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.

Digitalization and networking are taking on an increasingly important role in manufacturing. Fifth Generation mobile networks (5G) allow us to wirelessly connect multiple assets in factories with guaranteed quality of service (QoS). A 5G non-public network (5G-NPN) realizes a dedicated network with secure communication within the factory. Time-sensitive networking (TSN) provides deterministic connectivity and reliability in local networks. Edge computing moves computing power near factory locations, reducing the latency of edge applications. Making production processes more flexible, more robust, and resilient induces a great challenge for integrating these technologies. This paper presents the benefits of the joint use of 5G-NPN, TSN, and edge computing in manufacturing. To that end, first, the characteristics of the technologies are analyzed. Then, the integration of different 5G-NPN deployment options with edge (and cloud) computing is presented to provide end-to-end services. For enhanced reliability, ways of interworking between TSN and edge computing domains are proposed. Afterward, as an example realization of edge computing, the investigation on the capabilities of the Kubernetes container orchestration platform is presented together with the gap analysis for smart manufacturing requirements. Finally, the different integration options, interworking models, and Kubernetes-based edge computing are evaluated to assist smart factories to use these new technologies in combination in the future.

The Time Sensitive Networking Task Group of the IEEE 802.1 Working Group has developed a number of enhancements to the priority queueing architecture used by Ethernet. Perhaps the most notable one is the time-aware shaper (TAS), which can perfectly isolate priority classes in time with periodically scheduled traffic gates, thereby enabling ultra low latency applications to coexist with lower priority traffic on the same network. It requires near-perfect clock synchronization across the entire network, though, and a central network controller to calculate the timings for the traffic gates, based on known propagation delays on the network links. As a cheaper alternative, we have developed the asynchronous TAS that needs no clock synchronization, network planning, or a central controller. It uses local processes at each switch port to track the high priority streams, predict the arrival times of their next frames, and control the traffic gates such that the low priority streams are not interfering. We show how a simple predictor with minimal computation requirements can be used to track the high priority streams, and we demonstrate via simulations that this gating mechanism can protect the high priority traffic from interference.