无人机辅助的铁路无线传感网智能数据收集与计算卸载方法

闫莉; 王俊凯; 方旭明; 蔺伟; 梁轶群

doi:10.11999/JEIT250340

无人机辅助的铁路无线传感网智能数据收集与计算卸载方法

doi: 10.11999/JEIT250340 cstr: 32379.14.JEIT250340

1.
西南交通大学信息编码与传输四川省重点实验室成都 610031
2.
中国铁道科学研究院北京 100081

基金项目: 国家自然科学基金(62101460, 62071393, U2268201)

详细信息

作者简介:
闫莉：女，博士，副教授，研究方向为铁路下一代移动通信

王俊凯：男，硕士生，研究方向为铁路下一代移动通信

方旭明：男，博士，教授，研究方向为无线与移动通信网络、交通通信与信息系统等

蔺伟：男，硕士，正高级工程师，研究方向为铁路通信

梁轶群：男，博士，正高级工程师，研究方向为铁路通信

通讯作者:
王俊凯　kai9032@my.swjtu.edu.cn

中图分类号: TN929.5
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- 被引次数: 0
出版历程
- 收稿日期: 2025-04-29
- 修回日期: 2025-08-18
- 网络出版日期: 2025-08-26

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

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)

摘要

摘要: 针对铁路复杂环境运维时无线传感网存在监测点网络信号差、传感器更换电池难及监测数据计算量大等挑战，该文提出一种多无人机辅助的铁路无线传感网智能数据收集与计算任务卸载方法。为保障铁路运营安全，方案考虑了铁路安全保护区对无人机飞行的限制，并对不同类型无线传感业务进行优先级划分，优先保障安全型传感业务传输性能，利用基站与列车的可用计算资源进行传感数据计算处理，设计了基于多智能体软演员-评论家(MASAC)深度强化学习算法的多无人机飞行轨迹与数据卸载决策联合优化，实现无人机能耗、无线传感网能耗以及数据信息年龄的加权和最小化。仿真结果表明，所提算法能够显著提升系统整体能耗和数据信息新鲜度性能。
- 铁路无线传感网 /
- 无人机 /
- 计算卸载 /
- 多智能体深度强化学习
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.
- Railway Wireless Sensor Networks (WSNs) /
- Unmanned Aerial Vehicle (UAV) /
- Computation offloading /
- Multi-Agent Deep Reinforcement Learning (MADRL)

HTML全文

图 1 多无人机辅助铁路无线传感器网络数据收集与计算卸载系统架构

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图 2 任务执行周期的时隙划分图

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图 3 本文所提MASAC算法架构

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图 4 本文MASAC算法与基准算法的性能对比

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图 5 不同算法的多无人机飞行轨迹对比

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图 6 不同传感器数量/AoI下的目标函数对比

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图 7 不同UAV数量/AoI权重下UAV平均能耗性能对比

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图 8 不同SN数量/AoI权重下SN平均能耗性能对比

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图 9 不同SN数量/AoI权重下的平均AoI性能对比

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表 1 仿真参数

参数名称	取值	参数名称	取值
带宽$ B $ (MHz)	1	旋翼桨叶的叶尖速度$ {U}_{\mathrm{t}\mathrm{i}\mathrm{p}} $ (m/s)	120
传感数据$ r $ (Mbits)	8	平均旋翼感应速度$ {v}_{0} $ (m/s)	4.03
计算密度 (CPU周期/比特)	1000	机身阻力比$ {d}_{0} $	0.6
噪声功率$ {\sigma }^{2} $ (dBm)	–110	旋翼坚固程度$ s $	0.05
无人机高度$ H $ (m)	50	空气密度$ \rho $ (kg/m³)	1.225
无人机最大速度$ V $	20	旋翼盘面积$ A $ (m²)	0.503
载波频率$ {f}_{c} $ (GHz)	2	有效电容系数 $ \kappa $	10^-28
时隙个数$ N $	30	经验回放缓存区D	300000
时隙长度$ {\delta }_{t} $ (s)	2	Actor网络学习率	0.00001
计算资源$ {f}_{a},{f}_{b},{f}_{c} $ (Gcycles/s)	2, 5, 3.5	Critic网络学习率	0.0001
SN覆盖范围$ {D}_{\mathrm{c}\mathrm{o}\mathrm{v}\mathrm{e}\mathrm{r}} $ (m)	30	SN能耗放缩因子$ \beta $	10
无人机能耗放缩因子$ \alpha $	0.01	折扣因子$ \gamma $	0.95
无人机最大连接数$ {N}^{max} $	2	迭代回合数	20,000
桨叶轮廓功率$ {P}_{0} $ (W)	79.86	批次大小	1024
感应功率$ {P}_{i} $ (W)	88.63	学习间隔	5

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表 2 不同类型SN的AoI性能对比

算法	安全关键型 SN的AoI	结构监测型 SN的AoI	环境监测型 SN的AoI
MASAC	7.3178	13.8370	14.0416
MASAC-Greedy	9.9738	15.6170	10.7290
MASAC-AOU	14.2142	13.2253	18.2617
MADDPG	14.2068	17.3939	19.2440

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