Distance Matrix and Markov Chain Based Sensor Localization in WSN

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