ANFIS Aided AODV Routing Protocolfor Mobile Ad Hoc Networks

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1 Journal of Computer Scence Orgnal Research Paper ANFIS Aded AODV Routng Protocolfor Moble Ad Hoc Networks Vvek Sharma, Bashr Alam and M.N. Doja Department of Computer Engneerng, Jama MllaIslama New Delh, , Inda Artcle hstory Receved: Revsed: Accepted: Correspondng author: Vvek Sharma Department of Computer Engneerng, Jama MllaIslama New Delh, , Inda Tell: Emal: Abstract: Communcaton between moble nodes s the key purpose of Moble Ad Hoc Networks (MANETs). However, ncrease n the traffc leads to multple challengng ssues lke congeston among nodes, consumpton of more bandwdth, hgh energy requrement and low effcency output. A powerful soluton to these ssues of the MANET s to use ntellgent routng protocols. The Adaptve Neural Fuzzy Inference System (ANFIS) s one of those advanced technque whch adjust tself accordng to the change n system. Therefore, the use of ANFIS n routng protocol results n effcent gudng of packet from source to destnaton. In ths paper, the route selecton mechansm of tradtonal Ad Hoc on Demand Dstance Vector (AODV) routng protocol s mproved wth the use of ANFIS havng nput attrbutes hop count, energy consumpton and delay. Smulaton of the proposed ANFIS Aded AODV (AAODV) shows the clear mprovement n quanttatve parameters lke Throughput and Packet Delvery Rato (PDR). Keywords: ANFIS, AODV, MANET, Intellgent Routng System Introducton Moble ad hoc networks have the characterstcs of less nfrastructure, moblty, dynamc topology and the ablty to communcate wth each other even though they are not drectly n the range. Due to these characterstcs, MANETs have ganed tremendous attenton of researchers from the last decade. Wth the ncrease n moble users, the demand of wreless applcatons has also sgnfcantly ncreased. To meet these requrements, canon of routng protocols have been already developed (Murthy and Manoj, 2004; Sharma and Alam, 2012). Based on table update mechansm, the prevously reported routng protocols are dvded nto three groups (Murthy and Manoj, 2004; Pe et al., 2000; De Rango, 2010; Sharma and Alam, 2012): (1) ondemand; (2) table-drven; and (3) hybrd. On demand routng protocols share routng nformaton whenever t s needed by nodes, whle the table-drven routng protocols share the routng nformaton perodcally between nodes. The hybrd protocols are the combnaton of on demand as well as table drven protocols. The MANETs are wdely deployed n real tme applcatons lke mltary, conference room meetng, dsaster management and multmeda etc. Routng plays a vtal role n communcaton among data packets of MANET. In case of multple applcatons, the ad hoc networks are hghly dependent on ts envronment varables lke network topology, traffc strength, energy consumpton, delay, scalablty, securty and bandwdth. Whle desgnng the protocol for applcatons of MANET, the above sad envronment varables should be consdered for routng decson. To mprove the routng decson, the effects of ncorporatng ANFIS to tradtonal AODV s consdered. The ANFIS s hghly capable of thnkng, reasonng, decson makng and have hgh degree of parallelsm (Jang, 1993). The tradtonal AODV routng protocol consder only hop count as a metrc n routng decson whereas the proposed ANFIS aded AODV (AAODV) consder three metrces hop count, nodeenergy and delay n routng decson. The rest of the paper s organzed as follows. In Secton 2, the related works relevant to the areas of routng decson strateges s dscussed, Secton 3 dscusses about the adaptve neural fuzzy nference system, Secton 4 presents system model, Secton 5 shows smulaton results andsecton 6 concludes the work. Related Work The am of the routng protocol n MANET s to dscover route n the network from source node to 2017 Vvek Sharma, Bashr Alam and M.N. Doja. Ths open access artcle s dstrbuted under a Creatve Commons Attrbuton (CC-BY) 3.0 lcense

