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LEACH Protocol Optimization Based on Weighting Strategy and the Improved Ant Colony Algorithm.

Xuezhen Cheng¹, Chuannuo Xu¹, Xiaoqing Liu^1,2

¹College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao, China.

Frontiers in Neurorobotics

|April 4, 2022

Summary

This summary is machine-generated.

This study optimizes the Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol for wireless sensor networks using novel Optimal Combined Weighting (OCW) and Improved Ant Colony Optimization (IACO) algorithms, enhancing energy efficiency and path optimization.

Keywords:

LEACH optimization improved ant colony optimization optimal combination weighting path superiority routing protocol

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Area of Science:

Computer Science
Electrical Engineering
Network Engineering

Background:

Wireless Sensor Networks (WSNs) face energy consumption challenges.
Existing Low-Energy Adaptive Clustering Hierarchy (LEACH) protocols suffer from randomness and local optimum issues in path optimization.

Purpose of the Study:

To address the limitations of current LEACH protocols in WSNs.
To propose an optimized LEACH protocol using novel algorithms for improved performance.

Main Methods:

Proposed an Optimal Combined Weighting (OCW) method for selecting cluster heads based on residual energy, density, and distance to the sink.
Implemented an Improved Ant Colony Optimization (IACO) algorithm for optimizing transmission paths within clusters.
Introduced a dynamic replacement mechanism for cluster head nodes to reduce network energy consumption.

Main Results:

The OCW method enhances the quality of selected cluster head nodes.
IACO optimizes network partitioning and inter-cluster transmission paths.
Simulations demonstrate improved network energy efficiency and path optimization compared to standard LEACH.

Conclusions:

The proposed OCW and IACO algorithms effectively optimize the LEACH protocol for WSNs.
The enhanced LEACH protocol offers significant improvements in energy efficiency and path selection.
The method provides a viable solution for prolonging the operational life of WSNs.