An Efficient Method Based on Genetic Algorithms to Solve Sensor Network Optimization Problem

TL;DR

This paper presents a genetic algorithm-based method to optimize sensor network clustering, reducing energy consumption and extending network lifetime by minimizing communication distances and number of cluster heads.

Contribution

It introduces a novel GA-based approach for sensor network clustering that improves energy efficiency and network longevity compared to existing methods.

Findings

The algorithm effectively minimizes communication distances.

It reduces the number of cluster heads needed.

Simulation results demonstrate quick convergence to good solutions.

Abstract

Minimization of the number of cluster heads in a wireless sensor network is a very important problem to reduce channel contention and to improve the efficiency of the algorithm when executed at the level of cluster-heads. In this paper, we propose an efficient method based on genetic algorithms (GAs) to solve a sensor network optimization problem. Long communication distances between sensors and a sink in a sensor network can greatly drain the energy of sensors and reduce the lifetime of a network. By clustering a sensor network into a number of independent clusters using a GA, we can greatly minimize the total communication distance, thus prolonging the network lifetime. Simulation results show that our algorithm can quickly find a good solution.