Optimized Distance Range Free Localization Algorithm for WSN
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Optimized Distance Range Free Localization Algorithm for WSN Sumit Kumar1,2 · Shrawan Kumar3 · Neera Batra1
© Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract DV-Hop and its various improvements overexpose hop size and hop count for localization. Whereas hop size is always erroneous and hops path is not a straight line, which leads to a faulty location estimation. The proposed model optimized distance range free (ODR) localization algorithm limits the use of hop size and hop count to approximate nearly a straight line distance between a known and an unknown node without additional hardware and without increasing extra communication. The refrain use of hop size and hop count improves localization accuracy of ODR and makes it robust against those network variables which affect the hop size accuracy adversely. In fact, ODR modifies the last two steps of DV-Hop. DV-Hop finds hop size in its second step. Here ODR rectifies this hop size and then a centroid is obtained from the minimum distant anchor nodes for an unknown node. Now a minimum possible distance known as base distance is estimated with a routing table assistance. In the last step, DV-Hop uses least square regression to localize, while ODR exploits linear optimization to comprehend the base distance for localization. The paper establishes ODR analytically and rugged with ranging error of the omnidirectional antenna coverage pattern experimentally. Keywords DV-Hop algorithm · Localization · WSN · Euclidean distance · Hop size · Hop size correction · Localization error
* Sumit Kumar [email protected] Shrawan Kumar [email protected] Neera Batra [email protected] 1
Department of Computer Science and Engineering, Maharishi Markandeshwar Engineering College, Maharishi Markandeshwar University, Mullana, Ambala, Haryana 133207, India
2
Department of Computer Science and Engineering, Technology Education and Research Institute, Kurukshetra, Haryana 136119, India
3
Department of Computer Science, Indira Gandhi National Tribal University, Amarkantak, India
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1 Introduction Today we have a vast range of different applications of WSN. Among these applications [2] some of the applications are—environment monitoring, health monitoring, industrial monitoring, hazard detection, deep-sea underwater life monitoring, wildlife movement, defense operations, and many more of similar nature. WSN in itself faces a lot of challenges, like-self-management [25], limited computational power [2, 25], decentralized processing, scalability [25], physical topology, transmitter and receiver’s communication range, ad-hoc routing [2], etc., to prove its worth. Considering these challenges of WSN, in the above different applications, if we receive data without information about the location of its source then the received data carries no sense or simply it is worthless. This makes the localization (location estimation of the data source) a significant area of WSN and its allied field IoT (Internet
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