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A Novel WSN-Oriented Locating Approach Based on Density

T. Zhang1,2, D. G. Zhang1,2,*, X. H. Liu1,2, C. L. Gong1,2

1 Tianjin Key Lab of Intelligent Computing & Novel software Technology, Tianjin University of Technology, Tianjin 300384, China;
2 Key Laboratory of Computer Vision and System (Tianjin University of Technology), Ministry of Education, Tianjin 300384, China.

* Corresponding Author: D. G. Zhang, email

Intelligent Automation & Soft Computing 2020, 26(3), 429-437. https://doi.org/10.32604/iasc.2020.013919

Abstract

It is known that the locating accuracy of the traditional Distance Vector-HOP (DV-HOP) approach in a Wireless Sensor Network (WSN) depends on the density of the anchor node. A novel WSN-oriented locating approach based on a node's density is proposed in this paper. The approach can compute the distance of the node based on the maximum likelihood estimation strategy. It can improve the accuracy ratio of the measuring distance among the nodes. The relative nodes of a WSN can find the average hop distances by estimating the distances from themselves to their circular nodes. In order to assess the performance of the approach, the belief degree of locating is computed based on the possibility theory by our suggested fusion mechanism. This is a feasible method to solve the dependence problem of the DV-HOP algorithm on the anchor node density. Results of many simulation tests of the application show that our proposed WSN-oriented locating approach based on node density can improve the accuracy ratio of the measuring distance and the correctness of locating.

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Cite This Article

T. Zhang, D. G. Zhang, X. H. Liu and C. L. Gong, "A novel wsn-oriented locating approach based on density," Intelligent Automation & Soft Computing, vol. 26, no.3, pp. 429–437, 2020. https://doi.org/10.32604/iasc.2020.013919

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cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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