A range‐free localization algorithm for IoT networks

Internet of things (IoT) is a ubiquitous network that helps the system to monitor and organize the world through processing, collecting, and analyzing the data produced by IoT objects. The accurate localization of IoT objects is indispensable for most IoT applications, especially healthcare monitori...

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Bibliographic Details
Published in:International journal of intelligent systems Vol. 37; no. 12; pp. 10336 - 10379
Main Authors: Barshandeh, Saeid, Masdari, Mohammad, Dhiman, Gaurav, Hosseini, Vahid, Singh, Krishna K.
Format: Journal Article
Language:English
Published: New York John Wiley & Sons, Inc 01.12.2022
Subjects:
Algorithms
DV‐Hop
Heuristic methods
hybrid optimization
Intelligent systems
Internet of Things
Localization
localization error
Oceans
Optimization
ISSN:0884-8173, 1098-111X
Online Access:Get full text
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Summary:Internet of things (IoT) is a ubiquitous network that helps the system to monitor and organize the world through processing, collecting, and analyzing the data produced by IoT objects. The accurate localization of IoT objects is indispensable for most IoT applications, especially healthcare monitoring. Utilizing GPS as the positioning system is not cost‐efficient and does not apply to some environments (e.g., deep forests, oceans, inside the buildings, etc.). Hereupon, copious position estimation approaches are developed in the literature. Among range‐free approaches, distance vector‐Hop (DV‐Hop) is the widely used algorithm due to its straightforward applicability and can estimate the position of unknown objects that are far‐off the anchors. Due to its low accuracy, various techniques were proposed to increase the accuracy of basic DV‐Hop. In the most recent approach, meta‐heuristic algorithms were used, the results of which were promising. In the present paper, Tunicate Swarm Algorithm and Harris hawk optimization were initially hybridized. Afterthought, the resulting hybrid algorithm was enhanced by appending a new phase. Then, the proposed hybrid algorithm was intermingled with the DV‐Hop algorithm. In the first set of experiments, the proposed hybrid algorithm was evaluated on 50 test functions using average, SD, box plot, and p‐value criteria. In the second part, the proposed localization algorithm's efficiency was investigated in twenty‐eight different manners using node localization error, average localization error, and localization error variance metrics. The effectiveness of the contributions was evident from the experimental results.
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ISSN:0884-8173
1098-111X
DOI:10.1002/int.22524