Novel Strategy to Improve the Performance of Localization in WSN

TL;DR

This paper introduces a new energy model for wireless sensor networks that improves localization accuracy and reduces computation time.

Contribution

A novel discrete energy consumption model using quasi and crude Monte Carlo methods for WSN localization is proposed.

Findings

The proposed model achieves better localization accuracy than existing methods.

It requires minimum computational time for execution.

The model uses dynamic Bayesian networks to optimize sensor energy.

Abstract

A novel strategy of discrete energy consumption model for WSN based on quasi Monte Carlo and crude Monte Carlo method is developed. In our model the discrete hidden Markov process plays a major role in analyzing the node location in heterogeneous media. In this energy consumption model we use both static and dynamic sensor nodes to monitor the optimized energy of all sensor nodes in which every sensor state can be considered as the dynamic Bayesian network. By using this method the power is assigned in terms of dynamic manner to each sensor over discrete time steps to control the graphical structure of our network. The simulation and experiment result shows that our proposed methods are better in terms of localization accuracy and possess minimum computational time over existing localization method.

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