Fuzzy Based Node Disjoint QoS Routing in MANETs by Using Agents Vijayashree Budyal 1, S. S. Manvi 2, S. G. Hiremath 3

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1 Fuzzy Based Node Dsjont QoS Routng n MANETs by Usng Agents Vjayashree Budyal 1, S. S. Manv 2, S. G. Hremath 3 1 Basaveshwar Engneerng College, Bagalkot, Inda 2 Reva Insttute of Technology and Management, Bangalore, Inda 3 G. M. Insttute of Technology Davangere, Inda E-mal: vrbudyal@yahoo.co.n, sunl.manv@revansttuton.org, drsgh@yahoo.co.n Abstract Support for real tme multmeda applcatons such as, vdeo telephony, fnancal stock quote servces, and multplayer nteractve games etc., s very essental n Moble Ad hoc Networks (MANETs). Such applcatons requre multple Qualty of Servce (QoS) parameters to be satsfed, lke bandwdth, end-to- end delay, packet loss rate, jtter, etc. Ths paper consders the problem of fndng node dsjont and mult-constraned QoS multpaths from source to destnaton by usng agent based fuzzy nference system. The proposed scheme, Fuzzy based Node Dsjont Multpath QoS Routng (FNDMQR) operates n the followng steps by ntegratng statc and moble agents. (1) Determnaton of multple paths and pckng up of resource nformaton (avalable bandwdth, lnk delay, and packet loss rate) of the ntermedate nodes from source to destnaton. (2) Recognton of node dsjont, and mult-constraned QoS ft paths by usng Takag-Sugeno Fuzzy Inference System (TSFIS). TSFIS extracts a fuzzy QoS weght from avalable resource nformaton of the ntermedate nodes. (3) Selecton of the best path dependng on the fuzzy QoS weght. (4) Mantenance of QoS path when path breaks due to moblty of node or lnk falure. To test the performance effectveness of the approach, we have analyzed the performance parameters lke packet delvery rato, average end-to-end delay and overall control overhead. The scheme performs better as compared to a node-dsjont multpath routng n MANETs. Index Terms MANETs, QoS, Takag-Sugeno fuzzy Inference, software agents. I. INTRODUCTION Ad hoc wreless network conssts of collecton of moble devces lke, personal dgtal assstant (PDA), laptops, cell phones etc. These nodes are nterconnected by mult-hop communcaton path, due to lmted transmsson range. The route found between source and destnaton becomes nvald often because of the temporary topology of the network. Therefore routng n Moble Ad hoc Networks (MANETs) s a challengng task [1].Mult-path provdes more than one route to the destnaton node. Mult-path routng protocols are deemed superor over conventonal sngle path protocols for enhanced throughput, relablty, robustness, load balancng, fault-tolerance, offerng QoS, and to avod frequent route dscovery attempts [2]. Mult-path routng protocols can attempt to fnd node- dsjont, lnk-dsjont, or non-dsjont routes. Node- dsjont routes have no nodes or lnks n common on the routes. Lnk-dsjont routes have no lnks n common, but may have nodes n common. Non-dsjont routes 7 can have nodes and lnks n common. When a lnk or node s on several paths severe flow occurs when the ncomng traffc load s hgh. As a result shared lnk or the node becomes the bottleneck. Node dsjont paths provde more relablty than the lnk dsjont paths [3].Wth the ncreasng demand n realtme multmeda, applcaton n vdeo telephony, vdeo conferencng, and mltary arena requres mult-constraned Qualty of Servce (QoS) to be fulflled. The QoS requrement of connecton ncludes parameters lke bandwdth, end-toend delay, jtter, packet loss rate etc. Mult-constrant QoS parameters are mprecse and uncertan due to dynamc topology of MANETs. However, selectng a route, whch satsfes all multple constrants, s an NP complete problem [4]. There s no accurate mathematcal model to descrbe t. Fuzzy logc s used to provde a feasble tool to solve the mult-metrc QoS problem. Fuzzy logc s a theory that not only supports several nputs, but also explots the pervasve mprecson nformaton [5]. So adoptng fuzzy logc to solve mult metrc problems n ad hoc networks s an approprate choce. Mult-constrant based routng protocols use QoS satsfed paths other than the sngle shortest path to route the packets. If multple node dsjont paths wth multconstrant QoS paths are set up between a source and a destnaton, then source node can use these routes as prmary and backup routes,.e., a new route dscovery s nvoked only when all of the routng paths fal or when there only remans a sngle path avalable, whenever node or lnk fals. Ths helps to reduce overhead n fndng alternatve routes and extra delay n packet delvery ntroduced. Therefore, n ths paper we adopt