Decentralized compression and predistribution via randomized gossiping
Proceedings of the 5th international conference on Information processing in sensor networks
Information fusion for wireless sensor networks: Methods, models, and classifications
ACM Computing Surveys (CSUR)
Distributed Activity Recognition with Fuzzy-Enabled Wireless Sensor Networks
DCOSS '08 Proceedings of the 4th IEEE international conference on Distributed Computing in Sensor Systems
Incremental distributed identification of Markov random field models in wireless sensor networks
IEEE Transactions on Signal Processing
Distributed in-network channel decoding
IEEE Transactions on Signal Processing
Innovations diffusion: a spatial sampling scheme for distributed estimation and detection
IEEE Transactions on Signal Processing
Averaging approach for distributed event detection in wireless sensor networks
Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human
Distributed detection in sensor networks with limited range multimodal sensors
IEEE Transactions on Signal Processing
Recognition of user activity sequences using distributed event detection
EuroSSC'07 Proceedings of the 2nd European conference on Smart sensing and context
Message quantization in belief propagation: structural results in the low-rate regime
Allerton'09 Proceedings of the 47th annual Allerton conference on Communication, control, and computing
Distributed consensus-based demodulation: algorithms and error analysis
IEEE Transactions on Wireless Communications
Diffusion LMS-based distributed detection over adaptive networks
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
Order-optimal consensus through randomized path averaging
IEEE Transactions on Information Theory
Dual-decomposition approach for distributed optimization in wireless sensor networks
WASA'11 Proceedings of the 6th international conference on Wireless algorithms, systems, and applications
Efficient in-network processing through local ad-hoc information coalescence
DCOSS'06 Proceedings of the Second IEEE international conference on Distributed Computing in Sensor Systems
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We consider the problem of classifying among a set of M hypotheses via distributed noisy sensors. The sensors can collaborate over a communication network and the task is to arrive at a consensus about the event after exchanging messages. We apply a variant of belief propagation as a strategy for collaboration to arrive at a solution to the distributed classification problem. We show that the message evolution can be reformulated as the evolution of a linear dynamical system, which is primarily characterized by network connectivity. We show that a consensus to the centralized maximum a posteriori (MAP) estimate can almost always reached by the sensors for any arbitrary network. We then extend these results in several directions. First, we demonstrate that these results continue to hold with quantization of the messages, which is appealing from the point of view of finite bit rates supportable between links. We then demonstrate robustness against packet losses, which implies that optimal decisions can be achieved with asynchronous transmissions as well. Next, we present an account of energy requirements for distributed detection and demonstrate significant improvement over conventional decentralized detection. Finally, extensions to distributed estimation are described