Abstract
Wireless sensor networks (WSNs) 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. In this paper, we focus on faults occurred due to low battery and calibration in WSNs. Machine learning algorithms have been successfully used to identify and classify various types of faults. In this paper, we evaluate and compare the performance of k-nearest neighbour, support vector machine (SVM), and Naive Bayes machine learning algorithms by using the real-world datasets to identify and classify the faults. We present here a comparative study of the above mentioned approaches on experimental datasets. The approach is validated using real data obtained from over one month of samples from motes deployed in an actual living lab. The results show that the k-nearest neighbour (kNN) algorithm obtained a better fault detection rate than other algorithms based on given performance metrics.
| Original language | English |
|---|---|
| Pages (from-to) | 1-13 |
| Number of pages | 13 |
| Journal | International Journal of Sensor Networks |
| Volume | 24 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 2017 Jan 1 |
| Externally published | Yes |
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Keywords
- Data analysis
- Data and system faults
- Fault detection
- Internet of things
- Machine learning
- Reliability
- Wireless sensor networks
- WSNs
ASJC Scopus subject areas
- Control and Systems Engineering
- Computer Science Applications
- Computer Networks and Communications
- Electrical and Electronic Engineering
Cite this
A comparative analysis of machine learning algorithms for faults detection in wireless sensor networks. / Warriach, Ehsan Ullah; Tei, Kenji.
In: International Journal of Sensor Networks, Vol. 24, No. 1, 01.01.2017, p. 1-13.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - A comparative analysis of machine learning algorithms for faults detection in wireless sensor networks
AU - Warriach, Ehsan Ullah
AU - Tei, Kenji
PY - 2017/1/1
Y1 - 2017/1/1
N2 - Wireless sensor networks (WSNs) 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. In this paper, we focus on faults occurred due to low battery and calibration in WSNs. Machine learning algorithms have been successfully used to identify and classify various types of faults. In this paper, we evaluate and compare the performance of k-nearest neighbour, support vector machine (SVM), and Naive Bayes machine learning algorithms by using the real-world datasets to identify and classify the faults. We present here a comparative study of the above mentioned approaches on experimental datasets. The approach is validated using real data obtained from over one month of samples from motes deployed in an actual living lab. The results show that the k-nearest neighbour (kNN) algorithm obtained a better fault detection rate than other algorithms based on given performance metrics.
AB - Wireless sensor networks (WSNs) 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. In this paper, we focus on faults occurred due to low battery and calibration in WSNs. Machine learning algorithms have been successfully used to identify and classify various types of faults. In this paper, we evaluate and compare the performance of k-nearest neighbour, support vector machine (SVM), and Naive Bayes machine learning algorithms by using the real-world datasets to identify and classify the faults. We present here a comparative study of the above mentioned approaches on experimental datasets. The approach is validated using real data obtained from over one month of samples from motes deployed in an actual living lab. The results show that the k-nearest neighbour (kNN) algorithm obtained a better fault detection rate than other algorithms based on given performance metrics.
KW - Data analysis
KW - Data and system faults
KW - Fault detection
KW - Internet of things
KW - Machine learning
KW - Reliability
KW - Wireless sensor networks
KW - WSNs
UR - http://www.scopus.com/inward/record.url?scp=85019739871&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85019739871&partnerID=8YFLogxK
U2 - 10.1504/IJSNET.2017.084209
DO - 10.1504/IJSNET.2017.084209
M3 - Article
AN - SCOPUS:85019739871
VL - 24
SP - 1
EP - 13
JO - International Journal of Sensor Networks
JF - International Journal of Sensor Networks
SN - 1748-1279
IS - 1
ER -