Title: Optimised recurrent neural network-based localisation in wireless sensor network: a composite approach

Authors: Shivakumar Kagi; Basavaraj S. Mathapati

Addresses: Computer Science & Engineering, Sharnbasva University, Kalaburagi, Karnataka, India ' Computer Science & Engineering, Sharnbasva University, Kalaburagi, Karnataka, India

Abstract: Localisation is one of the key techniques in the wireless sensor network. The location estimation methods can be classified into target/source localisation and node self-localisation. There are several challenges in some special scenarios. Therefore, the anchor node-based distance estimation scheme is utilised in this research work. In the anchor-based localisation technique, the unknown node utilises the position of the anchor node to estimate its location. The trained Recurrent Neural Network (RNN) with the extracted Angle of Arrival (AoA) and RSSI features of the anchor node and the estimated nodes makes the localisation of the unknown node more precise. Further, to lessen the localisation errors in RNN, its weights are fine-tuned by an Improved Whale Optimisation Algorithm (IWOA).

Keywords: WSN; node localisation; AoA and RSSI-based feature computation; RNN; IWOA.

DOI: 10.1504/IJWMC.2025.146629

International Journal of Wireless and Mobile Computing, 2025 Vol.28 No.4, pp.353 - 369

Received: 27 Apr 2021
Received in revised form: 22 Jul 2022
Accepted: 06 Aug 2022
Published online: 10 Jun 2025 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article