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Fuzzy Based Node Disjoint QoS Routing in MANETsby Using Agents

Vijayashree Budyal1, S. S. Manvi2, S. G. Hiremath3

1 Basaveshwar Engineering College, Bagalkot, India2Reva Institute of Technology and Management, Bangalore, India

3G. M. Institute of Technology Davangere, IndiaE-mail: vrbudyal@yahoo.co.in, sunil.manvi@revainstitution.org, drsgh@yahoo.co.in

Abstract— Support for real time multimedia applications suchas, video telephony, financial stock quote services, andmultiplayer interactive games etc., is very essential in MobileAd hoc Networks (MANETs). Such applications requiremultiple Quality of Service (QoS) parameters to be satisfied,like bandwidth, end-to- end delay, packet loss rate, jitter, etc.This paper considers the problem of finding node disjoint andmulti-constrained QoS multipaths from source to destinationby using agent based fuzzy inference system. The proposedscheme, Fuzzy based Node Disjoint Multipath QoS Routing(FNDMQR) operates in the following steps by integratingstatic and mobile agents. (1) Determination of multiple pathsand picking up of resource information (available bandwidth,link delay, and packet loss rate) of the intermediate nodesfrom source to destination. (2) Recognition of node disjoint,and multi-constrained QoS fit paths by using Takagi-SugenoFuzzy Inference System (TSFIS). TSFIS extracts a fuzzy QoSweight from available resource information of theintermediate nodes. (3) Selection of the best path dependingon the fuzzy QoS weight. (4) Maintenance of QoS path whenpath breaks due to mobility of node or link failure. To test theperformance effectiveness of the approach, we have analyzedthe performance parameters like packet delivery ratio, averageend-to-end delay and overall control overhead. The schemeperforms better as compared to a node-disjoint multipathrouting in MANETs.

Index Terms—MANETs, QoS, Takagi-Sugeno fuzzy Inference,software agents.

I. INTRODUCTION

Ad hoc wireless network consists of collection of mobiledevices like, personal digital assistant (PDA), laptops, cellphones etc. These nodes are interconnected by multi-hopcommunication path, due to limited transmission range. Theroute found between source and destination becomes invalidoften because of the temporary topology of the network.Therefore routing in Mobile Ad hoc Networks (MANETs) isa challenging task [1].Multi-path provides more than one routeto the destination node. Multi-path routing protocols aredeemed superior over conventional single path protocols forenhanced throughput, reliability, robustness, load balancing,fault-tolerance, offering QoS, and to avoid frequent routediscovery attempts [2]. Multi-path routing protocols canattempt to find node- disjoint, link-disjoint, or non-disjointroutes. Node- disjoint routes have no nodes or links incommon on the routes. Link-disjoint routes have no links incommon, but may have nodes in common. Non-disjoint routes

can have nodes and links in common. When a link or node ison several paths severe flow occurs when the incoming trafficload is high. As a result shared link or the node becomes thebottleneck. Node disjoint paths provide more reliability thanthe link disjoint paths [3].With the increasing demand in real-time multimedia, application in video telephony, videoconferencing, and military arena requires multi-constrainedQuality of Service (QoS) to be fulfilled. The QoS requirementof connection includes parameters like bandwidth, end-to-end delay, jitter, packet loss rate etc.

Multi-constraint QoS parameters are imprecise anduncertain due to dynamic topology of MANETs. However,selecting a route, which satisfies all multiple constraints, isan NP complete problem [4]. There is no accurate mathematicalmodel to describe it. Fuzzy logic is used to provide a feasibletool to solve the multi-metric QoS problem. Fuzzy logic is atheory that not only supports several inputs, but also exploitsthe pervasive imprecision information [5]. So adopting fuzzylogic to solve multi metric problems in ad hoc networks is anappropriate choice.

Multi-constraint based routing protocols use QoSsatisfied paths other than the single shortest path to routethe packets. If multiple node disjoint paths with multi-constraint QoS paths are set up between a source and adestination, then source node can use these routes as primaryand backup routes, i.e., a new route discovery is invokedonly when all of the routing paths fail or when there onlyremains a single path available, whenever node or link fails.This helps to reduce overhead in finding alternative routesand extra delay in packet delivery introduced. Therefore, inthis paper we adopt both node disjoint and multi-constraintQoS routing in MANETs.

Software agents based applications are an emergingdiscipline, which can be applied to provide flexible, adaptable,and intelligent services in MANETs. Software agents areautonomous and intelligent programs that execute tasks onbehalf of a process or a user. They have two special properties:mandatory and orthogonal, which make them different fromthe standard programs. Mandatory properties are: autonomy,reactive, proactive and temporally continuous. Theorthogonal properties are: communicative, mobile, learningand believable [6].

