UAV-Assisted Intelligent Data Collection and Computation Offloading for Railway Wireless Sensor Networks

YAN Li; WANG Junkai; FANG Xuming; LIN Wei; LIANG Yiqun

doi:10.11999/JEIT250340

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YAN Li, WANG Junkai, FANG Xuming, LIN Wei, LIANG Yiqun. UAV-Assisted Intelligent Data Collection and Computation Offloading for Railway Wireless Sensor Networks[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250340

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YAN Li, WANG Junkai, FANG Xuming, LIN Wei, LIANG Yiqun. UAV-Assisted Intelligent Data Collection and Computation Offloading for Railway Wireless Sensor Networks[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250340

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UAV-Assisted Intelligent Data Collection and Computation Offloading for Railway Wireless Sensor Networks

doi: 10.11999/JEIT250340 cstr: 32379.14.JEIT250340

1.
Sichuan Province Key Laboratory of Information Coding and Transmission, Southwest Jiaotong University, Chengdu·610031, China
2.
China Academy of Railway Sciences, Beijing·100081, China

Funds: The National Natural Science Foundation of China (62101460, 62071393, U2268201)

Received Date: 2025-04-29
Rev Recd Date: 2025-08-18

Available Online: 2025-08-26

Abstract

Abstract

Objective Ensuring the safety and stability of train operations is essential in the advancement of railway intelligence. The growing maturity of Wireless Sensor Network (WSN) technology offers an efficient, reliable, low-cost, and easily deployable approach to monitoring railway operating conditions. However, in complex and dynamic maintenance environments, WSNs encounter several challenges, including weak signal coverage at monitoring sites, limited accessibility for tasks such as sensor node battery replacement, and the generation of large volumes of monitoring data. To address these issues, this study proposes a multi-Unmanned Aerial Vehicle (UAV)-assisted method for data collection and computation offloading in railway WSNs. This approach enhances overall system energy efficiency and data freshness, offering a more effective and robust solution for railway safety monitoring. Methods An intelligent data collection and computation offloading system is constructed for multi-UAV-assisted railway WSNs. UAV flight constraints within railway safety protection zones are considered, and wireless sensing services are prioritized to ensure preferential transmission for safety-critical tasks. To balance energy consumption and data freshness, the system optimization objective is defined as the weighted sum of UAV energy consumption, WSN energy consumption, and the Age of Information (AoI). A joint optimization algorithm based on Multi-Agent Soft Actor-Critic (MASAC) is proposed, which balances exploration and exploitation through entropy regularization and adaptive temperature parameters. This approach enables efficient joint optimization of UAV trajectories and computation offloading strategies. Results and Discussions (1) Compared with the Multi-Agent Deep Deterministic Policy Gradient (MADDPG), MASAC-Greedy, and MASAC-AOU algorithms, the MASAC-based scheme converges more rapidly and demonstrates greater stability (Fig. 4), ultimately achieving the highest reward. In contrast, MADDPG exhibits slower learning and less stable performance. (2) The comparison of multi-UAV flight trajectories under different algorithms shows that the proposed MASAC algorithm enables effective collaboration among UAVs, with each responsible for monitoring distinct regions while strictly adhering to railway safety protection zone constraints (Fig. 5). (3) The MASAC algorithm yields the best objective function value across all evaluated algorithms (Fig. 6). (4) As the number of sensors and the AoI weight increase, UAV energy consumption rises for all algorithms; however, the MASAC algorithm consistently maintains the lowest energy consumption (Fig. 7). (5) In terms of sensor node energy consumption, MADDPG achieves the lowest value, but at the expense of information freshness (Fig. 8). (6) Regarding average AoI performance, the MASAC algorithm performs best across a range of sensor densities and AoI weight settings, with the greatest improvements observed under higher AoI weight conditions (Fig. 9). (7) The AoI performance comparison by sensor type (Table 2) confirms that the system effectively supports priority-based data collection services. Conclusions This study proposes a MASAC-based intelligent data collection and computation offloading scheme for railway WSNs supported by multiple UAVs, addressing critical challenges such as limited WSN battery life and the high real-time computational demands of complex railway environments. The proposed algorithm jointly optimizes UAV flight trajectories and computation offloading strategies by integrating considerations of UAV and WSN energy consumption, data freshness, sensing service priorities, and railway safety protection zone constraints. The optimization objective is to minimize the weighted sum of average UAV energy consumption, average WSN energy consumption, and average WSN AoI. Simulation results demonstrate that the proposed scheme outperforms baseline algorithms across multiple performance metrics. Specifically, it achieves faster convergence, efficient multi-UAV collaboration that avoids resource redundancy and spatial overlap, and superior results in UAV energy consumption, sensor node energy consumption, and average AoI.

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References(24)

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