Adaptive Payoff Balance Among Mobile Wireless Chargers for Rechargeable Wireless Sensor Networks.

Wireless power transfer (WPT) based on mobile platforms such as unmanned aerial vehicle (UAV) has been recognized as a promised technique to prolong battery lifetime of resource-constrained wireless sensors in the Internet of Things (IoT) era. However, it is challenging to collaborate multiple mobile wireless chargers (MWCs) for coordinating heterogeneous energy requirements of massive rechargeable wireless sensors, where the efficient optimization of quantitative collaboration utility among MWCs is difficult. Hence, this paper investigates the adaptive payoff balance among MWCs for rechargeable wireless sensor networks (RWSN). Firstly, the collaborative wireless powered system based on charging sectors control among MWCs is proposed. Then, the charging payoff function and the corresponding optimization problem for maximizing the minimum payoff are designed, and it is decomposed into two layers by the hierarchical decompose method to be solved quickly. In the bottom layer, the payoff of each MWC with the given charging sector is maximized by the convex optimization theory. According to the payoff feedback of the bottom layer, intelligent charging sectors allocation is realized by the deep reinforcement learning. The simulation results show that our algorithm can ensure efficient energy allocation of any single MWC, and the overall utility of collaborative wireless powered system on this basis can be optimized by rationally allocating charging sectors, which significantly improves the sustainability of RWSN.