Using Particle Swarm Optimization for Enhancing the Hierarchical Cell Relay Routing Protocol

Transcription

1 2012 Thrd Internatonal Conference on Networkng and Computng Usng Partcle Swarm Optmzaton for Enhancng the Herarchcal Cell Relay Routng Protocol Hung-Y Ch Department of Electrcal Engneerng Natonal Sun Yat-Sen Unversty Kaohsung, Tawan Png-Cheng Chung Department of Electrcal Engneerng Natonal Sun Yat-Sen Unversty Kaohsung, Tawan student.nsysu.edu.tw Tsung-Chuan Huang Department of Electrcal Engneerng Natonal Sun Yat-Sen Unversty Kaohsung, Tawan Abstract In moble ad hoc networks (MANETs), the maxmum transmsson radus s generally used for transmsson. The advantage s that each moble node can communcate wth more moble nodes wthn the transmsson range and packets can reach the destnaton rapdly n fewer hops. However usng the maxmum transmsson radus wll nduce hgh power consumpton. We proposed the Herarchcal Cell Relay (HCR) scheme earler, whch s a herarchcal topology routng protocol for the MANETs. In order to enhance the performance of HCR, n ths paper we use the Partcle Swarm Optmzaton (PSO) algorthm to fnd a sutable transmsson radus for each node. The nodes do not use maxmum transmsson radus n order to prolong the network lfetme. In addton to comparng the enhanced HCR wth the orgnal HCR, we also compare t to another herarchcal topology routng protocol Adaptve Cell Relay (ACR). The comparson results show that usng PSO n HCR can reduce energy consumpton wthout sacrfcng the packet transmsson delay. Index Terms partcle swarm optmzaton (PSO), HCR scheme, moble ad hoc networks (MANETs), energy consumpton. I. INTRODUCTION In moble ad hoc networks (MANETs), the topology of routng protocols can be dvded nto two categores, flat and herarchcal. Generally, flat topology routng protocols are worse n regard to scalablty [3] [8]. Ths means that network performance wll descend rapdly as the network area or nodes ncrease. Relatvely, herarchcal topology routng protocols reduce the sze of routng tables, so they have better scalablty [2] [4] [11] [16]. In addton, t has been proven that routng protocols usng geographc locaton nformaton on MANETs are useful for scalablty and routng strategy [1] [6] [8]. The Herarchcal Cell Relay (HCR) [12] s a herarchcal topology routng protocol whch utlzes the Global Postonng System (GPS) to locate the moble node poston. In HCR, the entre network regon s dvded nto multple regular trangles of equal sze, called cells. The sde length of cell s equal to the node transmsson radus, as llustrated n Fg. 1. The man dea of HCR s to relay the route dscovery packets or data packets n selected cells whch s determned by the drecton from source node to the destnaton. By restrctng packet forwardng n certan cells, HCR can avod the consderable overhead resultng from complete floodng. In a general routng strategy, every node uses the maxmum Fg. 1. Network structure of HCR. radus to transmt packets. The advantage s that more nodes wll be n ts transmsson range and packets use fewer hops to arrve destnaton. The dsadvantage s that large transmsson raduses consume more energy. On the other hand, whle a shorter transmsson radus can reduce the energy consumpton, the hop counts of routng path wll ncrease, makng the packet delay tme longer. Partcle Swarm Optmzaton (PSO), ntroduced by Kennedy and Eberhart [7] n 1995, was nspred by the paradgm of brds flockng. It s a populaton-based and selfadaptve search optmzaton technque. In PSO, partcle represents a canddate soluton, and the entre canddate soluton set s called a swarm. Each partcle frst tres to return to ts own prevous best poston and also attempts to move to the best poston of all partcles. The whole acton of the swarm s rapd concentratng on probable regons of search space. Ths evolutonary computng method s useful n searchng for the soluton space for problems requrng defnte optmzaton targets [5] [9]. To enhance the performance of HCR, we am to fnd an approprate transmsson radus to reduce energy consumpton and not to ncrease the packet transmsson tme. PSO has been used to determne optmum solutons n varous domans, ncludng networkng [10] [17], and obtaned rather good results. In ths paper we utlze PSO to fnd the transmsson radus so as to strke the optmum balance between energy consumpton and transmsson delay n HCR /12 $ IEEE DOI /ICNC

