TY - GEN
T1 - Outlier Detection and Decision Tree for Wireless Sensor Network Fault Diagnosis
AU - Febriansyah, Irfanur Ilham
AU - Saputro, Whika Cahyo
AU - Achmadi, Galih Ridha
AU - Arisha, Fadila
AU - Tursina, Dara
AU - Pratomo, Baskoro Adi
AU - Shiddiqi, Ary Mazharuddin
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Wireless Sensor Network (WSN) has been used in the industrial world and the household. The increasing number of WSN-based smart home devices requires intensive monitoring and automation. Problems may arise when a fault occurs on these devices that result in misinterpretation of the data received. Existing approaches to fault detection and diagnosis have led to the development of fault diagnosis methods for large-scale data. One of the effective methods for fault diagnosis is the Multi-Scale Principal Component Analysis (MSPCA). This research implements a combination of MSPCA and Decision Tree to detect fault data and diagnose the type of fault cause. The classification of faults is based on significant changes in temperature, humidity, light, voltage, as measured from the Normal Profile extracted by the MSPCA. Experiment results showed that our method was able to determine faults with an accuracy score of 0.913.
AB - Wireless Sensor Network (WSN) has been used in the industrial world and the household. The increasing number of WSN-based smart home devices requires intensive monitoring and automation. Problems may arise when a fault occurs on these devices that result in misinterpretation of the data received. Existing approaches to fault detection and diagnosis have led to the development of fault diagnosis methods for large-scale data. One of the effective methods for fault diagnosis is the Multi-Scale Principal Component Analysis (MSPCA). This research implements a combination of MSPCA and Decision Tree to detect fault data and diagnose the type of fault cause. The classification of faults is based on significant changes in temperature, humidity, light, voltage, as measured from the Normal Profile extracted by the MSPCA. Experiment results showed that our method was able to determine faults with an accuracy score of 0.913.
KW - Decision Tree
KW - Fault Diagnosis
KW - Multi-scale PCA
KW - Normal Profile
KW - Outlier Detection
KW - Wireless Sensor Networks
UR - http://www.scopus.com/inward/record.url?scp=85123302404&partnerID=8YFLogxK
U2 - 10.1109/ICTS52701.2021.9608955
DO - 10.1109/ICTS52701.2021.9608955
M3 - Conference contribution
AN - SCOPUS:85123302404
T3 - Proceedings of 2021 13th International Conference on Information and Communication Technology and System, ICTS 2021
SP - 56
EP - 61
BT - Proceedings of 2021 13th International Conference on Information and Communication Technology and System, ICTS 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 13th International Conference on Information and Communication Technology and System, ICTS 2021
Y2 - 20 October 2021 through 21 October 2021
ER -