Faris, Al-Baadani, Yousef, Sufian, Aldmour, Rakan, Al-Jabouri, Laith, Tapaswi, Shashikala and Patnaik, Kiran Kumar 2017. A novel energy sleep mode based on standard deviation ‘ESMSD’ algorithm for adaptive clustering in MANETs. Presented at: ICGS3 2017, London, UK, 18-20 January 2017. Published in: Jahankhani, Hamid, Carlile, Alex, Emm, David, Hosseinian-Far, Amin, Brown, Guy, Sexton, Graham and Arshad, Jamal eds. Global Security, Safety and Sustainability - The Security Challenges of the Connected World. ICGS3 2017. Communications in Computer and Information Science (630) Cham: Springer, pp. 385-393. 10.1007/978-3-319-51064-4_31 |
Abstract
Redundant transmissions to the sink is one of the real issue that mobile ad-hoc networking (MANET) is still facing; therefore clustering is one of the most proven technique to avoid such complexity in MANET. Clustering will result in a better utilization of the MANET limited network resources such as energy consumption. Recent studies which have been proposed in the literature have introduced different algorithms for clustering. Unfortunately, these algorithms are resulting in a massive number of message exchanges, which lead to a high usage of the energy residual. In this research, a unique technique for clustering using the standard deviation as our base of choosing the optimum cluster head is proposed. The new ESMSD protocol uses the distance and the connectivity as the main factors which will be involved in choosing the cluster head. This work has been extended to add the sleeping mode to the entire member nodes in the cluster in order to come with a better utilization in terms of the energy consumption. Simulation results have proved that ESMSD new protocol outperforms the AODV protocol in all the parameters used in this study.
Item Type: | Conference or Workshop Item (Paper) |
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Date Type: | Publication |
Status: | Published |
Schools: | Computer Science & Informatics |
Uncontrolled Keywords: | AODV, SD, ESMSD, MANET, Throughput, Average delay |
Publisher: | Springer |
ISBN: | 978-3-319-51064-4 |
ISSN: | 1865-0937 |
Last Modified: | 09 Jun 2020 01:41 |
URI: | https://orca.cardiff.ac.uk/id/eprint/114611 |
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