2 The rest of ths paper s organzed as follows. Secton II ntroduces a number of related works. Secton III apples the PSO on the determnaton of optmum transmsson radus n HCR. Secton IV demonstrates the smulaton results. Fnally, secton V concludes the paper. II. RELATED WORK Many researches ndcated that geographc locaton nformaton can mprove the performance of routng n MANETs. Typcal routng protocols such as Dstance Routng Effect Algorthm for Moblty (DREAM) [1], Locaton-Aded Routng (LAR) [8], and Greedy Permeter Stateless Routng (GPSR) [6] are based on the locaton nformaton. Combnng herarchcal topology wth geographc locaton nformaton, the Multclass (MC) routng protocol [15] has been appled n heterogeneous MANETs. A routng area n MC s dvded nto equally szed square cells, and nodes n the network are classfed nto two types: backbone node (B-node) and general node (G-node). B-node has a wder transmsson range (power), hgher data rate and processng capablty, and s more relable and robust than G-node. The prmary prncple s that most routng traffc wthn an MC routng goes through B-nodes whch connect to each other n dfferent cells. However, n realstc MANETs, most nodes have the dentcal abltes, so the MC routng wll lack B-nodes to structure the network. The Adaptve Cell Relay (ACR) routng protocol [14] s another routng protocol combnng herarchcal topology wth geographc locaton nformaton. ACR has two routng strateges: one s the Cell Relay (CR) routng strategy appled n dense networks, and the other s the Large Cell (LC) routng strategy appled n sparse networks. ACR can adjust the routng strategy as global node densty changes. Both MC and ACR can send routng packets to neghborng cells only. Snce the dagonal s the half of transmsson radus, ther actual transmsson range s smaller than the maxmum transmsson range. In addton, these two routng protocols take only the maxmum transmsson range nto consderaton to transmt packets, so they wll consume more energy. Raza et al. researched the use of PSO to enhance CGSR n a wreless sensor network [10]. Each partcle represents the locaton of each sensor n a cluster and calculates total energy loss as ts ftness value. After a number of teratons, all sensors move to ther fnal locatons n the setup phase and the sensor whch has the global best value wll act as the cluster head. Whenever the ftness value of the global best value goes below any of the local best values, the swappng of servce wll take place. Jn et al. proposed PCPSO, an mproved PSO, for optmzng multple constraned Qualty of Servce (QoS) attrbutes on a multcast routng [17]. In ths research, the network s modeled as a weghted graph, and each partcle n the swarm represents a multcast tree. PCPSO uses four QoS attrbutes (delay, delay jtter, packet loss rate and cost) to construct the ftness functon. Each partcle n PCPSO s updated by a computed probablty, whch makes convergng to the optmum soluton faster than the orgnal PSO. Although PSO s appled to each doman, we dd not fnd any research usng PSO to fnd the optmum transmsson radus n MANETs. By rasng energy effcency, Feng et al. [13] tred to fnd the optmum transmsson radus n a rectangular wreless area. There was only one statc snk n the corner n that envronment. Nodes whch were near the snk used a small transmsson radus to reduce congeston and energy consumpton, and nodes whch were farther from the snk used large transmsson radus for transmttng farther. It s not smlar to our study because we do not consder only one statc destnaton node and we use a unform transmsson radus on all nodes. III. USING PSO TO FIND OPTIMUM TRANSMISSION RADIUS In the study, we utlze PSO to look for the most feasble transmsson radus for all nodes, and construct the HCR nto cell structure based on the transmsson radus. We use energy consumpton and tme delay to evaluate the effcency of HCR. The scheme s detaled as below. 