Predicting Types of Failures in Wireless Sensor Networks Using an Adaptive Neuro-fuzzy Inference System

Lim, C. L., Goh, C. , Khan, A., Syed, A. and Li, Y. (2016) Predicting Types of Failures in Wireless Sensor Networks Using an Adaptive Neuro-fuzzy Inference System. In: 2016 IEEE 12th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), New York, NY, USA, 17-19 Oct 2016, ISBN 9781509007240 (doi: 10.1109/WiMOB.2016.7763207)

Abstract

In this paper, Adaptive Neuro-Fuzzy Interference System (ANFIS) technique is used to develop models to predict two conditions commonly found in a Wireless Sensor Network's deployment; these conditions are failure due to (i) poorly deployed environment and (ii) human movements. ANFIS models are trained using parameters obtained from actual ZigBee PRO nodes' Neighbour Table experimented under the influence of associated network challenges. These parameters are Mean RSSI, Standard Deviation RSSI, Average Coefficient of Variation RSSI and Neighbour Table Connectivity. The individual and combined effects of parameters are investigated in-depth. Results showed the mean RSSI is a critical parameter and the combination of mean RSSI, ACV RSSI and NTC produced the best prediction results (~92%) for all ANFIS models.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Goh, Dr Cindy Sf and Lim, Cheng Leong
• Authors: Lim, C. L., Goh, C., Khan, A., Syed, A., and Li, Y.
• College/School: College of Science and Engineering > School of Engineering
• ISBN: 9781509007240
• Copyright Holders: Copyright © 2016 IEEE
• First Published: First published in 2016 IEEE 12th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob)
• Publisher Policy: Reproduced in accordance with the publisher copyright policy