水下无线传感器网络节点覆盖及其自定位

Coverage and Self-Localization of Underwater Wireless Sensor Network Nodes

节点自定位技术是水下无线传感器网络应用的关键技术之一,较高的覆盖概率能够提高节点自定位的精度。节点定位精度受到很多因素的影响,本文通过采用感知概率模型模拟传感节点测量概率分布模型,再对覆盖概率较高的传感器节点进行定位误差迭代,最后采用遗传算法对定位误差进行优化。仿真结果表明,覆盖概率受感知半径和迭代次数影响,定位误差受信标节点密度影响,采用遗传算法的优化能够实现水下传感器节点的自定位精度。

Self-localization is one of the key technologies of applications to underwater wireless sensor network. Coverage probability can improve the accuracy of the node self-localization. Accuracy of nodes self- localization is influenced by many factors. Distribution model to sensor nodes measuring is simulated by the perceived probability model probability in this paper. Higher coverage probability of sensor nodes are iterated on error, genetic algorithm is used to optimize the positioning error finally. Simulation results show that, cover- age probability is impacted by perceived radius and the number of iterations, error to self-localization is impacted by beacon node density, genetic algorithms is used to optimize to achieve the self-positioning accuracy of the underwater sensor nodes.