Enhancing the network performance of wireless sensor networks on metaheuristic approach: Grey Wolf Optimization

HARI MOHAN PANDEY; Biswa Mohan Sahoo; Tarachand Amgoth

doi:10.1007/978-981-16-3067-5_35

Enhancing the network performance of wireless sensor networks on metaheuristic approach: Grey Wolf Optimization

HARI MOHAN PANDEY, Biswa Mohan Sahoo^*, Tarachand Amgoth

^*Corresponding author for this work

Computer Science

Research output: Contribution to journal › Conference proceeding article (ISSN) › peer-review

8 Citations (Scopus)

99 Downloads (Pure)

Abstract

The sensing technology has brought all advancements in the human lives. Wireless Sensor Network (WSN) has proven to be a promising solution to acquire the information from the remote areas. However, the energy constraints of the sensor nodes have obstructed the widely spread application zone of WSN. There has been a great magnitude of efforts reported for acquiring the energy efficiency in WSN, these efforts varying from conventional approaches to the meta-heuristic method for enhancing the network performance. In this
paper, we have presented a comparative evaluation of state of art metaheuristic approaches that helps in acquiring energy efficiency in the network. We have proposed Grey Wolf Optimization (GWO-P) algorithm with the empirical analysis of the existing methods PSO, GA and WAO that will help the readers to select the
appropriate approach for their applications. It is similarly exposed that in different other execution measurements GWO-P beats the contender calculations for length of stability, network lifetime, expectancy, and so on.

Original language	English
Pages (from-to)	469-482
Number of pages	14
Journal	Lecture Notes in Electrical Engineering
Volume	778
Early online date	27 Jul 2021
DOIs	https://doi.org/10.1007/978-981-16-3067-5_35
Publication status	Published - 2021

Keywords

WSN
Meta-heuristic
Empirical analysis
Gray wolf optimization
Network performance

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10.1007/978-981-16-3067-5_35

PaperID 166_CamerarAccepted author manuscript, 536 KBLicence: CC BY-NC-ND

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title = "Enhancing the network performance of wireless sensor networks on metaheuristic approach: Grey Wolf Optimization",

abstract = "The sensing technology has brought all advancements in the human lives. Wireless Sensor Network (WSN) has proven to be a promising solution to acquire the information from the remote areas. However, the energy constraints of the sensor nodes have obstructed the widely spread application zone of WSN. There has been a great magnitude of efforts reported for acquiring the energy efficiency in WSN, these efforts varying from conventional approaches to the meta-heuristic method for enhancing the network performance. In thispaper, we have presented a comparative evaluation of state of art metaheuristic approaches that helps in acquiring energy efficiency in the network. We have proposed Grey Wolf Optimization (GWO-P) algorithm with the empirical analysis of the existing methods PSO, GA and WAO that will help the readers to select theappropriate approach for their applications. It is similarly exposed that in different other execution measurements GWO-P beats the contender calculations for length of stability, network lifetime, expectancy, and so on.",

keywords = "WSN, Meta-heuristic, Empirical analysis, Gray wolf optimization, Network performance",

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AU - Sahoo, Biswa Mohan

AU - Amgoth, Tarachand

PY - 2021

Y1 - 2021

N2 - The sensing technology has brought all advancements in the human lives. Wireless Sensor Network (WSN) has proven to be a promising solution to acquire the information from the remote areas. However, the energy constraints of the sensor nodes have obstructed the widely spread application zone of WSN. There has been a great magnitude of efforts reported for acquiring the energy efficiency in WSN, these efforts varying from conventional approaches to the meta-heuristic method for enhancing the network performance. In thispaper, we have presented a comparative evaluation of state of art metaheuristic approaches that helps in acquiring energy efficiency in the network. We have proposed Grey Wolf Optimization (GWO-P) algorithm with the empirical analysis of the existing methods PSO, GA and WAO that will help the readers to select theappropriate approach for their applications. It is similarly exposed that in different other execution measurements GWO-P beats the contender calculations for length of stability, network lifetime, expectancy, and so on.

AB - The sensing technology has brought all advancements in the human lives. Wireless Sensor Network (WSN) has proven to be a promising solution to acquire the information from the remote areas. However, the energy constraints of the sensor nodes have obstructed the widely spread application zone of WSN. There has been a great magnitude of efforts reported for acquiring the energy efficiency in WSN, these efforts varying from conventional approaches to the meta-heuristic method for enhancing the network performance. In thispaper, we have presented a comparative evaluation of state of art metaheuristic approaches that helps in acquiring energy efficiency in the network. We have proposed Grey Wolf Optimization (GWO-P) algorithm with the empirical analysis of the existing methods PSO, GA and WAO that will help the readers to select theappropriate approach for their applications. It is similarly exposed that in different other execution measurements GWO-P beats the contender calculations for length of stability, network lifetime, expectancy, and so on.

KW - WSN

KW - Meta-heuristic

KW - Empirical analysis

KW - Gray wolf optimization

KW - Network performance

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EP - 482

JO - Lecture Notes in Electrical Engineering

JF - Lecture Notes in Electrical Engineering

ER -

Enhancing the network performance of wireless sensor networks on metaheuristic approach: Grey Wolf Optimization

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Keywords

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