Abstract
Since more than one decade, Wireless Sensor Networks (WSN) have been emerged as a promising and interesting area which increasingly drawing researcher attention. So, the attraction to WSNs is due to their large applicability having growing tendency to fit almost all domains in our daily life. WSNs consist of a large number of heterogeneous/homogeneous sensor nodes communicating through wireless medium and working cooperatively to sense or monitor environment sizes related to physical phenomena. As a corner stone involved in WSN design, fault detection is indispensable to offer WSN applications robustness capability allowing them to meet mission success requirements. In order to ensure high quality of service, it is essential for a WSN to be able to detect its faulty sensor nodes before carrying out necessary recovery actions. In this paper, we propose a fault detection scheme (FDS) to identify faulty sensor nodes. FDS performs in two levels; the first level is conducted locally inside the sensor nodes, while the second level is carried out in a higher level (e.g., in a cluster head or gateway). The performance evaluation is tested through simulation to evaluate some factors such as: detection accuracy, false alarm rate, control overhead and memory overhead. We compared our results with referenced algorithm: Fault Detection in Wireless Sensor Networks (FDWSN), and found that FDS performance outperforms that of FDWSN.
Similar content being viewed by others
References
Benini, L., Castelli, G., Macii, A., Macii, E., Poncino, M., & Scarsi, R. (2000). A discrete-time battery model for high-level power estimation. In Proceeding of the design, automation and test in europe conference and exhibition (pp. 35–39).
Subramaniam, S., Palpanas, T., Papadopoulos, D., Kalogeraki, V., & Gunopulos, D. (2006). Online outlier detection in sensor data using non-parametric models. In Proceeding of the 32nd international conference on very large data bases (pp. 187–198).
Martincic, F., & Schwiebert, L. (2006). Distributed event detection in sensor networks. In Proceedings of the international conference on systems and networks communication (pp. 43–48).
Titouna, C., Aliouat, M., & Gueroui, A.-M. Outlier detection approach using bayes classifiers in wireless sensor networks. In Wireless Personal Communications Journal, WIRE-D-14-00341, doi:10.1007/s11277-015-2822-3, Springer.
Chen, J., Kher, S., & Somani, A. (2006). Distributed fault detection of wireless sensor networks. In Proceedings of the workshop on dependability issues in wireless ad hoc networks and sensor networks (pp. 65–72).
Peng, J. (2009). A new method for node fault detection in wireless sensor networks. Sensors, 9(2), 1282–1294.
Chen, X., Kim, Y.-A., Wang, B., Wei, W., Shi, Z.-J., & Song, Y. (2012). Fault-tolerant monitor placement for out-of-band wireless sensor network monitoring. Ad Hoc Networks, 10(1), 62–74.
Bari, A., Jaekel, A., Jiang, J., & Xu, Y. (2012). Design of fault tolerant wireless sensor networks satisfying survivability and lifetime requirements. Computer Communications, 35(3), 320–333.
Geeta, D.-D., Nalini, N., & Biradar, R.-C. (2013). Fault tolerance in wireless sensor network using hand-off and dynamic power adjustment approach. Journal of Network and Computer Applications, 36(4), 1174–1185.
Lee, M.-H., & Choi, Y.-H. (2008). Fault detection of wireless sensor networks. Computer Communications, 31(14), 3469–3475.
Pearl, J. (1988). Probabilistic reasoning in intelligent systems: Networks of plausible inference. Burlington: Morgan Kaufmann.
John, G.-H., & Pat, L. (1995). Estimating continuous distributions in Bayesian classifiers. In Proceedings of the Eleventh conference on uncertainty in artificial intelligence (pp. 338–345).
Gregory, F.-C., & Herskovits, E. (1992). A Bayesian method for the induction of probabilistic networks from data. Machine Learning, 9(4), 309–347.
Sahami, M. (1996). Learning limited dependence bayesian classifiers. In Proceedings of the second international conference on knowledge discovery and data mining (pp. 334–338).
Sahami, M., Dumais, S., Heckerman, D., & Horvitz, E. (1998). A bayesian approach to filtering junk e-mail. In AAAI-98 workshop on learning for text categorization.
Jingnian, C., Houkuan, H., Shengfeng, T., & Youli, Q. (2009). Feature selection for text classification with Naïve Bayes. Expert Systems with Applications, 36(3), 5432–5435.
Abid, S., & Vinod, S. (2012). Intelligent Naïve Bayes approach to diagnose diabetes type-2. International Journal of Computer Applications, Special Issue on Issues and Challenges in Networking, Intelligence and Computing Technologies, 3, 14–16.
Saurabh, M., & Neelam, Sharma. (2012). Intrusion detection using naive Bayes classifier with feature reduction. Procedia Technology, 4, 119–128.
Levis, P., Lee, N., Welsh, M., & Culler, D. (2003). TOSSIM: accurate and scalable simulation of entire TinyOS applications. In Proceedings of the 1st international conference on Embedded networked sensor systems (SenSys).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Titouna, C., Aliouat, M. & Gueroui, M. FDS: Fault Detection Scheme for Wireless Sensor Networks. Wireless Pers Commun 86, 549–562 (2016). https://doi.org/10.1007/s11277-015-2944-7
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11277-015-2944-7