Research Article Distributed Dynamic Memetic Algorithm Based Coding Aware Routing for Wireless Mesh Sensor Networks

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1 Hinwi Pulishing Corportion Interntionl Journl of Distriute Sensor Networks Volume 26, Artile ID , 7 pges Reserh Artile Distriute Dynmi Memeti Algorithm Bse Coing Awre Routing for Wireless Mesh Sensor Networks See Hmm n Ahm S. Almogren Computer Siene Deprtment, College of Computer n Informtion Sienes, King Su University, Riyh 543, Sui Ari Corresponene shoul e resse to Ahm S. Almogren; hlmogren@ksu.eu.s Reeive 2 Novemer 25; Revise 25 Ferury 26; Aepte Mrh 26 Aemi Eitor: Wen-Hung Cheng Copyright 26 S. Hmm n A. S. Almogren. This is n open ess rtile istriute uner the Cretive Commons Attriution Liense, whih permits unrestrite use, istriution, n reproution in ny meium, provie the originl work is properly ite. Network oing hs een onfirme s potentil tehnology to improve performne of wireless mesh networks (WMNs); network oing hs gret vntges for sensor networks like minimiztion of ommunition neee to ollet sensor t n error reovery. A few network oing wre routings hve een propose. However, these mehnisms etet oing opportunities through lol trffi pttern heking, whih hrly otins optiml routes. This pper proposes Distriute Dynmi Memeti Algorithm Bse Coing Awre Routing (DDMCAR) for wireless mesh networks, whih employs memeti lgorithm to optimize routes n oing opportunities. This pper proposes n improvement over GCAR lgorithm y employing memeti lgorithm n lso meme shring mong noes to inrese the hne of fining optiml solution n ontinuous monitoring n upte of meme fitness to hieve ynmi trking of network onitions, n finlly using most fit memes for lo lning elevting ongestion. Through simultions on rnomly generte wireless mesh networks, DDMCAR is to e shown to fin optiml routes within short time n hieve more improvements thn GCAR.. Introution Wireless network pity n esily e extene with little ost if ess points re onnete to eh other using wireless links; lso ommunition nwith n energy requirements n e minimize through network oing whih is very importnt for Wireless Sensor Networks s epite in Figure. Suh networks re lle wireless mesh networks (WMNs) [, 2]. Network oing tkes vntge of the opportunisti nture n sptil iversity of the wireless meium n opes with unrelile trnsmissions [3], where Opportunisti Routing (OR) n gretly improve the throughput of multihop wireless network y using shre rio meium. In the Opportunity Routing protools, ll neighoring noes loser to the trget listen to proe pket n lso forwr the pket to the estintion noe s nite noe [4]. Optimiztion lgorithm for WMNs oing wre routing using GCAR geneti lgorithms is esrie in [5] with simultion results showing improvement over lol trffi pttern heking lgorithms n over DCAR s shown in [6], where the uthors esrie issues relte to geneti lgorithms like hromosome representtion, geneti opertion of rossover n muttion, n fitness funtion onsiering oing opportunity n interferene. In our work, we propose n improvement over the GCAR lgorithm tht uses memeti lgorithm; the new lgorithm oes not terminte one fining n eptle solution ut inste ontinues to fin more optiml routes n shre its informtion (memes) mong other noes; the ontinuous oservtion n improvement enle the lgorithm to hnle the issue of hnging network onitions; hene we propose memeti lgorithm to improve the results of GCAR y employing lol serh to improve on the results of hromosomes rossover n muttions just efore popultion reution; this step (memeti lgorithm) hs shown generlly onsierle improvement over geneti lgorithms; nother improvement is to keep the optimiztion proess running inefinitely n ontinuously upte the QoS t ollete fromthewmnnuseittorelultethefitnessvlues for the urrent popultion, hene moifying the optiml routes through the new fitness vlues; nother improvement is shring of memes mong noe s immeite neighors

