Joint AoI and SWIPT-Aware Scheduling via Multi- Agent Deep Reinforcement Learning

Abstract

This work investigates the joint optimization of Age of Information (AoI) and energy harvesting (EH) in wireless edge computing systems, where edge servers not only process IoT data but also act as wireless power suppliers via simultaneous wireless information and power transfer (SWIPT). Building upon the asynchronous model-free fractional multi-agent reinforcement learning framework and the Lyapunov drift-plus-penalty (DPP) concept, we design a fractional-based reward function for AoI and construct a virtual queue to enforce long-term energy stability under battery storage constraints. The overall reward is formulated as a weighted sum, capturing the trade-off between timeliness and energy sustainability, with update decisions, task offloading, and power splitting ratios as key control variables. Simulation results demonstrate that the developed multi-agent deep reinforcement learning approach achieves superior AoI–energy trade-offs compared to related baseline algorithms. These findings highlight the effectiveness of our framework in balancing information freshness and sustainable energy harvesting under resource-constrained edge environments.