OODIDA: On-board/Off-board Distributed Data Analytics for Connected Vehicles
Year: 2019
Type: preprint
Abstract: Connected vehicles may produce gigabytes of data per hour, which makes centralized data processing impractical at the fleet level. In addition, there are the problems of distributing tasks to edge devices and processing them efficiently. Our solution to this problem is OODIDA (On-board/off-board Distributed Data Analytics), which is a platform that tackles both task distribution to connected vehicles as well as concurrent execution of large-scale tasks on arbitrary subsets of clients. Its message-passing infrastructure has been implemented in Erlang/OTP, while the end points are language-agnostic. OODIDA is highly scalable and able to process a significant volume of data on resource-constrained clients.
Source: arXiv (Cornell University)
Authors: Gregor Ulm, Emil Gustavsson, Mats Jirstrand
Language: English
Topic: IoT and Edge/Fog Computing
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2912901301