QoS-Aware Fault Detection in Wireless Sensor Networks

Wireless sensor networks (WSNs) are a fundamental building block of many pervasive applications. Nevertheless the use of such technology raises new challenges regarding the development of reliable and fault-tolerant systems. One of the most critical issues is the detection of corrupted readings amidst the huge amount of gathered sensory data. Indeed, such readings could significantly affect the quality of service (QoS) of the WSN, and thus it is highly desirable to automatically discard them. This issue is usually addressed through “fault detection” algorithms that classify readings by exploiting temporal and spatial correlations. Generally, these algorithms do not take into account QoS requirements other than the classification accuracy. This paper proposes a fully distributed algorithm for detecting data faults, taking into account the response time besides the classification accuracy. We adopt the Bayesian networks to perform classification of readings and the Pareto optimization to allow QoS requirements to be simultaneously satisfied. Our approach has been tested on a synthetic dataset in order to evaluate its behavior with respect to different values of QoS constraints. The experimental evaluation produced good results, showing that our algorithm is able to greatly reduce the response time at the cost of a small reduction in classification accuracy.

[1]  Giuseppe Lo Re,et al.  Sensor9k : A testbed for designing and experimenting with WSN-based ambient intelligence applications , 2012, Pervasive Mob. Comput..

[2]  Giuseppe Lo Re,et al.  Multi-sensor Fusion through Adaptive Bayesian Networks , 2011, AI*IA.

[3]  Giuseppe Lo Re,et al.  Rule based reasoning for network management , 2005, Seventh International Workshop on Computer Architecture for Machine Perception (CAMP'05).

[4]  Giuseppe Lo Re,et al.  A Logical Architecture for Active Network Management , 2005, Journal of Network and Systems Management.

[5]  Ramesh Govindan,et al.  Sensor faults: Detection methods and prevalence in real-world datasets , 2010, TOSN.

[6]  M. Potkonjak,et al.  On-line fault detection of sensor measurements , 2003, Proceedings of IEEE Sensors 2003 (IEEE Cat. No.03CH37498).

[7]  Oliver Obst,et al.  Spatiotemporal Anomaly Detection in Gas Monitoring Sensor Networks , 2008, EWSN.

[8]  Ian F. Akyildiz,et al.  Wireless sensor networks: a survey , 2002, Comput. Networks.

[9]  Gregory J. Pottie,et al.  Bayesian Selection of Non-Faulty Sensors , 2007, 2007 IEEE International Symposium on Information Theory.

[10]  Henk Corporaal,et al.  Quality-of-service trade-off analysis for wireless sensor networks , 2009, Perform. Evaluation.

[11]  Harry Zhang,et al.  The Optimality of Naive Bayes , 2004, FLAIRS.

[12]  Oliver Obst,et al.  Wireless Sensor Network Anomalies: Diagnosis and Detection Strategies , 2011 .

[13]  VARUN CHANDOLA,et al.  Anomaly detection: A survey , 2009, CSUR.

[14]  Giuseppe Lo Re,et al.  A distributed Bayesian approach to fault detection in sensor networks , 2012, 2012 IEEE Global Communications Conference (GLOBECOM).

[15]  Sameer Singh,et al.  Novelty detection: a review - part 2: : neural network based approaches , 2003, Signal Process..

[16]  Qingsheng Zhu,et al.  Subtractive Clustering Based RBF Neural Network Model for Outlier Detection , 2009, J. Comput..

[17]  Xiuzhen Cheng,et al.  Localized Outlying and Boundary Data Detection in Sensor Networks , 2007 .

[18]  Weili Wu,et al.  Localized Outlying and Boundary Data Detection in Sensor Networks , 2007, IEEE Transactions on Knowledge and Data Engineering.

[19]  M. Palaniswami,et al.  Distributed Anomaly Detection in Wireless Sensor Networks , 2006, 2006 10th IEEE Singapore International Conference on Communication Systems.

[20]  William T. Freeman,et al.  On the optimality of solutions of the max-product belief-propagation algorithm in arbitrary graphs , 2001, IEEE Trans. Inf. Theory.

[21]  Arun Somani,et al.  Distributed fault detection of wireless sensor networks , 2006, DIWANS '06.