DRIVEN: A framework for efficient Data Retrieval and clustering in Vehicular Networks Abstract   The growing interest in data analysis applications for Cyberxe2x80x93Physical Systems stems from the large amounts of data such large distributed systems sense in a continuous fashion. A key research question in this context is how to jointly address the efficiency and effectiveness challenges of such data analysis applications.  DRIVEN proposes a way to jointly address these challenges for a data gathering and distancebased clustering tool in the context of vehicular networks. To cope with the limited communication bandwidth (compared to the sensed data volume) of vehicular networks and data transmissionxe2x80x99s monetary costs, DRIVEN avoids gathering raw data from vehicles, but rather relies on a streamingbased and errorbounded approximation, through Piecewise Linear Approximation (PLA), to compress the volumes of gathered data. Moreover, a streamingbased approach is also used to cluster the collected data (once the latter is reconstructed from its PLAapproximated form). DRIVENxe2x80x99s clustering algorithm leverages the inherent ordering of the spatial and temporal data being collected to perform clustering in an online fashion, while data is being retrieved. As we show, based on our prototype implementation using Apache Flink and thorough evaluation with realworld data such as GPS, LiDAR and other vehicular signals, the accuracy loss for the clustering performed on the gathered approximated data can be small (below 10%), even when the raw data is compressed to 535% of its original size, and the transferring of historical data itself can be completed in up to onetenth of the duration observed when gathering raw data.

MobilityBased Clustering With Link Quality Estimation for Urban Vanets Owing to high mobility and a large amount of vehicles in a vehicular ad hoc network (VANET), it is challenging to overcome the issues of frequent topology changes and network scalability. To mitigate these issues, a vehicle clustering and management scheme can be applied to VANETs. Towards this goal, a mobilitybased clustering scheme with a clustering link quality estimation (CLQE) metric considering both mobility information and link quality estimation is proposed in this paper. The proposed clustering scheme is evaluated through the NS3 simulator and our simulation results show that our proposed scheme outperforms the closely related schemes in most scenarios.