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W-GUN：基于鲸鱼优化算法的能量和时延为中心的绿色水下网络。

W-GUN: Whale Optimization for Energy and Delay-Centric Green Underwater Networks.

机构信息

Department of Computer Science and Engineering, Deenbandhu Chhotu Ram University of Science and Technology, Murthal (Sonepat), Haryana 131039, India.

School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi 110067, India.

出版信息

Sensors (Basel). 2020 Mar 3;20(5):1377. doi: 10.3390/s20051377.

DOI:10.3390/s20051377
PMID:32138260
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7085705/
Abstract

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.

摘要

水下传感器网络 (UWSNs) 由于其不断增长的应用领域，如边境安全、海上或河流货运、自然石油生产和渔业，在学术界和工业界都受到了极大的关注。考虑到深水下的接入限制，以能量为中心的通信是延长 UWSNs 中小传感器节点寿命的关键研究主题之一。现有的绿色 UWSNs 文献主要是从传统无线传感器网络的现有技术中借鉴而来，这些技术依赖于地理位置和服务质量为中心的水下中继节点选择，而没有过多关注动态水下网络环境。为此，本文提出了一种基于鲸鱼和狼优化的能量和延迟为中心的绿色水下网络框架 (W-GUN)。它通过有效地利用水下鲸鱼优化在中继节点选择中，重点利用动态水下网络特性。首先，从数学上推导出水下中继节点优化模型，重点关注水下鲸鱼动力学，以将现实水下特性纳入网络中。其次，利用优化模型开发了一种自适应鲸鱼和灰狼优化算法，用于选择中心水下通信路径的最佳和稳定的中继节点。第三，提出了 W-GUN 框架的完整工作流程，并给出了优化流程图。对比性能评估证明了所提出框架的优势，并与考虑与水下网络环境相关的各种指标的最新技术进行了比较。

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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b3e/7085705/3cb1c600e87b/sensors-20-01377-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b3e/7085705/d1df4a4687f6/sensors-20-01377-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b3e/7085705/ddf3d1a1f4d7/sensors-20-01377-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b3e/7085705/c42397ce3ece/sensors-20-01377-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b3e/7085705/027e831bef39/sensors-20-01377-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b3e/7085705/c70467c7b6b9/sensors-20-01377-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b3e/7085705/76158ec2a1a1/sensors-20-01377-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b3e/7085705/4b885764b9c2/sensors-20-01377-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b3e/7085705/dfead5271a2c/sensors-20-01377-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b3e/7085705/9e14923048c3/sensors-20-01377-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b3e/7085705/f0ded6940a8c/sensors-20-01377-g010.jpg
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