Wireless integrated network sensors
Communications of the ACM
Wireless sensor networks: a survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
Bayesian Fault Detection and Diagnosis in Dynamic Systems
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Learning Bayesian Networks
Intelligent sensor fault detection and identification for temperature control
ICCOMP'07 Proceedings of the 11th WSEAS International Conference on Computers
WSEAS TRANSACTIONS on SYSTEMS
Data fusion and topology control in wireless sensor networks
WSEAS Transactions on Signal Processing
A distributed adaptive scheme for detecting faults in wireless sensor networks
WSEAS TRANSACTIONS on COMMUNICATIONS
Time-space-sequential algorithms for distributed Bayesian state estimation in serial sensor networks
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Bayesian Networks for Fault Detection under Lack of Historical Data
ISPAN '09 Proceedings of the 2009 10th International Symposium on Pervasive Systems, Algorithms, and Networks
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This paper presents some considerations related to fault detection and diagnosis, using Bayesian networks, in the complex distributed parameter systems with time and space variables, where the intelligent wireless sensor networks are used as a distributed sensor. These miniaturized intelligent sensors may be placed in the area of multivariable distributed parameter systems and even with limited resources of energy, memory, computational power and bandwidth they may add to solve applications on a large space. Multivariable estimation techniques are easier to applied when a multi-sensor network is used. Bayesian networks bring their main characteristics as graphic models with a node topology and treating information by probabilistic inference. The usage of Bayesian networks is chosen considering the distributed parameter system as a system with continuous variable, but digitally surveyed in discrete time, the sensor placed to measure the time variation of system variables been affected by random noises. The paper presents as an application how Bayesian networks could be applied to fault detection and diagnosis in an on-line estimation of a dynamic model for distributed parameter systems, with exemplification in the case of the city road traffic.