Abstract
In recent years, wireless sensor networks (WSNs) have transitioned from being objects of academic research interest to a technology that is frequently employed in real-life applications and rapidly being commercialized. Nowadays the topic of lifetime maximization of WSNs has attracted a lot of research interest owing to the rapid growth and usage of such networks. Research in this field has two main directions into it. The first school of researchers works on energy efficient routing that balances traffic load across the network according to energy-related metrics, while the second school of researchers takes up the idea of sleep scheduling that reduces energy cost due to idle listening by providing periodic sleep cycles for sensor nodes. As energy efficiency is a very critical consideration in the design of low-cost sensor networks that typically have fairly low node battery lifetime, this raises the need for providing periodic sleep cycles for the radios in the sensor nodes. Until now, these two fields have remained more or less disjoint leading to designs where to optimize one component, the other one must be pre-assumed. This in turn leads to many practical difficulties. To circumvent such difficulties in the performance of sensor networks, instead of separately solving the problem of energy efficient routing and sleep scheduling for lifetime maximization, we propose a single optimization framework, where both the components get optimized simultaneously to provide a better network lifetime for practical WSN. The framework amounts to solving a constrained non-convex optimization problem by using the evolutionary computing approach, based on one of the most powerful real-parameter optimizers of current interest, called Differential Evolution (DE). We propose a DE variant called modified semi-adaptive DE (MSeDE) to solve this optimization problem. The results have been compared with two state-of-the-art and widely used variants of DE, namely JADE and SaDE, along with one improved variant of the Particle Swarm Optimization (PSO) algorithm, called comprehensive learning PSO (CLPSO). Moreover, we have compared the performance of MSeDE with a well-known constrained optimizer, called \(\varepsilon \)-constrained DE with an archive and gradient-based mutation that ranked first in the competition on real-parameter constrained optimization, held under the 2010 IEEE Congress on Evolutionary Computation (CEC). Again to demonstrate the effectiveness of the optimization framework under consideration, we have included results obtained with a separate routing and sleep scheduling method in our comparative study. Our simulation results indicate that in all test cases, MSeDE can outperform the competitor algorithms by a good margin.
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References
Akyildiz IF, Su W, Sankarasubramaniam Y, Cayirci E (2002) Wireless sensor networks: a survey. Comput Netw 38(4):393–422
Ali MM, Kajee-Bagdadi Z (2009) A local exploration-based differential evolution algorithm for constrained global optimization. Appl Math Comput 208(1):31–48
Al-Karaki JN, Kamal AE (2004) Routing techniques in wireless sensor networks: a survey. IEEE Wirel Commun 11:6–28
Bojkovic Z, Bakmaz B (2008) A survey on wireless sensor networks deployment. WSEAS Trans Commun 7(12):1172–1181
Brest J, Maucecc V (2006) Control parameters in self-adaptive differential evolution. In: Filipic B, Silc J (eds) Bioinspired optimization methods and their applications. Jozef Stefan Institute, Ljubljana, pp 35–44
Bulusu N, Jha S (2005) Wireless sensor network: a systems perspective. Artech House, Norwood
Bulut E, Korpeoglu I (2007) DSSP: a dynamic sleep scheduling protocol for prolonging the lifetime of wireless sensor networks. In: Proceedings of the 21st international conference on advanced information networking and applications, workshop, pp 725–730, May 2007
Callaway EH Jr (2003) Wireless sensor networks: architectures and protocols. CRC Press, Boca Raton
