Fuzzy modeling and control of multilayer incinerator
Fuzzy Sets and Systems - Special issue: Dedicated to the memory of Richard E. Bellman
Real Time Fault Monitoring of Industrial Processes
Real Time Fault Monitoring of Industrial Processes
Wireless sensor networks: a survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
IEEE Transactions on Computers
Fault management in event-driven wireless sensor networks
MSWiM '04 Proceedings of the 7th ACM international symposium on Modeling, analysis and simulation of wireless and mobile systems
On Distributed Fault-Tolerant Detection in Wireless Sensor Networks
IEEE Transactions on Computers
Distributed fault detection of wireless sensor networks
DIWANS '06 Proceedings of the 2006 workshop on Dependability issues in wireless ad hoc networks and sensor networks
Fault detection of wireless sensor networks
Computer Communications
Robust fault detection for switched linear systems with state delays
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fault management in wireless sensor networks
IEEE Wireless Communications
Engineering Applications of Artificial Intelligence
Probabilistic fault detector for Wireless Sensor Network
Expert Systems with Applications: An International Journal
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In this paper we present a fault detection strategy for wireless sensor networks. The strategy is based on modeling a sensor node by Takagi-Sugeno-Kang (TSK) fuzzy inference system (FIS), where a sensor measurement of a node is approximated by a function of the sensor measurements of the neighboring nodes. We also model a node by recurrent TSK-FIS (RFIS), where the sensor measurement of the node is approximated as function of real measurements of the neighboring nodes and the previously approximated value of the node itself. Temporary errors in sensor measurements and/or communication are overcome by redundancy of data gathering. A node which has developed a faulty sensor is not completely discarded because it is useful for relaying the information among the other nodes. Each node has its own fuzzy model that is trained with input of neighboring sensors' measurements and an output of its actual measurement. A sensor is declared faulty if the difference between the outcome of the fuzzy model and the actual sensor measurement is greater than the prescribed amount depending on the physical quantity being measured. Simulations are performed using the fuzzy logic toolbox of Matlab. We also give a comparison of obtained results to those from a feed-forward artificial neural network, recurrent neural network and the median [1] of measured values of the neighboring nodes.