基于模拟并行蚁群算法的无线传感器网络异常节点自适应定位方法  

Adaptive Localization Method for Abnormal Nodes in Wireless Sensor Networks Based on Simulated Parallel Ant Colony Algorithm

作　　者：李庐[1] Li Lu(Anhui University of Finance and Economics)

机构地区：[1]安徽财经大学

出　　处：《哈尔滨师范大学自然科学学报》2024年第1期55-60,共6页Natural Science Journal of Harbin Normal University

摘　　要：由于无线传感器网络自身节点数量庞大,导致在异常节点自适应定位过程中,定位到的异常节点个数较少的问题.针对上述问题,提出基于模拟并行蚁群算法的无线传感器网络异常节点自适应定位方法.从网络中提取出异常节点的原始数据,并对异常节点数据属性进行详细的解析,根据解析后的数据属性,利用蚁群算法的优化搜索特性,结合并行计算的思想,建立并行蚁群算法模型,模拟蚂蚁在寻找食物过程中的协作和寻优行为,运行并行蚁群算法,获取异常节点的估计坐标值,实现节点的自适应定位.实验结果表明,该方法在面对复杂网络环境和多种异常类型时能够定位到多个异常节点,增强了定位方法的鲁棒性和自适应性.Due to the large number of nodes in wireless sensor networks,there is a problem of locating fewer abnormal nodes in the adaptive localization process of abnormal nodes.A wireless sensor network anomaly node adaptive localization method based on simulated parallel ant colony algorithm is proposed to address the above issues.Extracted the raw data of abnormal nodes from the network,and the data attributes of abnormal nodes are analyzed in detail.Based on the parsed data attributes,utilized the optimization search characteristics of ant colony algorithm,combined with the idea of parallel computing,a parallel ant colony algorithm model is established,the cooperative is simulated and the behavior of ants in the process of searching for food is optimizated,the parallel ant colony algorithm is run,and the estimated coordinate values of abnormal nodes is obtained,the adaptive localization of nodes is implemented.The experimental results show that this method can locate multiple abnormal nodes in complex network environments and multiple types of anomalies,enhancing the robustness and adaptability of the localization method.

关 键 词：模拟并行蚁群算法 无线传感器 网络异常节点 节点自适应定位方法 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]