Latency-Aware Energy-Efficient Far-Field Wireless Charging for IoT

The worldwide rapid development of the Internet of Things (IoT) has led to an exaggerated scale and explosive increase of IoT devices (IoTDs). However, owing to embedded capacity limited batteries, most IoTDs have relatively short lifespans, which is a crucial factor of enervating their IoT applications. Far-field wireless charging is a means to extend IoTDs' lifespans. However, many far-field wireless charging scheduling strategies overlook the work load imbalances among different charging base stations, thus leading to low charging throughput (CTP) and prolonged waiting times for IoTDs. This adversely affects the IoTDs wireless charging network performance. Based on the M/G/1 queuing model, in this work, we first analyze the wireless charging workload of a charging base station and then aim to maximize CTP in the network by jointly considering the charging base stations' wireless charging workloads and the IoTDs' non-preemptive charging priorities. As the CTP maximization problem is NP-hard, we further propose the woRkload bAlancing and non-Preemptive TempOral pRiority (RAPTOR) algorithm to efficiently solve this problem. Finally, extensive simulations have validated the performance of RAPTOR in improving the CTP of the whole network.