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基于距离优化和移动锚节点辅助的无线传感器定位：一种新算法。

Wireless sensor localization based on distance optimization and assistance by mobile anchor nodes: a novel algorithm.

作者信息

Yang Hui

机构信息

School of Media Engineering, Lanzhou University of Arts and Science, Lanzhou, Gansu, China.

出版信息

PeerJ Comput Sci. 2024 Jul 31;10:e2179. doi: 10.7717/peerj-cs.2179. eCollection 2024.

DOI:10.7717/peerj-cs.2179
PMID:39145245
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11323089/
Abstract

Wireless sensor networks (WSNs) have wide applications in healthcare, environmental monitoring, and target tracking, relying on sensor nodes that are joined cooperatively. The research investigates localization algorithms for both target and node in WSNs to enhance accuracy. An innovative localization algorithm characterized as an asynchronous time-of-arrival (TOA) target is proposed by implementing a differential evolution algorithm. Unlike available approaches, the proposed algorithm employs the least squares criterion to represent signal-sending time as a function of the target position. The target node's coordinates are estimated by utilizing a differential evolution algorithm with reverse learning and adaptive redirection. A hybrid received signal strength (RSS)-TOA target localization algorithm is introduced, addressing the challenge of unknown transmission parameters. This algorithm simultaneously estimates transmitted power, path loss index, and target position by employing the RSS and TOA measurements. These proposed algorithms improve the accuracy and efficiency of wireless sensor localization, boosting performance in various WSN applications.

摘要

无线传感器网络（WSN）依靠协作连接的传感器节点，在医疗保健、环境监测和目标跟踪等领域有着广泛应用。该研究调查了无线传感器网络中目标和节点的定位算法，以提高准确性。通过实施差分进化算法，提出了一种创新的定位算法，其特征为异步到达时间（TOA）目标。与现有方法不同，该算法采用最小二乘准则将信号发送时间表示为目标位置的函数。利用具有反向学习和自适应重定向的差分进化算法估计目标节点的坐标。引入了一种混合接收信号强度（RSS）-TOA目标定位算法，以应对未知传输参数的挑战。该算法通过采用RSS和TOA测量值同时估计发射功率、路径损耗指数和目标位置。这些提出的算法提高了无线传感器定位的准确性和效率，提升了在各种无线传感器网络应用中的性能。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/85a3/11323089/099c0b302836/peerj-cs-10-2179-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/85a3/11323089/33305ab793da/peerj-cs-10-2179-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/85a3/11323089/68e1d8b4600c/peerj-cs-10-2179-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/85a3/11323089/aa7474ae8e2f/peerj-cs-10-2179-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/85a3/11323089/ef8076e9899a/peerj-cs-10-2179-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/85a3/11323089/bdba01c00ded/peerj-cs-10-2179-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/85a3/11323089/75eca1d61199/peerj-cs-10-2179-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/85a3/11323089/099c0b302836/peerj-cs-10-2179-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/85a3/11323089/33305ab793da/peerj-cs-10-2179-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/85a3/11323089/68e1d8b4600c/peerj-cs-10-2179-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/85a3/11323089/aa7474ae8e2f/peerj-cs-10-2179-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/85a3/11323089/ef8076e9899a/peerj-cs-10-2179-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/85a3/11323089/bdba01c00ded/peerj-cs-10-2179-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/85a3/11323089/75eca1d61199/peerj-cs-10-2179-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/85a3/11323089/099c0b302836/peerj-cs-10-2179-g007.jpg

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Sensors (Basel). 2023 Oct 12;23(20):8426. doi: 10.3390/s23208426.
2
A Novel Method for Asynchronous Time-of-Arrival-Based Source Localization: Algorithms, Performance and Complexity.一种基于异步到达时间的源定位新方法：算法、性能与复杂度
Sensors (Basel). 2020 Jun 19;20(12):3466. doi: 10.3390/s20123466.