2 destnaton node\s so that the qualty of servce s enhanced. Whle desgnng routng protocol, one should consder challenges n MANETs lke large delay, less energy, low trust factor, to fnd an optmal route among nodes. Delay of data packets between the nodes decrease the performance of network, f delay s small t s acceptable n most of the applcatons (Sheng et al., 2006; Wang et al., 2009; Zuo et al., 2010) but f delay s large sgnfcant nformaton s lost. Sometmes, small delay also causes havoc lke n real tme multmeda applcatons. Packet lost due to congeston or death of certan node n MANET also degrade the network performance. So, the researchers also consder the average lfe tme and trust factor whle desgnng the routng protocol. Su et al. (2008) proposed modfcaton of AODV routng protocol wth fuzzy logc to multcast n moble ad hoc network. The fuzzy logc based mult crtera of the AODV routng protocol s dynamcally evaluated to determne the approprate route and actve route lfetme. Paul et al. (1999) proposed routng algorthm n whch the route lfe tme must be greater than some threshold value. Lang (2002) proposes the fuzzy logc system based routng protocol that dynamcally calculate the route expry tme. Jang et al. (2005) proposes routng protocol that uses the probablty concept, Farkas et al. (2008) and Snghal and Danel (2014) uses fuzzy based uses pattern matchng approach to evaluate the node stablty that ncreases the trust among nodes. Mallapur and Patl (2014) proposed fuzzy logc system based routng protocol for MANET that choose hgh qualty path among the nodes. Pasupulet et al. (2002; Song and Fang, 2006; Sheng et al., 2006), proposed adaptve routng algorthm based on fuzzy theory whch ams to solve traffc management problem and acheve good trade-off between qualty of servce (QoS) and network performance. Snce MANET s self-confgured and completely moble n nature therefore securty becomes essental parameter whle consderng applcatons lke mltary, bankng. Therefore, many routng protocols are desgned wth securty features along-wth QoS. Ne et al. (2006; Jn et al., 2006) presented fuzzy logc and securty based routng protocol that determne the most secure path for routng. In real tme applcatons lke rescue operatons, the overhead to establsh the route should be low, because the moble node of MANET totally depends on ts battery for power. Hence, battery lfe s very mportant and keepng ths n mnd researchers desgn energy aware routng algorthms. Torshz et al. (2008) proposed modfcaton n AODV protocol based on energy that provdes the optmal route based on bandwdth and hop count of each route. To obtan the optmal route the authors, also ncorporated fuzzy logc (Ross, 1995) to AODV for energy aware routng. Wang et al. (2005) proposed fuzzy based dynamc routng protocol that depends on degree of membershp for confgurng and categorzng the network. Ortz et al. (2011) proposed the use of fuzzy logc based on energy aware metrc to evaluate node condtons n AODV protocol, that selectthose nodes whch are n better state for routng. Dehyadegar et al. (2011) proposed an adaptve routng algorthm based on fuzzy logc n whch each lnk cost s dynamcally determned for current network condton. Sharma et al. (2015). The researchers have provded many successful protocols n the recent past but however they are faled to ncorporate adaptve learnng strateges. Therefore, the model of proposed protocol presented n next secton wll nclude the advanced adaptve learnng method ANFIS to the AODV routng protocol. Model Formulaton In ths secton, we propose an Adaptve neural fuzzy nference aded system that mproves the routng decson of AODV routng protocol. It stores the values and judge the preference accordng to desgn behavor of the system. Route Selecton Metrcs In the route path selecton decson of AODV routng protocol (Perkns, 1999; Murthy and Manoj, 2004; Khmsara et al., 2009; Fahad and Al, 2010) based on only hop count metrc. The route path s also nfluenced by other varable lke delay, energy, congeston etc. The Table 1 shows the major characterstc of on demand routng protocol when moblty s hgh and thus affect the routng decson. However only three varables wll be consdered n ths model, consst of: Hop count: Number of hopes between the sources to destnaton Resdual energy (E) (Su et al., 2008): The remanng resdual energy of a node s computed as multplcaton of resdual tme T for a node n whch ts energy s consumed by a factor E wth power energy consumpton rate PCR : E = T PCR (1) where, PCR s computed as: ( Pr Nr + Ps Ns + Po No) PCR = (2) T where, P r, P s and P o s the power consumed when the network receves, sends and overhears of the packet respectvely and N r, N s, N o s the total number of packets receves, send and overhears by a network respectvely. 515

