On Energy-Efficient UAV Route Scheduling to Offload Health Data from Under-Served Rural Communities

Digital connectivity in distant, under-served rural communities is regarded as a major barrier to improving healthcare services. To alleviate this issue, in this paper, we consider Unmanned Aerial Vehicles (UAV)-based health data offloading from the wireless terminals (WTs) of the inhabitants of a rural area that include user-smartphones, Internet of Things (IoT) devices, vital monitors, and so forth. However, serving a large area using the UAVs emerges as a challenging problem due to their limited energy resources. Therefore, the need for an energy-efficient route planning of the UAVs to maximize the offloaded health data from the WTs is discussed in this paper. This is then formulated as an optimization problem, which is identified to be computationally hard. To solve this problem, we first develop a randomized energy-efficient path selection scheme, and then improve its performance with a greedy heuristic. Next, we design a genetic algorithm-based technique to provide a much improved solution with a fast execution time when compared with commercial optimization solver for large scenarios. The effectiveness of our proposal is clearly demonstrated through extensive computer-based simulations for various scenarios.