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This paper investigates the problem of designing decentralized representations to support monitoring and inferences in sensor networks. State-space models of physical phenomena such as those arising from tracking multiple interacting targets, while commonly used in signal processing and control, suffer from the curse of dimensionality as the number of phenomena of interest increases. Furthermore, mapping an inference algorithm onto a distributed sensor network must appropriately allocate scarcedoi:10.1145/984622.984657 dblp:conf/ipsn/LiuCLRZ04 fatcat:qx5xhy7zwrbt3h2fbdiyl7a5fi