Opportunistic Wireless Crowd Charging of IoT Devices from Smartphones

Current research that use wireless charging for the energy replenishment of nodes in a network mostly considers charging of sensors from special mobile charging vehicles (MCV) and focuses on optimal path planning of these MCVs. However, it may not be practical to use such vehicles due to its operational cost and other restrictions. To this end, in this paper, we consider to utilize smartphones owned by people and let the low cost Internet of Things (IoT) devices harvest energy from the smartphones that pass by. We study the wireless crowd charging of such IoT devices from these smartphones in an opportunistic manner, without changing their actual trajectories. As each smartphone user will limitedly support such a crowd charging process, the selection of IoT devices that will be charged from each smartphone has to be determined based on the trajectories of smartphone users. To address that, we model the problem using Mixed Integer Linear Programming (MILP) and decide the optimal charging relation between smartphones and IoT devices. Through simulations on both synthetic and real user traces, we show that MILP based solution offers a more successful crowd charging outcome with a better charging ratio than the greedy approach where the IoT devices can harvest maximum possible energy from all users encountered.