Abstract
As Vehicular Networks rely increasingly on sensed data to enhance functionality and safety, efficient and distributed data analysis is needed to effectively leverage new technologies in real-world applications. Considering the tens of GBs per hour sensed by modern connected vehicles, traditional analysis, based on global data accumulation, can rapidly exhaust the capacity of the underlying network, becoming increasingly costly, slow, or even infeasible. Employing the edge processing paradigm, which aims at alleviating this drawback by leveraging vehicles’ computational power, we are the first to study how to localize, efficiently and distributively, relevant data in a vehicular fleet for analysis applications. This is achieved by appropriate methods to spread requests across the fleet, while efficiently balancing the time needed to identify relevant vehicles, and the computational overhead induced on the Vehicular Network. We evaluate our techniques using two large sets of real-world data in a realistic environment where vehicles join or leave the fleet during the distributed data localization process. As we show, our algorithms are both efficient and configurable, outperforming the baseline algorithms by up to a $40\times $ speedup while reducing computational overhead by up to $3\times $ , while providing good estimates for the fraction of vehicles with relevant data and fairly spreading the workload over the fleet. All code as well as detailed instructions are available at https://github.com/dcs-chalmers/dataloc_vn .
Highlights
W ITH the recent advancements in connected Vehicular Networks [1], often facilitated by Vehicular Ad Hoc Networks [2], the automotive industry is witnessing an unprecedented growth of possible ways for leveraging the fine-grained data sensed in modern vehicles and enhance drivers’ safety and experience
To how Mobile Edge Computing [4] pushes parts of data analysis applications, previously run entirely in the cloud, towards mobile users, to achieve lower latency and bandwidth consumption, Vehicular Edge Computing [5] aims at better utilizing the cumulative computational power of Vehicular Networks while coping with high-mobility networks and the challenges stemming from their dynamic topologies and communications
The focus lies on the vehicle selection phase necessarily performed prior to data gathering over large vehicular fleets, typically for selecting vehicles that triggered a particular condition or event [6], [20], [7]
Summary
W ITH the recent advancements in connected Vehicular Networks [1], often facilitated by Vehicular Ad Hoc Networks (or VANETs) [2], the automotive industry is witnessing an unprecedented growth of possible ways for leveraging the fine-grained data sensed in modern vehicles and enhance drivers’ safety and experience. Several recent studies in the literature are focusing on how to avoid general central data gathering by transitioning to models in which the data selection processes, or even the analysis itself, are pushed towards the vehicles [10], [11], [12], for efficient and continuous filtering [13], preprocessing through online compression [14], [7], and conversion of raw data into information in Federated Learning [15]. These distributed analysis models often lack mechanisms that attempt to involve only valid vehicles into the analysis, to avoid unnecessary computational load and data transfers
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
