An Adaptive and Distributed Clustering Scheme for Wireless Sensor Networks

Transcription

1 2007 Internatonal Conference on Convergence Informaton Technology An Adaptve and Dstrbuted Clusterng Scheme for Wreless Sensor Networs Xnguo Wang, Xnmng Zhang, Guolang Chen, Shuang Tan Department of Computer Scence and Technology Unversty of Scence and Technology of Chna Hefe, , P.. Chna Abstract In recent years, wreless sensor networs have ganed extensve attentons due to ther brght future n mltary and cvl felds. Innovatve routng technques that can prolong the networ lfetme are hghly demanded. Clusterng s an effectve technque n applcatons of perodcal data gatherng. Most clusterng schemes mae the common assumpton that sensors are unformly dspersed wthn an area. In ths paper, we propose an adaptve and dstrbuted clusterng scheme (ADCS) for wreless sensor networs, whch can wor very well n both unform and non-unform scenes. Our approach elects nodes wth comparatvely more resdual energy and more neghbors as the cluster heads. In the cluster formaton phase, the plan nodes choose to jon n cluster consderng both dstance and load balance. ADCS can generate an even dstrbuton of the clusters, whch helps to prolong the networ lfetme. Smulaton results show that ADCS outperforms LEACH sgnfcantly by prolongng the networ lfetme over 40% n unform scenes and 75% n non-unform scenes respectvely. Keywords-adaptve and dstrbuted; clusterng scheme; energy effcent; routng protocol; wreless sensor networ I. INTODUCTION ecent rapd advances n MEMS technologes, wreless communcatons, and dgtal electroncs mae the deployment of large scale wreless sensor networs (WSN) feasble. Wreless sensor networs have attracted more and more attentons for ther wde-range potental applcatons ncludng envronmental montorng, target tracng and mltary [1]. Wreless sensor networs usually contan thousands or mllons of sensors, whch are randomly and wdely deployed. Sensors are powered by battery, whch s mpossble to get recharged after deployment. Thus, energy effcency s an mportant ssue n sensor networs. Snce routng consumes a lot of energy, an effcent routng scheme n sensor networs s hghly requred. For perodcal data gatherng applcatons, mostly aded routng technque s clusterng scheme and data aggregaton. LEACH [2], the frst clusterng protocol, proposes a two-phase mechansm that the plan nodes wll turn off completely F untl ther pre-allocated tme slot. LEACH has a drawbac that the clusters are not evenly dstrbuted due to ts randomzed rotaton of cluster heads. PEGASIS [3] mproves the performance of LEACH and prolongs the networ lfetme greatly wth a chan topology. The delay s sgnfcant although the energy s saved. HEED [4] selects cluster heads through O (1) tme teraton accordng to some metrc and ads the mult-hop communcaton to further reduce the energy consumpton. EECS [5] elects cluster heads wth more resdual energy through local rado communcaton whle achevng good cluster heads dstrbuton. Most clusterng schemes mae the common assumpton that sensors are unformly dspersed wthn an area. However, the topology may be non-unform n factual applcatons. Obvously, the area where the node densty s hgher needs more cluster heads than other areas for load balance. In ths paper, we propose and evaluate an adaptve and dstrbuted clusterng scheme (ADCS) for perodcal data gatherng applcatons. In the cluster head electon phase, a number of canddate nodes are elected and compete for cluster heads accordng to the node resdual energy and node densty nearby. The method s fully localzed and produces an even dstrbuton of cluster heads approxmately. In the cluster formaton phase, the plan nodes choose to jon the cluster accordng to both dstance and load balance. ADCS s absolutely dstrbuted, energy effcent and adaptve to all nds of topology. The smulaton results show that t prolongs the networ lfetme to as much as 145% of LEACH n unform scenes and 175% n non-unform scenes respectvely. The remander of ths paper s organzed as follows. Secton II outlnes the ssues of clusterng scheme. Secton III descrbes the detals of ADCS and Secton IV analyzes the performance of ADCS. Secton V gves smulaton results and compares t wth LEACH. Secton VI gves the concluson and future wor. II. POBLEM STATEMENTS A. Networ Model To be smplfcaton, we mae some reasonable assumptons about networ as follows: 1) All sensors and BS are statonary after deployment and the former always have data to send to the latter. 2) We just consder the energy consumed by wreless communcaton and data aggregaton. 3) Sensors are hghly connected to each other and the communcaton s constrant wthn a sngle-hop /07 $ IEEE DOI /ICCIT

