Predicting types of failures in wireless sensor networks using an adaptive neuro-fuzzy inference system

Cheng Leong Lim, Cindy Goh, Asiya Khan, Aly Syed, Yun Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

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.

Original languageEnglish
Title of host publication2016 IEEE 12th International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob 2016
Number of pages7
DOIs
Publication statusPublished - 30 Nov 2016
Event12th IEEE International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob 2016 - New York, United States
Duration: 17 Oct 201619 Oct 2016

Conference

Conference12th IEEE International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob 2016
CountryUnited States
CityNew York
Period17/10/1619/10/16

Keywords

  • ANFIS
  • human movements
  • poor deployment
  • wireless sensor network
  • ZigBee PRO

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    Lim, C. L., Goh, C., Khan, A., Syed, A., & 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 2016 [7763207] https://doi.org/10.1109/WiMOB.2016.7763207