Abstract
Industrial cyber-physical systems rely increasingly on data from Internet-of-Things (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 article 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.
Highlights
I NDUSTRIAL production systems are cyber-physical systems (CPSs) that integrate raw materials, equipment, humans, and processes on multiple sites into complex system of systems (SoS) that depend on data to operate efficiently and sustainably [1], [2]
In [19], a system design method and system architecture is proposed for the smart product-quality monitoring systems for intellectual system design considerations for different needs, and how these can be implemented in Industry 4.0 settings based on big data, Internet of Things (IoT), and artificial intelligence (AI)
1) Flexible operation based on the set of running services, i.e., the composition of needed services can be accomplished based on services available at a given time
Summary
I NDUSTRIAL production systems are cyber-physical systems (CPSs) that integrate raw materials, equipment, humans, and processes on multiple sites into complex system of systems (SoS) that depend on data to operate efficiently and sustainably [1], [2]. This article researches functionalities and technical features required to flexibly orchestrate cloud- and edge-based services in production IoT applications. A unified approach is proposed to manage heterogeneous data flows from IoT devices and other production systems, and an integration architecture is suggested to orchestrate configurations to and between application service components both on edge and cloud. More flexible orchestration of computational services across edge and cloud is required due to new emerging data use cases, evolving application systems, and balancing network traffic and computing resources. 2) Evaluating the AHF service integration model and the implemented conceptual architecture for meeting the requirements in two real industrial use cases when developing SoS applications for the CPS. A discussion is provided in Section VIII, and Section IX concludes this article
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