both node dsjont and mult-constrant QoS routng n MANETs. Software agents based applcatons are an emergng dscplne, whch can be appled to provde flexble, adaptable, and ntellgent servces n MANETs. Software agents are autonomous and ntellgent programs that execute tasks on behalf of a process or a user. They have two specal propertes: mandatory and orthogonal, whch make them dfferent from the standard programs. Mandatory propertes are: autonomy, reactve, proactve and temporally contnuous. The orthogonal propertes are: communcatve, moble, learnng and belevable [6]. A. Related Work Some of the related works to buld mult-constraned QoS routng n MANETs are as follows: Fuzzy cost based mult-

2 constraned qualty of servce routng s dscussed n [7] to select an optmal path by consderng multple ndependent QoS metrcs such as bandwdth, end-to-end delay, and number of ntermedate hops.the work gven n [8] explores the node dsjont path routng subject to dfferent degrees of path couplng, wth and wthout packet redundancy. Multpath routng problem of MANETs wth multple QoS constrants, whch may deal wth the delay, bandwdth and relablty metrcs, and researchng the routng problem s explaned n [9]. Archtecture for guaranteeng QoS based on nodedsjont mult-path routng protocol n MANETs s explaned n [10]. The work gven n [11] uses fuzzy set and roughs set theory to select an effectve routng path n MANETs. In the frst stage, the data set consstng of resources and paths are fuzzfed. In the second stage, nformaton gan s calculated by usng ID3 algorthm for evaluatng the mportance among attrbutes. In the thrd stage, a decson table s reduced by removng redundant attrbutes wthout any nformaton loss. Fnally, f-then decson rules are extracted from the equvalence class to select the best routng path. Fuzzy based prorty scheduler for moble ad-hoc networks, to determne the prorty of the packets usng Destnaton Sequenced Dstance as the routng protocols s presented n [12]. The proposed fuzzy agent based Node Dsjont Mult-path QoS Routng n MANETs s motvated by observng nherent drawbacks of exstng QoS routng schemes lke: lack of support of mult-constrant QoS routng and mantenance of the QoS path when lnk/node fals. B. Our Contrbutons In ths work, we nvestgate on the use of Takag-Sugeno fuzzy nference system (TSFIS) for mult-constraned QoS route selecton n MANETs, ntegratng statc and moble agents. Source knows the multple nodes dsjont paths to the destnaton, and collects the resource nformaton (avalable bandwdth, delay, and packet loss rate) of ntermedate nodes. The source uses gathered ntermedate node nformaton to select the QoS path by usng TSFIS model. Ths model accepts uncertan and mprecse crsp parameters lke, avalable bandwdth, lnk delay, and packet loss rate as nput and s beng processed n stages,.e., fuzzfcaton, nference, and defuzzfcaton. After experencng all the stages, a sngle value score fuzzy QoS weght s generated from the combnaton metrcs for each node on the path. Ths s used to measure QoS satsfacton on the path. The performance of our scheme Fuzzy based Node dsjont Multpath QoS Routng (FNDMQR) s compared to nodedsjon mult-path routng n MANETs (NDMRP) [9].The rest of paper s organzed as follows. Secton II explans proposed work on fuzzy agent based mult-constraned QoS routng. Secton III descrbes an evaluaton of our approach usng smulaton. Fnally, secton IV concludes our paper. II. PROPOSED WORK Ths secton descrbes network model, Takag-Sugeno Fuzzy Inference System (TSFIS), QoS routng agency, and 8 fuzzy and agent based mult-constrant QoS routng scheme. A. Network Model An ad hoc network conssts of set of moble nodes and set of lnks between the moble nodes as shown n fgure 1. Due to moblty of the nodes n the ad hoc network, lnk connecton vares wth respect to tme. Each moble node has certan transmsson range. Each node s equpped wth an agent platform and an agency n whch agents resde. We assumed that agents have protecton from hosts on whch they execute. Smlarly, hosts have protecton from agents that can communcate on avalable platform. The secured platform conssts of protecton from denal of executon, masqueradng, eavesdroppng, etc. Recently developed technques for moble agent securty have technques for protectng the agent platform. Fg. 1. A Moble Ad hoc Network B. Takag-Sugeno Fuzzy Inference System Fuzzy system s classfed as Mamdan and Takag-Sugeno models. In ths paper we propose Takag Sugeno (frst-order) fuzzy nference system for reasonng, because as t has hgh nterpretablty and computatonal effcency, and bult-n optmal and adaptve technque. And also, t s not necessary to defne a pror lngustc terms for conclusons, snce the mappng s drect. And also, the effort of performng defuzzfcaton s saved, because the crsp output s drectly determned by the fuzzy mean formula. Our Takag-Sugeno fuzzy system conssts of three crsp nputs and one output. The system nputs are avalable bandwdth AB and lnk delay TD, and packet loss rate PR of the ntermedate nodes and output s QoS weght γ. Three nputs are characterzed by bell shaped membershp functons. Bell functon for AB s defned by equaton 1. 