Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Wireless Personal Communications
Published in: Wireless Personal Communications 3/2022

10-09-2021

Data Fault Detection in Wireless Sensor Networks Using Machine Learning Techniques

Authors: P. Indira Priya, S. Muthurajkumar, S. Sheeba Daisy

Published in: Wireless Personal Communications | Issue 3/2022

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Wireless Sensor Network (WSN) is a wireless network that consists of spatially distributed autonomous devices with sophisticated subsystem called sensors to monitor the environmental conditions. WSN’s measure environmental conditions like temperature, sound, pollution levels, humidity, wind, etc. Data fault detection is a challenging problem due to presence of sensors in unforeseeable areas. This detection has to be precise and accurate, so that it can be used for weather prediction, disease prediction, traffic monitoring, etc. In recent years, Machine Learning plays a vital role in accurate fault detection. In this paper, we propose an Enhanced Minimum Redundancy Maximum Relevance algorithm. Our work combines the principle of filter and wrapper methods by using F-Mutual Information Difference (FMID) and FMIQ as the objective functions to determine the relevance and redundancy. The core analysis carried out in various datasets shows that the classification accuracy ranges from 93 to 100%, which is promising compared to the existing systems. The strength of our algorithm can be leveraged in situations where data fault detection accuracy is a real matter of concern.
previous article Architectural Design, Improvement, and Challenges of Distributed Software-Defined Wireless Sensor Networks
next article Design and Implementation of a Bandpass–Bandpass Diplexer Using Coupled Structures

