Abstract
Reduction of unnecessary energy consumption is becoming a major concern in green wireless sensor networks. Sleep scheduling is one of the efficient strategies to achieve energy saving. In this paper, we propose a novel scheme for the sleep scheduling, which is based on Decentralized Partially Observable Markov Decision Process (Dec-POMDP). A sleep scheduling algorithm with online planning (Dec-POP-SSA) with respect to Dec-POMDP is also presented. In Dec-POMDP, due to the hardness of obtaining the state spaces and the reward with mold-free environment, quasi-Monte Carlo is applied to collect state spaces such that the real-time acquisition of beliefs state is achieved, and the reward is evaluated in tracking reward and coverage connectivity intensity. Instead of producing the entire plan, Dec-POP-SSA need only find actions for the current step. We also give the theoretical analysis on the upper bound for Dec-POP-SSA. The numerical experiments show that Dec-POP-SSA may receive the highest reward.
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Wei Q, Jianfeng G, Changqiao X, Hongke Z (2012) G-MER: green mobility estimation-based routing protocol in wireless sensor network. China Commun 9(5):10–21
Morreale P, Qi F, Croft P (2011) A green wireless sensor network for environmental monitoring and risk identification. Int J Sens Netw 10(1):73–82
Bianzino AP, Chaudet C, Rossi D, Rougier JL (2012) A survey of green networking research. IEEE Commun Surv Tutor 14(1):3–20
Chen Y, Zhang S, Xu S, Li GY (2011) Fundamental trade-offs on green wireless networks. IEEE Commun Mag 49(6):30–37
Jabbar S, Minhas AA, Paul A, Rho S (2014) Multilayer cluster designing algorithm for lifetime improvement of wireless sensor networks. J Supercomput 70(1):104–132
Kim YH, Han YH, Jeong YS, Park DS (2013) Lifetime maximization considering target coverage and connectivity in directional image/video sensor networks. J Supercomput 65(1):365–382
Wang WN, Mi ZK, Wang B (2012) Green networking based energy efficient communications. China Commun 9(2):22–30
Zeng Y, Sreenan CJ, Xiong N, Yang LT, Park JH (2010) Connectivity and coverage maintenance in wireless sensor networks. J Supercomput 52(1):23–46
Zhao Y, Wu J, Li F, Lu S (2012) On maximizing the lifetime of wireless sensor networks using virtual backbone scheduling. IEEE Trans Parallel Distrib Syst 23(8):1528–1535
Nan G, Shi G, Mao Z, Li M (2012) CDSWS: coverage-guaranteed distributed sleep/wake scheduling for wireless sensor networks. EURASIP J Wirel Commun Netw 1:1–14
Liu C, Wu K, Xiao Y, Sun B (2006) Random coverage with guaranteed connectivity: joint scheduling for wireless sensor networks. IEEE Trans Parallel Distrib Syst 17(6):562–575
Wang L, Wei RZ, Tian ZH (2012) Cluster based node scheduling method for wireless sensor networks. Sci China Inf Sci 55(4):755–764
Ding Y, Wang C, Xiao L (2009) An adaptive partitioning scheme for sleep scheduling and topology control in wireless sensor networks. IEEE Trans Parallel Distrib Syst 20(9):1352–1365
Yu J, Zhang Q, Yu D, Chen C, Wang G (2014) Domatic partition in homogeneous wireless sensor networks. J Netw Comput Appl 37:186–193
Censor-Hillel K, Ghaffari M, Kuhn F (2014) A new perspective on vertex connectivity. Proc SODA 2014:546–561
Zhang DQ, Li D (2014) High-density randomly deployed nodes sleep scheduling algorithm in wireless sensor networks. Adv Mater Res 846:446–451
Fuemmeler JA, Veeravalli VV (2008) Smart sleeping policies for energy-efficient tracking in sensor networks. In: Networked sensing information and control. Springer, USA, pp 267–287
Fuemmeler JA, Veeravalli VV (2010) Energy efficient multi-object tracking in sensor networks. IEEE Trans Signal Process 58(7):3742–3750
Fuemmeler JA, Atia GK, Veeravalli VV (2011) Sleep control for tracking in sensor networks. IEEE Trans Signal Process 59(9):4354–4366
