计算机与现代化 ›› 2025, Vol. 0 ›› Issue (03): 66-70.doi: 10.3969/j.issn.1006-2475.2025.03.010

• 算法设计与分析 • 上一篇    下一篇

基于改进遗传算法的电力物联网安全通信协作干扰策略




  
  

  1. (1.中国科学院上海微系统与信息技术研究所,上海 200050; 2.国网江苏省电力有限公司电力科学研究院,江苏 南京 211103)
  • 出版日期:2025-03-28 发布日期:2025-03-28
  • 基金资助:
    基金项目:国家电网有限公司科技项目(5108-202218280A-2-201-XG)

Collaborative Interference Strategy for Secure Communication in Power IoT Based on Improved Genetic Algorithm

  1. (1. Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 200050, China;
    2. State Grid Jiangsu Electric Power Company Ltd. Research Institute, Nanjing 211103, China)
  • Online:2025-03-28 Published:2025-03-28

摘要: 随着无线通信在电力物联网中应用越来越广泛,通信安全问题越来越受到重视。在电力物联网中,安全传输速率和功耗是保障无线通信安全的关键性因素。为了保障电力物联网无线通信系统的安全,本文提出一种基于集群协作干扰策略的物理层安全通信方式。首先,在变电站场景下,窃听节点处于变电站外进行窃听,在站内汇聚节点间互相通信过程中,汇聚节点簇内的多个传感器节点利用接收到的正交码片进行协助干扰。其次,由于传感器节点采用电池供电,综合考虑安全通信速率和能耗,将安全传输能耗作为优化目标。最后,本文利用改进的遗传算法学习优化协作干扰策略。仿真结果表明,与传统的遗传算法和节点随机选择算法相比,本文算法能够加快收敛速度且降低安全传输能耗。

关键词: 集群, 协作干扰, 通信物理层安全, 遗传算法, 能量受限

Abstract:  As wireless communication is more and more widely used in power IoT, the security of wireless communication in power IoT is getting increasing attention. In power IoT, secure transmission rate and power consumption are the key factors to guarantee the security of wireless communication. In order to guarantee the security of wireless communication system in power IoT, a physical layer secure communication method based on cluster collaborative interference strategy is proposed. Firstly, in the substation scenario, the eavesdropping nodes are outside the substation for eavesdropping, and multiple sensor nodes within the cluster of aggregation nodes use the received orthogonal code pieces to assist in the interference during the mutual communication between the sink nodes in the station. Secondly, since the sensor nodes are battery-powered, the secure transmission energy consumption is taken as the optimization target by considering the secure communication rate and energy consumption comprehensively. Finally, an improved genetic algorithm is used to learn an optimized collaborative jamming strategy. The simulation results show that the proposed algorithm can accelerate the convergence speed and reduce the secure transmission energy consumption compared with the traditional genetic algorithm and node random selection algorithm.

Key words: cluster-based, cooperative jamming, communication physical layer security, genetic algorithm, energy-constrained

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