Treffer: Analysis of machine learning based LEACH robust routing in the Edge Computing systems

Title:
Analysis of machine learning based LEACH robust routing in the Edge Computing systems
Authors:
Rajpoot, Vikram, Garg, Lalit, Zahid Alam, M., Sangeeta, Parashar, Vivek, Tapashetti, Pratibhadevi, Arjariya, Tripti
Publisher Information:
Elsevier Ltd.
Publication Year:
2021
Collection:
University of Malta: OAR@UM / L-Università ta' Malta
Subject Terms:
Wireless sensor networks, Edge computing -- Case studies, Machine learning -- Computer simulation, Statistical matching
Document Type:
Fachzeitschrift article in journal/newspaper
Language:
English
Relation:
Rajpoot, V., Garg, L., Alam, M. Z., Parashar, V., Tapashetti, P., & Arjariya, T. (2021). Analysis of machine learning based LEACH robust routing in the Edge Computing systems. Computers & Electrical Engineering, 96, 107574.; https://www.um.edu.mt/library/oar/handle/123456789/108999
DOI:
10.1016/j.compeleceng.2021.107574
Availability:
https://www.um.edu.mt/library/oar/handle/123456789/108999
https://doi.org/10.1016/j.compeleceng.2021.107574
Rights:
info:eu-repo/semantics/restrictedAccess ; The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder
Accession Number:
edsbas.5BD85ADD
Database:
BASE

Weitere Informationen

Wireless sensor networks (WSN) are used to detect real-time changes in the deployed environment. This dynamic behaviour is either triggered by the deployed environment or by the user from outside. Because of their ability to monitor complex scenarios that change rapidly over time, wireless sensor networks are critical components of most advanced computing systems. These complex activities are influenced by different methods or even by the designers of their networks. Machine learning encourages many real solutions that optimise resource use and increase the network's lifespan in sensor networks. LEACH routing protocol has many limitations due to sudden energy utilisation & cluster head nodes due to direct communication with the base station node. This fast node energy leak creates several black hole structures in the networks, resulting in data redundancy, data packets transmission, node upgrade costs, and end-to-end delay for WSN. The proposed model with LEACH protocol functionality has improved network performance, network (WSN) efficiency, and solving data redundancy issues. By using an independent Recurrent Neural Network (IRNN)-based data fusion algorithm, namely, DFAIRNN. The simulation and comparative results indicate that the mean method & minimum distance method used in the LEACH-DFAIRNN protocol can effectively resolve data redundancy issues caused by the adjacent sensor nodes by flooding data simultaneously to a single node. ; peer-reviewed