.69 5.15 6.81 6.79 4.74 4.71

500X500

100 8.02 7.28 10.35 8.54 8.81 9.28

150 8.11 10.91 9.43 9.02 8.12 9.04

200 8.25 8.98 11.23 10.88 9.06 9.04

250 10.41 12.34 10.04 10.41 8.64 8.52

700X700

100 13.23 11.09 13.36 13.19 13.75 14.01

150 12.78 13.71 12.28 13.86 11.33 13.48

200 13.8 11.28 14.07 14.63 12.43 11.4

250 13.26 14.7 14.51 11.59 11.2 13.81

k=2,

m=1

300X300

100 4.32 5.97 5.51 6.17 4.24 5.45

150 5.21 5.21 5.25 6.77 4.17 4.94

200 6.47 5.37 4.95 5.25 4.3 4.59

250 5.46 5.81 5.42 5.01 4.23 4.4

500X500

100 8.69 9.36 9.03 9.35 8.13 7.55

150 9.03 11.41 11.13 8.02 10.03 7.07

200 9.98 7.39 8.92 9.49 9.27 7.91

250 8.52 12.55 10.47 10.25 9.46 9.15

700X700

100 12.16 13.16 12.11 13.15 13.88 11.66

150 11.59 11.72 14.4 14.93 11 14.75

200 14.52 11.58 12.16 14.22 14.47 11.53

250 12.54 11.76 14.02 11.86 11.25 11.8

k=2,

m=2

300X300

100 4.07 5.49 4.82 5.19 4.99 4.82

150 6.51 5.69 4.45 6.81 6.94 4.27

200 6.67 5.82 6.28 5.12 4.96 6.42

250 6.49 6 6.27 6.86 6.31 4.73

500X500

100 9.67 12.04 9.03 10.8 9.18 9.29

150 9.34 7.57 11.13 9.27 10.99 7.05

200 10.91 10.87 8.92 8.69 10.89 9.51

250 10.43 10.69 10.47 8.61 8.17 7.87

700X700

100 14.28 11.58 13.55 12.12 11.92 14.31

150 12.81 12.14 14.47 13.49 14.03 11.79

200 12.25 13.15 12.82 14.73 12.26 13.25

250 13.69 14.58 12.76 12.4 12.23 13.75

Table 5 shows the computational time on 200 target nodes with different number of available potential positions.


Table 5. Computational Time on 200 target nodes with varied #potential positions\