A. Related WorkSome of the related works to build multi-constrained QoS

routing in MANETs are as follows: Fuzzy cost based multi-7

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constrained quality of service routing is discussed in [7] toselect an optimal path by considering multiple independentQoS metrics such as bandwidth, end-to-end delay, andnumber of intermediate hops.The work given in [8] exploresthe node disjoint path routing subject to different degrees ofpath coupling, with and without packet redundancy. Multi-path routing problem of MANETs with multiple QoSconstraints, which may deal with the delay, bandwidth andreliability metrics, and researching the routing problem isexplained in [9].

Architecture for guaranteeing QoS based on nodedisjointmulti-path routing protocol in MANETs is explained in [10].The work given in [11] uses fuzzy set and roughs set theoryto select an effective routing path in MANETs. In the firststage, the data set consisting of resources and paths arefuzzified. In the second stage, information gain is calculatedby using ID3 algorithm for evaluating the importance amongattributes. In the third stage, a decision table is reduced byremoving redundant attributes without any information loss.Finally, if-then decision rules are extracted from theequivalence class to select the best routing path. Fuzzy basedpriority scheduler for mobile ad-hoc networks, to determinethe priority of the packets using Destination SequencedDistance as the routing protocols is presented in [12]. Theproposed fuzzy agent based Node Disjoint Multi-path QoSRouting in MANETs is motivated by observing inherentdrawbacks of existing QoS routing schemes like: lack ofsupport of multi-constraint QoS routing and maintenance ofthe QoS path when link/node fails.

B. Our ContributionsIn this work, we investigate on the use of Takagi-Sugeno

fuzzy inference system (TSFIS) for multi-constrained QoSroute selection in MANETs, integrating static and mobileagents. Source knows the multiple nodes disjoint paths tothe destination, and collects the resource information(available bandwidth, delay, and packet loss rate) ofintermediate nodes. The source uses gathered intermediatenode information to select the QoS path by using TSFISmodel. This model accepts uncertain and imprecise crispparameters like, available bandwidth, link delay, and packetloss rate as input and is being processed in stages, i.e.,fuzzification, inference, and defuzzification. After experiencingall the stages, a single value score fuzzy QoS weight isgenerated from the combination metrics for each node on thepath. This is used to measure QoS satisfaction on the path.The performance of our scheme Fuzzy based Node disjointMultipath QoS Routing (FNDMQR) is compared to node-disjoin multi-path routing in MANETs (NDMRP) [9].The restof paper is organized as follows. Section II explains proposedwork on fuzzy agent based multi-constrained QoS routing.Section III describes an evaluation of our approach usingsimulation. Finally, section IV concludes our paper.

II. PROPOSED WORK

This section describes network model, Takagi-SugenoFuzzy Inference System (TSFIS), QoS routing agency, and

fuzzy and agent based multi-constraint QoS routing scheme.

A. Network ModelAn ad hoc network consists of set of mobile nodes and

set of links between the mobile nodes as shown in figure 1.Due to mobility of the nodes in the ad hoc network, linkconnection varies with respect to time. Each mobile node hascertain transmission range. Each node is equipped with anagent platform and an agency in which agents reside. Weassumed that agents have protection from hosts on whichthey execute. Similarly, hosts have protection from agentsthat can communicate on available platform. The securedplatform consists of protection from denial of execution,masquerading, eavesdropping, etc. Recently developedtechniques for mobile agent security have techniques forprotecting the agent platform.

Fig. 1. A Mobile Ad hoc Network

B. Takagi-Sugeno Fuzzy Inference SystemFuzzy system is classified as Mamdani and Takagi-Sugeno

models. In this paper we propose Takagi Sugeno (first-order)fuzzy inference system for reasoning, because as it has highinterpretability and computational efficiency, and built-inoptimal and adaptive technique. And also, it is not necessaryto define a prior linguistic terms for conclusions, since themapping is direct. And also, the effort of performingdefuzzification is saved, because the crisp output is directlydetermined by the fuzzy mean formula.

Our Takagi-Sugeno fuzzy system consists of three crispinputs and one output. The system inputs are availablebandwidth ‘AB’ and link delay ‘TD’, and packet loss rate‘PR’ of the intermediate nodes and output is QoS weight ‘γ’.Three inputs are characterized by bell shaped membershipfunctions. Bell function for ‘AB’ is defined by equation 1.

ib

i

ii

acABAB

2)(1

1)(

………. (1)

Where a, b and c are the parameters of membership func-tion governing the centre, width and slope of the bell-shapedmembership function. ‘TD’ and ‘PR’ take similar kind of bellfunction. The steps involved in FIS are fuzzification,inference

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and defuzzification. µi(AB) is Membership function value forthe available bandwidth.

