AoI-and-energy tradeoff scheduling for multi-UAV-enabled data acquisition in Wireless Sensor Networks

Leveraging their flexibility and mobility, Unmanned Aerial Vehicles (UAVs) have the potential to significantly bolster wireless communications by providing high-quality services and ensuring ubiquitous connectivity across extensive Wireless Sensor Networks (WSNs). However, the limited onboard energy of UAVs poses a considerable challenge to sustaining prolonged operational tasks. To address this issue, we introduce Mobile Unmanned Vehicles (MUVs) as mobile charging stations, tasked with providing timely energy replenishment to UAVs and thereby enabling extended data acquisition missions. This paper delves into a multi-UAV and multi-MUV-assisted data acquisition framework, with the aim of exploring and optimizing the intricate trade-off between maximizing the Age of Information (AoI) and minimizing the energy consumption of UAVs. Given the non-convex nature of the resultant multi-objective optimization problem, we propose a comprehensive joint optimization strategy that integrates sensor scheduling, resource allocation, and UAV trajectory planning. Specifically, we decompose the original problem into three manageable subproblems and solve them using a successive convex approximation (SCA) technique within an efficient iterative algorithm. Extensive simulations validate the proposed framework, underscoring its effectiveness in striking a balance between data freshness and energy efficiency. Consequently, this approach enhances the sustainability and overall performance of large-scale WSNs.