Open Access

ARTICLE

Distance Matrix and Markov Chain Based Sensor Localization in WSN

by Omaima Bamasaq¹, Daniyal Alghazzawi², Surbhi Bhatia³, Pankaj Dadheech^4,*, Farrukh Arslan⁵, Sudhakar Sengan⁶, Syed Hamid Hassan²

1 Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
2 Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
3 Department of Information Systems, College of Computer Science and Information Technology, King Faisal University, Saudi Arabia
4 Department of Computer Science and Engineering, Swami Keshvanand Institute of Technology, Management & Gramothan, Jaipur, Rajasthan, India
5 University of Engineering and Technology, Lahore, Pakistan
6 Department of Computer Science and Engineering, PSN College of Engineering and Technology, Tirunelveli, Tamilnadu, India

* Corresponding Author: Pankaj Dadheech. Email:

Computers, Materials & Continua 2022, 71(2), 4051-4068. https://doi.org/10.32604/cmc.2022.023634

Received 15 September 2021; Accepted 26 October 2021; Issue published 07 December 2021

Abstract

Applications based on Wireless Sensor Networks (WSN) have shown to be quite useful in monitoring a particular geographic area of interest. Relevant geometries of the surrounding environment are essential to establish a successful WSN topology. But it is literally hard because constructing a localization algorithm that tracks the exact location of Sensor Nodes (SN) in a WSN is always a challenging task. In this research paper, Distance Matrix and Markov Chain (DM-MC) model is presented as node localization technique in which Distance Matrix and Estimation Matrix are used to identify the position of the node. The method further employs a Markov Chain Model (MCM) for energy optimization and interference reduction. Experiments are performed against two well-known models, and the results demonstrate that the proposed algorithm improves performance by using less network resources when compared to the existing models. Transition probability is used in the Markova chain to sustain higher energy nodes. Finally, the proposed Distance Matrix and Markov Chain model decrease energy use by 31% and 25%, respectively, compared to the existing DV-Hop and CSA methods. The experimental results were performed against two proven models, Distance Vector-Hop Algorithm (DV-HopA) and Crow Search Algorithm (CSA), showing that the proposed DM-MC model outperforms both the existing models regarding localization accuracy and Energy Consumption (EC). These results add to the credibility of the proposed DC-MC model as a better model for employing node localization while establishing a WSN framework.

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