A substantial criterion with the use of wireless communication is the missing location information of the mobile participants. The RSSI (Received Signal Strength Indicator)-based localization technique is an easy and well known method to predict the position of an unknown node in indoor environments whereas additional measures are required for a sufficient accuracy. The distance-pending path loss is affected by strong variations, especially appearing as frequency specific signal dropouts. A diversity concept with redundant data transmission in different frequency bands can reduce the dropout probability. Not only the availability of the communication and the positioning, but also the accuracy of the localization can be increased by the diversity concept. Another improvement can be reached by a sensor fusion of the RSSI-based position data with an Inertial Navigation System. First experimental results with miniaturized transceiver prototypes show that a good performance for precision and availability can also be reached with low infrastructural costs.

The fourth industrial revolution, or Industry 4.0 (I4.0), makes use of wireless technologies together with other industrial Internet-of-Things (IIoT) technologies, cyber–physical systems (CPS), and edge computing to enable the optimization and the faster re-configuration of industrial production processes. As I4.0 deployments are ramping up, the practical integration of 5G wireless systems with existing industrial applications is being explored in both Industry and Academia, in order to find optimized strategies and to develop guidelines oriented towards ensuring the success of the industrial wireless digitalization process. This paper explores the challenges arisen from such integration between industrial systems and 5G wireless, and presents a framework applicable to achieve a structured and successful integration. The paper aims at describing the different aspects of the framework such as the application operational flow and its associated tools, developed based on analytical and experimental applied research methodologies. The applicability of the framework is illustrated by addressing the integration of 5G technology into a specific industrial use case: the control of autonomous mobile robots. The results indicate that 5G technology can be used for reliable fleet management control of autonomous mobile robots in industrial scenarios, and that 5G can support the migration of the on-board path planning intelligence to the edge-cloud.

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A common approach of range-based indoor posi tioning methods use the received signal strength (RSS) of RF packets from several anchor nodes to estimate their distances to a mobile emitter with unknown position. The range-based weighted centroid localization (WCL) approach with link quality information (LQI) is known to be more accurate than range free centroid localization (CL) methods using only a binary radio link information according to the cell of origin (COO) principle. With the combining of redundant RF channels using spatial and frequency diversity the WCL position estimation is proved to be even more reliable, although the RSS-based distance estimations of a single RF channel are known to be error-prone in multi path indoor environments. A novel range free approach using the exact number of available diversity channels -the link quantity information (LQnI) -is proposed. It needs no more infrastructural effort or processing power and can easily be applied to the range-based WCL estimation technique with redundant sensor information. Especially the combining of the redundant RSS-based distance estimations together with the LQnI approach leads to a more accurate position estimation. Experimental results in an office building and in a real-life tracking application for maintenance staff in the underground coal mining show the improvements of the additional range-free approach.