1) Issue fomalzaton: PSO defnes N partcles n a swarm. Each partcle s located at D-dmensonal coordnates whch ndcates a D-parameter soluton of the problem wthn the D-dmensonal soluton space. In our study, the poston of partcle represents the radus of transmsson. We apply the transmsson radus gven by partcle on HCR, and all nodes use the fxed transmsson radus untl each smulaton fnsh. We use these partcles to fnd the optmum transmsson radus to make that HCR has mnmum power consumpton and end-to-end delay. Snce the transmsson radus of nodes s the unque parameter that we want to solve, the soluton space for our problem s onedmensonal. 2) Partcle swarm ntalzaton: The swarm sze N s set to 20 n ths study. Partcles n swarm are located at random postons n frst teraton. In our study, the range of each partcle s poston s between 0 and 250 whch represents the transmsson radus n meter. Each partcle has a correspondng velocty V whch s responsble to update the poston of partcle and ntalzed to 0. 3) Ftness functon desgn: Ftness functon s used to determne the ftness of the canddate soluton (partcle). In the study, we use total energy consumpton and total delay tme to establsh the ftness functon for determnng the ftness of transmsson radus represented by partcle. Suppose that there are m nodes and k data packets n a network envronment and the followng notatons are used. a) n : the -th node. b) pkt : the -th packet. c) E ntal ( n ) : the ntal energy of node n. d) E nt _ total : the total energy of nodes before smulaton. e) E reman ( n ) : the remanng energy of node n. f) E consum : the total energy consumed by all nodes durng smulaton. g) T arr ( pkt ) : the arrval tme of packet pkt. h) T dep ( pkt ) : the departure tme of packet pkt. 126

3 ) D total : the sum of end-to-end delay for each packet. Then, total energy and delay functon s E E D m nt _ total ntal n ) consum total = E = 1 m ( (1) [ E n ) E ( n )] = ( (2) = k 1 ntal reman [ T pkt ) T ( pkt )] = ( (3) = 1 arr and the ftness functon s dep a( Econsum / Ent _ total ) + b( Dtotal / smulaton _ tme) (4) Eq. (1) calculates the total energy of all nodes before smulaton. Eq. (2) calculates the total energy consumpton by all nodes. Eq. (3) calculates the total end-to-end delay by addng each packet delay. In the ftness functon of equaton (4), a and b are weght constants and smulaton_tme s the total tme of smulaton. By assumng the energy consumpton and end-to-end delay are of equal weght, we set both a and b to ) Optmum value calculaton: Partcle calculates the ftness value of ts poston by ftness functon, and uses the ftness value to determne whether the poston s optmum. Thus, we apply the transmsson radus gven by partcle s poston on HCR to calculate total energy consumpton and total end-to-end delay n equatons (2) and (3). After smulaton completed, we substtute total energy consumpton and end-to-end delay nto the ftness functon (equaton (4)), and then we can get the ftness value of each partcle s current poston. In our study, the partcle n better poston wll have a smaller ftness value, so the partcle located n the optmum poston wll have the mnmum ftness value. After obtanng the ftness value, we determne local optmum values and the global optmum value by the followng steps. a) Local optmum value: Local optmum s the mnmum ftness value of each partcle. Whenever a partcle gets a new ftness value, the partcle compares that value wth the local optmum. If the new ftness value s less than the local optmum, the local optmum wll be replaced by ths new value, and PSO denotes the -th partcle poston as Pbest. The ftness value calculated n the frst teraton wll be set as the local optmum. b) Global optmum value: After all partcles fnd ther local optmums, PSO wll use the smallest one as the global optmum. After the next teraton, f the global optmum n ths teraton s less than the global optmum n the prevous teraton, the global optmum wll be replaced by the new global optmum, and PSO wll mark the poston of that partcle as Gbest. Otherwse, the global optmum wll reman unchanged. 