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

doi:10.1109/WiMOB.2016.7763207

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

Faculty Of Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

1 Citation (Scopus)

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 language	English
Title of host publication	2016 IEEE 12th International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob 2016
Number of pages	7
DOIs	https://doi.org/10.1109/WiMOB.2016.7763207
Publication status	Published - 30 Nov 2016
Event	12th IEEE International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob 2016 - New York, United States Duration: 17 Oct 2016 → 19 Oct 2016

Conference

Conference	12th IEEE International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob 2016
Country/Territory	United States
City	New York
Period	17/10/16 → 19/10/16

Keywords

ANFIS
human movements
poor deployment
wireless sensor network
ZigBee PRO

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10.1109/WiMOB.2016.7763207

Cite this

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

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.",

keywords = "ANFIS, human movements, poor deployment, wireless sensor network, ZigBee PRO",

author = "Lim, {Cheng Leong} and Cindy Goh and Asiya Khan and Aly Syed and Yun Li",

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day = "30",

doi = "10.1109/WiMOB.2016.7763207",

language = "English",

booktitle = "2016 IEEE 12th International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob 2016",

note = "12th IEEE International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob 2016 ; Conference date: 17-10-2016 Through 19-10-2016",

}

Lim, CL, 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, 12th IEEE International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob 2016, New York, United States, 17/10/16. https://doi.org/10.1109/WiMOB.2016.7763207

Predicting types of failures in wireless sensor networks using an adaptive neuro-fuzzy inference system. / Lim, Cheng Leong; Goh, Cindy; Khan, Asiya et al.
2016 IEEE 12th International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob 2016. 2016. 7763207.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

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AU - Lim, Cheng Leong

AU - Goh, Cindy

AU - Khan, Asiya

AU - Syed, Aly

AU - Li, Yun

PY - 2016/11/30

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N2 - 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.

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

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