Multi-scale query processing in vehicular networks
In the last decade, a number of wireless and small-sized devices (e.g., PDAs, smartphones, sensors, laptops, etc.) with increasing computing capabilities have appeared in the market at very affordable costs. These devices have started to be embedded in modern cars in the form of on-board computers, GPS navigators, or even multimedia centers. Thus, the vehicles can carry useful information, acting as data sources for other vehicles. Recently, some works have addressed the problem of processing queries in such highly dynamic vehicular networks in order to share information between drivers. The proposed query processing techniques usually rely on a push model. Hence, each vehicle receives data from its neighbors and decides whether they are relevant enough to be stored in a local data cache. Then, the data may be used by a query processor to retrieve relevant data for the driver. In this paper, we look at the problem from a broader perspective and discuss the interest of multi-scale query processing techniques in such context. The goal of such techniques is to exploit, at the mobile devices level, different access modes (e.g., push, pull) and various data sources (e.g., data cached locally, data stored by vehicles nearby, remote Web services, etc.) to provide the users with results for their queries. We highlight the most important challenges and outline some possible approaches. We also present a prototype of a first query evaluator developed using the Microsoft LINQ API.
multi-scale query processing vehicular networks services mobile data management network applications