5) Partcle updatng: After determnng the optmum values, PSO wll update the velocty, and then update the partcle poston by the velocty. The new poston of the partcle wll be closer to the poston of the global optmum, and fnally the optmum transmsson radus for HCR s found. In PSO, both the poston of partcle and the velocty are ndcated by vectors. Let P be the poston vector of -th partcle and V be the velocty vector correspondng to P. The updatng method s as below: V [ Pbest P ] + c r [ Gbest P ] V c r (5) P P + V (6) In equaton (5), s the nerta weght for the velocty; c 1 and c 2 are weght constant. In ths paper, we concentrate on whether the optmum transmsson radus can be found by PSO, so we set the parameter the same as n orgnal PSO; namely both c 1 and c2 are equal to 2 and s equal to 1. r 1 and r 2 are random values between 0 and 1 whch keep partcles from fallng nto the local optmum space. The change of velocty wll be affected not only by the local optmum but also by the global optmum of each partcle and the last velocty. After gettng the new velocty, the partcle changes to the new poston by usng equaton (6). 6) Iteraton and termnaton: After partcles updatng ther poston, we obtan new transmsson raduses. By applyng these raduses on HCR, we calculate new energy consumptons and end-to-end delays and fnally get new ftness values. Local optmums and the global optmum wll be determned agan. Ths procedure forms an teraton. The teratons wll contnue untl the assgned teraton tme s reached or the teraton termnaton condtons are satsfed. In our study, we set the number of teraton to 10 and the termnaton condton s that the dfference between the last global optmum and the current global optmum s less than 1. Fnally, the ftness value of the global optmum s the best transmsson radus n meter. IV. SIMULATION In ths secton, we evaluate the proposed scheme by usng network smulator NS-2. Before usng PSO to fnd optmum transmsson radus of HCR, we observe the performance by usng some dfferent transmsson radus n envronment 1 as shown n Table I. Fg. 2 shows that energy consumpton gets larger when transmsson radus and flow amount ncreases. Fg. 3 shows that end-to-end delay gets larger when the transmsson radus decreases; ths s because short transmsson radus leads to more hop count. However, the endto-end delay also ncreases when the transmsson radus gets larger; ths s because large transmsson radus leads to more nterference between nodes. 127

4 TABLE I. THE SIMULATION ENVIRONMENT 1 Fg. 2. Transmsson radus vs. energy consumpton. Smulaton envronment 1 area 500 x 500 square meters nodes number 100 Smulaton tme 300 packet sze 64 bytes packet generaton rate 4 packets / second maxmum speed 10 meters/ second TABLE II. OPTIMUM TRANSMISSION RADIUS IN SIMULATION ENVIRONMENT 1 flows transmsson radus of PSO-HCR meters meters meters meters Fg. 3. Transmsson radus vs. end-to-end delay. Next we compare our protocol wth ACR. ACR s a routng protocol strategy whch also ncludes a herarchcal structure and cell relayng scheme smlar to HCR. We use HCR-PSO to denote our protocol. Tables I and III lst two smulaton envronments; each usng four flow amounts: 5, 10, 15 and 20 flows. Envronment 1 and envronment 2 are of equal densty, but envronment 2 has more nodes and wder area than envronment 1. We calculate the optmum transmsson radus by usng PSO n each envronment. The results are shown n Tables II and IV. In smulaton envronment 1 (see Fg. 4) we fnd that HCR- PSO consumes less energy than HCR. Ths s because HCR- PSO uses a shorter transmsson radus nstead of the maxmum transmsson radus to reduce energy consumpton. Due to usng maxmum transmsson radus n ACR and HCR, ther energy consumptons are hgher than HCR-PSO. Fg. 4. Flows vs. energy consumpton n envronment 1. area TABLE III. THE SIMULATION ENVIRONMENT 2 Smulaton envronment 2 nodes number 400 Smulaton tme 300 packet sze packet generaton rate maxmum speed 1000 x 1000 square meters 64 bytes 4 packets / second 10 meters/ second TABLE IV. OPTIMUM TRANSMISSION RADIUS IN SIMULATION ENVIRONMENT 2 flows transmsson radus of PSO-HCR meters meters meters meters The delay tme of HCR-PSO and HCR are only slghtly dfferent (see fg. 5). Ths confrms that HCR-PSO has the capablty of fndng best transmsson radus for save energy, wthout sacrfcng the average end-to-end delay. To confrm that HCR-PSO can also perform well n envronments whch have a larger network area and more nodes, we take smulaton n envronment 2, and do the same comparsons as n envronment 1. When the network area gets larger and nodes ncrease, the hop count of each path wll also ncrease, and routes wll break 128