3 Table 1. Characterstc of on demand routng protocols when moblty s hgh On-demand routng Characterstc protocol Scalablty weak Adaptablty of topology changes weak Bandwdth hgh Delay large Power consumpton hgh Routng overhead hgh Delay: The delay s defned the dfference of the packet arrval tme (P arr ) for whch ts packet s arrved at node and acknowledged tme (P ack ) at whch data packet s acknowledged at a node. The average lnk delay for m packet wthn tme perod (T d ) s gven as: D = m ( Pack Parr ) = 1 T d (3) Anf s cost value: It s the value computed at each node. The th an fs cost s calculated by ANFIS system. These nputs are taken nto account for types of varaton n data values. Membershp functon and Rules depends on many parameters. If there s varaton n these parameters then the dfferent rule s appled to these varables. Based upon nput parameter value each node s appled to ANFIS system and t provdes the output value as cost. Based upon that value each node takes ts routng decson. Adaptve Neural Fuzzy Inference System (ANFIS) ANFIS s a mult-layer adaptve network-based fuzzy nference system ntroduced by Jang (1993). The archtecture of an ANFIS used n ths paper conssts of fve layers, whch s shown n Fg. 1. A hybrd learnng mode s used to mplement the dfferent node functons to learn and tune parameters n a FIS. The ANFIS s an extenson of the Takag-Sugeno-Kang (TSK) fuzzy model (Ross, 1995; Rea and Pesch, 2004; Jn et al., 2006; Ne et al., 2006; Lma et al., 2008; Zuo et al., 2010) TSK or sugeno model s used n ths paper that has advantage over hgh dmenson problem. Layer 1. Generate the Membershp Grade Every node n ths layer wth functon s where x s the nput to node. A, B, C s the lngustc label assocated wth ths node functon. We choose µa (x) maxmum equal to 1and mnmum equal to 0: O1 = µ ( x) (4) A O1 = µ B( y) (5) O1 = µ C ( z) (6) where, O 1 s output of the node n a layer l. Layer 2. Generate the Frng Rule Nodes n ths layer are labeled as π. Multply the sgnal before outputtng. The Output are gven by: O2 = w = µ A ( x) µ B( y) µ c( z) (7) Layer 3. Normalze the Frng Strength Every node n ths layer s fxed and labeled as N and Perform a normalzaton of the frng strength from the prevous layer. The output of each node s gven by: w O3 = w = w 1 + w 2 + w 3 (8) Layer 4. Calculate rule Outputs based on the Consequent Parameters Output s the product of normalzed frng rule and the order of frst order polynomal and s gven by: O4 = w f = w ( p x + q y + r ) (9) where, (p, q, r ) are consequent parameters that deals wth then part of fuzzy rule and t can be modfed. Layer 5 Sum of all the nputs from layer 4. It compute the overall ncomng sgnal: 5 O = f = w f = wf w (10) ANFIS modfes the membershp functons to the nput/output data n order to account for types of varatons n the data values, rather than choosng the parameter assocated wth a gven membershp functon arbtrarly. Where the membershp functons and rules depend on varous parameters, changes these parameters wll change the membershp functon rule whch are saved n the knowledge base. Instead of lookng the data to choose the membershp functon parameter or set the rule base, they can be modfed automatcally based on route choce logc through ANFIS. Path Selecton n AODV In AODV (Perkns et al., 1999; Ko and Vadya, 1998; Msra and Manda, 2005; Ba and Snghal, 2006; Manckam and Shanmugavel, 2007; Klen, 2008; De Rango et al., 2003), when a node wants to communcate wth other node, n order to fnd a path, t broadcasts a RREQ packet to the network. RREQ packet contans the nformaton of traversed nodes. Each node updates ts routng table based upon the nformaton stored n control message. 516