2 2 Interntionl Journl of Distriute Sensor Networks Server Sensor t olletion Wireless Sensor Network Figure : Network oing in Wireless Sensor Networks. Figure 2: Insert ommon neighor noe into route. where it n e e to the gene pool just efore popultion reution step resulting in ontinuous improvement over time n exploittion of newly isovere routes for optimizing other routes. As the lgorithm requires running the proess ontinuously over ll router noes it n e lime tht it will reue the noes t proessing power; this issue n e mitigte y giving the route optimiztion proess very low priority, hene preventing it from onsuming noe s resoures ffeting noe t proessing power uring high lo perios while tking vntge of ile time. Lstly we propose using ll memes in the gene pool inste of only the est one ut through stohsti proess tht will fvor memes with high fitness over low fitness memes; this will enle the lgorithm to test memes fitness in rel-time n get feek from the WMN tht will e use to upte eh meme s fitness over time; lso this will reue ongestion usully ffeting optiml routes s propose in [7] through route iversifition. 2. Relte Work The uthors in [8] first propose the onept of network oing,ntheuthorsin[6]proposecope,thefirst prtil XOR oing system. The limittions of COPE uner prtil physil lyer n link-sheuling lgorithms were esriein[9],nthereforetheuthorsproposetheonept of oing-effiient link sheuling for prtil network oing. The ETT (Expete Trnsmission Time) routing metri ws investigte in []. The uthors in [] nlyze severl routing metris, inluing hop ount, ETX, ETT, WCETT,nMIC.Theuthorsin[2]proposeDCAR,n lgorithm tht is se on pssing RREQ (Route Request)/ RREP (Route Reply) messges to etermine the route with the highest oing gin; lso it provies neessry n suffiient onitions for opportunisti network oing, hene llowing opportunisti oing for more thn two pkets; simultion showe tht it proues higher throughput ompre to previous lgorithms. In [3], the uthors propose improvements upon HWMP with new network oing wre routing protool (CAHWMP) for WMN. The uthors in [4] propose network oing wre queue mngement sheme (NCAQM) t intermeite noes wheres the uthors in [5] propose n effiient error reovery sheme tht ouples network oing n multipth in unerwter sensor networks. Anlysis n simultion results onfirm tht the sheme is effiient in oth error reovery n normlize Figure 3: Delete noe from route. energy. The uthors in [6] presente prtil network oing (PNC) s generi tool for storge restrite ontinuous t olletion sensor network pplitions. PNC generlizes the existing network oing (NC) prigm. The uthors in [5] propose geneti lgorithm se oing wre routing (GCAR) for wireless mesh networks; GCAR is n improvement on DCAR, whih employs geneti lgorithm to optimize routes n oing opportunities using geneti lgorithm. In ition, the key ingreients in GCAR, for exmple, hromosome representtion, geneti opertion of rossover n muttion, n fitness funtion onsiering oing opportunity n interferene, re formlize n use in this pper. Through simultions on rnomly generte wireless mesh network, GCAR is shown to fin optiml routes within short time n hieve more improvements thn the previously propose methos on route setup time, throughput, n lo lning. A new lgorithm to overome ongestion prolem tht fes WMNs ue to the rupt use of single pth ue to its optimlity ws propose in [7]. In this lgorithm priority se seletion mehnism for the pths is opte whih n ensure the performne of the network; simultion results shows reue ongestion. 3. The Propose Metho for Distriute Dynmi Memeti Coing Awre Routing We propose new oing wre routing lgorithm, Distriute Dynmi Memeti Algorithm Bse Coing Awre Routing (DDMCAR), tht improves the oing gin n throughput while reuing the route setup time; the lgorithm (Figure 4) is esrie in the following setions.