1 ( AB) AB c 1 ( ) a 2b. (1) Where a, b and c are the parameters of membershp functon governng the centre, wdth and slope of the bell-shaped membershp functon. TD and PR take smlar knd of bell functon. The steps nvolved n FIS are fuzzfcaton,nference

3 and defuzzfcaton. µ (AB) s Membershp functon value for the avalable bandwdth. Fuzzfcaton: The frst step s to consder the crsp nputs and determne the degree to whch they belong to each of the approprate lngustc sets va bell membershp functons whch s termed as fuzzfcaton. Fuzzfcaton converts nput data nto sutable fuzzy values (lngustc terms). The lngustc terms, whch dvde the membershp functons for avalable bandwdth, are {ABless, ABmore} and s as shown n fgure 2. Lnk delay lngustc terms are {TDless, TDmore}, and for packet loss rate the lngustc terms are {PRless, PRmore}. Vertcal coordnates represent the degree of membershp, whch dstrbutes n the nterval of [0-1]. Defuzzfcaton: The fnal output fuzzy QoS weght γ of the system s the weghted average of all rule outputs, computed as gven n equaton 3. N 1 N 1 w z w C. QoS Routng Agency. (3) Each node comprses of Fuzzy based Node Dsjont Multpath QoS Routng agency (FNDMQR). Components of agency and ther nteractons are depcted n fgure 3. Agency conssts of Knowledge Base (KB), statc agents and moble agents. Statc agent are Admnstrator Agent (AA), and QoS Decson Agent (QDA). Moble agents are Dsjont Agent (DA) and Recovery Agent (RA). Fg. 2. Membershp functons for avalable bandwdth Inference: Fuzzfed data trgger one or several rules n the fuzzy model to calculate the result. The fuzzy rules are realzed n the form of IF-THEN. The nput parameters are combned usng T-norm operator AND. The total number of rules formed s as follows: Rule 1: If AB s ABless and TD s TDless and PR s PRless Then z1 = Ψ 1 AB + ζ 1 T D + φ 1 PR + σ 1 Rule 2: If AB s ABless and TD s TDless and PR s PRmore Then z2 = Ψ 2 AB + ζ 2 TD + φ 2 PR + σ 2 Rule 3: If AB s ABless and TD s TDmore and PR s PRless Then z3 = Ψ 3 AB + ζ 3 TD + φ 3 PR + σ 3 Rule 5: If AB s ABmore and TD s TDless and PR s PRless Then z5 = Ψ 5 AB + ζ 5 TD + φ 5 PR + σ 5 Rule 6: If AB s ABmore and TD s TDless and PR s PRmore Then z6 = Ψ 6 AB + ζ 6 TD + φ 6 PR + σ 6 Rule 7: If AB s ABmore and TD s TDmore and PR s PRless Then z7 = Ψ 7 AB + ζ 7 TD + φ 7 PR + σ 7 Rule 8: If AB s ABmore and TD s TDmore and PR s PRmore Then z8 = Ψ 8 AB + ζ 8 TD + φ 8 PR + σ 8 The output level z of each rule s weghted by the frng strength w of the rule gven by 2. Ψ, ζ, φ, and σ are constants chosen between 0-1. Where = 1 to N. N s the number of rules. For example, for and AND rule wth nputs AB and TD, and PR have a frng strength as gven by equaton 2. w = µ(ab). µ (TD). µ (PR).. (2) Where µ (AB), µ (TD), and µ (PR) are the membershp values for nputs avalable bandwdth and lnk delay and packet loss rate. 9 Fg. 3. Fuzzy based node dsjont multpath QoS routng agency KB: KB of source comprses of nformaton of node ID, destnaton, resource nformaton {AB, TD, PR} of the ntermedate nodes on the paths, multple path IDs from source to destnaton and ther fuzzy QoS weght γ obtaned by usng TSFIS and runnng applcaton(s) detals.. Intermedate node KB conssts of node status (connected/dsconnected to network), Node dsjont Forward QoS Routng Table (NDFQRT), {AB, TD, PR} of ts own. KB s read, updated and s used by agences (AA, QDA, DA and RA) to establsh QoS route and to mantan the path between source and destnaton. Admnstrator Agent: It s a statc agent and performs the followng functons at source, (1) creates and dspatches DA to fnd multple paths to destnaton, (2) collects multple node dsjont paths and resource nformaton of ntermedate nodes from DA, (3) computes γ for each node by usng TSFIS and Γ for each node dsjont path (4) selects a QoS node dsjont path from multple node dsjont paths, and (5) ntates reconstructon of QoS path upon request from RA durng lnk/node falure. Dsjont Agent: It s a moble agent trggered by AA of source whenever t wshes to send data to the destnaton.

