UAV-Assisted Joint Data Collection and Wireless Power Transfer for Batteryless Sensor Networks

2026-01-23

AI Summary

This work addresses the challenges of energy scarcity, environmental dynamics, and link instability in battery-free sensor networks deployed in remote areas by proposing a UAV-assisted joint wireless power transfer and data collection scheme. The approach co-optimizes the UAV’s trajectory and transmit power to enable sensors to efficiently utilize harvested energy for data backhaul. A key innovation lies in the design of a deep reinforcement learning algorithm that integrates prioritized experience replay with the Performer attention mechanism to effectively solve the resulting non-convex dynamic optimization problem. Experimental results demonstrate that the proposed method significantly outperforms existing benchmarks in terms of total data collected, fairness among sensors, and UAV energy consumption, while also enhancing system stability and convergence speed.

Technology Category

• Search and Optimization: Learning to Search
• Intelligent Robots: Learning & Optimization for ROB
• Constraint Satisfaction and Optimization: Constraint Learning and Acquisition

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

The development of wireless power transfer (WPT) and Internet of Things (IoT) offers significant potential but faces challenges such as limited energy supply, dynamic environmental changes, and unstable transmission links. This paper presents an unmanned aerial vehicle (UAV)-assisted data collection and WPT scheme to support batteryless sensor (BLS) networks in remote areas. In this system, BLSs harvest energy from the UAV and utilize the harvested energy to transmit the collected data back to the UAV. The goal is to maximize the collected data volume and fairness index while minimizing the UAV energy consumption. To achieve these objectives, an optimization problem is formulated to jointly optimize the transmit power and UAV trajectory. Due to the non-convexity and dynamic nature of the problem, a deep reinforcement learning (DRL)-based algorithm is proposed to solve the problem. Specifically, this algorithm integrates prioritized experience replay and the performer module to enhance system stability and accelerate convergence. Simulation results demonstrate that the proposed approach consistently outperforms benchmark schemes in terms of collected data volume, fairness, and UAV energy consumption.

Problem

• UAV
• Wireless Power Transfer
• Batteryless Sensor Networks
• Data Collection
• Energy Harvesting

Innovation

• UAV-assisted
• wireless power transfer
• batteryless sensor networks
• deep reinforcement learning
• trajectory optimization