4 Fg. 1. Archtecture of an ANFIS wth 3 nputs (hop count, delay and energy) one output When a node receves RREQ packet, t updates the route to source node. Afterwards t checks ntermedate nodes accumulated n the path. A new entry s made n the routng table for any of the ntermedate nodes, f one dd not already exst. If a route entry for a node exsts and f the hop count to any of the ntermedate node s less than the prevously known hop count to that node, the routng table entry s updated for that node. The entry s updated by retanng the prevously known sequence number of that node. Path Selecton n ANFIS based AODV Here, we propose a routng decson method to decde whether a node wll appear n the path to contnue or not. Route metrcs that make the routng decson are energy of partcpatng node, hop count and end-to-end delay between nodes. Three lngustc varables for nputs are: Low, Medum and Hgh. Fve lngustc varables for outputs are very low, low, medum, hgh and very hgh, where each one s assgned a value between {0,1}. Trangle membershp s functon s used to represent the output and nput. These evaluatons are passed through a fuzzy nference engne that apples a set of fuzzy rules to obtan the desred behavor of the system as shown n Table 2. Due to the broadcast nature of route dscovery process of AODV, the RREQ packet of AODV carres the fuzzy nput parameter: hop count, delay, energy. Each node embedded a fuzzy logc system that dynamcally evaluates the fuzzy cost each tme when RREQ packet arrves. If t fnds path wth lesser fuzzy cost then t s chosen and update reverse route entry. Ths process s contnued untl get the destnaton. Ths scheme eradcates unsutable paths from the route dscovery process and optmzes the routng protocol. Table 2. Rule base for ANFIS AODV Rule Hop count Energy Delay Output Rule1: Low Low Low Very Hgh Rule2: Low Medum Low Very Low Rule3: Low Hgh Low Very Low Rule4: Low Low Medum Very Hgh Rule5: Low Medum Medum Low Rule6: Low Hgh Medum Low Rule7: Low Low Hgh Very Hgh Rule8: Low Medum Hgh Medum Rule9: Low Hgh Hgh Medum Rule10: Medum Low Medum Hgh Rule11: Medum Medum Medum Low Rule12: Medum Hgh Medum Low Rule13: Medum Low Medum Hgh Rule14: Medum Medum Medum Medum Rule15: Medum Hgh Medum Medum Rule16: Medum Low Hgh Very Hgh Rule17: Medum Medum Hgh Hgh Rule18: Medum Hgh Hgh Hgh Rule19: Hgh Low Low Medum Rule20: Hgh Medum Low Medum Rule21: Hgh Hgh Low Medum Rule22: Hgh Low Medum Medum Rule23: Hgh Medum Medum Hgh Rule24: Hgh Hgh Medum Hgh Rule25: Hgh Low Hgh Hgh Rule26: Hgh Medum Hgh Very hgh Rule27: Hgh Hgh Hgh Very hgh Unstable path s classfed as paths that have a large assocated sgnal loss, conssts of low-energy nodes, hgh number of hops or paths spread over a large dstance between source and destnaton. The proposed algorthm s depcted n the gven flowchart (Fg.2): 517

5 Fg. 2. Flowchart of proposed Algorthm (AAODV) Model Implementaton The dstance between two nodes s counted as one hop unt. The delay s the tme taken by a packet to reach from one node to another. It ranges between 0 to 10ms.Energy s the total amount of energy avalable on each node, so that t can partcpate n routng process. Each node has ts energy rangng from 0 to 100j. To explan the above dscussed terms, a hypothetcal network consstng 14 nodes and 19 lnks s shown n Fg. 3. It can be seen that n the presented network, there are 6 feasble routes (a-g-l-m-n-k, a-b-c-d-e-f-k, a-b-c-dj-n-k,a-b-c-i-j-n-k, a-b-h-i-j-n-k, a-g-h-i-j-n-k) from source node a to destnaton node k. System Procedure Least cost path s chosen based on total cost calculated from three data nputs (.e., hop count, energy, delay) lsted n Table 3. Route a-g-l-m-n-k s recommended by the tradtonal AODV routng algorthm because t has the least hop count but the system decdes to take route a-b-h--j-n-k due to mnmum total cost. Therefore, the system wll recommend route a-b-h--j-n-k n the future. To perform the learnng process, the costs nterchangng between dfferent routes wll be the nput for ANFIS data tranng. The total cost of varous routes after the executon of tranng process are lsted n Table