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
Literature
1.
Mannan, A., Javed, K., & Noo, S. K. (2017). Maximum relevance maximum anti-redundancy (MRMA) feature selection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 21(5), 1–14.
2.
Onal, A. C., Sezer, O. B., Ozbayoglu, M., & Dogdu, E. (2017). Weather data analysis and sensor fault detection using an extended IoT framework with semantics. IEEE Transactions on Signal Processing Big Data and Machine Learning, 53(1), 2037–2046.
3.
Bhargava, A., & Raghuvanshi, A. S. (2016). Anomaly fault detection in wireless sensor networks using S-transform in combination with SVM. IEEE Transactions on Wireless Communications, 34(9), 111–116.
4.
Adil, B.-H., Youssef, G., & Abderrahim, E. Q. (2017). HVS-MRMR wrapper methods for feature selection. IEEE Transactions on Neural Networks and Learning Systems, 17(3–4), 4062–4069.
5.
Zhang, D., Qian, L., Mao, B., Huang, C., Huang, B., & Si, Y. (2018). A data-driven design for fault detection of wind turbines using random forests and XGboost ensembling approaches. IEEE Open Access Journal, 6, 21020–21031.CrossRef
6.
Ehsan Ullah Warriach and Kenji Tei. (2014). Fault detection in wireless sensor networks: A machine learning approach. IEEE Transactions on Wireless Communications, 1(13), 758–764.
7.
Feng, J., Fu, Q., & Wang, Y. (2016). Weighted distributed fault detection for wireless sensor networks Based on the distance. In: Proceedings of 33rd China control conference (CCC) (pp. 322–326).
8.
Peng, H., Long, F., & Ding, C. (2015). Feature selection based on mutual information: Criteria of max-dependency, max-relevance and min-redundancy. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(5), 1226–1238.
9.
Yang, Jae-Wan, Lee, Young-Doo, & Koo, In-Soo (2018). Convolutional autoencoder-based sensor fault classification. In: IEEE international conference on ubiquitous and future networks (pp. 865–867).
10.
Shams, R., Khan, F. H., & Amir, M. (2021). Critical analysis of localization and time synchronization algorithms in underwater wireless sensor networks: Issues and challenges. Wireless Personal Communications, 116, 1231–1258.CrossRef
11.
Yang, J., Chen, Y., Zhang, L., & Sun, Z. (2016). Fault detection, isolation and diagnosis of self-validating multifunctional sensors. Review of Scientific Instruments, 87, 1–14.
12.
Ferreira, J. G., & Warzecha, A. (2017). An application of machine learning approach to fault detection of a synchronous machine. In International symposium on electrical machines (SME) (pp. 406–413).
13.
Karthick Raghunath, K. M., & Rengarajan, N. (2013). Investigation of faults, errors and failures in wireless sensor network: A systematical survey. International Journal of Advanced Computer, 3, 151–163.
14.
Edomwandekhoe, K., & Liang, X. (2018). Advanced feature selection for broken rotor bar faults in induction motors. IEEE Communication Letters, 54(5), 1–10.
15.
Lily, H. R., Leahy, K., Konstantakopoulos, I. C., Auslander, D. M., Spanos, C. J., & Agogino, A. M. (2016). Using domain knowledge features for wind turbine diagnostics. IEEE international conference on machine learning and applications (pp. 300–305).
16.
Luis, P. F., & Garcıa1, A. C. P. L. F., de Carvalho & Lorena, A. C. (2013). Noisy data set identification, springer international conference on hybrid artificial intelligence systems (pp. 629–638).
17.
Radovic, M., Ghalwash, M., Filipovic, N., & Obradovic, Z. (2017). Minimum redundancy maximum relevance feature selection approach for temporal gene expression data. IEEE Open Access Journal, 18, 2–14.
18.
Xue, Q., Pantelidou, A., & Latva-aho, M. (2011). Optimal relay selection for energy-efficient multicast. Wireless Personal Communications, 59, 433–446.CrossRef
19.
Mandal, M., Mukhopadhyay, A. (2013). An improved minimum redundancy maximum relevance for feature selection in gene data, Elsevier international conference on computational intelligence: Modelling, techniques and Applications (CIMTA) (vol. 10, pp. 20–27).
20.
Derbali, M., Buhari, S. M., Tsaramirsis, G., Stojmenovic, M., Jerbi, H., Abdelkrim, M., & Al-beirutty, M. H. (2017). Water desalination fault detection using machine learning approaches: A comparative study. IEEE Open Access Journal, 5, 23266–23275.CrossRef
21.
Amir, R. B., Gul, S. T., & Khan, A. Q. (2016). A comparative analysis of classical and one class SVM classifiers for machine fault detection using vibration signals. In IEEE International Conference on Emerging Technologies (pp. 373–380).
22.
Kour, H., Jha, R. K., & Jain, S. (2019). Protocol design and resource allocation for power optimization using spectrum sharing for 5G networks. Telecommunication Systems, 72, 95–113.
23.
