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传感器节点的位置信息在无线传感器网络的监测活动等应用中起着至关重要的作用,而实现节点定位较好的方法是采用定位算法进行估计,因此定位算法的研究是目前热门的研究课题之一.本文主要研究分析了分布式加权多维标度定位算法,针对其不能适应网络连通度变化、网络拓扑不规则且收敛速度较慢等不足,提出了一种改进算法.该算法采用的加权机制与邻居选择机制综合考虑1跳邻居数目、节点自身定位精度与测距误差,并且引入最速下降法优化其目标代价函数.实验结果表明:在相同的实验环境下改进算法与原算法相比,在定位精度提高的情况下对不规则、大规模网络有很好的适应性且有更好的鲁棒性.
The location information of sensor nodes plays an important role in the monitoring activities of wireless sensor networks and other applications. The better method to locate the nodes is to use the location algorithm to estimate, so the research of location algorithm is a hot research topic This paper mainly studies and analyzes the distributed weighted multidimensional scaling positioning algorithm, and proposes an improved algorithm for its inability to adapt to changes in network connectivity, network topology is irregular and the convergence speed is slow etc. The weighted mechanism And the neighbor selection mechanism, considering the number of neighbors in one hop, the localization accuracy of the node and the ranging error, and introducing the steepest descent method to optimize its objective cost function.The experimental results show that in the same experimental environment, compared with the original algorithm, When the positioning accuracy is improved, it has good adaptability to irregular and large-scale networks and has better robustness.