Title: A novel neuro-fuzzy-based localisation system for WSN using node proximity

Authors: Somnath Sinha; Aditi Paul

Addresses: Department of Computer Science, Amrita School of Arts and Sciences, Mysuru, Amrita Vishwa Vidyapeetham, India ' Department of Computer Science, Banasthali Vidyapith, India

Abstract: Localisation is a challenging issue in wireless sensor network. This paper describes a neural network and fuzzy logic-based approach for localisation in wireless sensor network (WSN). The received signal strength indicators (RSSIs) of some anchor nodes are used as basic parameter to estimate the location of sensor nodes. Using fuzzy logic the RSSI values of anchor nodes are categorised into some predefined regions and RSSI patterns are generated using fuzzy inference rules. These patterns are then used as input to a trained neural network (NN) for estimating a proximity factor of sensor nodes which in turn is used to calculate their positions. The RSSI patterns are used to find out the weighted position of the anchor nodes which when divided by the proximity factor gives the estimated position of the sensor nodes. A modified back propagation method is used to train the neural network. Proposed model is tested using network simulator NS2 and result shows accuracy up to 95%.

Keywords: wireless sensor network; WSN; fuzzy logic system; localisation; received signal strength indicator; RSSI; neural network; proximity.

DOI: 10.1504/IJIPT.2021.113908

International Journal of Internet Protocol Technology, 2021 Vol.14 No.1, pp.49 - 58

Received: 13 Feb 2019
Accepted: 29 Jul 2019
Published online: 01 Apr 2021 *

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