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A multi-objective grey wolf optimization algorithm for energy-efficient cluster-based routing in IoT-enabled WSNs.

Selvaganapathi Sennan1, Sankar S2, Ramasubbareddy Somula3

  • 1Technical Architect, Hexaware Technologies, Jersey City, USA.

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|December 8, 2025
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Summary
This summary is machine-generated.

This study introduces a Multi-Objective Grey Wolf Optimization (MOGWO) algorithm to enhance Internet of Things (IoT) network lifetime. MOGWO improves energy efficiency and data transmission for IoT devices.

Keywords:
And whale optimisationGrey wolf optimisationInternet of thingsOptimisation algorithms

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Network Engineering

Background:

  • Internet of Things (IoT) nodes have limited resources, making network lifetime extension a critical challenge.
  • Clustering is vital for managing IoT data, particularly in applications like smart farming and environmental monitoring.
  • Effective Cluster Head (CH) selection is crucial for optimizing energy consumption in clustered IoT networks.

Purpose of the Study:

  • To propose a novel optimization algorithm for selecting Cluster Heads (CHs) in IoT networks.
  • To enhance the overall network lifetime and energy efficiency of IoT systems.
  • To address the limitations of existing optimization methods in CH selection.

Main Methods:

  • A Multi-Objective Grey Wolf Optimization (MOGWO) algorithm was developed for CH selection.
  • A fitness function incorporating fuzzy logic was designed to evaluate potential CHs based on distance, neighbor count, and residual energy.
  • Simulations were conducted using MATLAB 2019a for performance evaluation.

Main Results:

  • The MOGWO algorithm demonstrated a 10-20% extension in network lifetime compared to LEACH, IFFOA, SHO-CH, and SHO-OBL.
  • Communication overhead was reduced by 5-10% using the proposed MOGWO algorithm.
  • Packet Delivery Ratio saw an improvement of 2-10% with the MOGWO approach.

Conclusions:

  • The MOGWO algorithm offers a significant improvement in extending IoT network lifetime and enhancing performance metrics.
  • Fuzzy logic integration within MOGWO provides a robust method for CH selection, balancing multiple critical factors.
  • MOGWO presents a promising solution for energy-efficient data management in resource-constrained IoT environments.