2 4) Sensors can compute the dstance based on the receved sgnal strength f the transmsson power s gven. 5) Sensors can adjust the transmsson power accordng to the desred transmsson range. Notaton T com pete A TABLE I. MEANING OF THE NOTATIONS Meanng the radus of probng the node densty threshold used to control the number of canddates competton radus of canddate node the area of the networ E ( ) the expectaton of cluster radus n Addtonally, the operaton of data aggregaton consumes E energy. da C. Scheme Goals It s crucal to elect [7] cluster heads wth good dstrbuton at low cost. To prolong the lfetme of the networ, we ntroduce a novel adaptve and dstrbuted clusterng scheme (ADCS). Specfc goals n ADCS are as follows: 1) load balance: balance the load among the sensors, especally among the cluster heads; 2) low control load: the fnal goal s to gather data from the envronment, so t s advsable to reduce the control load to extend the tme of montorng; 3) fully dstrbuted scheme: Sensors nteract wth each other through localzed communcaton; 4) adaptve scheme: our approach should be adaptve to all nds of topologes. For example, Fg.1 shows that the elected cluster heads dstrbute evenly even n non-unform scene. N the number of nodes the number of neghbors of node Numneghbors n We consder the networ lfetme as the nterval from the networ ntalzaton tll the frst node des out. Because once a node drans ts energy, some area of the networ won t be covered or montored any longer. Moreover, the smulaton shows that the other nodes get down qucly, followng the frst dead node f node s balanced. We don t mae any assumpton about the node densty of the networ. Our scheme can be adaptve to the scenes that the nodes are arbtrarly dspersed n a fxed area. B. Energy Model In ths paper, we ad the frst rado model presented n [2], [6]. The energy consumpton ET (, l d ) of transmttng l bt data between two nodes separated by a dstance of d meters s: 2 l Eelec + l εfsd, d< do (1) Eld T(, ) = ETxelec () l+ ETxamp (, ld) = 4 l Eelec + l εmp d, d do (2) The frst tem presents the energy consumpton of rado dsspaton, whle the second presents the energy consumpton of amplfyng rado. The dstance between plan node and cluster head s commonly less than d o meters, so we ad equaton (1) to compute the transmsson energy consumpton for the plan node. For transmsson to the base staton, we ad equaton (2). To receve a l bt data, the rado expends: E () l = l E (3) elec Fgure 1. Even dstrbuton of cluster heads III. ADCS DETAILS ADCS s a LEACH-le clusterng scheme, where the networ s parttoned nto [7] clusters wth one cluster head n each cluster. We mplement a -phase to the node densty nearby before the frst round begns. It helps the networ form clusters wth good dstrbuton at low cost. A. Probe Node Densty In ths phase, each node sends a hello message to all nodes wthn and the TTL of message s set to one. In ths way each node can compute the number of ts neghbors (nodes wthn ). An even dstrbuton of cluster can be sought even n non-unform scenes by tang the local node densty nto consderaton. And there s less chance for the margnal nodes dspersed n sparse areas to be elected as the cluster head because of ther low node denstes nearby. We just node densty for one tme n the lfetme of the networ based on: 1) we consder the networ s dead when a node runs out of ts energy as mentoned above; 2) the node densty doesn t vary untl a node s dead; 3) the phase 523

