Thermal entropy based hesitant fuzzy linguistic term set analysis in energy efficient opportunistic clustering

TL;DR

This paper introduces a novel energy-efficient clustering method for wireless sensor networks using hesitant fuzzy linguistic analysis and thermal entropy, improving network lifetime and data delivery.

Contribution

It presents a dynamic, self-organizing opportunistic clustering approach based on hesitant fuzzy linguistic decision modeling, addressing energy and decision-making challenges in sensor networks.

Findings

Enhanced network lifetime and energy efficiency.

Improved packet delivery ratio.

Better performance compared to existing benchmarks.

Abstract

Limited energy resources and sensor nodes adaptability with the surrounding environment play a significant role in the sustainable Wireless Sensor Networks. This paper proposes a novel, dynamic, self-organizing opportunistic clustering using Hesitant Fuzzy Linguistic Term Analysis-based Multi-Criteria Decision Modeling methodology in order to overcome the CH decision making problems and network lifetime bottlenecks. The asynchronous sleep/awake cycle strategy could be exploited to make an opportunistic connection between sensor nodes using opportunistic connection random graph. Every node in the network observe the node gain degree, energy welfare, relative thermal entropy, link connectivity, expected optimal hop, link quality factor etc. to form the criteria for Hesitant Fuzzy Linguistic Term Set. It makes the node to evaluate its current state and make the decision about the required action (CH, CM or relay). The simulation results reveal that our proposed scheme leads to an improvement in network lifetime, packet delivery ratio and overall energy consumption against existing benchmarks.