Distributed detection and fusion in a large wireless sensor network of random size
EURASIP Journal on Wireless Communications and Networking
Distributed detection in a large wireless sensor network
Information Fusion
Decentralized detection in wireless sensor networks with channel fading statistics
EURASIP Journal on Wireless Communications and Networking
On the Average Case Communication Complexity for Detection in Sensor Networks
DCOSS '08 Proceedings of the 4th IEEE international conference on Distributed Computing in Sensor Systems
Distributed Detection in Wireless Sensor Networks Using Dynamic Sensor Thresholds
International Journal of Distributed Sensor Networks - Selected Papers in Innovations and Real-Time Applications of Distributed Sensor Networks
Distributed detection in the presence of Byzantine attacks
IEEE Transactions on Signal Processing
Resource-scalable joint source-channel MAP and MMSE estimation of multiple descriptions
IEEE Transactions on Signal Processing
Conditional dependence in distributed detection: how far can we go?
ISIT'09 Proceedings of the 2009 IEEE international conference on Symposium on Information Theory - Volume 1
IEEE Transactions on Signal Processing
Collaborative pairwise detection schemes for improving coverage in WSNs
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
Decentralized detection in censoring sensor networks under correlated observations
EURASIP Journal on Advances in Signal Processing
Energy efficient distributed detection via multi-hop transmission in sensor networks
MILCOM'06 Proceedings of the 2006 IEEE conference on Military communications
Distributed estimation over complex networks
Information Sciences: an International Journal
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Most results about quantized detection rely strongly on an assumption of independence among random variables. With this assumption removed, little is known. Thus, in this paper, Bayes-optimal binary quantization for the detection of a shift in mean in a pair of dependent Gaussian random variables is studied. This is arguably the simplest meaningful problem one could consider. If results and rules are to be found, they ought to make themselves plain in this problem. For certain problem parametrizations (meaning the signals and correlation coefficient), optimal quantization is achievable via a single threshold applied to each observation-the same as under independence. In other cases, one observation is best ignored or is quantized with two thresholds; neither behavior is seen under independence. Further, and again in distinction from the case of independence, it is seen that in certain situations, an XOR fusion rule is optimal, and in these cases, the implied decision rule is bizarre. The analysis is extended to the multivariate Gaussian problem.