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Energy Sharing for Multiple Sensor Nodes With Finite Buffers

Padakandla, Sindhu and Prabuchandran, KJ and Bhatnagar, Shalabh (2015) Energy Sharing for Multiple Sensor Nodes With Finite Buffers. In: IEEE TRANSACTIONS ON COMMUNICATIONS, 63 (5). pp. 1811-1823.

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Official URL: http://dx.doi.org/ 10.1109/TCOMM.2015.2415777

Abstract

We consider the problem of finding optimal energy sharing policies that maximize the network performance of a system comprising of multiple sensor nodes and a single energy harvesting (EH) source. Sensor nodes periodically sense the random field and generate data, which is stored in the corresponding data queues. The EH source harnesses energy from ambient energy sources and the generated energy is stored in an energy buffer. Sensor nodes receive energy for data transmission from the EH source. The EH source has to efficiently share the stored energy among the nodes to minimize the long-run average delay in data transmission. We formulate the problem of energy sharing between the nodes in the framework of average cost infinite-horizon Markov decision processes (MDPs). We develop efficient energy sharing algorithms, namely Q-learning algorithm with exploration mechanisms based on the epsilon-greedy method as well as upper confidence bound (UCB). We extend these algorithms by incorporating state and action space aggregation to tackle state-action space explosion in the MDP. We also develop a cross entropy based method that incorporates policy parameterization to find near optimal energy sharing policies. Through simulations, we show that our algorithms yield energy sharing policies that outperform the heuristic greedy method.

Item Type: Journal Article
Publication: IEEE TRANSACTIONS ON COMMUNICATIONS
Publisher: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Additional Information: Copy right for this article belongs to the IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
Keywords: Energy harvesting sensor nodes; energy sharing; Markov decision process; Q-learning; state aggregation
Department/Centre: Division of Electrical Sciences > Computer Science & Automation
Date Deposited: 22 Jun 2015 05:37
Last Modified: 22 Jun 2015 05:37
URI: http://eprints.iisc.ac.in/id/eprint/51729

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