6 Fg. 3. Hypothetcal network Table 3. Input value of route affectng varable Hop count Delay Energy Cost after No Lnk (Normalzed) (Normalzed) (Normalzed) tunng 1 (a, b) (a, g) (b, c) (b, h) (c, d) (c, I) (d, e) (d, j) (e, f) (e, k) (f, k) (g, h) (g, l) (h, I) (I, j) (j, m) (n, k) (l, m) (m, n) Table 4. Route selecton Route Path Cost before tranng Cost after tranng 1 Status a-b-c-d-e-f-k a-b-c-d-j-n-k a-b-c-i-j-n-k Least cost path after trg1 a-b-h-i-j-n-k a-g-h-i-j-n-k Least cost path before trg. It s observed that after the tranng, the total cost of route a- b-c--j-n-k s mnmum nstead of a-b-h--j-n-k. Therefore, the system re-learns and fnally recommends the new route. System Results The results have demonstrated some mportant characterstc of ANFIS based AODV routng protocol. Cost of route chosen by AODV detal s shown n Table 3. The system s desgned to mnmze the decson error close to zero. It means, the total cost of route recommended wll match the cost of route chosen by system when decson error comes to zero. Smulaton Model The envronment varablesconsdered for smulaton of model for proposed protocol are lsted n Table 5. In an area of m 2, 50 moble nodes are placed. The channel capacty s fxed at 54 Mbps. A traffc generator to generate the CBR (Constant Bt Rate) traffc and the random waypont moblty model are used. The speed of ndvdual node schoosen n the range of 0 to 10 m/s. The data payload sze s set 512 bytes. These network parameters are smulated for 250s by NS 2.35 smulator by usng the system mplemented wth C++ and Tcl language. ANFIS s mplemented n MATLAB. 519

7 Table 5. Smulaton envronment varables Parameter Values Routng Protocol AODV, MBCR, FAODV, AAODV No. of Nodes 50 Area m 2 Channel Capacty 54 Mbps Traffc type CBR Moblty Model Random Way pont Smulaton Tme 250s Durng the route dscovery phase, ANFIS possessed wth three nputs hop count, delay and energy. The fuzzfer rule base of ANFIS contans 27 rules and ts defuzzfer has one output. To acheve hghly stable and balanced route, data tranng sets are requred. The data set comprsng 1000 data ponts, whch s dvded nto two sets of 300 and 700 data ponts for tranng and testng respectvely. The frst set s used to tran the network and the model s valdated wth all the 1000 ponts ncludng the 700 ponts that were not used n the tranng process. Data sets are derved from fuzzy f then rules. The tranng data set s fed as an nput to ANFIS tool under MATLAB envronment. As a result, the optmal fuzzy membershp functon s obtaned as an output whch s used n NS2 envronment to take the routng decson. The tranng s done for error tolerance 1e-5, epochs 100 and hybrd learnng method. To evaluate the proposed ANFIS based AAODV routng protocol, the followng performance based metrcs were used for smulaton model: Table 6. Parameters consdered for route selecton method Parameters consdered for Route Selecton Method Protocol Hop count Delay Energy Tradtonal AODV MBCR (C.K.Toh, 2001) FAODV (Fahad,2010) ANFIS aded AODV (proposed) Fg. 4. Average Throughput Vs Pause Tme Packet Delvery Rato (PDR): It can be expressed as the rato of summaton of all the packets that arrved at the destnaton nodes to the summaton of all packets that are transmtted by the source nodes Average routng overhead load: It can be expressed as the rato of summaton of all the overhead routng control packets sent from all nodes wthn the entre MANET network over the smulaton tme Average network throughput: It can be expressed as the rato of summaton of data packets successfully arrved at the destnaton per unt of the smulaton perod tme The parameters consdered for route selecton method for the tradtonal AODV routng protocol, Mnmum Battery Cost routng (MBCR) protocol l(et al.), fuzzy AODV (FAODV) routng protocol and proposed AAODV protocol are presented n Table 6. These parameters are smulated for varyng pause tme from 0 to 100 sec. The performance comparson of all the above sad protocols are presented n Fg The average network throughput of the proposed AAODV protocol s hghest among the all and s observed n Fg. 4. The throughput s hgh due to less number of broken lnks are present n the network because hgher the broken lnks leads to lower the successful number of transmtted packets at destnaton node. Fg. 5. Packet delvery rato Vs pause tme The varatons of packet rato wth respect to varyng pause tme for all these protocols are demonstrated n Fg. 5. The proposed AAODV protocol has the hghest packet delvery rato. Ths s due to consderaton of delay and energy parameters n route selecton of the proposed protocol. The varatons of routng load wth respect to varyng pause tme for all these protocols are presented n Fg