Caponio A, Neri F, Tirronen V (2009) Super-fit control adaptation in memetic differential evolution frameworks. Soft computing-a fusion of foundations, methodologies and applications, Springer 13(8):811–831
Chachra S, Marefat M (2006) Distributed algorithm for sleep scheduling in wireless sensor networks. In: Proceedings of IEEE international conference on robotics automation, pp 3101–3107, May 2006
Chang J-H, Tassiulas L (2004) Maximum lifetime routing in wireless sensor networks. IEEE/ACM Trans Netw 12(4):609–619
Chang C-Y, Sheu J-P, Chen Y-C, Chang S-W (2009) An obstacle-free and power-efficient deployment algorithm for wireless sensor networks. IEEE Trans Syst Man Cybern Part A: Syst Hum 39(4):795–806
Chong C, Kumar S (2003) Sensor networks: evolution, opportunities, and challenges. Proc IEEE 91(8):1247–1256
Dagher JC, Marcellin MW, Neifield MA (2007) A theory for maximizing the lifetime of sensor networks. IEEE Trans Commun 55(2):323–332
Deb K (2000) An efficient constraint handling method for genetic algorithms. Comput Methods Appl Mech Eng 186(2/4):311–338
Dong Q (2005) Maximizing system lifetime in wireless sensor networks. In: Proceedings of the international conference information process sensor networks, pp 13–19, April 2005
Feoktistov V (2006) Differential evolution in search of solutions. Springer, New York
Gamperle R, Muller SD, Koumoutsakos A (2002) Parameter study for differential evolution. In: WSEAS NNA-FSFS-EC, Interlaken, 11–15 Feb 2002
Gamperle R, Muller SD, Koumoutsakos P (2002) A parameter study for differential evolution. In: Proceedings of the conference in neural networks and applications, fuzzy sets and fuzzy systems (FSFS) and evolutionary computation (EC), WSEAS, 2002, pp 293–298
Heinzelman WR, Chandrakasan A, Balakrishnan H (2000) Energy-efficient communication protocol for wireless microsensor networks. In: Proceedings of the 33rd Hawaii international conference on system sciences
Hou YT, Shi Y, Sherali HD (2008) Rate allocation and network lifetime problems for wireless sensor networks. IEEE/ACM Trans Netw 16(2):321–334
Hua C, Yum T-S (2008) Optimal routing and data aggregation for maximizing lifetime of wireless sensor networks. IEEE/ACM Trans Netw 16(4):892–903
Huang VL, Qin AK, Suganthan PN (2006) Self-adaptive differential evolution algorithm for constrained real-parameter optimization. In: IEEE congress on evolutionary computation (CEC’2006), IEEE, Vancouver, pp 324–331, July 2006
Huang F, Wang L, He Q (2007) An effective co-evolutionary differential evolution for constrained optimization. Appl Math Comput 186(1):340–356
Islam SM, Das S, Ghosh S, Roy S, Suganthan PN (2012) An adaptive differential evolution algorithm with novel mutation and crossover strategies for global numerical optimization. IEEE Trans Syst Man Cybern Part B Cybern 42(2):482–500
Kim S-J, Wang X, Madihian M (2007) Distributed joint routing and medium access control for lifetime maximization of wireless sensor networks. IEEE Trans Wirel Commun 6(7):2669–2677
Kukkonen S, Lampinen J (2006) Constrained real-parameter optimization with generalized differential evolution. In: IEEE congress on evolutionary computation (CEC’2006), IEEE, Vancouver, pp 911–918, July 2006
Lampinen J (2002) A constraint handling approach for the differential evolution algorithm. In: Proceedings of the congress on evolutionary computation 2002 (CEC’2002), vol 2, Piscataway, pp 1468–1473, May 2002
Lewis RM, Torczon V (1999) Pattern search algorithms for bound constrained minimization. SIAM J Optim 9(4):1082–1099
Li J, Alregib G (2009) Network lifetime maximization for estimation in multihop wireless networks. IEEE Trans Signal Process 57(7):2456–2466
Liang JJ, Runarsson TP, Mezura-Montes E, Clerc M, Suganthan PN, Coello Coello CA, Deb K (2006) Problem definitions and evaluation criteria for the CEC 2006. Special session on constrained real-parameter optimization, Technical report, Nanyang Technological University, Singapore
