My paper “OODIDA: On-Board/Off-Board Distributed Real-Time Data Analytics for Connected Vehicles” was recently published in the Springer journal Data Science and Engineering. The article has been made freely available via Open Access. The abstract is below.
A fleet of connected vehicles easily produces many gigabytes of data per hour, making centralized (off-board) data processing impractical. In addition, there is the issue of distributing tasks to on-board units in vehicles and processing them efficiently. Our solution to this problem is On-board/Off-board Distributed Data Analytics (OODIDA), which is a platform that tackles both task distribution to connected vehicles as well as concurrent execution of tasks on arbitrary subsets of edge clients. Its message-passing infrastructure has been implemented in Erlang/OTP, while the end points use a language- independent JSON interface. Computations can be carried out in arbitrary programming languages. The message-passing infrastructure of OODIDA is highly scalable, facilitating the execution of large numbers of concurrent tasks.