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作 者:罗清波 朱星星 LUO Qingbo;ZHU Xingxing(Department of Information Engineering,Suzhou Technician College,Suzhou Jiangsu 215000,China;School of Mechatronic and Automation,Huaqiao University,Xiamen Fujian 361021,China)
机构地区:[1]江苏省苏州技师学院信息工程系,江苏苏州215000 [2]华侨大学机电及自动化学院,福建厦门361021
出 处:《传感技术学报》2025年第3期533-542,共10页Chinese Journal of Sensors and Actuators
基 金:江苏省职教学会项目(XHYBLX2023150);江苏省人才学会项目(2023SRC026)。
摘 要:为提高无线传感器网络高覆盖率,降低无线传感器网络能耗,提出了一种基于对立竞争群优化器的WSN部署策略。首先,采用二进制传感器模型监测感知区域,为每个传感器节点生成一条避障路径,建立传感器覆盖模型和移动模型。其次,采用基于对立的学习改进竞争群优化器算法,将虚拟力算法与边界机制相结合开发了一种混合边界机制,并运用维诺图的分区能力分解每个传感器的感知区域,从而感知半径分配的网络信息。最后,选择了几种典型应用场景进行了仿真分析,并与其他四种方法进行了对比,验证所提方法的有效性。实验结果表明,所提方法能够在实现最大化覆盖区域的同时最小化网络能量消耗,且对于所测试场景,所提方法的移动距离与覆盖收敛速度均优于其他对比算法。To improve the coverage rate and reduce the energy consumption of wireless sensor network,a WSN deployment strategy based on opposition-based competitive swarm optimizer is proposed.Firstly,a binary sensor model is used to monitor the sensing area,an obstacle avoidance path is generated for each sensor node,and the sensor coverage model and mobility model are established.Second-ly,the competitive swarm optimizer algorithm based on opposition-based learning is used to improve the competitive swarm optimizer al-gorithm,and a hybrid boundary mechanism is developed by combining the virtual force algorithm with the boundary mechanism.The Voronoi diagrams partitioning ability is used to performed decomposition of the sensing area of each sensor,so as to sense the network information of radius allocation.Finally,several typical application scenarios are selected for simulation analysis and compared with other four methods to verify the effectiveness of the proposed method.The experimental results show that the proposed method can maxi-mize the coverage area and minimize the network energy consumption.Moreover,for the tested scenarios,the proposed method has shor-ter moving distance and faster coverage convergence than other comparison algorithms.
关 键 词:无线传感器网络 节点部署 网络覆盖率 竞争群优化器 虚拟力算法
分 类 号:V244.12[航空宇航科学与技术—飞行器设计]
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