Fuzzification: The first step is to consider the crisp inputsand determine the degree to which they belong to each of theappropriate linguistic sets via bell membership functionswhich is termed as fuzzification. Fuzzification converts inputdata into suitable fuzzy values (linguistic terms). The linguisticterms, which divide the membership functions for availablebandwidth, are {ABless, ABmore} and is as shown in figure2. Link delay linguistic terms are {TDless, TDmore}, and forpacket loss rate the linguistic terms are {PRless, PRmore}.Vertical coordinates represent the degree of membership,which distributes in the interval of [0- 1].

Fig. 2. Membership functions for available bandwidth

Inference: Fuzzified data trigger one or several rules inthe fuzzy model to calculate the result. The fuzzy rules arerealized in the form of IF-THEN. The input parameters arecombined using T-norm operator ‘AND’. The total numberof rules formed is as follows:

Rule 1: If AB is ‘ABless’ and TD is ‘TDless’ and PRis‘PRless’ Then z1 = Ψ1AB + ζ 1T D + φ1PR + σ1

Rule 2: If AB is ‘ABless’ and TD is ‘TDless’ and PRis‘PRmore’ Then z2 = Ψ2AB + ζ 2TD + φ2PR + σ2

Rule 3: If AB is ‘ABless’ and TD is ‘TDmore’ and PRis‘PRless’ Then z3 = Ψ3AB + ζ 3TD + φ3PR + σ3

Rule 5: If AB is ‘ABmore’ and TD is ‘TDless’ and PRis‘PRless’ Then z5 = Ψ5AB + ζ5TD + φ5PR + σ5

Rule 6: If AB is ‘ABmore’ and TD is ‘TDless’ and PRis‘PRmore’ Then z6 = Ψ6AB + ζ6TD + φ6PR + σ6

Rule 7: If AB is ‘ABmore’ and TD is ‘TDmore’ and PRis‘PRless’ Then z7 = Ψ7 AB + ζ7TD + φ7 PR + σ7

Rule 8: If AB is ‘ABmore’ and TD is ‘TDmore’ and PRis‘PRmore’ Then z8 = Ψ8AB + ζ8TD + φ8 PR + σ8

The output level zi of each rule is weighted by the firingstrength wi of the rule given by 2. Ψi , ζi , φi , and σi areconstants chosen between 0-1. Where i = 1 to N. N is thenumber of rules. For example, for and ‘AND’ rule with inputsAB and TD, and PR have a firing strength as given by equation2. wi = µ(AB) . µ (TD). µ (PR) …….. (2)Where µ (AB), µ (TD), and µ (PR) are the membership valuesfor inputs available bandwidth and link delay and packetloss rate.

Defuzzification: The final output fuzzy QoS weight ‘γ’ of thesystem is the weighted average of all rule outputs, computedas given in equation 3.

N

ii

N

iii

w

zw

1

1………. (3)

C. QoS Routing AgencyEach node comprises of Fuzzy based Node Disjoint Multi-

path QoS Routing agency (FNDMQR). Components ofagency and their interactions are depicted in figure 3. Agencyconsists of Knowledge Base (KB), static agents and mobileagents. Static agent are Administrator Agent (AA), and QoSDecision Agent (QDA). Mobile agents are Disjoint Agent(DA) and Recovery Agent (RA).

Fig. 3. Fuzzy based node disjoint multipath QoS routing agency

KB: KB of source comprises of information of node ID,destination, resource information {AB, TD, PR} of theintermediate nodes on the paths, multiple path IDs from sourceto destination and their fuzzy QoS weight γ obtained by usingTSFIS and running application(s) details.. Intermediate nodeKB consists of node status (connected/disconnected tonetwork), Node disjoint Forward QoS Routing Table(NDFQRT), {AB, TD, PR} of its own. KB is read, updatedand is used by agencies (AA, QDA, DA and RA) to establishQoS route and to maintain the path between source anddestination.

Administrator Agent: It is a static agent and performs thefollowing functions at source, (1) creates and dispatches DAto find multiple paths to destination, (2) collects multiplenode disjoint paths and resource information of intermediatenodes from DA, (3) computes γ for each node by using TSFISand ‘Γ’ for each node disjoint path (4) selects a QoS nodedisjoint path from multiple node disjoint paths, and (5) initiatesreconstruction of QoS path upon request from RA duringlink/node failure.

