Genetic Algorithm for the Nodes Deployment Problem in Industrial Wireless Sensor Networks

Rocha-Rocha, David; Vasquez-Salgado, Francisco; Durán-Faúndez, Cristian; Duraan, Rosa Medina; Costa, Daniel G.; Galdames, Patricio

doi:10.1109/ica-acca.2018.8609779

Cited by 1 publication

(1 citation statement)

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“…Farsi et al , (2019) presents the study on the coverage and the connectivity issues in wireless sensor network along with the advantages, disadvantages and the performance metrics that must be considered for the wireless sensor networks. The major concern in improving the network behavior for the industrial wireless sensor networks is done by proposing a genetic algorithm model for the deploying of the nodes in the wireless sensor networks by Rocha-Rocha et al (2018). Salem and Shudifat, (2019) propose an enhanced LEACH protocol for the wireless sensor network to overcome the challenges incurred in the wireless sensor networks in terms of energy consumption and the network lifetime.…”

Section: Related Workmentioning

confidence: 99%

Performance analysis of ant-lion optimization based routing algorithm for wireless sensor networks

Swapna¹,

Santhosh

2020

IJIUS

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PurposeThe miniscule wireless sensor nodes, engaged in the wide range of applications for its capability of monitoring the physical changes around, requires an improved routing strategy with the befitting sensor node arrangement that plays a vital part in ensuring a completeness of the network coverage.Design/methodology/approachThis paves way for the reduced energy consumption, the enhanced network connections and network longevity. The conventional methods and the evolutionary algorithms developed for arranging of the node ended with the less effectiveness and early convergence with the local optimum respectively.FindingsThe paper puts forward the befitting arrangement of the sensor nodes, cluster-head selection and the delayless routing using the ant lion (A-L) optimizer to achieve the substantial coverage, connection, the network-longevity and minimized energy consumption.Originality/valueThe further performance analysis of the proposed system is carried out with the simulation using the network simulator-2 and compared with the genetic algorithm and the particle swarm optimization algorithm to substantiate the competence of the proposed routing method using the ant lion optimization.

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Section: Related Workmentioning

confidence: 99%