Toward Adaptive IoT Service Balance in Low-Altitude Economy: Multi-UAV-Aided Bi-Objective Wireless Data Collection and Wireless Energy Transfer

Abstract

The rapid development of the low-altitude economy (LAE) has significantly enhanced the service diversity of the Internet of Things (IoT) networks, necessitating efficient coordination among the unmanned aerial vehicles (UAVs). In this work, we utilize multiple UAVs to provide both wireless data collection (WDC) service and wireless energy transfer (WET) service, and divide the IoT devices into the I-devices that only need the WDC service and the E-devices that only need the WET service, respectively, from the multiple UAVs. Due to their conflicting service demands on the UAVs with limited resources, we formulate the bi-objective optimization problem (BOOP) to minimize the age of information (AoI) for the I-devices and the hungry-level of energy (HoE) for the E-devices at the same time, by jointly optimizing all the UAVs’ trajectories and WET decisions over time slots and their WDC decisions over sub-slots. To efficiently solve the complex BOOP, we innovatively transform it into a single-objective optimization problem (SOOP), in which the two conflicting objectives are scalarized via a self-adaptive objective weight. Unlike the conventional approach reliant on fixed and pre-defined objective weights, we optimize the objective weight jointly with other decision variables, enabling automatic adaptation to various network environments without human intervention. However, the proposed SOOP is NP-hard with a large number of decision variables. Accordingly, we propose a new Multi-Agent Adaptive and Hierarchical Deep Reinforcement Learning (MA$^{2}$HDRL) framework, which leverages a central controller (CC) to guide the local training of multiple individual UAV agents. In this framework, each UAV agent employs a two-tier hierarchical DRL model: tier-1 optimizes the trajectory and WET policies over the time slots, while tier-2 optimizes the WDC policy across the sub-slots. Meanwhile, the CC trains the global reward preference for all the UAV agents over the training episodes, to adaptively balance the WDC and WET service demands. Finally, extensive simulation results are conducted to demonstrate the outstanding performance of the proposed MA$^{2}$HDRL approach as compared to state-of-the-art benchmarks.