al GA-R GA-G HPG

ABC-

NM

k=1,

m=1

300X300

200 7.84 8.23 7.05 8.85 7.68 8.71

250 7.26 8.37 7.88 8.2 7.36 8.83

300 7.18 8.48 8.31 8.55 7.23 8.73

350 8.43 7.22 7.98 7.57 8.92 8.75

500X500

200 12.83 12.55 10.32 10.59 10.19 10.48

250 13 12.53 10.57 10.69 11.6 11.96

300 11.86 11.28 12.07 11.75 12.09 12.93

350 10.61 10.15 10.04 11.2 11.95 12.49

700X700

200 19.58 17.18 17.71 17.74 18.86 18.51

250 19.41 17.96 17.31 19.1 17.47 19.37

300 19.9 19.06 18.48 18.55 18.26 17.98



350 19.26 18.66 18.24 19.78 17.04 18.55

k=1,

m=2

300X300

200 7.88 8.94 8.25 8.72 8.59 8.34

250 7 7.09 8.25 7.06 8.23 7.35

300 8.8 7.57 7.34 7.2 8.12 8.37

350 8.62 7.8 8.77 8.02 8.21 8.73

500X500

200 11.91 12.88 11.93 10.1 12.59 10.55

250 11.74 11.23 12.14 11.93 12.82 12.81

300 10.38 12.89 11.51 11.12 10.88 10.08

350 12.44 11.26 11.93 11.66 12.07 10.02

700X700

200 19.59 18.9 17.69 19.34 18.5 18.53

250 18.93 17.09 18.59 19.59 17.1 18.35

300 18.88 17.47 18.49 19.96 17.96 17.18

350 17.57 19.31 19.19 17.1 19.8 18.43

k=2,

m=1

300X300

200 7.94 8.4 8.27 7.16 7.74 7.34

250 8.83 8.88 7.76 7.41 7.85 7.21

300 7.1 8.65 7.3 8.94 7.48 7.97

350 8.1 7.15 8 8.69 8.67 8.26

500X500

200 11.31 10.23 11.11 12.68 12.19 12.62

250 12.5 10.28 10.25 11 10.14 11.51

300 10.2 12.42 12.98 12.88 10.17 11.8

350 12.01 11.74 10.02 11.49 11.07 10.18

700X700

200 19.62 19.47 19.72 18.49 19.69 19.29

250 18.84 18.52 19.39 17.96 18.27 18.84

300 17.12 19.43 19.69 18.9 18.72 17.83

350 19.88 18.35 19.95 19.41 18.03 17.81

k=2,

m=2

300X300

200 8.51 8.64 7.55 8.19 7.26 8.58

250 8.64 7.06 7.21 7.15 8.2 7.73

300 8.12 8.23 8.12 7.21 7.69 7.78

350 8.11 7.01 8.11 8.94 8.95 7.51

500X500

200 12.23 10.16 11.08 12.97 10.05 10.4

250 11.92 12.69 10.82 12.04 12.08 12.03

300 11.09 12.49 12.53 11.74 10.84 11.52

350 12.48 12.69 13 11.43 10.34 12.59

700X700

200 17.6 19.74 17.3 18.83 17.34 17.08

250 17.31 18.93 17.6 17.49 18.72 17.64

300 17.07 19.9 17.54 18.38 17.64 17.85

350 19.29 19.37 18.22 17.93 18.91 18.66

On comparing the mean of computational time for 100 target nodes our proposed ABC-NM outperforms existing

algorithms with 10% against Greedy, 7% against Mini, 6% against GA-R, 16% against GA-G on 300X300 grid.

However, it takes high computational time against HPG with 1%. On 500X500 grid ABC-NM outperforms existing

algorithms with 9% against Greedy, 18% against Mini, 17% against GA-R, 10% against GA-G and 9% against

HPG. On 700X700 grid ABC-NM outperforms existing algorithms with 3% against Greedy, 13% against Mini, 3%

against GA-R, 5% against. Also, ABC-NM shows a degrade of 2% against Min, et al. and 1% against HPG.

On comparing the mean of computational time for 200 target nodes our proposed ABC-NM outperforms existing

algorithms with 2% against Greedy, 2% against Mini, 66% against GA-R, 1% against GA-G and shows high

computational time with 1% against GA-R and 2% against HPG on 500X500 grid size. On 700X700 grid ABC-NM

outperforms existing algorithms with 3% against Greedy, 3% against Mini, 1% against GA-R, 2% against GA-G.

However proposed ABC-NM performance has been degraded by 0.3% against HPG. On 300X300 grid in all the

instances proposed ABC-NM takes high computational time.

Table 6 shows the simulated results in terms of average coverage of each sensor on 100 target nodes with different

number of available potential positions. The simulation has been carried out on six different algorithms with the

same simulation parameters.



