Elements of information theory
Elements of information theory
Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
An introduction to signal detection and estimation (2nd ed.)
An introduction to signal detection and estimation (2nd ed.)
Introduction to statistical signal processing with applications
Introduction to statistical signal processing with applications
Optimal dimensionality reduction of sensor data in multisensor estimation fusion
IEEE Transactions on Signal Processing
Robust Distributed Estimation Using the Embedded Subgraphs Algorithm
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Target Location Estimation in Sensor Networks With Quantized Data
IEEE Transactions on Signal Processing
IEEE Transactions on Information Theory
Universal decentralized estimation in a bandwidth constrained sensor network
IEEE Transactions on Information Theory
IEEE Communications Magazine
IEEE Journal on Selected Areas in Communications
On rate-constrained distributed estimation in unreliable sensor networks
IEEE Journal on Selected Areas in Communications
Decentralized Estimation using distortion sensitive learning vector quantization
Pattern Recognition Letters
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This work addresses the problem of robust distributed estimation in the presence of sensor faults when the fusion center sequentially receives quantized messages from local sensors. The mean square error (MSE) of distributed estimation schemes increases dramatically if the information received from the faulty sensors within the network is not excluded from the estimation process. Accordingly, an efficient collaborative sensor-fault detection (CSFD) scheme is proposed in which the results of a homogeneity test are used to identify the faulty nodes within the network such that their quantized messages can be filtered out when estimating the parameter of interest. Utilizing an asymptotic analytical technique, a lower bound is derived for the MSE of the proposed distributed estimation scheme. A good agreement is observed between the simulated MSE results and the lower bound values, and thus it is inferred that the lower bound provides a convenient and reliable means of predicting the performance of the proposed estimation scheme in real-world sensor networks. In addition, a low-complexity CSFD (LC-CSFD) scheme is proposed to identify faulty sensors in WSNs with a very large number of nodes. The simulation results confirm that the accuracy of the estimates obtained from the CSFD and LCCSFD schemes is significantly better than that obtained from a conventional estimation scheme when applied in sensor networks characterized by an unknown number of sensor faults of various types.