Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
System architecture directions for networked sensors
ASPLOS IX Proceedings of the ninth international conference on Architectural support for programming languages and operating systems
Embedded Everywhere: A Research Agenda for Networked Systems of Embedded Computers
Embedded Everywhere: A Research Agenda for Networked Systems of Embedded Computers
Tree approximation for belief updating
Eighteenth national conference on Artificial intelligence
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A parallel framework for loopy belief propagation
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Calibrating distributed camera networks using belief propagation
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Distributed probabilistic inferencing in sensor networks using variational approximation
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EUC'05 Proceedings of the 2005 international conference on Embedded and Ubiquitous Computing
ACM Transactions on Sensor Networks (TOSN)
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Sensor networks are an exciting new kind of computer system. Consisting of a large number of tiny, cheap computational devices physically distributed in an environment, they gather and process data about the environment in real time. One of the central questions in sensor networks is what to do with the data, i.e. how to reason with it and how to communicate it. This paper argues that the lessons of the UAI community, in particular that one should produce and communicate beliefs rather than raw sensor values, are highly relevant to sensor networks. We contend that loopy belief propagation is particularly well suited to communicating beliefs in sensor networks, due to its compact implementation and distributed nature. We investigate the ability of loopy belief propagation to function under the stressful conditions likely to prevail in sensor networks. Our experiments show that it performs well and degrades gracefully. It converges to appropriate beliefs even in highly asynchronous settings where some nodes communicate far less frequently than others; it continues to function if some nodes fail to participate in the propagation process; and it can track changes in the environment that occur while beliefs are propagating. As a result, we believe that sensor networks present an important application opportunity for UAI.