Operation State Scheduling Towards Optimal Network Utility in RF-Powered Internet of Things

RF power transfer is becoming a reliable solution to energy supplement of Internet of Things (IoT) in recent years, thanks to the emerging off-the-shelf wireless charging and sensing platforms. However, as a core component of IoT, sensor nodes mounted with these platforms can not work and harvest energy simultaneously, due to the low-manufacture-cost requirement. This leads to a new design challenge of optimally scheduling sensor nodes' operation states: working or recharging, to achieve a desirable network utility. In our design, we first consider a single-hop special case of small-scale networks. We transform the operation state scheduling problem into a linear programming problem, and obtain an optimal analytical solution. Then a general case of large-scale multi-hop networks is investigated. The multi-hop operation state scheduling problem is proved to be NP-hard. We show that the spatiotemporal coupling caused by time-varying network topology makes the problem quite challenging. Based on Lyapunov optimization technique, we design a State Scheduling Algorithm (SSA) with a proved performance guarantee. Our algorithm decouples the primal problem by defining a dynamic energy threshold vector, which successfully schedules each sensor node to the desirable state according to its energy level. To verify our design, the SSA is implemented on a Powercast wireless charging and sensing testbed, achieving about 85 percent of the theoretical optimal with quite low time complexity. Furthermore, numerous simulation results demonstrate that the SSA outperforms the baseline algorithms and achieves good performance under different network settings.