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Industrial cyber-physical systems rely increasingly on data from IoT devices and other systems as continuously emerging use cases implement new intelligent features. Edge computing can be seen as an extension of the cloud in close physical proximity, in which some of the typical cloud computing loads are beneficial to run. This paper studies data analytics application development for integration of industrial IoT data and composition of application services executed on edge and cloud. A solution is designed to support heterogeneous hardware and run-time platforms, and focuses on the service layer that enables flexible orchestration of data flows and dynamic service compositions. The unified model and system architecture implemented, using the open Arrowhead Framework model, is verified through two representative industrial use cases.

No abstract

Industrial applications, including autonomous systems and vehicles, rely on processing data on multiple physical devices. The composition of functionality across heterogeneous computing infrastructure is challenging, and will likely get even more challenging in the future as software in vehicles is updated to introduce new features and ensure the safety. New soft real-time use cases emerge and in such cases the model of offloading processing from a limited or malfunctioning device is a viable solution. This study examines orchestration of services across edge and cloud for an industrial vehicle application use case involving image based object detection using machine learning (ML) based models. First, service orchestration requirements are defined taking into account the dependable nature of industrial vehicle applications. Second, an implementation based on Arrowhead framework is presented and evaluated. The open Arrowhead framework offers means for dynamic service discovery, authorization and late binding of computational units. The feasibility of object detection as a service and the suitability of Arrowhead framework to support such orchestrations across edge and cloud is assessed.