Dynamic Clustering and Data Aggregation for the Internet-of-Underwater-Things Networks

Omeke, K. G., Mollel, M. , Shah, S. T. , Arshad, K., Zhang, L. , Abbasi, Q. H. and Imran, M. A. (2022) Dynamic Clustering and Data Aggregation for the Internet-of-Underwater-Things Networks. In: 2022 14th International Conference on Computational Intelligence and Communication Networks (CICN), Al-Khobar, Saudi Arabia, 04-06 Dec 2022, pp. 322-328. ISBN 9781665487719 (doi: 10.1109/CICN56167.2022.10008249)

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Abstract

Advances in semiconductor technology have made it possible to have high processing powers in cheap microcontrollers, which is spawning off a revolution in the range of applications of the Internet-of-Things (IoT) and its underwater counterpart, the Internet-of-Underwater-Things (IoUT). As a result, it has now become possible and cost effective to implement powerful data processing algorithms on very cheap microcontrollers and achieve network intelligence on edge devices. In this paper, we evaluate the impact of implementing an unsupervised machine learning technique based on the k-means algorithm, as well as data aggregation, on the performance of a wireless underwater sensor network. A clustering algorithm based on the k-means algorithm is used to divide the network into clusters and to select cluster heads based on network topology and residual energy. Each cluster head collects and aggregates data from nodes within its cluster's coverage and forwards the data to the sink. The network is deployed in a shallow seabed, and it is assumed that the nodes can reach the sink using their full transmission powers. Hence, the performance evaluation compares the sum-throughput, energy efficiency and coverage probability for direct transmissions to the sink against transmissions using the cluster heads. We also propose a special consideration for disaster early warning data, which packets are assigned priority delivery and handled with minimum delay. The evaluation is performed through computer simulations and the results show over a 100% improvement in throughput for clusterbased transmissions compared to direct transmissions.

Item Type:Conference Proceedings
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Shah, Dr Syed Tariq and Zhang, Professor Lei and Imran, Professor Muhammad and Omeke, Dr Kenechi and Mollel, Dr Michael and Abbasi, Professor Qammer
Authors: Omeke, K. G., Mollel, M., Shah, S. T., Arshad, K., Zhang, L., Abbasi, Q. H., and Imran, M. A.
College/School:College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity
College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
College of Science and Engineering > School of Engineering > Systems Power and Energy
ISSN:2472-7555
ISBN:9781665487719
Published Online:13 January 2023
Copyright Holders:Copyright © 2022 IEEE
First Published:First published in Proceedings of the 2022 14th International Conference on Computational Intelligence and Communication Networks (CICN): 322-328
Publisher Policy:Reproduced in accordance with the publisher copyright policy

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