Mobile Location in Wireless Sensor Networks Based on Multi Spot Measurements Model.

Chao Zheng, Wei Hu, Jiyan Huang, Pengfei Wang, Yufei Liu, Chenyu Yang

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

6 authors.

Chao ZhengSichuan Jiuzhou Aerocont Technologies Co., Ltd., Mianyang 621000, China.

Wei HuSchool of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.

Jiyan HuangSchool of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.ORCID 0000-0002-2204-4552

Pengfei WangSchool of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.

Yufei LiuSchool of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.

Chenyu YangSchool of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.

Funding

Fundamental Research Funds for the Central Universities ZYGX2020ZB030

6 · The paper itself

Abstract

The localization of sensors in wireless sensor networks has recently gained considerable attention. The existing location methods are based on a one-spot measurement model. It is difficult to further improve the positioning accuracy of existing location methods based on single-spot measurements. This paper proposes two location methods based on multi-spot measurements to reduce location errors. Because the multi-spot measurements model has more measurement equations than the single-spot measurements model, the proposed methods provide better performance than the traditional location methods using one-spot measurement in terms of the root mean square error (RMSE) and Cramer-Rao lower bound (CRLB). Both closed-form and iterative algorithms are proposed in this paper. The former performs suboptimally with less computational burden, whereas the latter has the highest positioning accuracy in attaining the CRLB. Moreover, a novel CRLB for the proposed multi-spot measurements model is also derived in this paper. A theoretical proof shows that the traditional CRLB in the case of single-spot measurements performs worse than the proposed CRLB in the case of multi-spot measurements. The simulation results show that the proposed methods have a lower RMSE than the traditional location methods.

Indexed as

AlgorithmsComputer SimulationCramer–Rao lower bound (CRLB)multi-spot measurements modelsensor localizationtime of arrival (TOA)

Identifiers

PMID36502260

DOI10.3390/s22239559

PMCPMC9737464

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LicenceCC BY

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