3 Interntionl Journl of Distriute Sensor Networks 3 Route request Seletion Meme reeive from neighor Meme representtion Crossover Continuous optimiztion Distriute meme to neighors Popultion initiliztion Muttion Selet n return n optiml meme Repir funtion Yes Fitness vlue lultion Lol serh Termintion onition stisfie? No Figure 4: MCAR lgorithm flowhrt. 3.. Memeti Algorithms. Memeti lgorithms re vrint over geneti lgorithms where lol serh step is use fter the rossover n muttion steps to improve upon the resulting hromosome, this tehnique generlly proues etter solutions n fster onvergene, hene using memeti lgorithm for routing in WMNs shoul give etter results over GCAR lgorithm Lol Serh. The lgorithm employs lol serh lgorithmthtttemptstoimprovethesolutionjustfterrossover n muttion steps (Figures 2 n 3); the lol serh lgorithm will () inspet ll links in the route n ttempt to ommon neighor noe if it improves the fitness of the meme; (2) try to remove links from the route tht o not ontriute to meme fitness; (3) repet steps () n (2) until no further improvement is possile Initilizing Noes Popultion. One noe strts it will ontt its immeite neighors n ollet ll ville memes; the noe will omine these memes with ville QoSt(orprior)togenertelolmemeswithnewfitness vlues, the noe immeitely performs popultion reution keeping only popultion of the fittest memes Meme Shring. It is more prole to fin etter routes y running memeti lgorithms in prllel (one per noe) on supopultions n shre only newly isovere memes tht survive popultion reution n improve fitness with the noes immeite neighors; one meme is reeive it will e omine with the extr link with the sener noe n the memes fitness is reompute; then memes re e to the lol popultion just efore the popultion reution step; if the newly e meme survives the popultion reution step, it will e use in rossover with the rest of the popultion, hene ing to the iversity of the gene pool, mking it more prole to reh improve solutions Continuous Optimiztion. Ifnetworktopologynonitions o not hnge noe s lol popultion will reh n equilirium fter some time, ut if network topology hnges y ing or removing noes or y noe movement or y hnge of network onitions like noise or interferene, tht oulreuelinkthroughput;henethehngeswille pture lolly n inorporte in the meme fitness or y retion of new memes (for new noes); then the upte memes re trnsmitte to immeite neighors; hene the new knowlege is propgte to ll noes tht will enefit from the new knowlege; over time the hnge in network topology or onitions will result in the retion of new memes tht will mke use of new routes or non less fit routes. This enles the WMN to respon to hnges fster n mintin ynmi routing optimlity Congestion Control. Usully using one optiml pth to trnsmit introues the prolem of ongestion; hene y iversifition of routes trffi is ivie on multiple routes. Author in [7] esrie n lgorithm where est routes re prioritize oring to optimlity n the sener noe hooses to sen trffi y stohstilly lternting etween them, hene less proility for ongestion; we propose to hve similr solution to the ongestion prolem y llowing the noe to hoose stohstilly etween est N routes with proility proportionl to the fitness of eh route (meme) Algorithm Desription () Upon sening messge if no route to estintion exists in the meme pool, set up lol popultion of

