Corrigendum to Energy-Efficient Coverage Optimization in Wireless Sensor Networks based on Voronoi-Glowworm Swarm Optimization-K-means algorithm [AdHoc Networks Volume 122 (November 2021) 102660]

Chowdhury, Aparajita; De, Debashis; Raychaudhuri, Anindita; Bakshi, Mohana

doi:10.1016/j.adhoc.2022.102907

Cited by 3 publications

(1 citation statement)

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“…To optimize the coverage requirements in WSNs, Elhoseny et al [20] introduced a model that utilized the genetic algorithm for continuous monitoring of specific targets with limited energy resources. Chowdhury et al [21] presented an energy-efficient optimization technique using the Voronoi-glowworm swarm optimization-K-means algorithm. Jebi and Baulkani [22] proposed a multi-objective randomized grasshopper optimization algorithm-based selective activation method for optimal WSN management.…”

Section: Introductionmentioning

confidence: 99%

A new optimal strategy for energy minimization in wireless sensor networks

Ouchitachen,

Darif,

Er-rouidi

et al. 2024

IJ-AI

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In recent years, evolutionary and metaheuristic algorithms have emerged as crucial tools for optimization in the field of artificial intelligence. These algorithms have the potential to revolutionize various aspects of our lives by leveraging the multidisciplinary nature of wireless sensor networks (WSNs). This study aims to introduce genetic and simulated annealing algorithms as effective solutions for enhancing WSN performance. Our contribution entails two main phases. Firstly, we establish mathematical models and formulate objectives as a nonlinear constrained optimization problem. Secondly, we develop two algorithmic solutions to address the formulated optimization problem. The obtained results from multiple simulations demonstrate the positive impact of the proposed strategies on improving network performance in terms of energy consumption.

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Section: Introductionmentioning

confidence: 99%