5 large-scale envronment. Ths confrms that usng PSO can fnd the optmum transmsson radus to conserve total energy consumpton. Fg. 5. Flows vs. end-to-end delay n envronment 1. more often. Ths can nfer that there has an optmum transmsson radus n such envronments whch can save more energy and reduce the end-to-end delay. The smulaton results usng envronment 2 are shown n Fg. 6 and Fg. 7 whch verfy that HCR-PSO can conserve energy consumpton and not sacrfce the end-to-end delay. V. CONCLUSION In ths paper, we use PSO to fnd the optmum transmsson radus n HCR. It does not sacrfce the end-to-end delay but can save energy consumpton. Compared to usng maxmum transmsson radus n HCR and ACR, HCR-PSO consumes less energy not only n small-scale envronment but also n Fg. 6. Flows vs. energy consumpton n envronment 2. Fg. 7. Flows vs. end-to-end delay n envronment 2. REFERENCES [1] S. Basagn, I. Chlamtac, V. R. Syrotuk, and B. A. Woodward, "A dstance routng effect algorthm for moblty (DREAM)," 4th annual ACM/IEEE nternatonal conference on Moble computng and networkng, Dallas, Texas, Unted States, 1998, pp [2] G. Pe, M. Gerla, and T. W. Chen, "Fsheye state routng: a routng scheme for ad hoc wreless networks," IEEE Internatonal Conference on Communcatons, 2000, vol.1, pp [3] Z. J. Haas and M. R. Pearlman, "The performance of query control schemes for the zone routng protocol," IEEE/ACM Transactons on Networkng, 2001, vol. 9, pp [4] K. Xu, X. Hong, and M. Gerla, "An ad hoc network wth moble backbones," IEEE Internatonal Conference on Communcatons, 2002, vol.5, pp [5] K. P. Wang, L. Huang, C. G. Zhou, and W. Pang, "Partcle swarm optmzaton for travelng salesman problem," Internatonal conference on machne learnng and cybernetcs, 2003, Vol.3, pp [6] B. Karp and H. T. Kung, "GPSR: greedy permeter stateless routng for wreless networks," 6th annual nternatonal conference on Moble computng and networkng, Boston, Massachusetts, Unted States, 2000, pp [7] J. Kennedy and R. Eberhart, "Partcle swarm optmzaton," IEEE Internatonal Conference on Neural Networks, vol. 4, pp [8] Y.-B. Ko and N. H. Vadya, "Locaton-aded routng (LAR) n moble ad hoc networks," 4th annual ACM/IEEE nternatonal conference on Moble computng and networkng, 2000, vol. 6, pp [9] M. S. Norouzzadeh, M. R. Ahmadzadeh, and M. Palhang, Plowng PSO: A novel approach to effectvely ntalzng partcle swarm optmzaton, 3rd IEEE Internatonal Conference on Computer Scence and Informaton Technology, 2010, pp [10] H. Raza, P. Nandal, and S. Makker, "Selecton of cluster-head usng PSO n CGSR protocol," Internatonal Conference on Methods and Models n Computer Scence (ICM2CS), 2010, pp [11] T. C. Huang, C. K. Lao, and C. R. Dow, "Zone-based herarchcal routng n two-ter backbone ad hoc networks," 12th IEEE Internatonal Conference on Networks, 2004, vol.2, pp [12] T. C. Huang, H. Y. Ch, and S. K. Huang, "Herarchcal cell relay scheme wth GPS for moble ad hoc networks," 10th Internatonal Symposum on Pervasve Systems, Algorthms, and Networks, 2009, pp [13] W. Feng, H. Alshaer, and J. M. H. Elmrghan, "Energy effcency: Optmal transmsson range wth topology management n rectangular ad-hoc wreless networks," Internatonal Conference on Advanced Informaton Networkng and Applcatons, 2009, pp [14] X. Du and D. Wu, "Adaptve cell relay routng protocol for moble ad hoc networks," IEEE Transactons on Vehcular Technology, 2006, vol. 55, pp [15] X. Du, D. Wu, and W. Lu, Y. Fang, "Multclass routng and medum access control for heterogeneous moble ad hoc networks," IEEE Transactons on Vehcular Technology, 2006, vol. 55, pp [16] X. Xang, Z. Zhou, and X. Wang, "Robust and Scalable Geographc Multcast Protocol for Moble Ad-hoc Networks," IEEE Internatonal Conference on Computer Communcatons, 2007, pp [17] X. Jn, L. Ba, Y. J, and Y. Sun, "Probablty convergence based partcle swarm optmzaton for multple constraned QoS multcast routng," 4th Internatonal Conference on Semantcs, Knowledge and Grd, 2008, pp