基于布谷鸟差分算法优化的DV-Hop改进算法

doi:10.16182/j.issn1004731x.joss.201704012

系统仿真学报 ›› 2017, Vol. 29 ›› Issue (4): 791-797.doi: 10.16182/j.issn1004731x.joss.201704012

基于布谷鸟差分算法优化的DV-Hop改进算法

刘登峰¹, 章力², 邴晓瑛¹, 邵玉倩¹, 徐保国¹

1.江南大学轻工过程先进控制教育部重点实验室,江苏无锡 214122;
2.展讯通信(上海)有限公司,上海 201203

收稿日期:2015-06-29 修回日期:2015-12-07 出版日期:2017-04-08 发布日期:2020-06-03
作者简介:刘登峰(1980-),女,河南南阳,博士,研究方向为发酵过程建模、控制,无线传感网络定位。
基金资助:
国家自然科学基金 (21276111, 21206053),江苏省自然科学基金(BK20160162), 江苏省博士后科研项目(1601009A), 江南大学自主科研计划青年基金(JUSRP11558), 中央高校基本科研业务费专项资金(JUSRP51510)

Localization Method Based on Modified Cuckoo Difference Optimization for Wireless Sensor Networks

Liu Dengfeng¹, Zhang Li², Bing Xiaoying¹, Shao Yuqian¹, Xu Baoguo¹

1. Key Laboratory of Industrial Advanced Process Control, Ministry of Education, Jiangnan University, Wuxi 214122, China;
2. Spreadtrum Communications Inc., Shanghai 201203, China

Received:2015-06-29 Revised:2015-12-07 Online:2017-04-08 Published:2020-06-03

摘要/Abstract

摘要： 经典DV-Hop定位算法中,三边测量法虽然避免了迭代运算,但对信标节点的依赖性较大;而极大似然估计法存在对误差进行累加的问题。针对传统定位算法存在的问题,提出了基于布谷鸟(CS)差分(DE)优化的DV-Hop改进算法,将定位问题转化为群体优化问题,利用CS和DE算法进行双种群并行搜索,动态调整CS中宿主发现入侵者的概率参数,随机缩放DE算法中变异因子,增强全局搜索能力,规避了距离误差在定位过程中的累加,有效提高了定位精度。

关键词: DV-Hop定位, 布谷鸟优化, 差分优化, WSN

Abstract: To solve the sensitive and accumulative ranging error issue in the trilateration method and the maximum likelihood estimation method for the DV-Hop localization algorithm, an algorithm based on the Cuckoo difference optimization was proposed. The proposed algorithm essentially turned the positioning calculation problem into a group optimization problem. Using the Cuckoo algorithm and the differential evolution algorithm for parallel optimization with double populations, the proposed algorithm fused the advantages of the two kinds of intelligent optimization algorithms, which dynamically rectified the abandoned factor and the scaled variation factor randomly at the same time. The Cuckoo differential evolution algorithm's global search ability was enhanced to maintain the population diversity, which made the estimated coordinates closer to the real values. Without any increase in communication overhead, the positioning precision was improved effectively.

Key words: DV-Hop, cuckoo optimization, differential evolution, WSN

中图分类号:

TP393

刘登峰, 章力, 邴晓瑛, 邵玉倩, 徐保国. 基于布谷鸟差分算法优化的DV-Hop改进算法[J]. 系统仿真学报, 2017, 29(4): 791-797.

Liu Dengfeng, Zhang Li, Bing Xiaoying, Shao Yuqian, Xu Baoguo. Localization Method Based on Modified Cuckoo Difference Optimization for Wireless Sensor Networks[J]. Journal of System Simulation, 2017, 29(4): 791-797.

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基于布谷鸟差分算法优化的DV-Hop改进算法

Localization Method Based on Modified Cuckoo Difference Optimization for Wireless Sensor Networks

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