4 DA s are dspatched by AA to reach all ts neghbors. Every DA performs the followng functons. (1) Traces all the feasble paths to the destnaton by clonng. Gathers ntermedate node resource nformaton {AB, TD, PR}. (2) Handover the multple path nformaton to AA of destnaton. (3) AA of destnaton separates out the node dsjont path from a number of multple paths dentfed by DA, and (4) DA traverses back through the node dsjont paths to reach source, gatherng resource nformaton of the ntermedate nodes. QoS Decson Agent: Ths agent s a statc agent trggered by AA only at the source node. It s responsble for computng the γ for each of the node on the dsjont paths by usng TSFIS. Updates computed γ of each node on the node dsjont paths n AA of source. Later t s dsposed off. Recovery Agent: It s a moble agent and performs the operaton of route mantenance whenever lnk/node fals. D. Fuzzy and Agent based Mult-constrant QoS Routng Scheme Ths secton descrbes the functonng of the proposed mult-constrant QoS routng scheme. The scheme operates n the followng steps. 1) Recognton of node dsjont multple paths to the destnaton: When a source node needs mult-constrant QoS path to the destnaton. Source AA dspatches DA to reach ts neghbors. DA carres source ID, sequence number, maxmum number of hops, and traveled node lst. Upon reachng the ntermedate node, AA of ntermedate node checks for the duplcaton of the DA by lookng at the sequence number. When recevng a duplcate DA, the possblty of fndng node dsjont multple paths s zero f t s dropped, for t may come from another path. But f all of the duplcate DA are broadcast, ths wll generate broadcast storm and decrease performance. In order to avod ths problem DA records the shortest routng hops to keep loop-free paths and decrease routng broadcast overhead. When ntermedate node receves DA for the frst tme, t checks the node lst of path traversed and calculates the number of hops from the source node to tself and records the number as the shortest number of hops n ts reverse routng table. If the node receves the duplcate DA, t computes the number of hops and compares wth shortest number of hops n ts reverse routng table. If the number of hops s more than shortest number of hops n the reverse routng table, then the DA s dropped. Only when t s less than or equal to the shortest number of hops, the node appends ts own address to the node lst of path n DA and s cloned to reach the neghbors or the destnaton. Agent clonng s a technque of creatng an agent smlar to that of parent, where cloned agent contans the nformaton of parent agent that t has traversed. A chld agent can communcate ether to any one of ts parents who are wthn the range or to any of ts parents at a gven level.when frst DA s receved by the destnaton, t records the lst of node IDs of entre route n ts reverse route table and DA traces the reverse 10 route to reach the source. When destnaton AA receves duplcate DA, t compares the whole node IDs of the entre route wth exstng node dsjont paths n ts reverse routng table. If there s no common node (except source and destnaton) between the node IDs from the the duplcate DA and node IDs of exstng node dsjont path n the destnaton reverse routng table then, the path n current DA s node dsjont path and s recorded n the reverse routng table of the destnaton and DA traces the reverse route to reach the source. Otherwse, current DA s dsposed. DA collects the ntermedate node s resource nformaton {AB, TD, PR} whle tracng back the reverse path from destnaton to source. The multple node sjont paths and resource nformaton of the ntermedate node s made avalable to the AA of source for further QoS verfcaton. 