Energy Transfer and Data Collection from Batteryless Sensors in Low-altitude Wireless Networks

TL;DR

This paper presents a UAV-assisted framework for energy transfer and data collection from batteryless sensors in extreme environments, optimizing flight and power allocation using advanced reinforcement learning.

Contribution

It introduces a novel joint optimization approach for UAV trajectory and power control in batteryless sensor networks, employing an enhanced SAC algorithm for complex WPT scenarios.

Findings

Proposed method outperforms benchmarks in data collection efficiency.

Enhanced SAC algorithm improves learning stability and exploration.

Framework enables reliable sensing in inaccessible, high-temperature environments.

Abstract

The integration of wireless power transfer (WPT) with Internet of Things (IoT) offers promising solutions for sensing applications, but faces significant challenges when deployed in hard-to-access areas such as high-temperature environments. In such extreme conditions, traditional fixed WPT infrastructure cannot be safely installed, and batteries rapidly degrade due to hardware failures. In this paper, we propose an uncrewed aerial vehicle (UAV)-assisted data collection and WPT framework for batteryless sensor (BLS) networks deployed in these challenging environments. Specifically, we consider a practical scenario where a UAV first transfers energy to BLS nodes via WPT, enabling these nodes to subsequently transmit their collected data to the UAV through orthogonal frequency-division multiple access (OFDMA). Then, we formulate a multi-objective optimization problem that aims to maximize the fair data collection volume while minimizing the UAV energy consumption through joint optimization of transmit power allocation and flight trajectory planning. Due to the non-convex nature and dynamic characteristics of this problem, conventional optimization methods prove inadequate. To address these challenges, we propose an enhanced soft actor-critic algorithm with parameter-free attention, prioritized experience replay, and value-based reward centering (SAC-PPV), thereby improving the exploration efficiency and learning stability of the algorithm in complex WPT scenarios. Simulation results demonstrate that the proposed approach consistently outperforms benchmark algorithms under various network configurations.