The manufacturing industry is currently subject to a fundamental change, which is often referred to as the "Fourth Industrial Revolution" or simply "Industry 4.0" [15]. The main goals of Industry 4.0 are—among others—the improvement of flexibility, versatility, resource efficiency, cost efficiency, worker support, and quality of industrial production and logistics. These improvements are important for addressing the needs of increasingly volatile and globalised markets. A major enabler for all this is cyber-physical production systems based on a ubiquitous and powerful connectivity, communication, and computing infrastructure. The infrastructure interconnects people, machines, products, and all kinds of other devices in a flexible, secure and consistent manner. Several different application areas can be distinguished:

1. Factory automation: Factory automation deals with the automated control, monitoring and optimisation of processes and workflows within a factory. This includes aspects like closed-loop control applications (e.g., based on programmable logic or motion controllers) and robotics, as well as aspects of computer-integrated manufacturing. Factory automation generally represents a key enabler for industrial mass production with high quality and cost-efficiency. Corresponding applications are often characterised by highest requirements on the underlying communication infrastructure, especially in terms of communication service availability, determinism, and latency. In the Factories of the Future, static sequential production systems will be more and more replaced by novel modular production systems offering a high flexibility and versatility. This involves many increasingly mobile production assets, for which powerful wireless communication and localisation services are required.
2. Process automation: Process automation refers to the control of production and handling of substances like chemicals, food & beverage, pulp, etc. Process automation improves the efficiency of production processes, energy consumption, and safety of the facilities. Sensors measuring process values, such as pressures or temperatures, are working in closed loops via centralised and decentralised controllers. In turn, the controllers interact with actuators, e.g., valves, pumps, heaters. Also, monitoring of attributes such as the filling levels of tanks, quality of material, or environmental data are important, as well as safety warnings or plant shut downs. Workers in the plant are supported by mobile devices. A process automation facility may range from a few 100 m² to several km², and the facility may be geographically distributed. Depending on the size, a production plant may have several 10,000 measurement points and actuators. Autarkic device power supply for years is needed in order to stay flexible and to keep the total costs of ownership low.
3. HMIs and production IT: Human-machine interfaces (HMIs) include all sorts of devices for the interaction between people and production facilities, such as panels attached to a machine or production line, but also standard IT devices, such as laptops, tablet PCs, smartphones, etc. In addition, augmented- and virtual-reality applications are expected to play an increasingly important role in future.
4. Logistics and warehousing: Organisation and control of the flow and storage of materials and goods in the context of industrial production. In this respect, intra-logistics is dealing with logistics within a certain property (e.g., within a factory), for example by ensuring the uninterrupted supply of raw materials on the shop floor level using automated guided vehicles (AGVs), fork-lifts, etc. This is to be seen in contrast to logistics between different sites. Warehousing particularly refers to the storage of materials and goods, which is also getting more and more automated, for example based on conveyors, cranes and automated storage and retrieval systems.
5. Monitoring and maintenance: Monitoring of certain processes and/or assets in the context of industrial production without an immediate impact on the processes themselves (in contrast to a typical closed-loop control system in factory automation, for example). This particularly includes applications such as condition monitoring and predictive maintenance based on sensor data, but also big data analytics for optimising future parameter sets of a certain process, for instance. For these use cases, the data acquisition process is typically not latency-critical.

For each of these application areas, a multitude of potential use cases exists, some of which are outlined in the following subclauses. These use cases can be mapped to the given application areas (see Table A.2.1-1).