3 consumes a lot of energy. The sum of ths nd of message s O (N). We wll gve the computaton of n secton IV. B. Cluster Head Electon In ths phase, cluster heads are elected. Nodes become CANDIDATE nodes wth a probablty T and then broadcast the COMPETE_MSG to advertse ther wlls. Each CANDIDATE node receves messages and checs whether there s a CANDIDATE node wth more resdual energy wthn ts range of compete. compete s converse to the number of ts neghbor nodes. Once the CANDIDATE node fnds a more powerful CANDIDATE node n ts competton range, t wll gve up the competton mmedately. Otherwse, t wll be elected as CLUSTE HEAD n the end. The specfc process s gven n Algorthm 1. It s not hard to prove that there s only one cluster head n the range of each compete. And t s more probable for canddate nodes n the dense area to compete successfully than other areas, because ther competton ranges are smaller. n N; 1: state PLAIN ; 2: α andom(0,1); 3: f ( α< T) then 4: state CANDIDATE ; 5: broadcastcompete _ MSGd (, E); 6: whle( Tmerhasnot exp red ) do 7: msg recevecompete _ MSG ; 8: nj msggeti. d(); 10: f ( d < ) then // f theresanothercanddatentsrange 11: f (( Eresdual < nj. Eresdual ) or ( Eresdual == nj. Eresdual andn < nj )) 12: state PLAIN ; 13: brea; 14: endf 15: endf 16: end whle 17: end f 18: f ( state == CANDIDATE ) 19: state HEAD; 20: endf j compete compete Algorthm 1 C. Cluster Formaton In ths phase, cluster heads broadcast HEAD_MSG across the networ whle plan nodes receve HEAD_MSG and decde whch cluster to jon n. Most of the exted strategy for plan nodes s to choose the nearest cluster head. However, n nonunform networ, ths may result n load unbalance. Some cluster heads may consume too much energy because of ther large szes. In our scheme, a plan node chooses ts cluster accordng to both dstance and load balance. As shown n Fg.2, f a plan node sn t n the compete range of any cluster head, t chooses the nearest cluster to jon n for reducng ts transmsson cost; otherwse, t chooses the smallest cluster to jon n for balancng loads among clusters. Fgure 2. Choose the cluster to jon IV. ADCS ANALYSIS In ths secton, we analyze the control load of ADCS frstly; and then gve the computaton of and compete ; fnally we prove that ADCS can form the cluster wth good dstrbuton. A. Control load analyss Lemma 1.The control load across the networ s O (N), where N s the number of nodes. Proof: In -phase, all nodes send a hello message to ther neghbors wthn the range of. Clearly, the sum of ths nd of message s N whle t just runs for one tme. In the electon phase, only NT CANDIDATE nodes broadcast and receve COMPETE_MSG to compete for cluster head. In the formaton phase, the sum of HEAD_MSG and JOIN_MSG s N. So the control load s O (N) for each round. B. Parameter Computaton Accordng to [5], the networ s parttoned nto clusters evenly. Ideally, the clusters should cover the entre networ (even dstrbuton). Namely, A 2 π E ( ) = A E( ) = (4) A Let = E( ) = (5), so the expectaton of π π 524

4 the number of neghbors ENum ( neghbors ) s equal to So we regard that a node wth more than N N. neghbors wthn the range of s located at dense area, otherwse at sparse area. Addtonally, we set compete ENum ( neghbors) Numneghbors = 1 + (6) ENum ( ) neghbors As mentoned n secton III, there s only one cluster head n the range of each and t s easy to prove that these compete clusters cover the entre networ wth hgh probablty. 2 ( E( π compete ) = A) = 1 C. Dstrbuton Alanyss Lemma2. There are more cluster heads n dense area than sparse area. Proof: We assume that there s CANDIDATE node n n the dense area and n j n the sparse area. Clearly node densty of dense area s larger, so Numneghbors > Numneghbors j. Accordng to equaton (6), compete < compete j. That means t s more probable for n to become cluster head than n n the end. So there are more cluster heads n dense area than sparse area. experments n two dfferent scenes. Set = 5, as T vares from 0.05 to 0.5, Fg.3 shows the relaton between T and the networ lfetme. In both scenes, T has the mal value. For unform networ, the mal value s 0.1 and for non-unform networ, the mal value s 0.2. That ndcates that nonunform networs need more canddate nodes than unform networs to guarantee the even dstrbuton of cluster heads. When T s set to less than the mal value, there aren t enough canddate nodes to guarantee the even dstrbuton of cluster heads. On the other hand, when T s set to larger than the mal value, there are too many canddate nodes whch consume a lot of energy n van. In general, unform networ survves for a longer tme than non-unform networ. When T s set to larger than the mal value, the lfetme of unform networ drops more qucly than non-unform networ because ts nodes dstrbute more evenly at the begnnng. TABLE II. Parameter SIMULATION PAAMETES Value Area Locaton of BS (50,200) N 100 Intal Energy 0.5 J(0.5J 1 J) E elec 50 nj / bt ε 2 fs 10 nj / bt / m ε mp nj / bt / m d crossover 87m E da 5 nj / bt / sgnal Pacet Sze 2000bts 4 V. SIMULATION In ths secton, we evaluate the performance of ADCS protocol mplemented wth MATLAB. In the smulaton, we ad the same parameters and MAC protocols wth LEACH. In order to prove that our scheme can be adaptve to both unform and non-unform topologes, we run smulaton n two dfferent scenes: a unform dstrbuton scene and a nonunform dstrbuton. The non-unform topology s generated usng a Gaussan functon. ( σ = 1, wth maxmum on the center of the area) In the smulaton, we measure the lfetme n terms of round when the frst node des and regard the lfetme as the most mportant metrc to chec the performance of protocol. The values of the prmary parameters used are lsted n Table II. A. Experment of T T s the threshold used to control the number of canddate nodes. Frstly, we examne the effect of T on the networ lfetme n both scenes. We have done two ndependent Fgure 3. The effect of T on lfetme B. Number of Clusters In [7], the author proposed and proved that there s the mal value ( ) for the number of clusters. In our scenes, we assume ths rule s stll n effect n non-unform scenes and set =5. However, both ADCS and LEACH are dstrbuted algorthms, snce centralzed algorthm wastes a lot of energy. We can t ensure that such a huge and dstrbuted system always 525

