  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.