Disjoint Agent: It is a mobile agent triggered by AA ofsource whenever it wishes to send data to the destination.

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DA’s are dispatched by AA to reach all its neighbors. EveryDA performs the following functions. (1) Traces all the feasiblepaths to the destination by cloning. Gathers intermediatenode resource information {AB, TD, PR}. (2) Handover themultiple path information to AA of destination. (3) AA ofdestination separates out the node disjoint path from anumber of multiple paths identified by DA, and (4) DAtraverses back through the node disjoint paths to reachsource, gathering resource information of the intermediatenodes.

QoS Decision Agent: This agent is a static agent triggeredby AA only at the source node. It is responsible for computingthe γ for each of the node on the disjoint paths by usingTSFIS. Updates computed γ of each node on the node disjointpaths in AA of source. Later it is disposed off.Recovery Agent: It is a mobile agent and performs theoperation of route maintenance whenever link/node fails.

D. Fuzzy and Agent based Multi-constraint QoS RoutingScheme

This section describes the functioning of the proposedmulti-constraint QoS routing scheme. The scheme operatesin the following steps.

1) Recognition of node disjoint multiple paths to thedestination:

When a source node needs multi-constraint QoS path tothe destination. Source AA dispatches DA to reach itsneighbors. DA carries source ID, sequence number, maximumnumber of hops, and traveled node list. Upon reaching theintermediate node, AA of intermediate node checks for theduplication of the DA by looking at the sequence number.When receiving a duplicate DA, the possibility of findingnode disjoint multiple paths is zero if it is dropped, for it maycome from another path. But if all of the duplicate DA arebroadcast, this will generate broadcast storm and decreaseperformance. In order to avoid this problem DA records theshortest routing hops to keep loop-free paths and decreaserouting broadcast overhead.

When intermediate node receives DA for the first time, itchecks the node list of path traversed and calculates thenumber of hops from the source node to itself and recordsthe number as the shortest number of hops in its reverserouting table. If the node receives the duplicate DA, itcomputes the number of hops and compares with shortestnumber of hops in its reverse routing table. If the number ofhops is more than shortest number of hops in the reverserouting table, then the DA is dropped. Only when it is lessthan or equal to the shortest number of hops, the nodeappends its own address to the node list of path in DA and iscloned to reach the neighbors or the destination. Agentcloning is a technique of creating an agent similar to that ofparent, where cloned agent contains the information of parentagent that it has traversed. A child agent can communicateeither to any one of its parents who are within the range or toany of its parents at a given level.When first DA is receivedby the destination, it records the list of node IDs of entireroute in its reverse route table and DA traces the reverse

route to reach the source. When destination AA receivesduplicate DA, it compares the whole node IDs of the entireroute with existing node disjoint paths in its reverse routingtable. If there is no common node (except source anddestination) between the node IDs from the the duplicate DAand node IDs of existing node disjoint path in the destinationreverse routing table then, the path in current DA is nodedisjoint path and is recorded in the reverse routing table ofthe destination and DA traces the reverse route to reach thesource. Otherwise, current DA is disposed.

DA collects the intermediate node’s resource information{AB, TD, PR} while tracing back the reverse path fromdestination to source. The multiple node isjoint paths andresource information of the intermediate node is madeavailable to the AA of source for further QoS verification.

2) Fuzzy Agent based QoS Path Selection: Multi-constrained QoS path is selected from numerous known nodedisjoint multi-paths placed in AA of source by using TSFIS(refer section II B). TSFIS computes the γ for every node oneach of the path by considering { AB, TD, PR } as inputmetrics to TSFIS. AA of source computes Γ by considering γof all the nodes on the path and is given by equation 4.

P

P

ij

1

……………. (4)

Where, P is the number of nodes on the path ‘j’. If à isgreater than QoS required by the user, it implies the pathsatisfies the requirement and QoS packets are transmittedthrough that path.As an example consider figure 4, which isconsisting of number of mobile nodes. There exist multiplepaths between source and destination shown with dottedlines. Destination decides a node disjoint paths fromnumerous multiple paths and these multiple node disjointpaths are shown with solid lines. Upon receiving node disjointpaths, source AA uses TSFIS to identify a paths whichsatisfies multi-constraint QoS shown with solid bidirectionalarrow. One among them with least number of hops is selectedas QoS path to route the packets. The other QoS satisfiednode disjoint paths act as back up paths.

