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In case of range\u2010free hop\u2010based localization approaches, position coordinates estimation is done through the common steps like the calculation of the minimum hop count, hop size, and then later on applying the multilateration or least square approach. There are many improvements that have been made in the form of various algorithms through some changes in the common steps for the reduction of localization error. It has been observed that the existing algorithms are still having the highest value of localization error. To overcome the deficiencies in the existing algorithms, a novel distance error correction metric\u2013based approach has been introduced. This paper presents a distance error correction\u2013based algorithm (DEC\u2010hop) for the hop\u2010based localization approach for a wireless sensor network. Distance\u2010based error correction factor that is introduced in this paper further reduces the localization error value to some extent. For measuring the effectiveness and the preciseness of the given approach, it is inspected with other improved algorithms based on DV\u2010hop. Simulation results have confirmed that the proposed error correction\u2013based algorithm outperforms the other variety of improved hop\u2010based algorithms. Results are analyzed in terms of various factors like the localization error, accuracy, and error variance against the parameters of interest taken as node amount, anchor ratio, and communication range of the node.<\/jats:p>","DOI":"10.1002\/ett.3547","type":"journal-article","created":{"date-parts":[[2018,11,28]],"date-time":"2018-11-28T18:35:50Z","timestamp":1543430150000},"update-policy":"http:\/\/dx.doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Design and analysis of distance error correction\u2013based localization algorithm for wireless sensor networks"],"prefix":"10.1002","volume":"29","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-8786-1115","authenticated-orcid":false,"given":"Deepak","family":"Prashar","sequence":"first","affiliation":[{"name":"Department of Computer Science IKG Punjab Technical Univesity Kapurthala India"}]},{"given":"Kiran","family":"Jyoti","sequence":"additional","affiliation":[{"name":"Department of Information Technology Guru Nanak Dev Engineering College Ludhiana India"}]},{"given":"Dilip","family":"Kumar","sequence":"additional","affiliation":[{"name":"Department of Electronics and Communication Engineering Sant Longowal Institute of Engineering and Technology Sangrur India"}]}],"member":"311","published-online":{"date-parts":[[2018,11,28]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"crossref","unstructured":"BibbDA YuanZ IskanderMF.Machine learning for source localization in urban environments. 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