Table 6. Average Coverage on 100 target nodes with varied #potential positions


al GA-R GA-G HPG

ABC-

NM

k=1,

m=1

300X300

100 2.17 3.13 3.45 3.57 3.85 4.17

150 2.33 3.23 3.70 3.70 4.00 4.35

200 2.56 3.45 3.85 4.00 4.00 4.76

250 2.70 3.57 4.17 4.17 4.35 4.76

500X500

100 1.69 1.79 1.82 1.89 2.04 2.08

150 1.79 1.85 1.89 1.92 2.17 2.17

200 1.89 1.96 1.96 1.92 2.22 2.17

250 1.92 2.04 2.08 2.08 2.33 2.22

700X700

100 1.33 1.39 1.41 1.45 1.54 1.56

150 1.41 1.45 1.47 1.49 1.64 1.61

200 1.47 1.52 1.52 1.49 1.67 1.72

250 1.59 1.67 1.69 1.69 1.75 1.82

k=1,

m=2

300X300

100 2.04 2.70 2.94 3.23 2.94 3.85

150 2.08 3.03 3.33 3.45 3.23 4.00

200 2.17 3.45 4.17 3.57 3.85 4.00

250 2.38 3.70 4.55 3.85 4.00 4.35

500X500

100 1.67 1.69 1.72 1.82 1.89 2.00

150 1.67 1.72 1.75 2.04 1.96 2.04

200 1.72 1.75 1.85 2.17 2.04 2.04

250 1.79 1.82 1.92 2.22 2.17 2.17

700X700

100 1.33 1.35 1.37 1.43 1.47 1.54

150 1.41 1.45 1.47 1.67 1.61 1.61

200 1.47 1.49 1.56 1.79 1.67 1.72

250 1.59 1.61 1.69 1.92 1.75 1.82

k=2,

m=1

300X300

100 2.27 3.33 3.70 3.70 4.17 4.35

150 2.50 3.70 3.70 3.85 4.35 4.76

200 2.78 3.85 4.17 3.85 5.00 4.76

250 2.94 3.85 4.35 4.17 5.26 5.00

500X500

100 1.75 1.82 1.89 1.96 2.04 2.13

150 1.82 1.89 1.92 2.00 2.13 2.17

200 1.89 2.00 2.22 2.08 2.17 2.17

250 2.00 2.13 2.22 2.08 2.27 2.22

700X700

100 1.33 1.37 1.41 1.45 1.49 1.54

150 1.41 1.45 1.47 1.52 1.59 1.61

200 1.47 1.54 1.67 1.59 1.67 1.72

250 1.59 1.67 1.72 1.64 1.75 1.82

k=2,

m=2

300X300

100 2.38 3.45 3.70 3.85 4.17 4.35

150 2.44 3.57 3.85 4.55 4.17 4.35

200 2.63 3.85 3.85 4.76 5.00 4.55

250 3.03 4.00 4.17 5.26 5.00 4.55

500X500

100 1.82 1.89 2.00 2.13 2.17 2.33

150 1.96 2.00 2.00 2.13 2.27 2.33

200 2.00 2.04 2.04 2.22 2.33 2.38

250 2.00 2.08 2.04 2.27 2.33 2.50

700X700

100 1.33 1.37 1.43 1.49 1.52 1.59

150 1.41 1.43 1.43 1.49 1.56 1.61

200 1.47 1.49 1.49 1.59 1.67 1.72

250 1.59 1.64 1.61 1.75 1.75 1.82



Table 7 shows the simulated results in terms of average coverage of each sensor on 200 target nodes with different



