Hovering Data Clouds for Organic Computing

As part of our project AutoNomos, we have investigated traffic information and management systems that motivate the usage of new methods and tools inspired by Organic Computing paradigms. Current traffic monitoring and management approaches with stationary infrastructure lack flexibility with respect to system deployment and have difficulties with detecting unpredictable events (e.g., accidents). One goal of AutoNomos is the development of a distributed and selforganising traffic information and management system without a centralised infrastructure. Our system relies on a GPS-based navigation system and a wireless radio interface; vehicles can gather information about the current position on the road network and form a vehicular ad-hoc network (VANET) to share information about traffic phenomena. In this article, we introduce Hovering Data Clouds (HDCs) as a tool to collect, aggregate and disseminate application-specific data. HDCs evolve in a self-organising manner at locations of relevant data in the system. Although their data is hosted on the nodes, HDCs exist independent of the individual carriers. While HDCs float between physical carriers, their corresponding HDC messages are disseminated in the network by a new effective transport protocol named AutoCast, designed according to Organic Computing paradigms. Finally, we demonstrate that HDCs detect traffic phenomena reliably and propagate them robustly within the network.