Industrial IoT has special communication requirements, including high reliability, low latency, flexibility, and security. These are instinctively provided by the 5G mobile technology, making it a successful candidate for supporting Industrial IoT (IIoT) scenarios. The aim of this paper is to identify current research challenges and solutions in relation to 5G-enabled Industrial IoT, based on the initial requirements and promises of both domains. The methodology of the paper follows the steps of surveying state-of-the art, comparing results to identify further challenges, and drawing conclusions as lessons learned for each research domain. These areas include IIoT applications and their requirements; mobile edge cloud; back-end performance tuning; network function virtualization; and security, blockchains for IIoT, Artificial Intelligence support for 5G, and private campus networks. Beside surveying the current challenges and solutions, the paper aims to provide meaningful comparisons for each of these areas (in relation to 5G-enabled IIoT) to draw conclusions on current research gaps.

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Abstract-Various forms of cloud computing principles and technologies are becoming important recently. This paper addresses cloud computing for automation and control applications. It's argued that the open Internet cloud idea has such limitations that its not appropriate for automation.Since automation is physically and geographically local, it is inevitable to introduce the concept of local automation clouds. It's here proposed that local automation clouds should be self contained an be able to execute the intended automation functionalities without any external resources. Thus providing a fence at the rim of the local cloud preventing any inbound or outbound communication. Such a local cloud provides possibilities to address key requirements of both todays and future automation solutions. Adding mechanisms for secure inter-cloud administration and data tranfere enables local automation cloud to meet IoT automation system requirements as: 1) Interoperability of a wide range of IoT and legacy devices 2) Automation requirement on latency guarantee/prediction for communication and control computations. 3) Scalability of automation systems enabling very large integrated automation systems 4) Security and related safety of automation systems 5) Ease of application engineering 6) Multi stakeholder integration and operations agility.How these requirements can be met in such a local automation cloud is discussed with references to proposed solutions. The local automation cloud concept is further verified for a compartment climate control application. The control application included an IoT controller, four IoT sensors and actuators, and a physical layer communication gateway. The gateway acted as host for local cloud core functionalities. The climate control application has successfully been implemented using the open source Arrowhead Framework and its supports for design and implementation of self contained local automation clouds.

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-The Arrowhead project aims to address the technical and applicative issues associated with cooperative automation based on Service Oriented Architectures. The problems of developing such kind of systems are mainly due to the lack of adequate development and service documentation methodologies, which would ease the burden of reusing services on different applications. The Arrowhead project proposes a technical framework to efficiently support the development of such systems, which includes several tools for documentation of services and to support the development of SOA-based installations. The work presented in this paper describes the approach which has been developed for the first generation pilots to support the documentation of their structural services. Each service, system and system-of-systems within the Arrowhead Framework must be documented and described in such way that it can be implemented, tested and deployed in an interoperable way. This paper presents the first steps of realizing the Arrowhead vision for interoperable services, systems and systems-of-systems.

Revolutionizing logistics and supply chain management in smart manufacturing is one of the main goals of the Industry 4.0 movement. Emerging technologies such as autonomous vehicles, Cyber-Physical Systems and digital twins enable highly automated and optimized solutions in these fields to achieve full traceability of individual products. Tracking various assets within shop-floors and the warehouse is a focal point of asset management; its aim is to enhance the efficiency of logistical tasks. Global players implement their own solutions based on the state of the art technologies. Small and medium companies, however, are still skeptic toward identification based tracking methods, because of the lack of low-cost and reliable solutions. This paper presents a novel, working, reliable, low-cost, scalable solution for asset tracking, supporting global asset management for Industry4.0. The solution uses high accuracy indoor positioning—based on Ultra-Wideband (UWB) radio technology—combined with RFID-based tracking features. Identifying assets is one of the most challenging parts of this work, so this paper focuses on how different identification approaches can be combined to facilitate an efficient and reliable identification scheme.

Determining the position of ourselves or our assets has always been important to humans. Technology has helped us, from sextants to outdoor global positioning systems, but real-time indoor positioning has been a challenge. Among the various solutions, network-based positioning became an option with the arrival of 5G mobile networks. The new radio technologies, minimized end-to-end latency, specialized control protocols, and booming computation capacities at the network edge offered the opportunity to leverage the overall capabilities of the 5G network for positioning—indoors and outdoors. This paper provides an overview of network-based positioning, from the basics to advanced, state-of-the-art machine-learning-supported solutions. One of the main contributions is the detailed comparison of machine learning techniques used for network-based positioning. Since new requirements are already in place for 6G networks, our paper makes a leap towards positioning with 6G networks. In order to also highlight the practical side of the topic, application examples from different domains are presented with a special focus on industrial and vehicular scenarios.