Table 7. Average Coverage on 200 target nodes with varied #potential positions


al GA-R GA-G HPG

ABC-

NM

k=1,

m=1

300X300

200 2.17 3.13 3.45 3.57 3.85 4.17

250 3.72 5.16 5.93 5.93 6.40 6.96

300 5.13 6.90 7.69 8.00 8.00 9.52

350 6.18 8.16 9.52 9.52 9.94 10.88

500X500

200 1.69 1.79 1.82 1.89 2.04 2.08

250 2.86 2.96 3.02 3.08 3.48 3.48

300 3.77 3.92 3.92 3.85 4.44 4.35

350 4.40 4.66 4.76 4.76 5.32 5.08

700X700

200 2.67 2.90 2.99 3.17 3.64 3.77

250 2.82 2.92 2.97 3.03 3.42 3.23

300 2.94 3.03 3.03 2.99 3.33 3.45

350 3.17 3.31 3.36 3.36 3.51 3.64

k=1,

m=2

300X300

200 2.04 2.70 2.94 3.23 2.94 3.85

250 3.33 4.85 5.33 5.52 5.16 6.40

300 4.35 6.90 8.33 7.14 7.69 8.00

350 5.44 8.47 10.39 8.79 9.14 9.94

500X500

200 1.64 1.69 1.72 1.82 1.89 2.00

250 2.67 2.76 2.81 3.27 3.14 3.27

300 3.45 3.51 3.70 4.35 4.08 4.08

350 4.08 4.16 4.40 5.08 4.97 4.97

700X700

200 2.67 2.82 2.90 3.17 3.39 3.77

250 2.82 2.92 2.97 3.49 3.35 3.23

300 2.94 2.99 3.13 3.57 3.33 3.45

350 3.17 3.22 3.36 3.75 3.51 3.64

k=2,

m=1

300X300

200 2.27 3.33 3.70 3.70 4.17 4.35

250 4.00 5.93 5.93 6.15 6.96 7.62

300 5.56 7.69 8.33 7.69 10.00 9.52

350 6.72 8.79 9.94 9.52 12.03 11.43

500X500

200 1.75 1.82 1.89 1.96 2.04 2.13

250 2.91 3.02 3.08 3.20 3.40 3.48

300 3.77 4.00 4.44 4.17 4.35 4.35

350 4.57 4.86 5.08 4.76 5.19 5.08

700X700

200 2.67 2.82 2.99 3.17 3.39 3.64

250 2.82 2.92 2.97 3.09 3.28 3.23

300 2.94 3.08 3.33 3.17 3.33 3.45

350 3.17 3.31 3.41 3.27 3.51 3.64

k=2,

m=2

300X300

200 2.38 3.45 3.70 3.85 4.17 4.35

250 3.90 5.71 6.15 7.27 6.67 6.96

300 5.26 7.69 7.69 9.52 10.00 9.09

350 6.93 9.14 9.52 12.03 11.43 10.39

500X500

200 1.82 1.89 2.00 2.13 2.17 2.33

250 3.14 3.20 3.20 3.40 3.64 3.72

300 4.00 4.08 4.08 4.44 4.65 4.76

350 4.57 4.76 4.66 5.19 5.32 5.71

700X700 200 2.67 2.82 3.08 3.39 3.51 3.92

250 2.82 2.87 2.87 3.03 3.21 3.23



300 2.94 2.99 2.99 3.17 3.33 3.45

350 3.17 3.27 3.22 3.46 3.51 3.64

On comparing the mean of average value for 100 target nodes our proposed ABC-NM outperforms existing


HPG on 300X300 grid size. On 500X500 grid ABC-NM outperforms existing algorithms with 16% against Greedy,

13% against Mini, 11% against GA-R, 6% against GA-G and 2% against HPG. On 700X700 grid ABC-NM

outperforms existing algorithms with 14% against Greedy, 11% against Mini, 9% against GA-R, 5% against GA-G

and 3% against HPG.




against Mini, 10% against GA-R, 6% against GA-G and 1% against HPG. On 700X700 grid ABC-NM outperforms

existing algorithms with 18% against Greedy, 15% against Mini, 12% against GA-R, 7% against GA-G and 3%

against HPG.

Table 8 shows the simulated results in terms of connection cost of each sensor on 100 target nodes with different



Table 8. Connection Cost on 100 target nodes with varied #potential positions


al GA-R GA-G HPG

ABC-

NM

k=1,

m=1

300X300

100 0.85 0.86 0.88 0.90 0.91 0.97

150 0.83 0.85 0.87 0.88 0.91 0.95

200 0.78 0.86 0.85 0.85 0.89 0.95

250 0.79 0.83 0.85 0.84 0.88 0.94

500X500

100 0.96 0.96 0.97 0.98 0.98 0.98

150 0.97 0.96 0.97 0.97 0.97 0.98

200 0.96 0.95 0.98 0.96 0.97 0.97

250 0.97 0.96 0.97 0.96 0.96 0.98

700X700

100 0.98 0.99 0.98 0.99 0.98 0.99

150 0.98 0.97 0.99 0.99 0.99 1.00

200 0.98 0.99 0.98 1.00 0.99 0.99

250 0.97 0.97 0.99 0.98 0.98 0.98

k=1,

m=2

300X300

100 0.85 0.92 0.91 0.93 0.94 0.97

150 0.86 0.91 0.89 0.90 0.91 0.97

200 0.84 0.86 0.88 0.85 0.89 0.97

250 0.83 0.86 0.87 0.81 0.88 0.96

500X500

100 0.97 0.98 0.98 0.98 0.98 0.99

150 0.97 0.97 0.98 0.97 0.97 0.98

200 0.97 0.97 0.97 0.97 0.97 0.98

250 0.96 0.96 0.96 0.97 0.97 0.97

700X700

100 0.98 0.98 0.98 1.00 0.98 0.99

150 0.99 0.99 0.98 0.99 0.99 0.99

200 0.99 0.98 0.97 0.98 0.98 0.99

250 0.98 0.97 0.98 0.98 0.99 0.98

k=2,

m=1

300X300

100 0.83 0.84 0.87 0.87 0.91 0.96

150 0.79 0.81 0.87 0.87 0.87 0.94

200 0.78 0.76 0.86 0.83 0.86 0.94

250 0.76 0.73 0.83 0.82 0.87 0.92

500X500

100 0.96 0.97 0.97 0.97 0.99 0.97

150 0.97 0.96 0.96 0.98 0.97 0.98

200 0.96 0.96 0.97 0.97 0.97 0.97

250 0.97 0.97 0.98 0.97 0.97 0.96



700X700

100 0.98 0.99 0.99 1.00 0.98 0.99

150 0.98 0.99 0.99 0.99 1.00 0.99

200 0.98 0.98 0.99 0.98 0.99 1.00

250 0.99 0.98 0.98 0.98 0.97 0.99

k=2,

m=2

300X300

100 0.83 0.84 0.87 0.87 0.88 0.95

150 0.82 0.83 0.80 0.85 0.87 0.95

200 0.81 0.75 0.78 0.86 0.87 0.94

250 0.81 0.77 0.74 0.83 0.86 0.92

500X500

100 0.95 0.96 0.97 0.98 0.98 0.98

150 0.96 0.95 0.96 0.96 0.98 0.97

200 0.95 0.95 0.96 0.96 0.96 0.97

250 0.95 0.96 0.97 0.96 0.97 0.97

700X700

100 0.99 0.99 0.99 1.00 0.98 0.99

150 0.99 0.98 1.00 0.99 0.99 0.98

200 0.98 0.98 0.98 0.98 0.98 0.99

250 0.98 0.97 0.98 0.98 0.99 0.99

Table 9 shows the simulated results in terms of connection cost of each sensor on 200 target nodes with different



