Intelligent Information Dissemination in Collaborative, Context-Aware Environments

Today’s mobile computing environments aggregate many entities, all of them with a number of internal sensors, processing applications and other, user given information that can be shared with others over the available networks. Tomorrow’s ubiquitous computing environments, where the number of sensors is assumed to be even significantly higher, face the challenge that information exchange between entities has to be controlled, not only to protect privacy and to unburden the wireless networks - but also to reduce load on the receiving entities that have to process all incoming information. The approach we propose in this work measures the importance of some information and the interest of the receiver in it, before it is transferred. We apply it in two scenarios with limited resources. In vehicle-to-vehicle communications the transmission time while cars meet and bandwidth availability of the wireless channel is the critical resource, forcing to reduce information exchange. In context inference on mobile devices processing power and battery life are limited and responsiveness to user actions is most important. Hence only the most important information should be processed

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

With ever smaller processors and ubiquitous Internet connectivity, the pervasive computing environments from Mark Weiser’s vision are coming closer. For their context-awareness, 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 non-deterministically 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