Atia GK, Veeravalli VV, Fuemmeler JA (2011) Sensor scheduling for energy-efficient target tracking in sensor networks. IEEE Trans Signal Process 59(10):4923–4937
Jiang B, Ravindran B, Cho H (2013) Probability-based prediction and sleep scheduling for energy-efficient target tracking in sensor networks. IEEE Trans Mob Comput 12(4):735–747
Mihaylov M, Le Borgne YA, Tuyls K, Now A (2013) Reinforcement learning for self-organizing wake-up scheduling in wireless sensor networks. In: Agents and artificial intelligence. Springer, Berlin, pp 382–396
Wu F, Zilberstein S, Chen X (2011) Online planning for multi-agent systems with bounded communication. Artif Intell 175(2):487–511
Bhandarkar SM, Arabnia HR (1995) The REFINE multiprocessor: theoretical properties and algorithms. Parallel Comput 21(11):1783–1806
Arabnia HR, Smith JW (1993) A reconfigurable interconnection network for imaging operations and its implementation using a multi-stage switching box. In: Proceedings of the 7th annual international high performance computing conference. The 1993 High Performance Computing: New Horizons Supercomputing Symposium, Calgary, Alberta, Canada, June 349–357
Arabnia HR, Oliver MA (1989) A transputer network for fast operations on digitised images. Int J Eurogr Assoc (Comput Graphics Forum) 8(1):3–12
Arabnia HR (1990) A parallel algorithm for the arbitrary rotation of digitized images using process-and-data-decomposition approach. J Parallel Distrib Comput 10(2):188–193
Shi Z, Beard C, Mitchell K (2009) Analytical models for understanding misbehavior and MAC friendliness in CSMA networks. Perform Eval 66(9):469–487
Shi Z, Beard C, Mitchell K (2013) Analytical models for understanding space, backoff, and flow correlation in CSMA wireless networks. Wirel Netw 19(3):393–409
Shi Z, Beard C, Mitchell K (2011) Competition, cooperation, and optimization in multi-hop CSMA networks. In: Proceedings of the 8th ACM symposium on performance evaluation of wireless ad hoc, sensor, and ubiquitous networks. ACM, pp 117–120
Shi Z, Beard CC, Mitchell K (2007) Misbehavior and MAC friendliness in CSMA networks. In: Proceedings of WCNC, pp 355–360
Shi Z, Beard C, Mitchell K (2008) Tunable traffic control for multihop CSMA networks. In: Proceedings of military communications conference, 2008, MILCOM 2008. IEEE, pp 1–7
Shi Z, Gu R (2013) Efficient implementation of particle Swarm optimization algorithm. Int J Soft Comput Math Control 2(4):1–13
Ragi S, Chong EKP (2013) Decentralized control of unmanned aerial vehicles for multitarget tracking. Int Conf IEEE Unmanned Aircr Syst 2013:260–268
Demchik V (2011) Pseudo-random number generators for Monte Carlo simulations on ATI graphics processing units. Comput Phys Commun 182(3):692–705
Halton JH (1960) On the efficiency of certain quasi-random sequences of points in evaluating multi-dimensional integrals. Numerische Mathematik 2(1):84–90
Gmez-Prez D, Hofer R, Niederreiter H (2013) A general discrepancy bound for hybrid sequences involving Halton sequences. Unif Distrib Theory 8(1):31–45
Levy BC (2008) Principles of signal detection and parameter estimation. Springer, New York, USA
Hoeffding W (1963) Probability inequalities for sums of bounded random variables. J Am Stat Assoc 58(301):13–30
Acknowledgments
The authors wish to thank National Natural Science Foundation of China (Grant No: 61072080, No. U1405255). Fujian Normal University Innovative Research Team (No. IRTL1207). The Natural Science Foundation of Fujian Province (No: 2013J01222, J01223, 2013J01221). The Education Department of Fujian Province science and technology project (JA13215).
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Zhang, J., Xu, L., Zhou, S. et al. A novel sleep scheduling scheme in green wireless sensor networks. J Supercomput 71, 1067–1094 (2015). https://doi.org/10.1007/s11227-014-1354-z
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DOI: https://doi.org/10.1007/s11227-014-1354-z