Game-Theoretic Multi-Channel Multi-Access in Energy Harvesting Wireless Sensor Networks

Energy harvesting (EH) has been proposed as a promising technology to extend the lifetime of wireless sensor networks (WSNs) by continuously harvesting green/renewable energy. However, the intermittent and random EH process as well as the complexity in achieving global network information call for efficient energy management and distributed resource optimization. Considering the complex interactions among individual sensors, we use the game theory to perform distributed optimization for the general multi-channel multi-access problem in an EH-WSN, where strict delay constraints are imposed for the data transmission. Sensors' competition for channel access is formulated as a non-cooperative game, which is proved to be an ordinal potential game that has at least one Nash equilibrium (NE). Furthermore, all the NE of the game is proved to be Pareto optimal, and Jain's fairness index bound of the NE is theoretically derived. Finally, we design a fully distributed, online learning algorithm for the multi-channel multi-access in the EH-WSN, which is proved to converge to the NE of the formulated game. Simulation results validate the effectiveness of the proposed algorithm.