2) Fuzzy Agent based QoS Path Selecton: Multconstraned QoS path s selected from numerous known node dsjont mult-paths placed n AA of source by usng TSFIS (refer secton II B). TSFIS computes the γ for every node on each of the path by consderng { AB, TD, PR } as nput metrcs to TSFIS. AA of source computes Γ by consderng γ of all the nodes on the path and s gven by equaton 4. P 1 j P. (4) Where, P s the number of nodes on the path j. If à s greater than QoS requred by the user, t mples the path satsfes the requrement and QoS packets are transmtted through that path.as an example consder fgure 4, whch s consstng of number of moble nodes. There exst multple paths between source and destnaton shown wth dotted lnes. Destnaton decdes a node dsjont paths from numerous multple paths and these multple node dsjont paths are shown wth sold lnes. Upon recevng node dsjont paths, source AA uses TSFIS to dentfy a paths whch satsfes mult-constrant QoS shown wth sold bdrectonal arrow. One among them wth least number of hops s selected as QoS path to route the packets. The other QoS satsfed node dsjont paths act as back up paths. Fg. 4. Fuzzy based node dsjont mult-path QoS routng agency

5 3) QoS Route Mantenance: The proposed scheme uses RA to mantan QoS path. Whenever node moves or fals, then RA sends error to the source. AA of the source checks to fnd a path from the exstng QoS satsfed node dsjont multpaths to reach destnaton. If not found t ntates new route dscovery. III. SIMULATION The proposed FNDMQR scheme s smulated along wth NDMRP n the network scenaro usng C programmng language to verfy the performance and operaton effectveness. Membershp functons and rule bases of the fuzzy are carefully desgned and the output s verfed usng Matlab 7.0 fuzzy logc toolbox wth FIS edtor. Then the nputs are dentfed n the lbrary of C code programmng. In ths secton, we descrbe the smulaton model. A. Smulaton Model A moble ad hoc smulaton model conssts of N = 80 number of moble nodes placed randomly wthn the area of A X B = 1000 X 1000 m2. A random way pont moblty model s used. Each node randomly selected a poston wth a speed rangng from Smn to Smax = 0-10 m/s. A pause tme Pau tme = 0-10 sec, s assgned for each node. If a node tres to go out of the boundary, ts drecton s reversed (Bouncng ball model). The rado propagaton range for each node s selected as R ran = 250 m and channel capacty s Ch cap = 10 Mbps. Lnk delays may vary between Ldmn to Ldmax = ms. The sources and destnatons are randomly selected wth unform probabltes. Resdual power of each node vared between pwr mn to pwr max = mw. Traffc sources are wth constant bt rate (CBR) wth data payload sze as Dt pld = 512 bytes. Each smulaton s executed for Sm tme = 600 seconds. Smulaton was carred out wth dfferent QoS requrements. The followng performance metrcs are used for evaluatng the proposed scheme. Packet delvery rato (PDR): It s the rato of the number of data packets delvered to the destnaton node to the number of data packets transmtted by the source node. It s expressed n percentage. Overall control Overhead: It s defned as the rato of the total number of control messages or agents to the total number of packets generated to perform communcaton. Average end-to-end delay: It s defned as the average tme taken to transmt predefned number of packets from source to destnaton. It s expressed n seconds. B. Results In ths secton, we dscuss varous results obtaned through smulaton. The results nclude packet delvery rato, overall control overhead, average end-to-end delay. Our scheme FNDMQR s compared wth exstng NDMRP.Fgure 5 depcts PDR wth varaton n node speed and number of nodes. PDR decreases, as node speed, ncreases n both FNDMQR and NDMRP because when node speed ncreases packets are lost whle reconstructng the QoS path. PDR of FNDMQR s more compared to NDMRP snce t accounts 11 Fg. 5. Packet delvery Rato vs. Node Speed the mult-constraned QoS on the path by usng TSFIS and stable path s dentfed by consderng mnmum number of hops. Average end-to-end delay generated for varyng number of nodes and speed s reported n the fgure 6. As the node speed ncreases average end to end delay also ncreases. The decrease of end-to-end delay n FNDMQR s manly presented by selectng a sutable QoS route that results n reducton of path breakage. Where as NDMRP suffers frequent lnk breaks and needs route reconstructon frequently whch results n ncrease n end-to-end delay. Fg. 6. Average end-to-end delay vs. Node Speed Fgure 7 shows that the average end to end delay rasesgradually as the number of source ncreases. The reason s that wth ncreasng number of sources, the total traffc load ncreases and the network becomes congested. So, more packets are kept watng n the queues for long tme whch causes the delay to ncrease. However FNDMQR outperforms NDMRP n reducng the end-to-end delay. Fg. 7. Average end-to-end delay vs. No. of Sources