Fig. 4. Fuzzy based node disjoint multi-path QoS routing agency

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3) QoS Route Maintenance: The proposed scheme uses RAto maintain QoS path. Whenever node moves or fails, thenRA sends error to the source. AA of the source checks tofind a path from the existing QoS satisfied node disjoint multi-paths to reach destination. If not found it initiates new routediscovery.

III. SIMULATION

The proposed FNDMQR scheme is simulated along withNDMRP in the network scenario using C programminglanguage to verify the performance and operationeffectiveness. Membership functions and rule bases of thefuzzy are carefully designed and the output is verified usingMatlab 7.0 fuzzy logic toolbox with FIS editor. Then the inputsare identified in the library of C code programming. In thissection, we describe the simulation model.

A. Simulation ModelA mobile ad hoc simulation model consists of N = 80

number of mobile nodes placed randomly within the area of AX B = 1000 X 1000 m2. A random way point mobility model isused. Each node randomly selected a position with a speedranging from Smin to Smax = 0-10 m/s. A pause time Pautime =0-10 sec, is assigned for each node. If a node tries to go outof the boundary, its direction is reversed (Bouncing ballmodel). The radio propagation range for each node is selectedas R ran = 250 m and channel capacity is Ch cap = 10 Mbps. Linkdelays may vary between Ldmin to Ldmax = 20-50 ms. Thesources and destinations are randomly selected with uniformprobabilities. Residual power of each node varied betweenpwrmin to pwrmax = 20- 200 mW. Traffic sources are withconstant bit rate (CBR) with data payload size as Dtpld = 512bytes. Each simulation is executed for Simtime = 600 seconds.Simulation was carried out with different QoS requirements.

The following performance metrics are used forevaluating the proposed scheme.

Packet delivery ratio (PDR): It is the ratio of the numberof data packets delivered to the destination node to thenumber of data packets transmitted by the source node. It isexpressed in percentage.

Overall control Overhead: It is defined as the ratio of thetotal number of control messages or agents to the total

number of packets generated to perform communication.Average end-to-end delay: It is defined as the average

time taken to transmit predefined number of packets fromsource to destination. It is expressed in seconds.

B. ResultsIn this section, we discuss various results obtained

through simulation. The results include packet delivery ratio,overall control overhead, average end-to-end delay. Ourscheme FNDMQR is compared with existing NDMRP.Figure5 depicts PDR with variation in node speed and number ofnodes. PDR decreases, as node speed, increases in bothFNDMQR and NDMRP because when node speed increasespackets are lost while reconstructing the QoS path. PDR ofFNDMQR is more compared to NDMRP since it accounts

Fig. 5. Packet delivery Ratio vs. Node Speed

the multi-constrained QoS on the path by using TSFIS andstable path is identified by considering minimum number ofhops.

Average end-to-end delay generated for varying numberof nodes and speed is reported in the figure 6. As the nodespeed increases average end to end delay also increases.The decrease of end-to-end delay in FNDMQR is mainlypresented by selecting a suitable QoS route that results inreduction of path breakage. Where as NDMRP suffersfrequent link breaks and needs route reconstruction frequentlywhich results in increase in end-to-end delay.

Fig. 6. Average end-to-end delay vs. Node Speed

Figure 7 shows that the average end to end delayraisesgradually as the number of source increases. Thereason is that with increasing number of sources, the totaltraffic load increases and the network becomes congested.So, more packets are kept waiting in the queues for long timewhich causes the delay to increase. However FNDMQRoutperforms NDMRP in reducing the end-to-end delay.

Fig. 7. Average end-to-end delay vs. No. of Sources

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Overall control overhead with respect to node speed andnumber of nodes are shown in figure 8. As the speed of thenodes increases control overhead increases. Because ofnetwork connectivity, as the node mobility increases mobileagents are generated for repairing the path for the QoScommunication.

Fig. 8. Overall Control Overhead vs. Node Speed

CONCLUSIONS

This paper presented fuzzy based multi-constrained QoSnode disjoint multi-path routing in MANETs by using agents.Fuzzy rule base is developed to unite the various uncertainQoS metrics such as available bandwidth, link delay, andpacket loss rate to generate single QoS weight for the nodedisjoint paths, which is used for path selection. The resultsfor our proposed FNDMQR show good packet delivery ratioand reduction in end-to-end delay and control overhead. Theagent-based architectures provide flexible, adaptable andasynchronous mechanisms for distr ibuted networkmanagement, and facilitate software reuse and maintenance.Future work includes optimization of membership functionof fuzzy system according to the user requirement, to supportQoS routing in MANETs

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