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.

Autonomous vehicles are at the forefront of interest due to the expectations of changing transportation for the better. In order to make better decisions on the road, vehicles use information from various sources: their own sensors, messages arriving from surrounding vehicles and objects, as well as from centralized entities—including their own Digital Twin. Certain decisions require the information to arrive with low latency and some of this information (such as video) requires broadband communication. Furthermore, the vehicles can populate an area, so they can represent mass communication endpoints that still need low latency and massive broadband. The mobility of the vehicles obviously requires the complete coverage of the roads with reliable wireless communication technologies fulfilling the previously mentioned needs. The fifth generation of cellular mobile technologies, 5G, addresses these requirements. The current paper presents real-life scenarios—on the M86 highway and the ZalaZONE proving ground in Hungary—for the demonstration of vehicular communication with 5G support, where the cars exchange sensor and control information with each other, their environment, and their Digital Twins. The demonstrations were carried out through the Scenario-in-the-Loop (SciL) methodology, where some of the actionable triggers were not physically present around the vehicles, but sensed or simulated around their Digital Twin. The measurements around the demonstrations aim to reveal the feasibility of the 5G Non-Standalone Architecture for certain communication scenarios, and they mainly aim to reveal the current latency and throughput limitations under real-life conditions.

A spectacular measurement campaign was carried out on a real-world motorway stretch of Hungary with the participation of international industrial and academic partners. The measurement resulted in vehicle based and infrastructure based sensor data that will be extremely useful for future automotive R&D activities due to the available ground truth for static and dynamic content. The aim of the measurement campaign was twofold. On the one hand, road geometry was mapped with high precision in order to build Ultra High Definition (UHD) map of the test road. On the other hand, the vehicles—equipped with differential Global Navigation Satellite Systems (GNSS) for ground truth localization—carried out special test scenarios while collecting detailed data using different sensors. All of the test runs were recorded by both vehicles and infrastructure. The paper also showcases application examples to demonstrate the viability of the collected data having access to the ground truth labeling. This data set may support a large variety of solutions, for the test and validation of different kinds of approaches and techniques. As a complementary task, the available 5G network was monitored and tested under different radio conditions to investigate the latency results for different measurement scenarios. A part of the measured data has been shared openly, such that interested automotive and academic parties may use it for their own purposes.

The Industry 4.0 initiative has been showing the way for industrial production to optimize operations based on collecting, processing, and sharing data. There are new requirements on the production floor: flexible but ultra-reliable, low latency wireless communications through interoperable systems can share data. Further challenges of data sharing and storage arise when diverse systems come into play at the Manufacturing Operations Management and Business Planning & Logistics levels. The emerging complex cyber-physical systems of systems need to be engineered with care. Regarding industrial requirements, the telecommunication industry has many similarities to production—including ultra-reliability, high complexity, and having humans “in-the-loop”. The current paper aims to provide an overview of converging telco-grade solutions that can be successfully applied in the wide sense of industrial production. These toolsets range from model-driven engineering through system interoperability frameworks, 5G- and 6G-supported manufacturing, and the telco-cloud to speech recognition in noisy environments.

To analyze next-generation mobile networks properly, there is a need to define key performance indicators (KPIs). Testing signaling only or just partial domains of the network have been replaced with end-to-end testing methodologies. With the appearing of machine-to-machine (M2M) applications, this question became even harder, since there is no direct user feedback. Quality of experience cannot be measured accurately in M2M applications, even if the network operates correctly and without failures. There are dozens of new—but theoretical—use-cases for 5G; however, these are not tested on a live network. The modeling methodology used throughout the paper follows the steps of observation, analysis, model creation, implementation, and verification. The first part of the paper examines the three application-types: enhanced mobile broadband (eMBB), critical Internet of Things (cIoT), and mass Internet of Things (mIoT). Afterwards, we introduce the main traffic characteristics based on current mobile networks’ traffic patterns and measurements. Considering the measurement results, we introduce a methodology and define traffic models for the simulation of different application-types. To validate these models, we compare the generated artificial traffic with real traffic patterns. In the second part of the paper, we examine what the main effects of these traffic patterns on a domestic 5G test-network are. Finally, we suggest some considerations on the possible main impacts regarding 5G network design.

Summary: The massive demand for broadband mobile network services are quite successfully covered already by 3G and 4G cellular mobile systems. The challenges for 5G are more diverse: answering the demands of Ultra‐Reliable Low Latency Communications (URLLC) and massive Machine Type Communications (mMTC) users—besides elevating mobile broadband to the next level. While generic, high‐level targets for KPIs (Key Performance Indicators) are widely communicated, it is not yet well‐understood how the various demands can affect the traffic mixture. Both the radio‐ and the core‐domains of the cellular network have to cope with traffic peaks, and have to obey various QoS (Quality of Service) guarantees. In order to cover these gaps, traffic‐related characteristics (data volume, signaling message types, and traffic peaks) should be determined, and this knowledge should be used during network planning, optimization, and service shaping.This paper aims to provide insights into user behavioral patterns for these three key application areas: enhanced Mobile Broadband (eMBB), URLLC and mMTC. Since traffic volume‐ and burst‐related user behavior is not expected to change suddenly, current targeted data collection on legacy mobile network links would provide a good basic insight for future, 5G usage—at least as traffic patterns. We have collected live pre‐5G mobile network data then analyzed them throughout this paper in order to reveal traffic patterns—and their distinguishing features—for the three key 5G application areas.