Human-centric autonomous systems, based at the convergence of wireless sensor networks (WSN) and unmanned aerial vehicles (UAV), mark a transformative epoch in the amalgamation of technology. The widespread adoption of UAVs is attributable to their multifaceted applications and services. Within this realm, the utilization of UAVs to augment WSNs, defined as UAV-aided wireless sensor networks (UAWSN), has emerged as a captivating focus of research. The paramount rationale lies in UAWSN's potential to significantly enhance coverage and mitigate energy consumption when compared to traditional WSNs. However, the substantial mobility, variable altitude, and dynamic trajectory of UAVs may induce unforeseen changes in network topology, giving rise to connectivity and coverage challenges that can adversely impact the routing performance of networks. In the context of WSNs, the imperative to minimize energy consumption during data transmission by sensors is a central concern. A promising solution involves leveraging UAVs to collect data from sensors distributed across the network. This paper formulates an optimization problem aimed at maximizing the residual energy of sensors post data transmission, while accounting for the limitations imposed by the UAV's travel distance. The proposed solution involves the derivation of an optimized solution through the adaptation of a Voronoi diagram to explore a set of UAV hovering locations, with a preference for Voronoi vertices for data acquisition from adjacent sensors. Capitalizing on the benefits afforded by Voronoi diagrams, the authors delineate a UAV route by iteratively adjusting each UAV's hovering location based on the energy status of the sensors.
Babbar et al. (Sat,) studied this question.