Sensors 2018, 18(2), 375; https://doi.org/10.3390/s18020375
Self-Learning Power Control in Wireless Sensor Networks
Michele Chincoli 1,*
and Antonio Liotta 2
and Antonio Liotta 21
Electrical Engineering Department, Eindhoven University of Technology, De Zaale (internal address: Groene Loper 19), 5612 AJ Eindhoven, The Netherlands
2
Data Science Centre, University of Derby, Lonsdale House, Quaker Way, Derby DE1 3HD, UK
*
Author to whom correspondence should be addressed.
Received: 14 December 2017 / Revised: 19 January 2018 / Accepted: 21 January 2018 / Published: 27 January 2018
(This article belongs to the Special Issue Smart Communication Protocols and Algorithms for Sensor Networks)
Abstract
Current trends in interconnecting myriad smart objects to monetize on Internet of Things applications have led to high-density communications in wireless sensor networks. This aggravates the already over-congested unlicensed radio bands, calling for new mechanisms to improve spectrum management and energy efficiency, such as transmission power control. Existing protocols are based on simplistic heuristics that often approach interference problems (i.e., packet loss, delay and energy waste) by increasing power, leading to detrimental results. The scope of this work is to investigate how machine learning may be used to bring wireless nodes to the lowest possible transmission power level and, in turn, to respect the quality requirements of the overall network. Lowering transmission power has benefits in terms of both energy consumption and interference. We propose a protocol of transmission power control through a reinforcement learning process that we have set in a multi-agent system. The agents are independent learners using the same exploration strategy and reward structure, leading to an overall cooperative network. The simulation results show that the system converges to an equilibrium where each node transmits at the minimum power while respecting high packet reception ratio constraints. Consequently, the system benefits from low energy consumption and packet delay. View Full-TextKeywords:
wireless sensor network; transmission power control; Q-learning; reinforcement learning; game theory; multi-agent; energy efficiency; quality of service
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Chincoli, M.; Liotta, A. Self-Learning Power Control in Wireless Sensor Networks. Sensors 2018, 18, 375.
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