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.
Highlights
The overwhelming amount of networking components has boosted the use of Wireless Sensor Networks (WSN) in a wide range of disciplines, including medical fields, industrial control, home automation and environmental monitoring [1,2]
This paper proposes the Distance Matrix and Markov Chain (DM-MC) approach for performing sensor node localization and resource optimization in WSN to achieve efficient routing
The proposed DM-MC method is compared to existing methods given by Bianchi et al 2020 [29,30] and Mubaraka’s Crow Search Algorithm (CSA) model presented in Kaur et al 2019 [31]
Summary
The overwhelming amount of networking components has boosted the use of Wireless Sensor Networks (WSN) in a wide range of disciplines, including medical fields, industrial control, home automation and environmental monitoring [1,2]. The localization technology has gained its importance in WSN since the sensor node’s physical location is vital in WSN-based applications. The accuracy of localization algorithms is critical since it has a direct impact on the performance of networks. Localization can be accomplished in two ways: (i) distributed localization, whereby each node can locate its position by itself, and (ii) centralized localization, where the data from the node are sent to a centralized unit, which processes the data to extract location information
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