4 4 Interntionl Journl of Distriute Sensor Networks Liner (lss DCAR) Throughput Liner (lss GCAR) Liner (lss MCAR) Avg en to en ely Figure 6: Offere lo Mps versus en to en ely (se). Figure 5: Offere lo Mps versus throughput ps. memes t the soure noe tht trverse the network from soure to estintion noes; memes re onstrute rnomly using the network topology (se on network stte tse (NSB)) n the heeling funtion; if there is lrey route to estintion existing in the meme pool go to step (2). (2) Perform severl yles of memeti optimiztion on the estintions meme pool. (3) Selet meme rnomly from the estintions meme pool with proility equl to the rtio of its fitness vlue to the totl fitness of ll the memes of tht estintion. (4) Use the route ssoite with selete meme for sening the messge. (5) Upon reeiving meme from neighor the link etween the urrent noe n its neighor to eginningoftherouteprouingnewmemen use the heling funtion to remove loops; if the meme oes not lrey exist in the meme pool, then the new meme to the meme pool of the estintion. (6) Perform memeti optimiztion for ll estintions whilenoeisile(theperentgeofiletimeuse y this step n e onfigure to reue power onsumption). (7) If step (2) or (6) proues new meme with higher fitness vlue, shre tht meme with immeite neighors. (8) When sening new messge go to step (). (9) When reeiving new meme go to step (5). ()Whileilegotostep(6). 4. Performne Evlution A simultion of instnes of 2 ifferent offere los ( mps 2 mps) rnom networks is generte with 2 noes eh with rnom positions (for eh instne positions re ientil ross networks to reue vrine) in 8 m 8 m re n then the networks re simulte over.... Avg Route Setup Time Figure 7: Offere lo Mps versus log (Avg Route Setup Time (se)). 82. MAC lyer with CD n pseuoknowlegments n performne re mesure n ompre to GCAR n DCAR performne on the sme network s referene; the numer of hromosomes in GCAR popultion is while DDMCAR meme popultion hs only 5 memes (sine the lgorithm will tke vntge of ontinuous optimiztion n memeshringsmllernumerofmemesneuse); lso less thn % of the proessor ile time ws use for ontinuous optimiztion ssuring tht more thn 99% of proessing time is reserve for other tsks. Figure 5 shows MCAR onsistent inrese in throughput over GCAR. This is ue to the lol serh step in the memeti metho tht enhnes the result of the geneti metho n veryigenhnementoverdcar;thisisuetomcar,the memeti lol serh step fining pths with lower queue size; this les to oth lower ongestion n more utiliztion n throughput. In Figure 6, MCAR shows more ely t lower los s it fouses more on oing gin optimiztion over ely; GCAR hs the sme teneny ut its ely is smller; this is ue to MCAR hieving higher gin pths tht re longer; ll lgorithms show lower ely s the lo is inrese; this is ue to interferene preventing seletion of longer routes. InFigure7,slinersleoftheAvgRouteSetupTime oes not properly show omprison etween the lgorithms ue to DCAR showing extremely high route setup times

5 Interntionl Journl of Distriute Sensor Networks Avg Route Setup Time Avg Collision Detetion Time Figure 8: Offere lo Mps versus Avg Route Setup Time (se) Avg TX time Figure 9: Offere lo Mps versus Avg Trnsmission Time (se) RX time Figure : Offere lo Mps versus Avg Reeption Time (se). ompretomcarngcar,heneweresortetolog sletoshowtheomprison. In Figure 8, MCAR shows onsierle erese in route setup time (5-fol) ompre to GCAR; this is ue to sving the memes for future use n euse of ontinuous optimiztion whih mke the memes keep up with the hnging network onitions n lso to meme shring whih Figure : Offere lo Mps versus Avg Collision Detetion Time (se) Liner (lss DCAR) Avg Queue Time Liner (lss GCAR) Liner (lss MCAR) Figure 2: Offere lo Mps versus Avg Pket Queue Time (se). expeites fining more optiml pths; hene MCAR oes not nee to spen too muh time setting up the route, while GCAR hs to strt rout setup from srth every time. In Figure 9, MCAR shows n inrese in trnsmission time over GCAR whih is onsistent with higher throughput, leing to MCAR onsuming more power for t trnsmission. In Figure, there is lmost mirror of the Avg Trnsmission Time grph; this is ue to MCAR spening more time listening to t trnsmission. In Figure, MCAR inrese oing gin effiieny les to more interferene, hene spening more time on ollision etetion. InFigure2,MCARshowsinresequeuetimetren with inrese of lo; this is ue to higher rrivl rte with inrese lo n onsistent with higher throughput. In Figure 3, MCAR shows eresing ongestion tren with inrese in lo ue to ongestion ontrol mehnism propose through stohsti route iversifition long with more effiient ongestion voine (in the fitness funtion) ue to the memeti lol serh step tht proues more effiient ongestion voine. In Figure 4, MCAR shows lower retrnsmission rte over GCAR; this is ue to MCAR voiing interferene more

