AoI-Constrained Efficient 3-D Far-Field Wireless Charging and Data Collection Using Multiple AAVs

As Internet of Things (IoT) networks continue to expand rapidly, remote IoT devices (IoTDs) face significant challenges related to energy supply and data collection. On the one hand, limited battery capacities hinder the long-term, intervention-free operation of IoTDs. On the other hand, the Age of Information (AoI) is a crucial metric for evaluating data freshness, and delays in data collection reduce its value. A promising solution to these challenges is the use of autonomous aerial vehicles (AAVs) to facilitate green energy far-field wireless charging and data collection. Although extensive research has been conducted on scenarios where AAVs operate at fixed altitudes, in many real-world applications, most AAVs operate in 3-D space. In this work, we focus on a 3-D scenario where AAVs first wirelessly charge IoTDs and then collect data. We investigate the 3-D trajectories of multiple AAVs, considering varying altitudes and velocities, and introduce models for AAV-based wireless charging and data collection. We then formulate a multi-AAV wireless charging efficiency maximization problem, taking into account the IoTDs’ average AoI. Given the NP-hard nature of this problem, we propose the joint charging and data collection (JCDC) algorithm, which aims to ensure data timeliness while replenishing as many IoTDs as possible. Finally, extensive simulations are conducted to validate the performance of the proposed JCDC algorithm.