8 Ethcs Ths publcaton s extended from an artcle named Performance enhancement of AODV routng protocol usng Adaptve neural fuzzy nference system Vvek Sharma, Bashr Alam, M.N Doja. publshed n 7 th nternatonal Conference on Qualty,Relablty,Infocom Technology and Busness operaton,2015. Fg. 6. Routng Load Vs Pause Tme From fgure, t s observed that the proposed protocol has least route overhead (routng load) due to less number of broken lnks, low delay and due to contrbuton of nodes wth hgh energy. The proposed AAODV protocol s capable of predctng the optmal route and effectveness of deployng the well traned ANFIS model n the AODV. In general, the proposed protocol AAODV has the hghest performance among the state of art protocols. Concluson In the study, new routng strategy that modfes AODV routng protocol n MANETs proposed. In AODV routng protocol the routng decson s based only upon hop count metrc, however n real tme scenaro there are varous parameters that affect the routng decson. Therefore, to fnd the optmal route, ANFIS system that deals wth uncertan nformaton s embedded nto the AODV routng protocol. The proposed AAODV routng protocol has the nputs of hop count, delay and energy for routng decson. The comparson results of the proposed AAODV protocol shows the mprovement n terms of throughput, PDR and routng load. The proposed AAODV protocol also have the potental to provde one drect path lnk from source node to destnaton node wth hgher effcency and accuracy. Acknowledgement The authors are hghly ndebted to faculty and staff members of network laboratory at jama mla slama, new delh to provde facltes for smooth conduct of ths research work. Author s Contrbutons All the authors are equally responsble n ths research work for collectng all the requred data,smulaton, analyss of results and manuscrpt preparton. References Ba, R. and M. Snghal, DOA: DSR over AODV routng for Moble Ad Hoc Networks. IEEE Trans. Moble Comput., 5: DOI: /TMC De Rango, F., A modfed locaton-aded routng Cprotocol for the reducton of control overhead n ad hoc wreless networks. Proceedngs of the 10th Internatonal Conference on Telecommuncaton, (ICT 10). De Rango, F., A. Iera, A. Molnaro and S. Marano, A modfed locaton-aded routng protocol for the reducton of control overhead n ad-hoc wreless networks. Proceedngs of the 10th Internatonal Conference on Telecommuncatons, Feb. 23-Mar. 1, IEEE Xplore Press, Papeete. DOI: /ICTEL Dehyadegar, M., M. Daneshtalab, M. Ebrahm, J. Plosla and S. Mohammad, An adaptve fuzzy Logc-based routng algorthm for networks-onchp. Proceedngs of the NASA/ESA conference on Adaptve Hardware and Systems, Jun. 6-9, leee Xplore Press, San Dego, pp: DOI: /AHS Fahad, T.O. and A.A. Al, Improvement of AODV Routng on MANETs Usng Fuzzy Systems. Proceedngs of 1st Internatonal Conference on Energy, Power and Control, Nov. 30-Dec. 2, IEEE Xplore Press, Basrah, Iraq. Farkas, K., T. Hossmann, F. Legendre, B. Plattner and S.K. Das, Lnk qualty predcton n mesh networks. Comput. Commun., 31: DOI: /j.comcom Jang, J.S.R., ANFIS: Adaptve-network-based fuzzy nference system. IEEE Trans. Syst. Man Cybernetcs, 23: DOI: / Jang, S., D. He and J. Rao, A predcton-based lnk avalablty estmaton for routng metrcs n MANETs. IEEE/ACM Trans. Netw., 13: DOI: /TNET Khmsara, S., K.K.R. Kambhatla, J., Hwang, S. Kumar and J.D. Matyjas, AM-AOMDV: Adaptve Multmetrc Ad-Hoc On-Demand Multpath Dstance Vector Routng. Proceedngs of the Internatonal Conference on Ad Hoc Networks, (AHN 09), Sprnger Internatonal Publshng AG., pp:

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