DEKCS: a dynamic clustering protocol to prolong underwater sensor networks

Omeke, K. G., Mollel, M. S. , Ozturk, M., Ansari, S. , Zhang, L. , Abbasi, Q. and Imran, M. (2021) DEKCS: a dynamic clustering protocol to prolong underwater sensor networks. IEEE Sensors Journal, 21(7), pp. 9457-9464. (doi: 10.1109/JSEN.2021.3054943)

Abstract

Energy consumption is a critical issue in the design of wireless underwater sensor networks (WUSNs). Data transfer in the harsh underwater channel requires higher transmission powers compared to an equivalent terrestrial-based network to achieve the same range. However, battery-operated underwater sensor nodes are energy-constrained and require that they transmit with low power to conserve power. Clustering is a technique for partitioning wireless networks into groups where a local base station (cluster head) is only one hop away. Due to the proximity to the cluster head, sensor nodes can lower their transmitting power, thereby improving the network energy efficiency. This paper describes the implementation of a new clustering algorithm to prolong the lifetime of WUSNs. We propose a new protocol called distance- and energy-constrained k-means clustering scheme (DEKCS) for cluster head selection. A potential cluster head is selected based on its position in the cluster and based on its residual battery level. We dynamically update the residual energy thresholds set for potential cluster heads to ensure that the network fully runs out of energy before it becomes disconnected. Also, we leverage the elbow method to dynamically select the optimal number of clusters according to the network size, thereby making the network scalable. Our evaluations show that the proposed scheme outperforms the conventional low-energy adaptive clustering hierarchy (LEACH) protocol by over 90% and an optimised version of LEACH based on k-means clustering by 42%.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Ozturk, Mr Metin and Zhang, Professor Lei and Ansari, Dr Shuja and Imran, Professor Muhammad and Omeke, Dr Kenechi and Mollel, Dr Michael and Abbasi, Professor Qammer
• Authors: Omeke, K. G., Mollel, M. S., Ozturk, M., Ansari, S., Zhang, L., Abbasi, Q., and Imran, M.
• College/School: College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering; College of Science and Engineering > School of Engineering > Systems Power and Energy
• Journal Name: IEEE Sensors Journal
• Publisher: IEEE
• ISSN: 1530-437X
• ISSN (Online): 1558-1748
• Published Online: 27 January 2021
• Copyright Holders: Copyright © 2020 IEEE
• First Published: First published in IEEE Sensors Journal 21(7):9457-9464
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher