The Internet of Things (IoT) has gained popularity and is increasingly used in large scale deployments for industrial applications. Such deployments rely on the flexibility and scalability of systems and devices. Heterogeneous systems need to be interoperable and work together seamlessly. In order to manage such system of systems, it is important to work with a framework that not only supports the flexible nature of IoT systems but also provides adequate support for industrial requirements, such as real-time and runtime features, architectural approaches, hardware constraints, standardization, industrial support, interoperability, and security. The selection of an appropriate framework results difficult due to the rising number of available frameworks and platforms, which offer different support for the aforementioned requirements. Therefore, this article investigates the features of seven prominent frameworks for the purpose of simplifying the selection of a suitable framework for an industrial application. The aim of this article is to present the recent developments and state-of-the-art of industrial IoT frameworks and provide a technical comparison of their features and characteristics.

Industry 4.0 is advancing the use of Internet of Things (IoT) devices in industrial applications, which enables efficient device-to-device (D2D) communication. However, these devices are often heterogeneous in nature, i.e. from different manufacturers, use different protocols, etc. and adds requirements such as security, interoperability, etc. To address these requirements, the Service-Oriented Architecture-Based (SOA) Arrowhead Framework was previously proposed using the concept of local clouds. These local clouds provide a set of mandatory and support core systems to enable industrial automation applications. One of these mandatory core systems is an Authentication, Authorisation and Accounting (AAA) system, which is used to authenticate and provide access control to the devices in a local cloud. In an industrial context, with multiple stakeholders, the AAA must support fine-grain access control. For example, in a distributed control loop, a controller should only have read access to its sensor such as a flow meter and write access to its actuator, such as a valve. The controller should not have access to any other information besides what is needed to implement the desired functionality. In this work, an NGAC-based AAA solution to achieve fine-grain service level access control between IoT devices has been proposed and implemented. The solution is presented using a district heating use case.

The Internet of Things (IoT) enables interaction from real-world physical objects using sensors to the virtual world of computers and the Internet. The use of service-oriented architecture (SOA) is one step in the creation of basic and complex interactions between several sensors and actuators. However, the use of SOA-enabled technologies alone does not meet all requirements of how sensor and actuator systems could be integrated to create distributed monitoring and control applications. The centralized, traditional method of communication in wireless sensor networks via a gateway presents drawbacks that have to be addressed; device-to-cloud communication adds higher latency and higher power consumption and is less robust than the device-to-device communication approach. Moreover, all these characteristics reduce the scalability of the network, thus limiting the use of IoT in the industry. In this paper, the proposed method utilizes the Arrowhead framework orchestration system to generate service composition within a (wireless) network formed by IoT devices. The aim is to achieve efficient device-to-device service invocation to reduce the drawbacks of today's widely used device-to-cloud approach. The method in this paper performs efficient service composition for industrial IoT, including mapping SOA service composition in very small resource-constrained devices using the Arrowhead orchestration. The results presented in this paper at the service level can increase performance and robustness in fog computing on resource-constrained devices.

In Industry 4.0, systems with a heterogeneous implementation, different architecture, protocols, and semantics have to be integrated into collaborative environments. The interoperability between them has become a major challenge in this new ecosystem of the industry, thereby generating several research questions about how to manage the information exchange and collaboration between systems with so vastly different properties.This paper presents a detailed investigation of the different mismatch problems that can happen in the information exchange in heterogeneous SOA-based environments where the interoperability is crucial. The proposed solution in this paper is an Adapter System that can aid generatation of new service consumer interfaces at both compile-time and run-time. The interfaces are based on Service Contracts, thereby allowing heterogeneous systems to communicate and interchange services.The proposed approach requires a new point of view in the service description field that can provide a holistic description of the information required for creating both run-time and compile-time consumer interfaces.

Insufficient resources and high costs are hindering industrial development, potentially impeding adaptation to market demands. Overcoming this challenge necessitates advancements in software engineering techniques to streamline processes and meet industrial requirements. Crucially, automating manual tasks and enhancing interoperability between engineering stages can yield efficiency gains. This paper presents a model‐based system engineering approach aimed at automating the transition from design to implementation, incorporating autonomous generation and validation features. Implemented as plugins and utilizing model transformation techniques, this solution targets reducing engineering time and facilitating the adoption of new technologies. Developed, implemented, and tested within the Arrowhead framework, the approach is followed by a discussion on its benefits and limitations.

Recent years have seen a surge of interest in using IoT systems for an increasingly diverse set of applications, with use cases ranging from medicine to mining. Due to the disparate needs of these applications, vendors are adopting a growing number of messaging protocols, encodings and semantics, which result in poor interoperability unless systems are explicitly designed to work together. Key efforts, such as Industry 4.0, put heavy emphasis on being able to compose arbitrary IoT systems to create emergent applications, which makes mitigating this barrier to interoperability a significant objective. In this paper, we present a theoretical method for translating message payloads in transit between endpoints, complementing previous work on protocol translation. The method involves representing and analyzing encoding syntaxes with the aim of identifying the concrete translations that can be performed without risk of syntactic data loss. While the method does not facilitate translation between all possible encodings or semantics, we believe that it could be extended to enable such translation.