Abstract
This paper presents a methodology for forecasting the average downlink throughput for an LTE cell by using real measurement data collected by multiple LTE probes. The approach uses data analytics techniques, namely forecasting algorithms to anticipate cell congestion events which can then be used by Self-Organizing Network (SON) strategies for triggering network re-configurations, such as shifting coverage and capacity to areas where they are most needed, before subscribers have been impacted by dropped calls or reduced data speeds. The presented implementation results show the prediction of network behaviour is possible with a high level of accuracy, effectively allowing SON strategies to be enforced in time.

Abstract
This chapter describes outdoor transmission tests and field measurements in TV white spaces (TVWS) carried out in Europe. TVWS Measurements in Germany showed that the extended Hata model is appropriate to describe the path loss over distances up to a few kilometers. During the TVWS trial in Slovenia, we combine infrastructure sensing with geo-location database access to protect not only DVB-T, but also wireless microphone (WM) signals from TVWS devices interference. © 2015 John Wiley & Sons, Inc.

Abstract
This paper approaches the permanent struggle that less favoured regions must deal with regarding economic opportunities, job creation, income and regional production increase. Since an increased demand for nature and protected areas is taking place in a more and more urban society, some innovation potential is emerging. The study we have developed is focused on sustainable tourism practices in a specific natural area (Malcata Mountain Reserve), using electric mobility, which is known for its zero emission, no polluting and noise-free travelling. The broader study is carried out under the Interreg Funding Program in the Moveletur Project. Our aims are to promote a model of sustainable and clean tourism for visitors of natural areas, to create a network of green tourism itineraries connecting sites of natural and/or cultural value using electric vehicles and to empower tourism sector entrepreneurs with a new added-value service for their activity. Joint work with other natural areas is required to increase results. After the project is finished (by the end of 2018) there will be an improved knowledge about natural and cultural values that natural areas hold and that can be used for visitors’ enjoyment. There will be a more respectful way of ‘doing tourism’ in natural areas and hopefully it will address employment creation and improved territorial competitiveness. Finally, tourism experiences will have more quality and the project will promote smart villages’ further development by using technological components. © 2019, Springer International Publishing AG, part of Springer Nature.

Abstract
The present paper describes the state of the art related to IIoT Devices and Cyber-Physical systems and presents a use case related to predictive maintenance. Industry 4.0 is the boost for smart manufacturing and demands flexibility and adaptability of all devices/machines in the shop floor. The machines must become smart and interact with other machines inside and outside the industries/factories. The predictive maintenance is a key topic in this industrial revolution. The reason is based on the idea that smart machines must be capable to automatically identify and predict possible faults and actuate before they occur. Vibrations can be problematic in electrical motors. For this reason, we address an experimental study associated with an automatic classification procedure, that runs in the smart devices to detect anomalies. The results corroborate the applicability and usefulness of this machine learning algorithm to predict vibration faults. © 2019, Springer International Publishing AG, part of Springer Nature.

Abstract
Industry 4.0 paradigm is a reality in the digitization of industrial processes and physical assets, as well as their integration into digital ecosystems with several suppliers of the value chain. In particular, Industry 4.0 is the technological evolution of embedded systems applied to Cyber-Physical Systems (CPSs). With this, a shift from the current paradigm of centralization to a more decentralized production, supported by Industrial Internet of Things (IIoT), is implied. The work reported in this paper focuses on the development of smart devices (SmartBoxes), based on low-cost hardware such as Raspberry Pi and also platforms certified for industrial applications, such as NI CompactRIO. Both platforms adopted the OPC-UA architecture to collect data from the shop-floor and convert it into OPC-UA Data Access standard for further integration in the proposed CPPS. Tests were also performed with the MQTT protocol for monitorization. Each SmartBox is capable of real-time applications that run on OPC-UA and MQTT, allowing easy interaction between supervisory systems and physical assets. © 2019 IEEE.

Abstract
Constant search for efficiency and productivity has led to innovation on the factory shop floor, representing an evolution of the current production systems combined with new technologies of industrial automation and information technology. This work presents an experimental demo of a smartbox for Industry 4.0 scenarios, allowing sensing, monitoring and data acquisition. We have tested two different approaches, depending on the communication protocol used for real time applications: OPC UA or MQTT. Raspberry Pi's platform act as an OPC UA server or MQTT broker, respectively. From the measurements, data stored in a cloud server can be accessed remotely with improved security and visualized from a computer dashboard. One of the conclusions that can be drawn is that both protocols allow data from the smartbox to be stored and easily monitored from a smartphone application or a computer web interface. MQTT is a good option in communications requiring very low bandwidth. However, there is a lack of suitable libraries to program alarm features for OPC UA Servers. © 2019 IEEE.