Abstract

This thesis examines reasoning under uncertainty in distributed systems. Unlike in centralized systems, where the observations reside in a single location, the observations in distributed systems are often scattered across the network. To reason accurately, a networked device often needs to incorporate observations from other nodes and must do so with limited computation and communication even for large problems. The reasoning is further complicated by unstable network conditions, characteristic to many real-world networks: the nodes may fail, communication links may become unreliable, and the entire network may get fragmented into several components that cannot communicate with each other. These aspects make distributed inference very challenging. We consider one general problem of distributed filtering for estimating the state of a dynamical system and three independent applications: simultaneous localization and tracking, where a camera network localizes itself by observing a moving object, internal localization of large-scale modular robots, where a robot determines the relative poses of its internal parts,

Citations