Wireless ad hoc/sensor networks are playing an increasingly important role in our lives, for which collaboration among distributed nodes is crucial for their success. While most existing approaches for node collaboration are restricted to local and link behavior, this research takes a new, network-wide view: a general methodology, belief propagation (BP), is employed to provide a systematic, accurate, and yet flexible framework for collaborative information processing and dissemination in wireless networks. Belief propagation is a computing algorithm operating on graphical models, while in wireless networks there is a communication graph reflecting connectivity topology. This research investigates the synergy of the two: where the computing graph meets the communication graph. First, it provides design guidelines and analytical tools to facilitate application of BP in wireless networks, leading to distributed, robust, scalable, and energy-efficient communication and network protocols. Meanwhile, the impact of real communication constraints on the design and analysis is explicitly studied. Extension to generalized BP and hybrid architectures is also addressed. Throughout this research, application-specific and data-centric approaches, and cross-layer approaches, are actively explored to facilitate design and analysis and improve performance. This research lies in the interface of networking, communications, and computing, and relies heavily on tools from information theory, communication theory, Bayesian inference, graphical theory and models, and communication/computation/information complexity. Its outcome may advance the theory and practice of these areas, and contribute to the evolution of next-generation wireless networks. The multi-disciplinary nature of this research also lends itself to cross-disciplinary education and well-rounded training of students.
