Distributed and Communication-Efficient Continuous Data Processing in Vehicular Cyber-Physical Systems
Licentiate thesis, 2020

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' 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.

Query Spreading

Data Streaming

Vehicular Cyber-Physical Systems

Compression

Edge Computing

Online
Opponent: Leonardo Querzoni, University of Rome

Author

Bastian Havers

Chalmers, Computer Science and Engineering (Chalmers), Networks and Systems (Chalmers)

DRIVEN: A framework for efficient Data Retrieval and clustering in Vehicular Networks

Future Generation Computer Systems,; Vol. 107(2020)p. 1-17

Journal article

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

Proceedings of the 2019 IEEE Intelligent Transportation Systems Conference - ITSC 2019,; (2019)p. 2604-2611

Paper in proceedings

HARE: Self-deploying and Adaptive Data Streaming Analytics in Fog Architectures

Swedish Research Council (VR), 2017-01-01 -- 2020-12-31.

BADA - On-board Off-board Distributed Data Analytics

VINNOVA, 2016-12-01 -- 2019-12-31.

AUTOSPADA (Automotive Stream Processing and Distributed Analytics) OODIDA Phase 2

VINNOVA, 2020-03-12 -- 2022-12-31.

Future factories in the Cloud (FiC)

Swedish Foundation for Strategic Research (SSF), 2016-01-01 -- 2020-12-31.

Subject Categories

Computer Science

Publisher

Chalmers University of Technology

Online

Online

Opponent: Leonardo Querzoni, University of Rome

More information

Latest update

11/16/2020