Table 9.Connection Cost on 200 target nodes with varied #potential positions


al GA-R GA-G HPG

ABC-

NM

k=1,

m=1

300X300

200 0.92 0.93 0.95 0.95 0.96 0.98

250 0.76 0.80 0.83 0.83 0.87 0.93

300 0.55 0.69 0.69 0.71 0.77 0.87

350 0.41 0.51 0.55 0.56 0.67 0.82

500X500

200 0.98 0.99 0.99 0.99 0.99 0.99

250 0.95 0.95 0.95 0.96 0.96 0.97

300 0.91 0.91 0.94 0.92 0.93 0.94

350 0.88 0.86 0.90 0.89 0.90 0.91

700X700

200 0.93 0.94 0.95 0.96 0.97 0.97

250 0.95 0.95 0.96 0.96 0.96 0.96

300 0.94 0.95 0.96 0.96 0.96 0.96

350 0.94 0.94 0.95 0.95 0.95 0.96

k=1,

m=2

300X300

200 0.93 0.96 0.96 0.97 0.97 0.99

250 0.80 0.87 0.85 0.87 0.89 0.95

300 0.68 0.71 0.75 0.66 0.77 0.91

350 0.52 0.59 0.62 0.47 0.65 0.86

500X500

200 0.99 0.99 0.99 0.99 0.99 0.99

250 0.95 0.96 0.95 0.97 0.97 0.97

300 0.92 0.92 0.91 0.94 0.94 0.95

350 0.88 0.88 0.88 0.91 0.92 0.92

700X700

200 0.93 0.94 0.96 0.97 0.96 0.97

250 0.96 0.95 0.94 0.96 0.96 0.96

300 0.95 0.95 0.94 0.95 0.96 0.96

350 0.94 0.94 0.94 0.95 0.95 0.96

k=2,

m=1

300X300

200 0.91 0.92 0.94 0.94 0.95 0.98

250 0.72 0.76 0.81 0.83 0.83 0.92

300 0.55 0.50 0.71 0.66 0.71 0.85

350 0.35 0.28 0.56 0.51 0.62 0.78

500X500 200 0.98 0.99 0.98 0.99 0.99 0.99

250 0.95 0.95 0.95 0.95 0.96 0.96



300 0.91 0.91 0.92 0.90 0.93 0.94

350 0.88 0.87 0.90 0.87 0.88 0.90

700X700

200 0.94 0.95 0.95 0.96 0.96 0.97

250 0.96 0.95 0.96 0.96 0.96 0.97

300 0.95 0.95 0.95 0.95 0.96 0.96

350 0.94 0.94 0.95 0.95 0.95 0.96

k=2,

m=2

300X300

200 0.91 0.92 0.93 0.93 0.94 0.97

250 0.77 0.78 0.74 0.82 0.84 0.93

300 0.59 0.51 0.55 0.71 0.71 0.87

350 0.47 0.35 0.28 0.56 0.59 0.77

500X500

200 0.98 0.98 0.98 0.99 0.99 0.99

250 0.94 0.94 0.95 0.95 0.96 0.96

300 0.89 0.89 0.91 0.92 0.92 0.93

350 0.84 0.87 0.87 0.90 0.89 0.90

700X700

200 0.93 0.94 0.95 0.95 0.97 0.97

250 0.96 0.95 0.96 0.96 0.97 0.97

300 0.95 0.95 0.96 0.96 0.96 0.96

350 0.94 0.95 0.95 0.96 0.95 0.96

On comparing the mean of connection cost for 100 target nodes our proposed ABC-NM outperforms existing

algorithms with 14% against Greedy, 13% against Mini, 11% against GA-R, 9% against GA-G and 7% against HPG

on 300X300 grid size. On 500X500 grid ABC-NM outperforms existing algorithms with 1% against Greedy, 1%

against Mini, 1% against GA-R, 1% against GA-G and performs equally with HPG. On 700X700 grid ABC-NM

outperforms existing algorithms with 1% against Greedy, 1% against Mini, and equally performs with GA-R, GA-G

and HPG.