6 Overall control overhead wth respect to node speed and number of nodes are shown n fgure 8. As the speed of the nodes ncreases control overhead ncreases. Because of network connectvty, as the node moblty ncreases moble agents are generated for reparng the path for the QoS communcaton. Fg. 8. Overall Control Overhead vs. Node Speed CONCLUSIONS Ths paper presented fuzzy based mult-constraned QoS node dsjont mult-path routng n MANETs by usng agents. Fuzzy rule base s developed to unte the varous uncertan QoS metrcs such as avalable bandwdth, lnk delay, and packet loss rate to generate sngle QoS weght for the node dsjont paths, whch s used for path selecton. The results for our proposed FNDMQR show good packet delvery rato and reducton n end-to-end delay and control overhead. The agent-based archtectures provde flexble, adaptable and asynchronous mechansms for dstrbuted network management, and facltate software reuse and mantenance. Future work ncludes optmzaton of membershp functon of fuzzy system accordng to the user requrement, to support QoS routng n MANETs REFERENCES [1] Yuh Shyan Chen, Yu-Chee Tseng, and Jang - Png Sheu,Po Hsuen Kuo, An On-demand, Lnk State, Mult - path QoS Routng n Wreless Moble Ad hoc network, Elsever Internatonal Journal of Computer Communcatons, vol. 27, no. 1, pp , [2] Georgos Parssds, Vncent Lenders, Martn May, Bernhard Platter, Mult-path Routng Protocols n Wreless Moble Ad hoc Networks: A Quanttatve Comparson, Proc. Sprnger Next Generaton Tele traffc and Wred/Wreless Advanced Networkng Lecture Notes n Computer Scence, vol. 4003, pp , [3] Luo Lu, Laure Cuthbert, Mult- rate QoS enabled NDMR for Moble Ad Hoc Networks, Proc. IEEE Internatonal Conference on Computer Scence and Software Engneerng, pp Wuhan, Chna, [4] Sanguankotchakorn T, Maharajan P., A New Approach for QoS Provson based on Mult-constraned feasble Path Selecton n MANETs, Proc. 8 th IEEE Internatonal Conference on Electrcal Engneerng/ Electroncs,Computer, Telecommuncaton and InformatonTechnology, pp , Khon Kaen Unversty, Thaland,2011. [5] V. R. Budyal, S. S. Manv, S. G. Hremath, Fuzzy Agent Based Qualty of Servce Multcast Routng n Moble Ad Hoc Networks, Proc. IEEE Internatonal Conference on Advances n Moble Network, Communcaton and ts Applcatons, pp , Bangalore, Inda, [6] S.S. Manv, P. Venkataram, Applcatons of Agent Technology n Communcatons: A Revew, Internatonal Journal of Computer Communcatons, vol. 27, pp , [7] G. Santh, Alamelu Nachappan, Fuzzy-cost based Multconstraned QoS Routng wth Moblty Predcton n MANETs, Elsever Egyptan Informatcs Journal, vol. 13, pp , [8] Xaoxa Huang, Yuguang Fang, Performance Study ofnode- Dsjont Mult-path Routng n Vehcular Ad Hoc Networks, IEEE Transactons on Vehcular Technology, vol. 58, no. 4, [9] Xu Y, Cu Me, Yang We, Xan Yn, A Node-dsjon Multpath Routng n Moble Ad hoc Networks, Proc.IEEE Internatonal Conference on Electrc Informaton and Control Engneerng, pp , Wuhan, Chna, [10] Luo Lu, Laure Cuthbert, A Novel QoS n Node-Dsjont Routng for Ad Hoc Networks, Proc. IEEE Internatonal Conference on Communcatons Workshops, pp , Bejng, Chna, [11] P. Seethalakshm, M.Gomath, G.Rajendran, Path Selecton n Wreless Moble Ad Hoc Network Usng Fuzzy and Rough Set Theory, Proc. IEEE Internatonal Conference on Wreless Communcaton, Vehcular Technology, Informaton Theory and Aerospace and Electronc System Technology, pp. 1-5, Denmark, [12] K. Manoj, S. C. Sharma, Leena Arya, Fuzzy Based QoS Analyss n Wreless Ad hoc Network for DSR Protocol, Proc. IEEE Internatonal Conference on Advance Computng Conference (IACC 2009), pp , Patala, Inda,