Article type: Research Article
Authors: Sathish Kumar, P.J.a; * | Ponnusamy, Murugananthamb | Radhika, R.c | Dhurgadevi, M.d
Affiliations:
[a] Department of Computer Science and Engineering, Panimalar Engineering College, Chennai, Tamil Nadu, India
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[b] Deputy Registrar, Indian institute of Information Technology, Kalyani, Tamil Nadu, India
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[c] Department of Electronics and Communication Engineering, S. A. Engineering College, Chennai, Tamil Nadu, India
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[d] Department of Computer science and Engineering, Mahendra Engineering College, Namakkal, Tamil Nadu, India
Correspondence:
[*]
Corresponding author. P.J. Sathish Kumar, Department of Computer Science and Engineering, Panimalar Engineering College, Chennai, Tamil Nadu, 600123, India. E-mail: sathishkumar.engineer12@gmail.com.
Abstract: Underwater wireless sensor networks (UWSNs) are designed to perform cooperative monitoring and data collection tasks by combining several elements, such as automobiles and sensors located in a particular acoustic area. Several studies have been carried out to improve energy efficiency and routing reliability. However, UWSN faces several challenges, such as high ocean interference and noise, long transmission delays, limited bandwidth, and low sensor node battery energy. In this work, a novel underwater clustering-based hybrid routing protocol (UC-HRP) has been proposed to address these issues. The overall process is carried out in three phases. In the first phase, the fuzzy-ELM approach is used to initialize the cluster based on parameters such as Doppler spread, path loss, noise, and multipath. In the second phase, the cluster head is selected using Cluster Centre Cluster Head Selection (C3HS) based on Link quality, distance, node degree, and residual energy. In the third phase, Hybrid Artificial Bee Colony (HABC) algorithm is used for selecting an optimal route based on the parameters such as reliability, bandwidth effectiveness, average path loss, and average transmission latency. The performance of the proposed UC-HRP method is evaluated using a variety of parameters, including the network lifetime, packet delivery ratio, alive nodes, and energy consumption. The proposed technique improves the network lifetime by 14.03%, 16.25%, and 18.34% better than ACUN, ANC-UWSNS, and MERP respectively.
Keywords: Underwater wireless sensor networks, fuzzy extreme learning machine, cluster
centre cluster head selection, hybrid artificial bee colony algorithm
DOI: 10.3233/JIFS-230172
Journal: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 831-843, 2023
