Load Balancing Energy Efficient Clustering for Wireless Sensor Networks

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1 JOURNAL OF APPLIED SCIENCES Electronics and Information Engineering Vol. 28 No. 6 Nov DOI: /j.issn , LEACH HEED. TP (2010) Load Balancing Energy Efficient Clustering for Wireless Sensor Networks LEI Lei 1,2, XUE Xiao-long 1, ZHOU Jin-hua 1, XU Zong-ze 1 1. College of Information Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing , China 2. The 28th Research Institute of China Electronic Technology Corporation, Nanjing , China Abstract: In this paper, the load balancing problem in clustering wireless sensor networks (WSN) is investigated, and an energy efficient clustering algorithm for achieving load balancing in WSN presented. proposed algorithm computes optimal network cluster numbers based on the network model, and adjusts the range of the cluster by coordinating the communication range of the node. The nodes select cluster headers in a distributed and iterative manner to form an appropriate architecture of the network topology. In simulations under different network conditions, performance of the algorithm is compared with two typical clustering algorithms of WSN, LEACH and HEED. The results show that the proposed algorithm performs better than the other two algorithms, and can effectively balance the load of nodes. Thus it reduces energy consumption of the nodes and prolongs the network s lifespan. Keywords: wireless sensor network, clustering algorithm, load balancing, power saving The (wireless sensor networks, WSN) [1-2] (No ) (No ) (No ) (No.BK ) (No.BY ) (No.NS ) leilei@nuaa.edu.cn

2 MAC(medium access control) [3-4] [5-6] [7] MAC... [8] LEACH(low energy adaptive clustering hierarchy) LEACH.. LEACH. [9] SEP(stable election protocol) LEACH. SEP LEACH. [10] HEED(hybrid energy-efficient distributed clustering). HEED. [11] HEED DACA(dynamic advanced clustering algorithm) [12] HEED RHEED. k k 1.. [13] EEUC(energy-efficient unequal clustering). EEUC EEUC. [13]. LEACH HEED (load balancing energy efficient clustering algorithm LBEEC).. LEACH HEED LBEEC.

3 Matlab LEACH HEED LEACH k. N k N/k. k P i (t) = N k(r mod N k ), C i(t) = 1 (1) 0, C i (t) = 0 r C i (t) C i (t) 0 i N/k 0 C i (t) 1 i N/k (1) N k(r mod N k ) N/k LEACH. Matlab LEACH m 100 m 100 LEACH. 1 LEACH. 1 LEACH Figure 1 Network topology generated by LEACH HEED. r HEED Matlab HEED. 300 m 300 m 100 2(a) HEED 2(b) 2 HEED. 2 LBEEC (LBEEC). 2.1 LBEEC

4 E R (k) { ET (k) = E elec k + E amp k E R (k) = E elec k (2) E elec / E amp (free space) (tworay) [16] / r E amp E amp = { εfs r 2, r r 0 ε tr r 4, r > r 0 (3) ε fs ε tr. / r 0 / r 0 4. r 0 εfs r 0 = (4) ε tr 2 HEED Figure 2 Simulation results of HEED 1) 2) 1 [14] 3) 4) [15]. / k E T (k) k E fusion (k) = E DA k (5) E DA. 2.2 LBEEC N M k 1 N/k 1.

5 6 555 N/k E CH = le elec ( N k 1 ) + l N k E DA+ le elec + lε tr d 4 tobs (6) l d tobs 1 (N/k) E non CH = le elec + lε fs d 2 toch (7) d toch k D M 2 /k. (X, Y ) ρ(x, y) d 2 toch E[d 2 toch] = (x 2 + y 2 )ρ(x, y)dxdy (8) D ρ(x, y) = πk M 2 (9) (9) (8) E[d 2 toch] = 2π 0 M πk 0 πk M 2 r3 drdθ = M 2 2k (10) (6) (7) ( N ) E cluster = E CH + k 1 E non-ch (11) (11) E total (k) = ke cluster = ke CH + (N k)e non-ch = ( l (2N k)e elec + NE DA + (12) kε tr dtobs 4 + ε fs(n k) M 2) 2k E total (k) k (12) k 0 k k opt ε fs N k opt = M 2(ε tr d 4 tobs E (13) elec) (12) (13) d 4 tobs 2.1 d 4 tobs E[d 4 tobs] = M/2 M/2 M/2 M/2 (x 2 + y 2 ) 2( 1 M 2 ) dxdy = 0.039M 6 (14) ρ r [17] ρπr 2 1. k opt N/k opt N nei N nei = αn k opt (15) α 3 α. N/M 2 (15) R ini N M 2 πr2 ini = N nei + 1 (16) (15) (16) R ini αn + k opt R ini = M k opt Nπ (17)

