ECRKQ: Machine Learning-Based Energy-Efficient Clustering and Cooperative Routing for Mobile Underwater Acoustic Sensor Networks

The dynamic topology, narrow transmission bandwidth, and limited energy of sensor nodes in mobile underwater acoustic sensor networks (UASNs) pose challenges to design an efficient and robust network for underwater communications. In this paper, we propose a novel machine learning-based clustering a...

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Main Authors: Jianying Zhu, Yougan Chen, Xiang Sun, Jianming Wu, Zhenwen Liu, Xiaomei Xu
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/9425498/
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author Jianying Zhu
Yougan Chen
Xiang Sun
Jianming Wu
Zhenwen Liu
Xiaomei Xu
author_facet Jianying Zhu
Yougan Chen
Xiang Sun
Jianming Wu
Zhenwen Liu
Xiaomei Xu
author_sort Jianying Zhu
collection DOAJ
description The dynamic topology, narrow transmission bandwidth, and limited energy of sensor nodes in mobile underwater acoustic sensor networks (UASNs) pose challenges to design an efficient and robust network for underwater communications. In this paper, we propose a novel machine learning-based clustering and routing scheme, named energy-efficient clustering and cooperative routing based on improved K-means and Q-learning (ECRKQ), to reduce and balance energy consumption among sensor nodes in a mobile UASN and improve the bandwidth utilization. In the cluster head (CH) selection stage, ECRKQ modifies the K-means algorithm to dynamically select a CH based on the residual energy of the node and the distance from the node to the centroid in a cluster. In the clustering stage, ECRKQ adopts the Q-learning algorithm by incorporating the residual energy of the CH, the energy consumption of data transmission from the node to the CH, and the energy consumption of the data transmission from the CH to the base station into the Q-value function. In the data transmission stage, ECRKQ applies the dynamic coded cooperation (DCC) transmission to improve the bandwidth utilization and the robustness of the underwater communications. In the DCC transmission, cooperative nodes are also dynamically selected based on the residual energy and the energy consumption of transmitting a packet to their destinations. In the simulation, we apply the ocean current drifting model to emulate the position variation of nodes caused by ocean currents in a mobile UASN. The simulation results show that the proposed ECRKQ scheme can achieve more balanced energy consumption among sensor nodes in a mobile UASN than that of the existing scheme.
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spelling doaj.art-86736dc4cbb84d238687a7e8b28ce14c2022-12-21T21:30:30ZengIEEEIEEE Access2169-35362021-01-019708437085510.1109/ACCESS.2021.30781749425498ECRKQ: Machine Learning-Based Energy-Efficient Clustering and Cooperative Routing for Mobile Underwater Acoustic Sensor NetworksJianying Zhu0https://orcid.org/0000-0002-4137-7950Yougan Chen1https://orcid.org/0000-0001-8345-1226Xiang Sun2https://orcid.org/0000-0002-6954-7018Jianming Wu3https://orcid.org/0000-0002-8158-0574Zhenwen Liu4https://orcid.org/0000-0001-7273-0019Xiaomei Xu5https://orcid.org/0000-0001-5677-2497Key Laboratory of Underwater Acoustic Communication and Marine Information Technology (Xiamen University), Ministry of Education, Xiamen, ChinaKey Laboratory of Underwater Acoustic Communication and Marine Information Technology (Xiamen University), Ministry of Education, Xiamen, ChinaDepartment of Electrical and Computer Engineering, The University of New Mexico, Albuquerque, NM, USAKey Laboratory of Underwater Acoustic Communication and Marine Information Technology (Xiamen University), Ministry of Education, Xiamen, ChinaCollege of Harbour and Environmental Engineering, Jimei University, Xiamen, ChinaKey Laboratory of Underwater Acoustic Communication and Marine Information Technology (Xiamen University), Ministry of Education, Xiamen, ChinaThe dynamic topology, narrow transmission bandwidth, and limited energy of sensor nodes in mobile underwater acoustic sensor networks (UASNs) pose challenges to design an efficient and robust network for underwater communications. In this paper, we propose a novel machine learning-based clustering and routing scheme, named energy-efficient clustering and cooperative routing based on improved K-means and Q-learning (ECRKQ), to reduce and balance energy consumption among sensor nodes in a mobile UASN and improve the bandwidth utilization. In the cluster head (CH) selection stage, ECRKQ modifies the K-means algorithm to dynamically select a CH based on the residual energy of the node and the distance from the node to the centroid in a cluster. In the clustering stage, ECRKQ adopts the Q-learning algorithm by incorporating the residual energy of the CH, the energy consumption of data transmission from the node to the CH, and the energy consumption of the data transmission from the CH to the base station into the Q-value function. In the data transmission stage, ECRKQ applies the dynamic coded cooperation (DCC) transmission to improve the bandwidth utilization and the robustness of the underwater communications. In the DCC transmission, cooperative nodes are also dynamically selected based on the residual energy and the energy consumption of transmitting a packet to their destinations. In the simulation, we apply the ocean current drifting model to emulate the position variation of nodes caused by ocean currents in a mobile UASN. The simulation results show that the proposed ECRKQ scheme can achieve more balanced energy consumption among sensor nodes in a mobile UASN than that of the existing scheme.https://ieeexplore.ieee.org/document/9425498/K-meansQ-learningcooperative communicationsclustering and routingunderwater acoustic communications
spellingShingle Jianying Zhu
Yougan Chen
Xiang Sun
Jianming Wu
Zhenwen Liu
Xiaomei Xu
ECRKQ: Machine Learning-Based Energy-Efficient Clustering and Cooperative Routing for Mobile Underwater Acoustic Sensor Networks
IEEE Access
K-means
Q-learning
cooperative communications
clustering and routing
underwater acoustic communications
title ECRKQ: Machine Learning-Based Energy-Efficient Clustering and Cooperative Routing for Mobile Underwater Acoustic Sensor Networks
title_full ECRKQ: Machine Learning-Based Energy-Efficient Clustering and Cooperative Routing for Mobile Underwater Acoustic Sensor Networks
title_fullStr ECRKQ: Machine Learning-Based Energy-Efficient Clustering and Cooperative Routing for Mobile Underwater Acoustic Sensor Networks
title_full_unstemmed ECRKQ: Machine Learning-Based Energy-Efficient Clustering and Cooperative Routing for Mobile Underwater Acoustic Sensor Networks
title_short ECRKQ: Machine Learning-Based Energy-Efficient Clustering and Cooperative Routing for Mobile Underwater Acoustic Sensor Networks
title_sort ecrkq machine learning based energy efficient clustering and cooperative routing for mobile underwater acoustic sensor networks
topic K-means
Q-learning
cooperative communications
clustering and routing
underwater acoustic communications
url https://ieeexplore.ieee.org/document/9425498/
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