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Computer Science > Distributed, Parallel, and Cluster Computing

arXiv:2311.17621 (cs)
[Submitted on 29 Nov 2023]

Title:The AutoSPADA Platform: User-Friendly Edge Computing for Distributed Learning and Data Analytics in Connected Vehicles

Authors:Adrian Nilsson, Simon Smith, Jonas Hagmar, Magnus Önnheim, Mats Jirstrand
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Abstract:Contemporary connected vehicles host numerous applications, such as diagnostics and navigation, and new software is continuously being developed. However, the development process typically requires offline batch processing of large data volumes. In an edge computing approach, data analysts and developers can instead process sensor data directly on computational resources inside vehicles. This enables rapid prototyping to shorten development cycles and reduce the time to create new business values or insights. This paper presents the design, implementation, and operation of the AutoSPADA edge computing platform for distributed data analytics. The platform's design follows scalability, reliability, resource efficiency, privacy, and security principles promoted through mature and industrially proven technologies. In AutoSPADA, computational tasks are general Python scripts, and we provide a library to, for example, read signals from the vehicle and publish results to the cloud. Hence, users only need Python knowledge to use the platform. Moreover, the platform is designed to be extended to support additional programming languages.
Comments: 14 pages, 4 figures, 3 tables, 1 algorithm, 1 code listing
Subjects: Distributed, Parallel, and Cluster Computing (cs.DC); Networking and Internet Architecture (cs.NI)
Cite as: arXiv:2311.17621 [cs.DC]
  (or arXiv:2311.17621v1 [cs.DC] for this version)
  https://doi.org/10.48550/arXiv.2311.17621

Submission history

From: Adrian Nilsson [view email]
[v1] Wed, 29 Nov 2023 13:30:26 UTC (130 KB)
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