Synergy-Payoff-Maximization-Based Rechargeable Adaptive Energy-Efficient Dual-Mode Data Gathering Using Renewable Energy Sources

Integrating wireless energy transfer (WET) and data gathering based on mobile platforms such as the unmanned aerial vehicle (UAV) has been recognized as a promising technique to prolong the battery lifetime of resource-constrained wireless sensors in the Internet of Things era. However, it is challenging to jointly schedule dynamic renewable energy sources and communications resources to coordinate heterogeneous performance requirements in rechargeable wireless sensor networks (RWSN). Hence, this paper researches rechargeable adaptive energy-efficient dual-mode data gathering using renewable energy sources. Firstly, considering the limited endurance of UAV and the uncertainty of renewable energy harvesting, a life-expectancy-balance-based adaptive energy-efficient dual-mode data gathering strategy is proposed for optimizing the communication energy efficiency of fixed data gathering (FDG) and mobile data gathering (MDG). Then, considering WET and MDG, the synergy payoff function of rechargeable MDG (RMDG) is designed, and the corresponding synergy payoff maximization problem is established. The problem is non-convex due to the coupling of MDG and WET, so it is decomposed into two layers to be quickly solved by the designed hierarchical decomposition framework. The simulation results prove that our algorithm can efficiently use renewable energy sources, whether in FDG or RMDG mode, thereby improving the sustainability of RWSN.