Segmenting Bayesian networks for intelligent information dissemination in collaborative, context-aware environments with Bayeslets

Abstract

With ever smaller processors and ubiquitous internet connectivity, the pervasive computing environments from Mark Weiser's vision are coming closer. For their contextawareness, they will have to incorporate data from the abundance of sensors integrated in everyday life and to benefit from continuous machine-to-machine communications. Along with huge opportunities, this also poses problems: sensor measurements may conflict, processing times of logical and statistical reasoning algorithms increase nondeterministically polynomially or even exponentially, and wireless networks might become congested by the transmissions of all measurements. Bayesian networks are a good starting point for inference algorithms in pervasive computing, but still suffer from information overload in terms of network load and computation time. Thus, this work proposes to distribute processing with a modular Bayesian approach, thereby segmenting complex Bayesian networks. The introduced "Bayeslets" can be used to transmit and process only information which is valuable for its receiver. Two methods to measure the worth of information for the purpose of segmentation are presented and evaluated. As an example for a context-aware service, they are applied to a scenario from cooperative vehicular services, namely adaptive cruise control.

A Bayesian network for diagnosis of networked mobile robots

“…Distributed BNs have been introduced in many works (Sayed and Lohse 2013), (Xiang and Lesser 2000), (Bloemeke and Valtorta 2002), (Langevin 2010), (Hwang and Cho 2006), (Hwang and Cho 2009) to shorten inference time, to reduce load on the calculations server and to infer information from different environments. In fact, the information coming from remote and smart devices should be preprocessed and inferred locally (Frank et al 2014 (Bloemeke and Valtorta 2002), (Langevin 2010), each agent implements its internal knowledge as a local BN which has its own probability distribution. It uses its local BN for its own reasoning.…”