Spatiotemporal charging scheduling in wireless rechargeable sensor networks Abstract   Wireless sensor networks have a wide range of applications. However, the batteryconstrained energy limits the scope of network applications and timeliness. In this paper, we study how to schedule charging and allocate charging time simultaneously for a wireless sensor network for the purpose of prolonging network lifetime and improve charging efficiency. To this end, a mixed integer optimization model for simultaneous charging scheduling and charging time allocation is established through the maximization of the charging efficiency. Then, an offline algorithm is developed to solve the problem. Further, an online charging node insertion algorithm is developed for realtime service. The simulation results show that the proposed algorithm can achieve near 100% charging success rate under periodic and hybrid services, which is significantly superior to the results obtained by the compared algorithms.

Optimal Charger Placement for Wireless Power Transfer Abstract   As a promising technology to achieve perpetual operation of batterypowered wireless sensor devices, wireless power transfer has attracted much attention recently. In wireless power transfer, the charger enables the energy to be wirelessly transmitted to the rechargeable sensor devices that are hungry for energy. Previous works mainly focus on maximizing the charging utility or minimizing the charging delay. This paper concerns two more practical issues of placing wireless chargers, the first one aims at minimizing the deployment cost of chargers while satisfying the overall requirement for charging levels, and the second one aims at maximizing the total charging levels subject to a deployment cost budget constraint. We investigate the above two optimal charger placement problems under two typical scenarios in which omni chargers and directional chargers are used, respectively. To resolve these two problems under the two charging models, we first prove their NPhardness and then propose four approximation algorithms with proven performance guarantees. Finally, we conduct extensive simulation experiments to validate our designs, and the experimental results demonstrate that the proposed algorithms significantly outperform the baselines.