System architecture directions for networked sensors
ASPLOS IX Proceedings of the ninth international conference on Architectural support for programming languages and operating systems
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
Distributed regression: an efficient framework for modeling sensor network data
Proceedings of the 3rd international symposium on Information processing in sensor networks
A collaborative approach to in-place sensor calibration
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
Loopy belief propagation as a basis for communication in sensor networks
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Distributed localization of networked cameras
Proceedings of the 5th international conference on Information processing in sensor networks
A robust architecture for distributed inference in sensor networks
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Distributed metric calibration of ad hoc camera networks
ACM Transactions on Sensor Networks (TOSN)
Calibrating distributed camera networks using belief propagation
EURASIP Journal on Applied Signal Processing
Distributed probabilistic inferencing in sensor networks using variational approximation
Journal of Parallel and Distributed Computing
Fully distributed EM for very large datasets
Proceedings of the 25th international conference on Machine learning
Adaptive dynamic probabilistic networks for distributed uncertainty processing
Journal of Experimental & Theoretical Artificial Intelligence
An autonomic sensing framework for body sensor networks
Proceedings of the ICST 2nd international conference on Body area networks
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
Efficient Distributed Bayesian Reasoning via Targeted Instantiation of Variables
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
A multi-agent systems approach to distributed bayesian information fusion
Information Fusion
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
Collaborative multiagent Gaussian inference in a dynamic environment using belief propagation
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Resource-aware junction trees for efficient multi-agent coordination
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Efficient design and inference in distributed bayesian networks: an overview
TbiLLC'09 Proceedings of the 8th international tbilisi conference on Logic, language, and computation
Global peer-to-peer classification in mobile ad-hoc networks: a requirements analysis
CONTEXT'11 Proceedings of the 7th international and interdisciplinary conference on Modeling and using context
Building graphical model based system in sensor networks
EUC'05 Proceedings of the 2005 international conference on Embedded and Ubiquitous Computing
Distributed data association in smart camera networks using belief propagation
ACM Transactions on Sensor Networks (TOSN)
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Probabilistic inference problems arise naturally in distributed systems such as sensor networks and teams of mobile robots. Inference algorithms that use message passing are a natural fit for distributed systems, but they must be robust to the failure situations that arise in real-world settings, such as unreliable communication and node failures. Unfortunately, the popular sum--product algorithm can yield very poor estimates in these settings because the nodes' beliefs before convergence can be arbitrarily different from the correct posteriors. In this paper, we present a new message passing algorithm for probabilistic inference which provides several crucial guarantees that the standard sum--product algorithm does not. Not only does it converge to the correct posteriors, but it is also guaranteed to yield a principled approximation at any point before convergence. In addition, the computational complexity of the message passing updates depends only upon the model, and is independent of the network topology of the distributed system. We demonstrate the approach with detailed experimental results on a distributed sensor calibration task using data from an actual sensor network deployment.