Liang JJ, Qin AK, Suganthan PN, Baskar S (2006) Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans Evolut Comput 10(3):281–295
Lindsey S, Raghavendra CS (2002) Power-efficient gathering in sensor information systems. IEEE Trans Parallel and Distrib Syst 13(9). doi:10.1109/AERO.2002.1035242
LinE-TA, Rabaey JM, Wolisz A (2004) Power-efficient rendezvous schemes for dense wireless sensor networks. In: Proceedings of 2004 IEEE international conference on communications, vol 7, pp 3769–3776, June 2004
Liu F, Tsui C-Y, Zhang YJ (2010) Joint routing and sleep scheduling for lifetime maximization of wireless sensor networks. IEEE Trans Wirel Commun 9(7):2258–2267
Madan R, Lall S (2006) Distributed algorithms for maximum lifetime routing in wireless sensor networks. IEEE Trans Wirel Commun 5(8):2185–2193
Madan R, Cui S, Lall S, Goldsmith A (2006) Cross-layer design for lifetime maximization in interference-limited wireless sensor networks. IEEE Trans Wirel Commun 5(11):3142–3152
Mallipeddi R, Suganthan PN (2010) Problem definitions and evaluation criteria for the CEC 2010 competition on constrained real-parameter optimization. Technical report, Nanyang Technological University, Singapore
Mezura-Montes E, Coello Coello CA, Tun-Morales EI (2004) Simple feasibility rules and differential evolution for constrained optimization. In: Proceedings of the 3rd Mexican international conference on artificial intelligence (MICAI’2004), lecture notes in artificial intelligence No. 2972, Springer Verlag, Heidelberg, pp 707–716, April 2004
Mezura-Montes E, Palomeque-Ortiz AG (2009) Parameter control in differential evolution for constrained optimization. In: IEEE congress on evolutionary computation (CEC ’09), Trondheim, vol 18–21, pp 1375–1382, May 2009
Mezura-Montes E, Velázquez-Reyes J, Coello Coello CA (2005) Promising infeasibility and multiple offspring incorporated to differential evolution for constrained optimization. ACM-SIGEVO proceedings of genetic and evolutionary computational conference (GECCO-2005), Washington, DC, pp 225–232, June 2005
Mezura-Montes E, Velázquez-Reyes J, Coello Coello CA (2005) Promising infeasibility and multiple offspring incorporated to differential evolution for constrained optimization. In: ACM-SIGEVO proceedings of genetic and evolutionary computation conference (GECCO- 2005), Washington, DC, pp 225–232, June 2005
Mezura-Montes E, Velázquez-Reyes J, Coello Coello CA (2006) A comparative study of differential evolution variants for global optimization. In: Genetic and evolutionary computation conference (GECCO 2006), pp 485–492
Mezura-Montes E, Velázquez-Reyes J, Coello Coello CA (2006) Modified differential evolution for constrained optimization. In: IEEE congress on evolutionary computation (CEC’2006), IEEE, Vancouver, pp 332–339, July 2006
Mininno E, Neri F, Cupertino F, Naso D (2011) Compact differential evolution. IEEE Trans Evolut Comput 15(1): 32–54
Munoz-Zavala AE, Herńandez-Aguirre A, Villa-Diharce ER, Botello-Rionda S (2006) PESO+ for constrained optimization. In: IEEE congress on evolutionary computation (CEC’2006), IEEE, Vancouver, pp 935–942, July 2006
Neri F, Iacca G, Mininno E (2011) Disturbed exploitation compact differential evolution for limited memory optimization problems. Inf Sci, Elsevier 181(12):2469–2487
Nojeong H, Varshney PK (2005) Energy-efficient deployment of intelligent mobile sensor networks. IEEE Trans Syst Man Cybern Part A: Syst Hum 35(1):78–92
Polastre J, Culler D (2004) Versatile low power media access for wireless sensor networks. In: Proceedings of the 2nd ACM conference embedded network sensor system, pp 95–107, Nov 2004
Pottie G, Kaiser W (2000) Wireless sensor networks. Commun ACM 43(5):51–58
Qin AK, Huang VL, Suganthan PN (2009) Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans Evolut Comput 13(2):398–417
Raghunathan V, Ganeriwal S (2006) Emerging techniques for long lived wireless sensor networks. IEEE Commun Mag 44(4):108–114
