Querying Large Vehicular Networks: How to Balance On-Board Workload and Queries Response Time?

Paper in proceeding, 2019

Data analysis plays a key role in designing today’s Intelligent Transportation Systems (ITS) and is expected to become even more important in the future. Connected vehicles, one of the main instantiations of ITS, produce large volumes of data that are hard to gather by centralized analysis tools. The even larger volumes of data expected from autonomous driving will further exacerbate the bottleneck problem of data retrieval. When analysts issue queries that seek data from vehicles satisfying certain criteria (e.g. those driving above a certain speed or in a certain area), the problem can nonetheless be overcome by pushing to vehicles themselves the job of checking and reporting the compliance of their local data, hence avoiding a costly data retrieval phase. To efficiently provide answers for such queries, we present in this work configurable query-spreading algorithms tailored for vehicular networks. Our tunable algorithms, which we evaluate on two large datasets of real-world vehicular data, outperform baseline solutions and are able to trade-off the overall on-board workload and the response time needed to resolve a set of queries.