6 HEED. LBEEC N nei R ini N opt R ini i R fin (i) N nei R ini, N opt R fin (i) = 1.5R ini, N nei R ini 1.5R ini N opt N nei N opt R ini > 1.5R ini (18) (18) LBEEC R fin (i) LBEEC R fin (i) R ini LBEEC 1) (13) (17) R ini. (neighbor discovery packet, NDP) NDP. R ini N opt (18) R fin (i) R fin (i) NDP. LBEEC 1.. LBEEC HEED H prob ( H prob = max C prob E ) res, P min E max (19) E res E max. 1 E max. C prob P min C prob P min C prob P min. 1 (initialize algorithm). : : 1 compute R ini according to the initial conditions of the network; 2 obtain the neighbor node set (S nbr ) through broadcasting, S nbr = {node i: i lies in the transmission range of R ini }; 3 compute the actural transmission range R fin (i); 4 update the neighbor set through secondary broadcasting, S nbr = {node i: i lies in the transmission range of R fin (i)}; 5 is_final_ch = FALSE; 6 H prob = max{c prob E res /E max, P min }. 2) H prob H prob 1. H prob. H prob 1 H prob 1 (0, 1). R fin (i) 2 (iterative algorithm)

7 6 557 ; (is_ final_ch). 1 H pre = H prob ; 2 the cluster header set (S CH ) = {node i: i is a cluster head within the range of R fin (i)}; 3 while (H pre < 1 ) 4 if (S CH Φ) 5 cluster_header = mini_r(s CH ); 6 if (cluster_header = = NodeID) 7 if (H prob = = 1) 8 broadcast_ch_msg (NodeID, final_ch, R fin (i)); 9 is_final_ch = TRUE; 10 else 11 broadcast_ch_msg (NodeID, tentative_ch, R fin (i)); 12 end if 13 end if 14 else if (H prob = = 1) 15 broadcast_ch_msg (NodeID, final_ch, R fin (i)); 16 is_final_ch = TRUE; 17 else if Random(0,1) H prob 18 broadcast_ch_msg (NodeID, tentative_ch, R fin (i)); 19 end if 20 H pre = H prob ; 21 H prob = min(h prob 2, 1); 22 end while 3) 3 (finalization algorithm) (is_ final_ch) ; 1 if (is_final_ch = FALSE) 2 if (S CH Φ) 3 cluster_header = mini_r(s CH ); 4 join_cluster(cluster_header _ID, NodeID); 5 else 6 broadcast_ch_msg (NodeID, final_ch, R fin (i)); 7 end if 8 end if LBEEC 1) R ini 2) 3). (19) LBEEC H prob P min H prob N ite N ite = lb (20) P min CPU LBEEC 3 Matlab LBEEC LEACH HEED [18] ARM 3.1 α 2.2 (15) α 3 α. LBEEC (13) 400 m 400 m

8 m 300 m. 3 α α [1.39, 1.43] (21) 1 Table 1 Value of the parameters in the simulations 6 J 100 bit/packet 25 bit 200 packet C prob 0.05 P min E elec J/bit E amp(fs) (J/bit)/m 2 E amp(tr) (J/bit)/m 4 E DA J/bit 30% 80%. HEED LBEEC LEACH. HEED LBEEC LEACH 4 3 Figure 4 Comparison of the simulation results when the nodes uniformly distributed α LBEEC Figure 3 Influence of the value of parameter α on the performance of LBEEC 3.2 HEED LBEEC LEACH. 400 m 400 m LBEEC HEED LEACH m 400 m 400, LBEEC 10% HEED LEACH 25% 50%. 3.4 LBEEC η η = 1 k k (N(i) N ave ) 2 (22) i=1 k N(i) i N ave

