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A novel sleep scheduling scheme in green wireless sensor networks

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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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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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Correspondence to Li Xu.

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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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