Title: Fuzzy clustering-based genetic evolutionary algorithm for 5G wireless sensor re-localisation

Authors: Vishal B. Patil; Ninad More

Addresses: Chatrapti Shivaji Maharaj University, Panvel, Navi Mumbai, India ' Department of Computer Science and Information Technology, Chatrapti Shivaji Maharaj University, Navi Mumbai, India

Abstract: Traffic analysis is required for successful packet transmission from source to destination after receiving network acknowledgement. Sensor nodes must send position information to nearby nodes, reducing accuracy and performance. However, precise node location determination improves network performance. This paper proposes a fuzzy clustering-based genetic evolutionary (FCGE) algorithm for effective re-localisation in heavy traffic. It chooses the best node for re-localisation, reducing network traffic, communication costs, and localisation inaccuracy. A proper intelligent re-localisation method is used within a predetermined time frame to avoid localisation errors. To determine the optimal retrigger time, the genetic algorithm uses time-bound re-localisation (TBR). It is more accurate even with many node migrations, so localisation errors are reduced. Fuzzy k-means improves network data collection by creating disjoint clusters and global time synchronisation for mobile agents. The proposed FCGE algorithm reduced RMSPE by 13.26%, 9.65%, and 6.78% for 5, 10, and 20 anchor nodes, respectively.

Keywords: FCGE; effective re-localisation; fuzzy K means; average root mean square position error; RMSPE.

DOI: 10.1504/IJES.2023.136370

International Journal of Embedded Systems, 2023 Vol.16 No.2, pp.83 - 95

Received: 05 Aug 2022
Received in revised form: 14 Oct 2022
Accepted: 03 Jan 2023
Published online: 31 Jan 2024 *

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