A Markov Process-based Opportunistic Trust Factor Estimation Mechanism for Efficient Cluster Head Selection and Extending the Lifetime of Wireless Sensor Networks

Abstract

INTRODUCTION: The lifetime of a sensor network completely relies on the potentialities of the utilized Cluster Head (CH) selection scheme that aids in building efficient Wireless Sensor Networks (WSNs). Most of the existing CH selection approaches use an impractical condition which mainly emphasizes that the nodes that are trustworthy and highly energy competitive have better likelihood of being selected as CHs.OBJECTIVES: In this paper, a Markov Process-based Opportunistic Trust Factor Estimation Mechanism (MPOTFEM) is proposed for achieving optimal CH selection that enhances the possibility of maintaining network lifetime and energy stability in the network. METHODS: MPOTFEM is proposed for ensuring efficient CH selection and thereby enhancing the lifetime of WSNs. The proposed MPOTFEM incorporates the merits of Markov process for computing the Opportunistic and Trust factors that assesses the maximum likelihood of nodes with the possibility of being selected as the CH by exploring multiple transition states of nodes in the networks. RESULTS: The results of the propounded MPOTFEM confirm to be significant in improving the network longevity by 39.21% with minimized energy consumption of 34.82% when compared to the baseline CH election mechanisms taken for analysis.CONCLUSION: The results prove that MPOTFEM is better when compared to the benchmarked CH selection schemes in terms of network lifespan and energy stability.

Keywords

• markov process
• opportunistic factor
• trust factor
• cluster head selection
• network lifetime
• maximum likelihood probability