On comparing the mean of connection cost for 200 target nodes our proposed ABC-NM outperforms existing


HPG on 300X300 grid size. On 500X500 grid ABC-NM outperforms existing algorithms with 2% against Greedy,

2% against Mini, 2% against GA-R, 1% against GA-G and equally performs with HPG. On 700X700 grid ABC-NM

outperforms existing algorithms with 2% against Greedy, 2% against Mini, 1% against GA-R, 1% against GA-G and

equal performance with HPG.

Table 10 shows the simulated results in terms of coverage cost of each sensor on 100 target nodes with different



Table 10.Coverage Cost on 100 target nodes with varied #potential positions


al GA-R GA-G HPG

ABC-

NM

k=1,

m=1

300X300

100 0.95 0.90 0.88 0.87 0.85 0.83

150 0.95 0.90 0.86 0.86 0.84 0.81

200 0.93 0.88 0.85 0.84 0.84 0.77

250 0.93 0.87 0.83 0.83 0.81 0.77

500X500

100 0.97 0.97 0.97 0.96 0.96 0.96

150 0.97 0.97 0.96 0.96 0.95 0.95

200 0.96 0.96 0.96 0.96 0.95 0.95

250 0.96 0.96 0.96 0.96 0.95 0.95

700X700

100 0.98 0.98 0.98 0.98 0.98 0.98

150 0.98 0.98 0.98 0.98 0.97 0.97

200 0.98 0.98 0.98 0.98 0.97 0.97

250 0.97 0.97 0.97 0.97 0.97 0.97

k=1,

m=2

300X300

100 0.96 0.93 0.91 0.90 0.91 0.85

150 0.96 0.91 0.89 0.88 0.90 0.84

200 0.95 0.88 0.83 0.87 0.85 0.84

250 0.94 0.86 0.79 0.85 0.84 0.81

500X500 100 0.97 0.97 0.97 0.97 0.96 0.96



150 0.97 0.97 0.97 0.96 0.96 0.96

200 0.97 0.97 0.97 0.95 0.96 0.96

250 0.97 0.97 0.96 0.95 0.95 0.95

700X700

100 0.98 0.98 0.98 0.98 0.98 0.98

150 0.98 0.98 0.98 0.97 0.97 0.97

200 0.98 0.98 0.98 0.97 0.97 0.97

250 0.97 0.97 0.97 0.96 0.97 0.97

k=2,

m=1

300X300

100 0.95 0.89 0.86 0.86 0.83 0.81

150 0.94 0.86 0.86 0.85 0.81 0.77

200 0.92 0.85 0.83 0.85 0.75 0.77

250 0.91 0.85 0.81 0.83 0.72 0.75

500X500

100 0.97 0.97 0.96 0.96 0.96 0.95

150 0.97 0.96 0.96 0.96 0.95 0.95

200 0.96 0.96 0.95 0.96 0.95 0.95

250 0.96 0.95 0.95 0.96 0.95 0.95

700X700

100 0.98 0.98 0.98 0.98 0.98 0.98

150 0.98 0.98 0.98 0.98 0.97 0.97

200 0.98 0.98 0.97 0.97 0.97 0.97

250 0.97 0.97 0.97 0.97 0.97 0.97

k=2,

m=2

300X300

100 0.94 0.88 0.86 0.85 0.83 0.81

150 0.94 0.87 0.85 0.79 0.83 0.81

200 0.93 0.85 0.85 0.77 0.75 0.79

250 0.91 0.84 0.83 0.72 0.75 0.79

500X500

100 0.97 0.96 0.96 0.95 0.95 0.95

150 0.96 0.96 0.96 0.95 0.95 0.95

200 0.96 0.96 0.96 0.95 0.95 0.94

250 0.96 0.96 0.96 0.95 0.95 0.94

700X700

100 0.98 0.98 0.98 0.98 0.98 0.97

150 0.98 0.98 0.98 0.98 0.98 0.97

200 0.98 0.98 0.98 0.97 0.97 0.97

250 0.97 0.97 0.97 0.97 0.97 0.97

Table 11 shows the simulated results in terms of coverage cost of each sensor on 200 target nodes with different



