GAMA: Genetic Algorithm for k-Coverage and Connectivity with Minimum Sensor Activation in Wireless Sensor Networks

Document Type

Conference Proceeding

Publication Date

1-1-2024

Abstract

In wireless sensor networks, ensuring k-coverage and connectivity is crucial in order to efficiently gather data and relay it back to the base station. We propose an algorithm to achieve k-coverage and connectivity in randomly deployed wireless sensor networks while minimizing the number of active sensors. It has been shown that selecting a minimum set of sensors to activate from an already deployed set of sensors is NP-hard. We address this by using a genetic algorithm that efficiently approximates a solution close to the optimal solution. The algorithm works by selecting random solutions and mutating them, retaining only the best solutions for the next generation until it converges to a near-optimal solution. We examine the time complexity of our approach and discuss possible optimizations. Our simulation results show that our approach works consistently across different types of wireless sensor networks and for different degrees of required coverage.

Identifier

85180531311 (Scopus)

ISBN

[9783031496103]

Publication Title

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

External Full Text Location

https://doi.org/10.1007/978-3-031-49611-0_17

e-ISSN

16113349

ISSN

03029743

First Page

239

Last Page

251

Volume

14461 LNCS

Grant

2338521

Fund Ref

National Science Foundation

This document is currently not available here.

Share

COinS