FDS: Fault Detection Scheme for Wireless Sensor Networks

被引:46
作者
Titouna, Chafiq [1 ]
Aliouat, Makhlouf [2 ]
Gueroui, Mourad [3 ]
机构
[1] Univ Bejaia, Dept Comp Sci, Bejaia 06000, Algeria
[2] Univ Setif 1, Dept Comp Sci, Setif 19000, Algeria
[3] Univ Versailles, PRISM Lab, F-78000 Versailles, France
关键词
Wireless sensor networks; Fault detection; Naive Bayesian classifier;
D O I
10.1007/s11277-015-2944-7
中图分类号
TN [电子技术、通信技术];
学科分类号
0809 ;
摘要
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.
引用
收藏
页码:549 / 562
页数:14
相关论文
共 19 条
[1]  
[Anonymous], 1998, AAAI 98 WORKSH LEARN
[2]   Design of fault tolerant wireless sensor networks satisfying survivability and lifetime requirements [J].
Bari, Ataul ;
Jaekel, Arunita ;
Jiang, Jin ;
Xu, Yufei .
COMPUTER COMMUNICATIONS, 2012, 35 (03) :320-333
[3]  
Benini L., 2000, Proceedings Design, Automation and Test in Europe Conference and Exhibition 2000 (Cat. No. PR00537), P35, DOI 10.1109/DATE.2000.840012
[4]  
Chen J., 2006, P 2006 WORKSH DEP IS, P65, DOI [DOI 10.1145/1160972.1160985, 10.1145/1160972.1160985]
[5]   Feature selection for text classification with Naive Bayes [J].
Chen, Jingnian ;
Huang, Houkuan ;
Tian, Shengfeng ;
Qu, Youli .
EXPERT SYSTEMS WITH APPLICATIONS, 2009, 36 (03) :5432-5435
[6]   Fault-tolerant monitor placement for out-of-band wireless sensor network monitoring [J].
Chen, Xian ;
Kim, Yoo-Ah ;
Wang, Bing ;
Wei, Wei ;
Shi, Zhijie Jerry ;
Song, Yuan .
AD HOC NETWORKS, 2012, 10 (01) :62-74
[7]  
COOPER GF, 1992, MACH LEARN, V9, P309, DOI 10.1007/BF00994110
[8]   Fault tolerance in wireless sensor network using hand-off and dynamic power adjustment approach [J].
Geeta, D. D. ;
Nalini, N. ;
Biradar, Rajashekhar C. .
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS, 2013, 36 (04) :1174-1185
[9]   A New Method for Node Fault Detection in Wireless Sensor Networks [J].
Jiang, Peng .
SENSORS, 2009, 9 (02) :1282-1294
[10]  
John G. H., 1995, Uncertainty in Artificial Intelligence. Proceedings of the Eleventh Conference (1995), P338