FDS: Fault Detection Scheme for Wireless Sensor Networks

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• 作者：Chafiq Titouna ; Makhlouf Aliouat ; Mourad Gueroui
• 关键词：Wireless sensor networks ; Fault detection ; Naive Bayesian classifier
• 刊名：Wireless Personal Communications
• 出版年：2016
• 出版时间：January 2016
• 年：2016
• 卷：86
• 期：2
• 页码：549-562
• 全文大小：983 KB
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• 作者单位：Chafiq Titouna (1)
  Makhlouf Aliouat (2)
  Mourad Gueroui (3)
  
  1. Computer Science Department, University of Bejaia, 06000, Bejaia, Algeria
  2. Computer Science Department, University of Setif 1, 19000, Setif, Algeria
  3. PRISM Laboratory, University of Versailles, Versailles, France
  
• 刊物类别：Engineering
• 刊物主题：Electronic and Computer Engineering
  Signal,Image and Speech Processing
  Processor Architectures
  
• 出版者：Springer Netherlands
• ISSN：1572-834X

文摘

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. Keywords Wireless sensor networks Fault detection Naive Bayesian classifier