LAUSR

Wireless charging technology is considered as a promising solution to address the energy limitation problem for wireless sensor networks (WSNs). In scenarios where the deployed chargers are static, we generally require a number of chargers to work simultaneously. However, due to the radio interference among different wireless chargers, scheduling these chargers is generally necessary. This scheduling problem is challenging since each charger's charging utility cannot be calculated independently due to the nonlinear superposition charging effect caused by radio interference. In this paper, based on the concurrent charging model, we formulate the concurrent charging scheduling problem (CCSP) with the objective of quickly fully charging all the sensor nodes. After proving the NP-hardness of CCSP, we propose two efficient greedy algorithms, and give the approximation ratio of one of them. Both the two greedy algorithms’ performances are very close to that of a well-designed genetic algorithm (GA) which performs almost as well as a brute force algorithm at small network and charger scale. However, the running time of the two greedy algorithms is far lower than that of the GA. We conduct extensive simulations and specially implemented a testbed for wireless chargers. The results verified the good performance of the proposed algorithms.