9 (22) η. LEACH HEED 5 3 Figure 5 Comparison of the simulation results when the nodes non-uniformly distributed HEED LBEEC LEACH 400 m 400 m (a) (b) 3. 6(a) HEED LBEEC LBEEC HEED LEACH HEED LBEEC 6(b) LEACH HEED LBEEC LBEEC Figure 6 Comparison of the load balancing index of the three algorithms (LBEEC).. LBEEC LEACH HEED. : [1] Akyildiz I, Su W, Cayirci E. A survey on sensor networks [J]. IEEE Communications Magazine, 2002, 40(8): [2] Akyildiz I, Su W, Cayirci E. Wireless sensor networks: a survey [J]. Computer Networks, 2002, 38(4):

10 [3] Ameer A, Mohamed Y. A survey on clustering algorithms for wireless sensor networks [J]. Compter Communications, 2007, 30(14): [4] Tian Q J, Coyle E. Optimal distributed detection in clustered wireless sensor networks [J]. IEEE Transactions on Signal Processing, 2007, 55(7): [5] Miao Peng, Yang Xiao, Wang P P. Error analysis and kernel density approach of scheduling sleeping nodes in cluster-based wireless sensor networks [J]. IEEE Transactions on Vehicular Technology, 2009, 58(9): [6] Fapojuwo A, Cano T. Energy consumption and message delay analysis of QoS enhanced base station controlled dynamic clustering protocol for wireless sensor networks [J]. IEEE Transactions on Wireless Communications, 2009, 8(10): [7] Liu Y, Xiong N, Zhao Y. Multi-layer clustering routing algorithm for wireless vehicular sensor networks [J]. IET Communications, 2010, 4(7): [8] Heinzelman W, Chandrakasan A. Energyefficient communication protocol for wireless micro sensor networks [C]// Proceedings of the 33rd Annual Hawaii Int l Conference on System Sciences, Maui, 2000: [9] Georgios S, Ibrahim M, Azer B. SEP: a stable election protocol for clustered heterogeneous wireless sensor networks [C]// Second International Workshop on Sensor and Actor Network Protocols and Applications, [10] Younis O, Fahmy S. HEED: a hybrid, energyefficient, distributed clustering approach for ad hoc sensor networks [J]. IEEE Transactions on Mobile Computing, 2004, 3(4): [11] Hamidreza A, Maghsoud A. DACA: dynamic advanced clustering algorithm for sensor networks [C]// Proceedings of the IEEE International Conference on Electronics, Circuits, and Systems, Marrakech, Morocco, 2007: [12] Younis O, Fahmy S, Santi P. Robust communications for sensor networks in hostile environments [C]//2004 Twelfth IEEE International Workshop on Quality of Service. Montreal, Ont., Canada, 2004: [13] Li Chengfa, Mao Ye, Chen Guihai. An energyefficient unequal clustering mechanism for wireless sensor networks [C]//2nd IEEE International Conference on Mobile Ad-hoc and Sensor Systems, Washington, United states, 2005: [14] Manish B, Timothy G, Anantha P. Upper bounds on the lifetime of sensor networks [C]//2001 IEEE International Conference on Communications, Helsinki, Finland, 2001: [15] Sim S H, Carbonell M. Efficient decentralized data aggregation in wireless smart sensor networks[c]//proceedings of the SPIE, San Diego, CA, USA, 2010: [16] Zhou Y H, Nettles S M. Balancing the hidden and exposed node problems with power control in CSMA/CA-based wireless networks [C]//IEEE Wireless Communications and Networking Conference. New Orleans, LA, USA, 2005: [17] Christian B. On the minimum node degree and connectivity of a wireless multi-hop network[c]// Proceedings of the International Symposium on Mobile Ad Hoc Networking and Computing, Lausanne, Switzerland, 2002: [18] Heinzelman W B, Einzelman A. An applicationspecific protocol architecture for wireless micro sensor networks [J]. IEEE Transactions on Wireless Communications, 2002, 1(4): ( : )

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