Rogers A, David E, Jennings NR (2005) Self-organized routing for wireless microsensor networks. IEEE Trans Syst Man Cybern Part A: Syst Hum 35(3):349–359
Singh HK, Ray T, Smith W (2010) Performance of infeasibility empowered memetic algorithm for CEC 2010 constrained optimization problems. In: 2010 IEEE congress on evolutionary computation (CEC), pp 1–8, 18–23 July 2010
Storn R, Price K (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359
Storn R (1999) System design by constraint adaptation and differential evolution. IEEE Trans Evolut Comput 3(1):22–34
Storn R, Price KV (1995) Differential evolution—a simple and efficient adaptive scheme for global optimization over continuous spaces. Technical report TR-95-012, ICSI. http://http.icsi.berkeley.edu/~storn/litera.html
Subramanian R, Fekri F (2006) Sleep scheduling and lifetime maximization in sensor networks–fundamental limits and optimal solutions. In: Proceedings of the international Information Processing in Sensor Networks, pp 218–225, April 2006
Takahama T, Sakai S (2006) Constrained optimization by the \(\varepsilon \) constrained differential evolution with gradient-based mutation and feasible elites. In: IEEE congress on evolutionary computation (CEC’2006), Vancouver, pp 308–315, July 2006
Takahama T, Sakai S (2010) Constrained optimization by the constrained differential evolution with an archive and gradient-based mutation. IEEE congress on evolutionary computation 2010, pp 1680–1688
Tasgetiren MF, Suganthan PN (2006) A multi-populated differential evolution algorithm for solving constrained optimization problem. In: IEEE congress on evolutionary computation (CEC’2006), Vancouver, pp 340–354, July 2006
Tseng L-Y, Chen C (2010) Multiple trajectory search for single objective constrained real-parameter optimization problems. In: 2010 IEEE congress on evolutionary computation (CEC), pp 1–7, 18–23 July 2010
Weber M, Neri F, Tirronen V (2011) A study on scale factor in distributed differential evolution. Inf Sci, Elsevier 181(12):2488–2511
Weber M, Tirronen V, Neri F (2010) Scale Factor inheritance mechanism in distributed differential evolution. Soft computing-a fusion of foundations, methodologies and applications, Springer 14(11):1187–1207
Wilcoxon F (1945) Individual comparisons by ranking methods. Biometrics 1(6):80–83
Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput I(I):67–82
Ye W, Heidemann J, Estrin D (2004) Medium access control with coordinated adaptive sleeping for wireless sensor networks. IEEE/ACM Trans Netw 12(6):493–506
Yick J, Mukherjee B, Ghosal D 2008) Wireless sensor network survey. Comput Netw 52(12):2292–2330
Yu Y, Wei G Energy aware routing algorithm based on layered chain in wireless sensor network. In: Wireless communications, networking and mobile computing, 2007. WiCom 2007. International conference on issue date: 21–25 Sept 2007, pp 2701–2704, Shanghai
Zaharie D (2009) Influence of crossover on the behavior of the differential evolution algorithm. Appl Soft Comput 9(3):1126–1138
Zhang J, Sanderson AC (2009) JADE: adaptive differential evolution with optional external archive. IEEE Trans Evolut Comput 13(5):945–958
Zhang M, Luo W, Wang X (2008) Differential evolution with dynamic stochastic selection for constrained optimization. Inf Sci 178(15):3043–3074
Zhao F, Guibas L (2004) Wireless sensor networks: an Information processing approach. Morgan Kaufmann, Boston
Zielinski K, Laur R (2006) Constrained single-objective optimization using differential evolution. In: IEEE congress on evolutionary computation (CEC’2006), IEEE, Vancouver, pp 927–934, July 2006
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Kundu, S., Das, S., Vasilakos, A.V. et al. A modified differential evolution-based combined routing and sleep scheduling scheme for lifetime maximization of wireless sensor networks. Soft Comput 19, 637–659 (2015). https://doi.org/10.1007/s00500-014-1286-9
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DOI: https://doi.org/10.1007/s00500-014-1286-9