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2000
Volume 15, Issue 2
  • ISSN: 2210-3279
  • E-ISSN: 2210-3287

Abstract

Aim

This research aims to explore and evaluate various strategies for improving energy efficiency within wireless sensor networks (WSNs). Specifically, the study focuses on the critical challenge of extending network lifespan through energy conservation by establishing balanced clusters within the WSN architecture.

Background

In wireless sensor networks (WSNs), ensuring prolonged network operation while conserving energy resources is a significant concern. One promising approach to address this challenge is the implementation of equalized clusters, which requires an effective selection of cluster heads (CHs). However, this task presents considerable complexity and demands innovative solutions to overcome.

Objective

The primary objective of this study is to develop and assess a novel methodology for selecting precise cluster heads (CHs) within WSNs. This methodology is based on the utilization of Bluetooth low energy (BLE) sensors deployed in a randomly distributed manner across the study area. By employing an enhanced artificial neural network and greedy approach (EANN-GA), the proposed technique seeks to identify CHs with optimal proximity to the cluster center and substantial remaining energy reserves.

Methods

The proposed methodology involves the deployment of BLE sensors distributed randomly throughout the study region, which are then organized into clusters. Using the enhanced artificial neural network and greedy approach (EANN-GA), the sensor node nearest to the cluster center with the highest remaining energy is selected as the cluster head (CH). Additionally, a mobile sink (MS) is introduced to harness the power of CHs, and the number of paths utilized by the MS is estimated through a genetic approach. Based on this path information, the MS enters each cluster to initiate the data-gathering process.

Results

Performance analysis of the presented methodology demonstrates significant improvements in energy efficiency and the extension of network lifetime. By employing the proposed EANN-GA technique for CH selection and optimizing MS path utilization, the study showcases enhanced operational effectiveness within WSNs.

Conclusion

The findings of this research underscore the effectiveness of the proposed methodology in enhancing energy efficiency and prolonging the lifespan of wireless sensor networks. Through the innovative integration of BLE sensors, EANN-GA CH selection, and genetic-based MS path estimation, the study contributes valuable insights toward addressing the critical challenges of energy conservation in WSNs.

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2024-06-24
2025-10-29

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A Novel Approach for Mobility-aware Energy-efficient Clustering-based Routing Using EANN With GA Algorithm on WSNs
Int J Sensors Wirel Commun Control 15, 96 (2025); https://doi.org/10.2174/0122103279296251240529105359
/content/journals/swcc/10.2174/0122103279296251240529105359
/content/journals/swcc/10.2174/0122103279296251240529105359
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  • Article Type:     Research Article
Keyword(s): ANN; BLE sensors; clustering; Energy efficiency; routing; WSN

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