6 6 Interntionl Journl of Distriute Sensor Networks Avg Congestion Also stohsti route iversifition showe lower ongestion. Competing Interests The uthors elre tht they hve no ompeting interests. 4 Aknowlegments This work ws supporte y the Reserh Center of the College of Computer n Informtion Sienes t King Su University. The uthors re grteful for this support. Liner (lss DCAR) Liner (lss GCAR) Liner (lss MCAR) Figure 3: Offere lo Mps versus Avg Congestion (pkets/se) Avg Pket Retrnsmission Figure4:OffereloMpsversusAvgRetrnsmissions(pkets/se). effiiently through the lol serh step in the memeti metho performe fter the geneti metho, hene giving higher oing gin n lower interferene routes. 5. Conlusions Ourproposemetho(MCAR)showsinresethroughput over GCAR n DCAR; lso it shows onsierly lower route setup time n lower ongestion thn GCAR, yet MCARshowshigherentoenelystlowerlosue to overoptimiztion of oing gin leing to longer routes; this n e improve y introuing route length in the memeti lgorithm ost funtion, ut with lower ost s the lo inreses. The memeti optimiztion metho showe inrese performne in oing gin n in reue interferene. Meme shring n ontinuous optimiztion resulte in onsierly fster route setup time; the ontinuous optimiztion ost ws less thn % of the proessing time, whih is n eptle ost for suh inrese in performne. Referenes [] T. Ikeng, K. Tsuouhi, D. Noyshi, n Y. Fuku, Disjoint pth routing for multi-hnnel multi-interfe wireless mesh network, Interntionl journl of Computer Networks & Communitions,vol.3,no.2,pp.65 78,2. [2] A. S. Almogren, Developing powerful n resilient smrt oy sensor network through hyperue interonnetion, Interntionl Journl of Distriute Sensor Networks, vol.25, Artile ID 6975, 8 pges, 25. [3] N. Gzoni, V. Angelkis, V. A. Siris, n R. Bruno, A frmework for opportunisti routing in multi-hop wireless networks, in Proeeings of the 7th ACM Workshop on Performne Evlu- tionofwirelessaho,sensor,nuiquitousnetworks(pe- WASUN ),ACM,Borum,Turkey,Otoer2. [4] X. Du, Y. Liu, K. Liu, n F. Yun, An ollision solutions mehnism in opportunisti routing in wireless mesh networks, in Proeeings of the 2n Interntionl Conferene on Control, Instrumenttion, n Automtion (ICCIA ), rtio, pp. 4 43, Shirz, Irn, Deemer 2. [5] X.Sho,R.Wng,H.Xu,H.Hung,nL.Sun, Genetilgorithm se oing wre routing for wireless mesh networks, Avnes in Informtion Sienes n Servie Sienes,vol.4,no. 5, pp , 22. [6] S. Ktti, H. Rhul, W. Hu, D. Kti, M. Mer, n J. Crowroft, XORs in the ir: prtil wireless network oing, IEEE/ACM Trnstions on Networking, vol.6,no.3,pp.497 5, 28. [7] M. Shrif, M. Murtz, W. Hier, n M. Rz, Priority se ongestion ontrol routing in wireless mesh network, Interntionl Journl of Avne Networking n Applitions, vol. 3, no. 3, pp. 47 5, 2. [8] R. Ahlswee, N. Ci, S. R. Li, n R. W. Yeung, Network informtion flow, IEEE Trnstions on Informtion Theory, vol. 46, no. 4, pp , 2. [9] J.Le,J.C.S.Lui,nD.M.Chiu, Howmnypketsnwe enoe? n nlysis of prtil wireless network oing, in Proeeings of the IEEE 27th Conferene on Computer Communitions(INFOCOM 8), Phoenix, Ariz, USA, April 28. [] R. Drves, J. Phye, n B. Zill, Routing in multi-rio, multihop wireless mesh networks, in Proeeings of the th Annul Interntionl Conferene on Moile Computing n Networking (MoiCom 4), pp. 4 28, ACM, Philelphi, P, USA, Otoer 24. [] Y. Yng, J. Wng, n R. Krvets, Designing routing metris for mesh networks, in Proeeings of the st IEEE Workshop on Wireless Mesh Networks (WiMesh 5), Snt Clr, Clif, USA, Septemer 25.

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