基于高斯-牛顿法和灰太狼优化算法的节能节点定位算法

Q3 Computer Science
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引用次数: 0

摘要

节点定位过程是无线传感器网络的一个重要前提。节点定位算法分为基于距离和无距离两种。由于其成本效益,无距离算法比基于距离的算法更受欢迎。DV-Hop及其变体由于其直接性、可扩展性和分布式特性,通常是受欢迎的无距离算法,但它也存在精度差、功耗高等缺点。为了解决这些限制,本文引入了一种称为GWOGN-LA的算法。GWOGN-LA采用灰狼优化和高斯-牛顿方法提高了精度。该算法通过限制数据包的转发来限制能耗。仿真结果表明,该算法在精度和功耗方面都优于现有算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Energy Efficient Node Localization Algorithm based on Gauss-Newton Method and Grey Wolf Optimization Algorithm
Node localization process is a crucial prerequisite in the area of Wireless Sensor Networks (WSNs). The algorithms for node localization process can either range-based or range-free. Range-free algorithms are preferred over range-based ones due to their cost-effectiveness. DV-Hop along with its variants is normally well-liked range-free algorithm because of its straightforwardness, scalability and distributed nature, but it has some disadvantages such as poor accuracy and high-power utilization. To deal with these limitations, this paper introduces an algorithm, called GWOGN-LA. GWOGN-LA improves accuracy by applying Grey-Wolf Optimization and Gauss-Newton method. The proposed algorithm restricts the forwarding of packets in order to limit energy consumption. Simulation results show that given proposal outperforms other state-of-art algorithms in terms of accuracy and power consumption.
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来源期刊
International Journal of Fuzzy System Applications
International Journal of Fuzzy System Applications Computer Science-Computer Science (all)
CiteScore
2.40
自引率
0.00%
发文量
65
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