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Smart Spider Monkey Optimization (SSMO) for Energy-Based Cluster-Head Selection Adapted for Biomedical Engineering

P Ajay1, B Nagaraj2, J Jaya3

  • 1Faculty of Information and Communication Engineering, Anna University, Chennai, India.

Contrast Media & Molecular Imaging
|February 17, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a sampling-based smart spider monkey optimization (SSMO) to improve energy efficiency in wireless sensor networks (WSNs) for biomedical applications. The SSMO approach enhances network longevity and stability by optimizing cluster head selection.

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Area of Science:

  • Wireless Sensor Networks (WSNs)
  • Biomedical Applications
  • Optimization Algorithms

Background:

  • Energy efficiency is crucial for the sustainability of biomedical wireless sensor networks (WSNs).
  • Clustering improves energy efficiency but faces challenges in cluster head selection, particularly with location-based methods that can lead to imprecise and redundant node choices.
  • Existing methods struggle with distributed nodes and effective cluster head identification.

Purpose of the Study:

  • To develop and evaluate a novel sampling-based smart spider monkey optimization (SSMO) approach for enhanced cluster head selection in WSNs.
  • To improve the lifetime and stability of WSNs in biomedical applications through optimized energy efficiency.
  • To compare the performance of SSMO against standard SMO and other established protocols like LEACH-C and PSO-C.

Main Methods:

  • Development of the sampling-based smart spider monkey optimization (SSMO) algorithm.
  • Implementation of SSMO for smart cluster head (CH) selection in WSNs.
  • Comparative analysis of SSMO with LEACH-C and PSO-C in homogeneous and heterogeneous network settings.

Main Results:

  • SSMO significantly boosts network longevity and stability periods.
  • Estimated improvements in network longevity and stability range from 1.22% to 32.652% across different scenarios.
  • The sampling-based approach effectively addresses challenges related to distributed nodes and cluster head selection.

Conclusions:

  • The proposed SSMO approach offers a superior method for cluster head selection in WSNs for biomedical applications.
  • SSMO enhances overall network performance, leading to increased lifespan and reliability.
  • This optimization technique provides a promising solution for energy efficiency challenges in WSNs.