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Energy efficient data transmission using multiobjective improved remora optimization algorithm for wireless sensor network with mobile sink

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Title: Energy efficient data transmission using multiobjective improved remora optimization algorithm for wireless sensor network with mobile sink
Authors: Jemla Naik, Anil Kumar, Parameswarappa, Manjunatha, Ramachandra, Mohan Naik
Contributors: I have not received any fund from any organization.
Source: International Journal of Electrical and Computer Engineering (IJECE); Vol 13, No 6: December 2023; 6476-6488 ; 2722-2578 ; 2088-8708 ; 10.11591/ijece.v13i6
Publisher Information: Institute of Advanced Engineering and Science
Publication Year: 2023
Subject Terms: Telecommunication, Wireless Sensor Network, Average residual energy, multiobjective ant colony optimization, multiobjective improved remora optimization algorithm, network lifespan, wireless sensor networks
Description: A wireless sensor network (WSN) is a collection of nodes fitted with small sensors and transceiver elements. Energy consumption, data loss, and transmission delays are the major drawback of creating mobile sinks. For instance, battery life and data latency might result in node isolation, which breaks the link between nodes in the network. These issues have been avoided by means of mobile data sinks, which move between nodes with connection issues. Therefore, energy aware multiobjective improved remora optimization algorithm and multiobjective ant colony optimization (EA-MIROA-MACO) is proposed in this research to improve the WSN’s energy efficiency by eliminating node isolation issue. MIRO is utilized to pick the optimal cluster heads (CHs), while multiobjective ant colony optimization (MACO) is employed to find the path through the CHs. The EA-MIROA-MACO aims to optimize energy consumption in nodes and enhance data transmission within a WSN. The analysis of EA-MIROA-MACO’s performance is conducted by considering the number of alive along with dead nodes, average residual energy, and network lifespan. The EA-MIROA-MACO is compared with traditional approaches such as mobile sink and fuzzy based relay node routing (MSFBRR) protocol as well as hybrid neural network (HNN). The EA-MIROA-MACO demonstrates a higher number of alive nodes, specifically 192, over the MSFBRR and HNN for 2,000 rounds.
Document Type: article in journal/newspaper
File Description: application/pdf
Language: English
Relation: https://ijece.iaescore.com/index.php/IJECE/article/view/32076/16973; https://ijece.iaescore.com/index.php/IJECE/article/view/32076
DOI: 10.11591/ijece.v13i6.pp6476-6488
Availability: https://ijece.iaescore.com/index.php/IJECE/article/view/32076
https://doi.org/10.11591/ijece.v13i6.pp6476-6488
Rights: Copyright (c) 2023 Anil Kumar Jemla Naik, Manjunatha Parameswarappa, Mohan Naik Ramachandra ; http://creativecommons.org/licenses/by-sa/4.0
Accession Number: edsbas.A284C4B1
Database: BASE
Description
Abstract:A wireless sensor network (WSN) is a collection of nodes fitted with small sensors and transceiver elements. Energy consumption, data loss, and transmission delays are the major drawback of creating mobile sinks. For instance, battery life and data latency might result in node isolation, which breaks the link between nodes in the network. These issues have been avoided by means of mobile data sinks, which move between nodes with connection issues. Therefore, energy aware multiobjective improved remora optimization algorithm and multiobjective ant colony optimization (EA-MIROA-MACO) is proposed in this research to improve the WSN’s energy efficiency by eliminating node isolation issue. MIRO is utilized to pick the optimal cluster heads (CHs), while multiobjective ant colony optimization (MACO) is employed to find the path through the CHs. The EA-MIROA-MACO aims to optimize energy consumption in nodes and enhance data transmission within a WSN. The analysis of EA-MIROA-MACO’s performance is conducted by considering the number of alive along with dead nodes, average residual energy, and network lifespan. The EA-MIROA-MACO is compared with traditional approaches such as mobile sink and fuzzy based relay node routing (MSFBRR) protocol as well as hybrid neural network (HNN). The EA-MIROA-MACO demonstrates a higher number of alive nodes, specifically 192, over the MSFBRR and HNN for 2,000 rounds.
DOI:10.11591/ijece.v13i6.pp6476-6488