Abstract:

The rapidly expanding Industrial Internet of Things (IIoT) has brought a number of significant challenges, the most of which are associated with the interoperability of several vendors and the scalability of the system, as the current frameworks for the Industrial Internet of Things (IIoT) often fail concerning appropriately address many industrial settings that are characterized by proprietary systems, a range of communication protocols, and need to monitor thousands of networked devices and considerable data volumes at the technological edge of the industry, when this thesis presents a novel Kubernetes-native architecture with the intention of overcoming these limitations by making full use of the orchestration capabilities offered by Kubernetes primitives, as well as maintaining communication consistency and fostering interoperability across a wide range of use cases, data formats, and automation methodologies are both facilitated by the architecture that has been recommended, while a robust resource management approach that distributes agents between edge and cloud layers is one of the essential aspects. Other essential elements include enhancing for a wide variety of data formats, complex data conversion procedures, and resource management, as an automated scheduling system that is specifically designed for distributed IIoT systems is included into the framework, this system makes it easier to allocate complex resources and improve energy efficiency, the effectiveness of the framework will be evaluated in order to establish its validation via the use of experimental scenarios that include a realistic industrial workload, it will demonstrate the framework's improved performance in terms of enhancing both interoperability and scalability across a variety of IIoT systems.