AutoSPADA - A platform for distributed data analytics
The AutoSPADA platform puts the data and computational resources of the edge directly in the hands of its users. Users write Python tasks that execute on any number of connected vehicles. With our provided Python library, users can easily access vehicle signals and publish results in their tasks. No assumption is made as to how the Python task is written. This means that you have full flexibility to implement anything you want, from simple statistics to complex use cases such as federated learning. Concrete applications include A/B testing, fuel economy analysis, and road condition monitoring.
As a general edge computing platform, you can still use AutoSPADA for traditional data collection. However, its true potential lies in its interactive nature that lets you perform analytics on live data and immediately see the results. This interactivity facilitates rapid prototyping, allowing users to quickly gain data-driven insights that support decision-making, and adapt to changes in customer needs or behaviour.
Partners: Alkit Communications, Chalmers, Volvo Cars
The client nodes are the edge component of the AutoSPADA platform and are responsible for spawning assigned tasks and reporting the results of these tasks.
The user nodes send tasks to be run on the clients and retrieve task results. Tasks are expressed in Python code and are executed in containers on the host, which makes the platform agnostic to the task implementation.
The server nodes are the bridge between the user and client nodes. Server nodes follow the stateless paradigm, meaning that servers read and write the necessary state to a shared database for each request. Paired with a partition-tolerant database, the backend achieves a high degree of horizontal scalability.
All inter-node communication is encrypted with TLS and authenticated by OIDC using JSON Web Tokens (JWTs) or by mutual TLS (mTLS) using X.509 certificates. By using OICD for user authentication, organisations can easily integrate their existing user databases.
A preprint that discusses the system in-depth is available on arXiv: arXiv:2311.17621