Abstract
Wireless Sensor Network (WSN) deployment experiences show that collected data is prone to be faulty. Faults are due to internal and external influences, such as calibration, low battery, environmental interference and sensor aging. However, only few solutions exist to deal with faulty sensory data in WSN. We develop a statistical approach to detect and identify faults in a WSN. In particular, we focus on the identification and classification of data and system fault types as it is essential to perform accurate recovery actions. Our method uses Hidden Markov Models (HMMs) to capture the fault-free dynamics of an environment and dynamics of faulty data. It then performs a structural analysis of these HMMs to determine the type of data and system faults affecting sensor measurements. The approach is validated using real data obtained from over one month of samples from motes deployed in an actual living lab.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 15th IEEE International Conference on Computational Science and Engineering, CSE 2012 and 10th IEEE/IFIP International Conference on Embedded and Ubiquitous Computing, EUC 2012 |
| Pages | 618-625 |
| Number of pages | 8 |
| DOIs | |
| Publication status | Published - 2012 Dec 1 |
| Externally published | Yes |
| Event | 15th IEEE International Conference on Computational Science and Engineering, CSE 2012 and 10th IEEE/IFIP International Conference on Embedded and Ubiquitous Computing, EUC 2012 - Paphos, Cyprus Duration: 2012 Dec 5 → 2012 Dec 7 |
Other
| Other | 15th IEEE International Conference on Computational Science and Engineering, CSE 2012 and 10th IEEE/IFIP International Conference on Embedded and Ubiquitous Computing, EUC 2012 |
|---|---|
| Country | Cyprus |
| City | Paphos |
| Period | 12/12/5 → 12/12/7 |
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ASJC Scopus subject areas
- Software
Cite this
A machine learning approach for identifying and classifying faults in wireless sensor networks. / Warriach, Ehsan Ullah; Aiello, Marco; Tei, Kenji.
Proceedings - 15th IEEE International Conference on Computational Science and Engineering, CSE 2012 and 10th IEEE/IFIP International Conference on Embedded and Ubiquitous Computing, EUC 2012. 2012. p. 618-625 6417349.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - A machine learning approach for identifying and classifying faults in wireless sensor networks
AU - Warriach, Ehsan Ullah
AU - Aiello, Marco
AU - Tei, Kenji
PY - 2012/12/1
Y1 - 2012/12/1
N2 - Wireless Sensor Network (WSN) deployment experiences show that collected data is prone to be faulty. Faults are due to internal and external influences, such as calibration, low battery, environmental interference and sensor aging. However, only few solutions exist to deal with faulty sensory data in WSN. We develop a statistical approach to detect and identify faults in a WSN. In particular, we focus on the identification and classification of data and system fault types as it is essential to perform accurate recovery actions. Our method uses Hidden Markov Models (HMMs) to capture the fault-free dynamics of an environment and dynamics of faulty data. It then performs a structural analysis of these HMMs to determine the type of data and system faults affecting sensor measurements. The approach is validated using real data obtained from over one month of samples from motes deployed in an actual living lab.
AB - Wireless Sensor Network (WSN) deployment experiences show that collected data is prone to be faulty. Faults are due to internal and external influences, such as calibration, low battery, environmental interference and sensor aging. However, only few solutions exist to deal with faulty sensory data in WSN. We develop a statistical approach to detect and identify faults in a WSN. In particular, we focus on the identification and classification of data and system fault types as it is essential to perform accurate recovery actions. Our method uses Hidden Markov Models (HMMs) to capture the fault-free dynamics of an environment and dynamics of faulty data. It then performs a structural analysis of these HMMs to determine the type of data and system faults affecting sensor measurements. The approach is validated using real data obtained from over one month of samples from motes deployed in an actual living lab.
UR - http://www.scopus.com/inward/record.url?scp=84874094695&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84874094695&partnerID=8YFLogxK
U2 - 10.1109/ICCSE.2012.90
DO - 10.1109/ICCSE.2012.90
M3 - Conference contribution
SN - 9780769549149
SP - 618
EP - 625
BT - Proceedings - 15th IEEE International Conference on Computational Science and Engineering, CSE 2012 and 10th IEEE/IFIP International Conference on Embedded and Ubiquitous Computing, EUC 2012
ER -