Table 11. Coverage Cost on 200 target nodes with varied #potential positions


al GA-R GA-G HPG

ABC-

NM

k=1,

m=1

300X300

200 0.98 0.95 0.94 0.94 0.93 0.91

250 0.93 0.87 0.82 0.82 0.80 0.76

300 0.87 0.76 0.70 0.68 0.68 0.55

350 0.81 0.67 0.55 0.55 0.51 0.41

500X500

200 0.99 0.98 0.98 0.98 0.98 0.98

250 0.96 0.96 0.95 0.95 0.94 0.94

300 0.93 0.92 0.92 0.93 0.90 0.91

350 0.90 0.89 0.89 0.89 0.86 0.87

700X700

200 0.96 0.96 0.96 0.95 0.93 0.93

250 0.96 0.96 0.96 0.95 0.94 0.95

300 0.96 0.95 0.95 0.96 0.94 0.94

350 0.95 0.95 0.94 0.94 0.94 0.93

k=1,

m=2 300X300

200 0.98 0.96 0.96 0.95 0.96 0.93

250 0.94 0.88 0.86 0.85 0.87 0.80



300 0.91 0.76 0.65 0.74 0.70 0.68

350 0.85 0.64 0.46 0.61 0.58 0.51

500X500

200 0.99 0.99 0.99 0.98 0.98 0.98

250 0.96 0.96 0.96 0.95 0.95 0.95

300 0.94 0.94 0.93 0.91 0.92 0.92

350 0.92 0.91 0.90 0.87 0.88 0.88

700X700

200 0.96 0.96 0.96 0.95 0.94 0.93

250 0.96 0.96 0.96 0.94 0.94 0.95

300 0.96 0.96 0.95 0.94 0.94 0.94

350 0.95 0.95 0.94 0.93 0.94 0.93

k=2,

m=1

300X300

200 0.97 0.94 0.93 0.93 0.91 0.91

250 0.92 0.82 0.82 0.81 0.76 0.71

300 0.85 0.70 0.65 0.70 0.50 0.55

350 0.77 0.61 0.51 0.55 0.28 0.35

500X500

200 0.98 0.98 0.98 0.98 0.98 0.98

250 0.96 0.95 0.95 0.95 0.94 0.94

300 0.93 0.92 0.90 0.91 0.91 0.91

350 0.90 0.88 0.87 0.89 0.87 0.87

700X700

200 0.96 0.96 0.96 0.95 0.94 0.93

250 0.96 0.96 0.96 0.95 0.95 0.95

300 0.96 0.95 0.94 0.95 0.94 0.94

350 0.95 0.95 0.94 0.95 0.94 0.93

k=2,

m=2

300X300

200 0.97 0.94 0.93 0.93 0.91 0.91

250 0.92 0.84 0.81 0.74 0.78 0.76

300 0.86 0.70 0.70 0.55 0.50 0.59

350 0.76 0.58 0.55 0.28 0.35 0.46

500X500

200 0.98 0.98 0.98 0.98 0.98 0.97

250 0.95 0.95 0.95 0.94 0.93 0.93

300 0.92 0.92 0.92 0.90 0.89 0.89

350 0.90 0.89 0.89 0.87 0.86 0.84

700X700

200 0.96 0.96 0.95 0.94 0.94 0.92

250 0.96 0.96 0.96 0.95 0.95 0.95

300 0.96 0.96 0.96 0.95 0.94 0.94

350 0.95 0.95 0.95 0.94 0.94 0.93

On addressing coverage cost, proposed ABC-NM shows a minimal deflection when compared with other existing

algorithms on both 100 and 200 target tables Table 10 and Table 11. However, the proposed ABC-NM satisfies a

complete coverage without any compromise in network lifetime.

Table 12 shows the simulated results in terms of network lifetime on 100 target nodes with different number of

available potential positions. The simulation has been carried out on six different algorithms with the same

simulation parameters.