Ramirez-Gallego, L. I., Martinez-Rego, D., Bolon Canego, V., Benetez, J. M., Herrera, F., & Alanso, A. (2016). Fast minimum redundancy maximum relevance for high dimensional data. International Journal of Intelligent Systems (pp. 1–19).
24.
Samarakoon, P. A., & Athukorala, D. A. S. (2017). System abnormality detection in stock market complex trading systems using machine learning techniques. IEEE International Conferences on Communications, 14(8), 125–130.
25.
Agarwal, A., Sinha, V. K., & Palisetty, R. (2019). Performance analysis and FPGA prototype of variable rate GO-OFDMA baseband transmission scheme. Wireless Personal Communications, 108, 785–809.CrossRef
26.
Jan, S. U., Lee, Y., Shin, J., & Koo, I. (2017). Sensor fault classification based on support vector machine and statistical time-domain features. IEEE Open Access Journal, 5, 8682–8690.CrossRef
27.
Sathiyavathi, R., & Bharathi, B. (2017). A review on fault detection in wireless sensor networks. In: IEEE international conference on communication and signal processing (pp. 1487–1490).
28.
Yin, S., Gao, H., Qiu, J., & Kaynak, O. (2017). Fault detection for nonlinear process with deterministic disturbances: A just-in-time learning based data driven method. IEEE Transactions on Cybernetics, 47(9), 3649–3657.CrossRef
29.
Rashid, S., Akram, U., Qaisar, S., & Khan, S. A. (2016). Wireless sensor network for distributed event detection based on machine learning. In: IEEE international conference on internet of things (iThings) and green computing and communications (GreenCom) (pp. 540–545).
30.
Titouna, C., Aliouat, M., & Guerou, M. (2015). Outlier detection approach using Bayes classifiers in wireless sensor networks. Wireless Sensor Commune, 85(3), 1009–1023.
31.
Abrol, A., Jha, R. K., & Jain, S. (2018). Joint power allocation and relay selection strategy for 5G network: A step towards green communication. Telecommunication Systems, 68, 201–215.CrossRef
32.
Miao, X., Liu, Y., Zhao, H., & Li, C. (2018). Distributed online one-class support vector machine for anomaly detection over networks. IEEE Transactions On Cybernetics, 14(4), 2168–2267.
33.
Selvi, M., Santhosh Kumar, S. V. N., & Ganapathy, S. (2021). An energy efficient clustered gravitational and fuzzy based routing algorithm in WSNs. Wireless Personal Communications, 116, 61–90.CrossRef
34.
Prabhu Kavin, B., Ganapathy, S., & Kanimozhi, U. (2020). An enhanced security framework for secured data storage and communications in cloud using ECC, access control and LDSA. Wireless Personal Communications, 115, 1107–1135.CrossRef
35.
Selvi, M., Thangaramya, K., & Ganapathy, S. (2019). An energy aware trust based secure routing algorithm for effective communication in wireless sensor networks. Wireless Personal Communications, 105, 1475–1490.CrossRef
36.
Yang, Y., Li, H., Lin, X., & Ming, D. (2010). Recursive feature selection based on minimum redundancy maximum relevance. In Algorithms and Programming: IEEE International Symposium on Parallel Architectures (pp. 281–285).
37.
Yunluo, Yu., Li, W., Shenga, D., & Chen, J. (2015). A novel sensor fault diagnosis method based on modified ensemble empirical mode decomposition and probabilistic neural network. Elsevier notes on Neural Networking, 68, 328–336.
38.
Yuvaraja, M., & Sabrigiriraj, M. (2017). Fault detection and recovery scheme for routing and lifetime enhancement in WSN. Wireless Networks Communications Journal, 23, 267–277.CrossRef
39.
40.
Muthurajkumar, S., Vijayalakshmi, M., & Kannan, A. (2017). Secured data storage and retrieval algorithm using map reduce techniques and chaining encryption in cloud databases. Wireless Personal Communications, 96, 5621–5633.CrossRef
Metadata
Title
Data Fault Detection in Wireless Sensor Networks Using Machine Learning Techniques
Authors
P. Indira Priya
S. Muthurajkumar
S. Sheeba Daisy
Publication date
10-09-2021
Publisher
Springer US
Published in
Wireless Personal Communications / Issue 3/2022
Print ISSN: 0929-6212
Electronic ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-021-09001-1

Other articles of this Issue 3/2022

Wireless Personal Communications 3/2022 Go to the issue

Correction

Correction to: Partially Overlapped Channel Assignment for Cloud-Based Heterogeneous Cellular and Mesh Networks

OriginalPaper

An Adaptive Neuro-Fuzzy Based Region Selection and Authenticating Medical Image Through Watermarking for Secure Communication

OriginalPaper

Improved K-Means Based Q Learning Algorithm for Optimal Clustering and Node Balancing in WSN

OriginalPaper

Quasi Oppositional Dragonfly Algorithm for Load Balancing in Cloud Computing Environment

OriginalPaper

Implementing Integrity Assurance System for Big Data

OriginalPaper

Design of 8-Bit Chipless Printable RFID Tags via Spectral Signature Encoding for Low-Cost Identifications