Choice of Detection Parameters on Fault Detection in Wireless Sensor Networks: A Multiobjective Optimization Approach

In this paper, the intermittent fault detection in wireless sensor networks is formulated as an optimization problem and a recently introduced multiobjective swarm optimization (2LB-MOPSO) algorithm is used to find an optimum trade-off between detection accuracy and detection latency. Faulty sensor nodes are identified based on comparisons of sensed data between one-hop neighboring nodes. Time redundancy is used to detect intermittent faults since an intermittent fault does not occur consistently. Simulation and analytical results show that sensor nodes with permanent faults are identified with high accuracy and by properly choosing the inter-test interval most of the intermittent faults are isolated with negligible performance degradation.

[1]  D. P. Kothari,et al.  Stochastic economic emission load dispatch , 1993 .

[2]  Tian He,et al.  FIND: faulty node detection for wireless sensor networks , 2009, SenSys '09.

[3]  Sai Ji,et al.  Distributed Fault Detection for Wireless Sensor Based on Weighted Average , 2010, 2010 Second International Conference on Networks Security, Wireless Communications and Trusted Computing.

[4]  Songlin Sun,et al.  Fault diagnosis of sensor by chaos particle swarm optimization algorithm and support vector machine , 2011, Expert systems with applications.

[5]  Azzedine Boukerche,et al.  A distributed fault identification protocol for wireless and mobile ad hoc networks , 2008, J. Parallel Distributed Comput..

[6]  Melvin A. Breuer,et al.  Testing for Intermittent Faults in Digital Circuits , 1973, IEEE Transactions on Computers.

[7]  Yoon-Hwa Choi,et al.  Fault detection of wireless sensor networks , 2008, Comput. Commun..

[8]  Mohd Fadlee A. Rasid,et al.  Cluster Based Routing protocol for Mobile Nodes in Wireless Sensor Network , 2009, CTS.

[9]  Xianghua Xu,et al.  Distributed fault diagnosis of wireless sensor networks , 2008, 2008 11th IEEE International Conference on Communication Technology.

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

[11]  Ponnuthurai Nagaratnam Suganthan,et al.  Two-lbests based multi-objective particle swarm optimizer , 2011 .

[12]  Wei Wang,et al.  A Cluster-based Real-time Fault Diagnosis Aggregation Algorithm for Wireless Sensor Networks , 2011 .

[13]  Yunhao Liu,et al.  Agnostic diagnosis: Discovering silent failures in wireless sensor networks , 2011, INFOCOM.

[14]  Pabitra Mohan Khilar,et al.  Fault Diagnosis in Wireless Sensor Networks: A Survey , 2013, IEEE Communications Surveys & Tutorials.

[15]  Carlos A. Coello Coello,et al.  Handling multiple objectives with particle swarm optimization , 2004, IEEE Transactions on Evolutionary Computation.

[16]  Pabitra Mohan Khilar,et al.  Intermittent Fault Diagnosis in Wireless Sensor Networks , 2007 .

[17]  Ma Yong-guang,et al.  Fault Diagnosis of Sensor Network Using Information Fusion Defined on Different Reference Sets , 2006, 2006 CIE International Conference on Radar.

[18]  Pabitra Mohan Khilar,et al.  SDDP: Scalable Distributed Diagnosis Protocol for Wireless Sensor Networks , 2011, IC3.

[19]  Peng Jiang,et al.  A New Method for Node Fault Detection in Wireless Sensor Networks , 2009, Sensors.

[20]  Walter Lang,et al.  Application of Computational Intelligence for Sensor Fault Detection and Isolation , 2007 .

[21]  Oliver Obst,et al.  Distributed Fault Detection in Sensor Networks using a Recurrent Neural Network , 2009, Neural Processing Letters.

[22]  B. Prabhakaran,et al.  Motion fault detection and isolation in Body Sensor Networks , 2011, Pervasive Mob. Comput..

[23]  Yoon-Hwa Choi,et al.  An Adaptive Fault-Tolerant Event Detection Scheme for Wireless Sensor Networks , 2010, Sensors.

[24]  Mohammad Ali Abido,et al.  Two-level of nondominated solutions approach to multiobjective particle swarm optimization , 2007, GECCO '07.

[25]  Paul Phillips,et al.  Model-based Intermittent Fault Detection , 2013 .

[26]  R.R. Selmic,et al.  Wireless Sensor Network Modeling Using Modified Recurrent Neural Networks: Application to Fault Detection , 2008, 2007 IEEE International Conference on Networking, Sensing and Control.

[27]  Gao Jian-Liang,et al.  Weighted-Median Based Distributed Fault Detection for Wireless Sensor Networks , 2007 .

[28]  Rainer Laur,et al.  Differential evolution with adaptive parameter setting for multi-objective optimization , 2007, 2007 IEEE Congress on Evolutionary Computation.

[29]  Qingfu Zhang,et al.  Multiobjective evolutionary algorithms: A survey of the state of the art , 2011, Swarm Evol. Comput..

[30]  Özgür B. Akan,et al.  Spatio-temporal correlation: theory and applications for wireless sensor networks , 2004, Comput. Networks.

[31]  Qing Zhao,et al.  On the lifetime of wireless sensor networks , 2005, IEEE Communications Letters.

[32]  R. K. Ursem.  Multi-objective Optimization using Evolutionary Algorithms , 2009 .

[33]  Pabitra Mohan Khilar,et al.  Online Distributed Fault Diagnosis in Wireless Sensor Networks , 2013, Wirel. Pers. Commun..

[34]  Neeraj Suri,et al.  On-Line Diagnosis and Recovery: On the Choice and Impact of Tuning Parameters , 2007, IEEE Transactions on Dependable and Secure Computing.

[35]  Stefano Chessa,et al.  Crash faults identification in wireless sensor networks , 2002, Comput. Commun..

[36]  Israel Koren,et al.  A Continuous-Parameter Markov Model and Detection Procedures for Intermittent Faults , 1978, IEEE Transactions on Computers.

[37]  Marvin Zelen,et al.  Mathematical Theory of Reliability , 1965 .

[38]  Feng Zhang,et al.  Sensor fault diagnosis and location for small and medium-scale wireless sensor networks , 2010, 2010 Sixth International Conference on Natural Computation.

[39]  Ahmad Khademzadeh,et al.  A new self-diagnosing approach based on petri nets and correlation graphs for fault management in wireless sensor networks , 2013, J. Syst. Archit..

[40]  Fan Yu,et al.  A Self Reorganizing MAC Protocol for Inter-vehicle Data Transfer Applications in Vehicular Ad Hoc Networks , 2007 .