5 elects the expected cluster heads. We just wsh t wll acheve ths wth hgh probablty. We count the numbers of cluster heads per round, and the results are gven n Fg.4. As shown n Fgures, ADCS mostly elects 4~6 cluster heads whle LEACH elects 3~7 cluster heads. ADCS performs better than LEACH n both scenes because t elects cluster heads wth hgher probablty, whch s one of the reasons why ADCS survves a longer tme. In [2], LEACH s on the assumpton that all nodes n the networ are homogeneous. However, the nodes may dffer n the amounts of energy n factual applcatons. The node wth more energy should become cluster head for more tmes. Otherwse, the node wth less energy wll de qucly. ADCS can avod ths by confnng the node wth less energy to be cluster head. In order to prove that, we have done another two experments. The only alteraton s that the ntal energy s set to a random number between 0.5 J and 1 J. Fg.6 shows that ADCS wors much better than LEACH. ADCS prolongs the lfetme of the networ over 50% and 100% aganst LEACH n two scenes respectvely. Fgure 4. The number of clusters n ADCS and LEACH: (a) unform scene, (b)non-unform scene C. Lfetme of the Networ As dscussed n secton II, the round when the frst node des out s regarded as the lfetme of the networ. We have done two experments where each node taes the same or dfferent levels of energy. Fg.5 shows how two protocols perform f each node taes the same amount of energy. In the unform scene, (T=0.1, Intal Energy= 0.5J, =5), ADCS prolongs the lfetme of the networ over 40% aganst LEACH. In the non-unform scene, (T=0.2, Intal Energy= 0.5J, =5), LEACH wors very worse whle ADCS s adaptve to non-unform topology and prolongs the lfetme of the networ over 75% aganst LEACH. The other nodes de qucly followng the frst dead node n ADCS. That ndcates that all nodes n ADCS consume ther energy wth smlar speed. Fgure 6. Lfetme comparson of ADCS and LEACH when all nodes are assembled dfferent energy: (a) unform scene, (b)non-unform scene VI. CONCLUSION AND FUTUE WOK In ths paper, we present a novel dstrbuted, energy effcent and load balanced clusterng scheme appled for perodcal data gatherng. ADCS can produce a unform dstrbuton of clusters even n non-unform networs. The dfferences among cluster szes are small and the load s balanced. All sensors consume ther energy averagely accordng to ther ntal energy. Smulaton results show that ADCS outperforms LEACH sgnfcantly wth prolongng the networ lfetme over by 40% n unform scenes and 75% n non-unform scenes. We have made the assumpton that all communcatons are based on sngle-hop. In large scale sensor networs, not all sensors can communcate to each other and sngle-hop may not be energy effcent n ntra-cluster transmsson. An effectve soluton s mult-hop communcaton. We wll desgn a novel adaptve and energy effcent routng protocol based on multhop n the future wor. Fgure 5. Lfetme comparson of ADCS and LEACH when all nodes are assembled the same energy: (a) unform scene, (b)non-unform scene ACKNOWLEDGMENT Ths paper s partally supported by the Natonal Natural Scence Foundaton of Chna under Grant No ; the Natonal Grand Fundamental esearch 973 Program of Chna under Grant No.2006CB303006; the Open Foundaton of Anhu Provnce Key Laboratory of Software n Computng and Communcaton ; Anhu Provnce-MOST Co-Key Laboratory of Hgh Performance Computng and Its Applcaton. 526

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