Time- and Computation-Efficient Data Localization at Vehicular Networks\u27 Edge

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\u27 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 7 speedup while reducing computational overhead by up to 3 7 , 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

Distributed and Communication-Efficient Continuous Data Processing in Vehicular Cyber-Physical Systems

Processing the data produced by modern connected vehicles is of increasing interest for vehicle manufacturers to gain knowledge and develop novel functions and applications for the future of mobility.Connected vehicles form Vehicular Cyber-Physical Systems (VCPSs) that continuously sense increasingly large data volumes from high-bandwidth sensors such as LiDARs (an array of laser-based distance sensors that create a 3D map of the surroundings).The straightforward attempt of gathering all raw data from a VCPS to a central location for analysis often fails due to limits imposed by the infrastructure on the communication and storage capacities. In this Licentiate thesis, I present the results from my research that investigates techniques aiming at reducing the data volumes that need to be transmitted from vehicles through online compression and adaptive selection of participating vehicles. As explained in this work, the key to reducing the communication volume is in pushing parts of the necessary processing onto the vehicles\u27 on-board computers, thereby favorably leveraging the available distributed processing infrastructure in a VCPS.The findings highlight that existing analysis workflows can be sped up significantly while reducing their data volume footprint and incurring only modest accuracy decreases. At the same time, the adaptive selection of vehicles for analyses proves to provide a sufficiently large subset of vehicles that have compliant data for further analyses, while balancing the time needed for selection and the induced computational load