W-GUN: Whale Optimization for Energy and Delay-Centric Green Underwater Networks
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Department of Computer Science and Engineering, Deenbandhu Chhotu Ram University of Science and Technology, Murthal (Sonepat), Haryana 131039, India
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School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi 110067, India
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School of Science and Technology, Nottingham Trent University, Nottingham NG11 8NS, UK
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School of Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
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Department of Computing and Mathematics, Manchester Metropolitan University, Manchester M15 6BH, UK
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Department of Electrical, Computer, Software, and Systems Engineering, Embry-Riddle Aeronautical University, Daytona Beach, FL 32114, USA
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(5), 1377; https://doi.org/10.3390/s20051377
Received: 24 January 2020
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Revised: 26 February 2020
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Accepted: 27 February 2020
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Published: 3 March 2020
(This article belongs to the Special Issue Underwater Sensor Networks)
Underwater sensor networks (UWSNs) have witnessed significant R&D attention in both academia and industry due to their growing application domains, such as border security, freight via sea or river, natural petroleum production and the fishing industry. Considering the deep underwater-oriented access constraints, energy-centric communication for the lifetime maximization of tiny sensor nodes in UWSNs is one of the key research themes in this domain. Existing literature on green UWSNs are majorly adapted from the existing techniques in traditional wireless sensor network relying on geolocation and the quality of service-centric underwater relay node selection, without paying much attention to the dynamic underwater network environments. To this end, this paper presents an adapted whale and wolf optimization-based energy and delay-centric green underwater networking framework (W-GUN). It focuses on exploiting dynamic underwater network characteristics by effectively utilizing underwater whale-centric optimization in relay node selection. Firstly, an underwater relay node optimization model is mathematically derived, focusing on underwater whale dynamics for incorporating realistic underwater characteristics in networking. Secondly, the optimization model is used to develop an adapted whale and grey wolf optimization algorithm for selecting optimal and stable relay nodes for centric underwater communication paths. Thirdly, a complete workflow of the W-GUN framework is presented with an optimization flowchart. The comparative performance evaluation attests to the benefits of the proposed framework and is compared to state-of-the-art techniques considering various metrics related to underwater network environments.
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MDPI and ACS Style
Rathore, R.S.; Sangwan, S.; Mazumdar, S.; Kaiwartya, O.; Adhikari, K.; Kharel, R.; Song, H. W-GUN: Whale Optimization for Energy and Delay-Centric Green Underwater Networks. Sensors 2020, 20, 1377. https://doi.org/10.3390/s20051377
AMA Style
Rathore RS, Sangwan S, Mazumdar S, Kaiwartya O, Adhikari K, Kharel R, Song H. W-GUN: Whale Optimization for Energy and Delay-Centric Green Underwater Networks. Sensors. 2020; 20(5):1377. https://doi.org/10.3390/s20051377
Chicago/Turabian StyleRathore, Rajkumar S., Suman Sangwan, Sukriti Mazumdar, Omprakash Kaiwartya, Kabita Adhikari, Rupak Kharel, and Houbing Song. 2020. "W-GUN: Whale Optimization for Energy and Delay-Centric Green Underwater Networks" Sensors 20, no. 5: 1377. https://doi.org/10.3390/s20051377
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