ew 16(8): e4

Research Article

Transmission Policies for Energy Harvesting Sensors Based on Markov Chain Energy Supply

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  • @ARTICLE{10.4108/eai.28-9-2015.2261406,
        author={Wenxiang Zhu and Pingping Xu and Maozong Zheng and Guilu Wu and Honglei Wang},
        title={Transmission Policies for Energy Harvesting Sensors Based on Markov Chain Energy Supply},
        journal={EAI Endorsed Transactions on Energy Web},
        volume={3},
        number={8},
        publisher={ACM},
        journal_a={EW},
        year={2015},
        month={12},
        keywords={energy harvesting, wireless sensor networks, energy management, markov decision process},
        doi={10.4108/eai.28-9-2015.2261406}
    }
    
  • Wenxiang Zhu
    Pingping Xu
    Maozong Zheng
    Guilu Wu
    Honglei Wang
    Year: 2015
    Transmission Policies for Energy Harvesting Sensors Based on Markov Chain Energy Supply
    EW
    EAI
    DOI: 10.4108/eai.28-9-2015.2261406
Wenxiang Zhu1,*, Pingping Xu1, Maozong Zheng1, Guilu Wu1, Honglei Wang2
  • 1: Southeast University
  • 2: Xuzhou College of Industrial Technology
*Contact email: zwx@seu.edu.cn

Abstract

Due to the small energy harvesting rates and stochastic energy harvesting processes, energy management of energy harvesting senor is still crucial for body network. Transmission polices for energy harvesting sensors with Markov chain energy supply over time varying channels is formulated as an infinite discounted reward Markov Decision Problem under the assumption of geometric distribution of sensors’ lifetime. In this paper, we firstly propose a low-storage transmission policy based on probability of successful transmission for body network. Then we narrow the feasible region of parameters in our policies from the real domain to a discrete set with limited number, which makes the method of combing optimal equations and enumeration algorithm to obtain optimal parameters workable. Finally, numerical results show that our presented transmission policies can achieve a good approximated performance of optimal policies, which can be derived by policy iteration algorithm. Compared with the optimal policies, our presented policies has the advantage of low storage.