Table 12. Network Lifetime on 100 target nodes with varied #potential positions


al GA-R GA-G HPG

ABC-

NM

k=1,

m=1

300X300

100 722 1073 839 1034 1132 1151

150 761 898 937 839 1034 1229

200 663 781 995 722 1210 995

250 761 898 742 839 995 1171

500X500

100 550 655 615 518 615 655

150 501 558 590 607 623 655

200 518 582 590 582 550 623

250 526 510 526 566 558 631

700X700 100 390 486 414 378 546 474



150 468 426 432 366 486 384

200 408 570 588 558 558 582

250 438 462 522 498 462 522

k=1,

m=2

300X300

100 689 972 1042 972 1113 1254

150 742 760 830 919 1025 1042

200 707 901 618 866 989 1025

250 601 654 601 707 919 1113

500X500

100 490 533 526 548 598 569

150 432 512 562 447 533 548

200 432 591 504 461 548 562

250 440 504 519 411 476 540

700X700

100 320 393 366 370 425 393

150 352 430 462 389 398 345

200 311 370 302 251 411 419

250 311 398 411 347 402 386

k=2,

m=1

300X300

100 620 788 821 637 1072 855

150 519 838 670 754 888 737

200 586 804 670 771 888 855

250 654 821 788 654 821 855

500X500

100 430 450 423 437 551 531

150 383 517 417 484 464 511

200 383 497 457 457 484 538

250 423 450 423 477 417 531

700X700

100 290 273 259 293 297 335

150 266 283 242 245 283 269

200 245 259 214 180 283 325

250 242 276 262 276 259 283

k=2,

m=2

300X300

100 580 836 887 614 1126 819

150 716 904 853 631 972 1058

200 597 699 751 648 904 921

250 648 734 802 529 785 921

500X500

100 390 415 390 365 484 497

150 365 415 409 403 409 465

200 384 440 371 421 396 453

250 333 421 377 352 390 409

700X700

100 250 340 352 352 352 340

150 301 363 371 375 328 336

200 270 297 273 273 305 328

250 289 270 246 254 285 340

Figure 2. Network Lifetime for 500X500 GRID on 100 Target Nodes



Figure 2 shows the lifetime of WSN under different and values of 500X500 Grid for 100 target nodes. For

value (1,1), (2,1) and (2,2) it outperforms all existing methods and for values (1,2) proposed ABC-NM outperforms

other existing algorithms and HPG gives a near optimal network lifetime as like ABC-NM.



value (2,1) and (2,2) it outperforms all existing methods and for values (1,1) and (1,2) proposed ABC-NM

outperforms other existing algorithms except HPG.



value (1,1), (1,2), (2,1) and (2,2) it outperforms all existing methods.





value (1,1), (1,2), (2,1) and (2,2) it outperforms all existing methods. This states that on performing on more number

of target nodes (i.e. when more number of combinations leads to converge towards global optimal combination

solution).



value (1,1), (1,2), (2,1) and (2,2) it outperforms all existing methods.

V. CONCLUSION

A major concern in wireless sensor networks is to maximize network lifetime (in terms of rounds) while maintaining

a high quality of services (QoS) at each round such as target coverage and network connectivity. Due to the power

scarcity of sensors, a mechanism that can efficiently utilize energy has a great impact on extending network lifetime.

Most existing works concentrate on scheduling sensors between sleep and active modes to maximize network

lifetime while maintaining target/area coverage and network connectivity. To enhance the lifetime of the network,

ABC-NM has been proposed. The proposed algorithm gives better lifetime of the network by conducting various

benchmark results.

VI. REFERENCE [1] Liu, Zheng. "Maximizing network lifetime for target coverage problem in heterogeneous wireless sensor networks." Mobile Ad-Hoc and

Sensor Networks (2007): 457-468.

[2] Cardei, Mihaela, My T. Thai, Yingshu Li, and Weili Wu. "Energy-efficient target coverage in wireless sensor networks." In INFOCOM

2005. 24th annual joint conference of the IEEE computer and communications societies. Proceedings IEEE, Vol. 3, pp. 1976-1984. IEEE, 2005.

[3] Hongwu, Zhang, Wang Hongyuan, Feng Hongcai, Liu Bing, and Gui Bingxiang. "A heuristic greedy optimum algorithm for target coverage

in wireless sensor networks." In Circuits, Communications and Systems, 2009. PACCS'09. Pacific-Asia Conference on, pp. 39-42. IEEE, 2009.

[4] Gu, Yu, Jie Li, Baohua Zhao, and Yusheng Ji. "Target coverage problem in wireless sensor networks: A column generation based

approach." In Mobile ADHOC and sensor systems, 2009. MASS'09. IEEE 6th international conference on, pp. 486-495. IEEE, 2009. [5] Zhao, Qun, and Mohan Gurusamy. "Lifetime maximization for connected target coverage in wireless sensor networks." IEEE/ACM

Transactions on Networking (TON), Vol. 16, No. 6 (2008): 1378-1391.

[6] Lu, Mingming, Jie Wu, Mihaela Cardei, and Minglu Li. "Energy-efficient connected coverage of discrete targets in wireless sensor networks." Networking and mobile computing (2005): 43-52.

enhancing the lifetime of network using hybrid artificial ... paper/volume 54/issue...

Documents