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The increase in irrigated crop areas, especially in countries such as Spain and Italy, has led to an increase in technological solutions for monitoring the irrigation. Furthermore, the centre pivot irrigation systems used in these crops have facilitated the daily work of farmers in recent years, however, the monitoring of these systems and the reporting of problems in their operation has become a key aspect during the growing season. For this reason, different monitoring solutions have been proposed from the area of Precision Agriculture and Information and Communication Technologies (ICTs). Nevertheless, it is necessary that these solutions take into account the digital divide their potential users may suffer and be low-cost solutions in order to be attractive to the end-users. This paper presents several low-cost solutions using novel user interfaces and wireless communication technologies for the monitoring of this type of irrigation systems. This paper presents the results obtained after the deployment of the proposed systems on a real environment and the main conclusions drawn after their use in irrigated maize crops. • LoRaWAN and GPS allow affordable monitoring systems for centre pivot irrigation. • Chatbots and voice assistants help to avoid the digital divide. • Edge Computing Devices and LPWANs enable new AMR solutions for water meters. [ABSTRACT FROM AUTHOR]

Multi-Media applications are amongst the most demanding services, requiring high amounts of network capacity and computational resources for synchronous audio-visual streaming with high quality. Gaming streaming is one of the most demanding of these media applications. Recent technological advances in the 5G domain, precisely Network Function Virtualization (NFV) and Multi-access Edge Computing (MEC), unlock media industries’ potential by offering high-quality media services through dynamic and efficient resource allocation with low latency. This work presents a multi-site gaming streaming use case implemented over an end-to-end 5G-enabled platform provided by the EU H2020 5G-PPP 5G EVE project. Furthermore, we present the executed use case experiment scenarios to validate the use case performance following a set of defined Quality of Experience(QoE)-related Key Performance Indicators (KPIs). In particular, this paper discusses the design workflow and orchestration of the multi-site gaming streaming use case across two 5G EVE sites (i.e., Spain and Greece), providing a detailed description of the network function applications and resources utilized for the use case. Subsequently, leveraging the 5G EVE monitoring platform, this paper elaborates on the executed experiment scenarios that provide the defined KPI metrics data. Finally, this paper presents, discusses, and analyzes the obtained KPI metrics data results and provides recommendations on future works for these kinds of use cases. [ABSTRACT FROM AUTHOR]

With the proliferation of unmanned aerial vehicles (UAVs) in different contexts and application areas, efforts are being made to endow these devices with enough intelligence so as to allow them to perform complex tasks with full autonomy. In particular, covering scenarios such as disaster areas may become particularly difficult due to infrastructure shortage in some areas, often impeding a cloud-based analysis of the data in near-real time. Enabling AI techniques at the edge is therefore fundamental so that UAVs themselves can both capture and process information to gain an understanding of their context, and determine the appropriate course of action in an independent manner. Towards this goal, in this paper, we take determined steps towards UAV autonomy in a disaster scenario such as a flood. In particular, we use a dataset of UAV images relative to different floods taking place in Spain, and then use an AI-based approach that relies on three widely used deep neural networks (DNNs) for semantic segmentation of images, to automatically determine the regions more affected by rains (flooded areas). The targeted algorithms are optimized for GPU-based edge computing platforms, so that the classification can be carried out on the UAVs themselves, and only the algorithm output is uploaded to the cloud for real-time tracking of the flooded areas. This way, we are able to reduce dependency on infrastructure, and to reduce network resource consumption, making the overall process greener and more robust to connection disruptions. Experimental results using different types of hardware and different architectures show that it is feasible to perform advanced real-time processing of UAV images using sophisticated DNN-based solutions. [ABSTRACT FROM AUTHOR]

5G is a key tool for the cloud-based wireless production of audio-visual contents. By providing higher throughput and lower latency than previous mobile network technologies, as well as flexible allocation of network resources, it enables that some content production pipelines, which are currently implemented using wired connectivity, are provided on top of a 5G network. This brings down production costs, reduces environmental impact and increases operational efficiency. In particular, live immersive production services, such as free-viewpoint video (FVV), can be provided in a much more efficient way. In this paper, we address the challenge of producing and streaming FVV services in real time through 5G networks. We have adapted a state-of-the-art FVV system to integrate it within a 5G architecture, using three key technological enablers: mmWave radio access network to support the uplink traffic requirements from FVV cameras, multi-access edge computing to run video processing algorithms with minimum latency, and end-to-end slicing to guarantee a sufficient quality of service (QoS) within the production pipeline. We have built a field trial over the production network of a telecommunication operator, including a mmWave pilot deployment, edge cloud processing, and remote content production, involving three different locations across Spain. We have measured the key performance indicators at the relevant parts of our trial deployment, showing that, with existing 5G technology, it is possible to achieve live FVV production, although with some limitations. We have also analyzed such limitations, obtaining some insights on how the next generation of 5G networks can overcome them to achieve higher quality of experience (QoE). [ABSTRACT FROM AUTHOR]

In this paper, we design an Anomaly Detection (AD) framework for mobile data traffic, capable of identifying different types of anomalous events generated by flash crowds in metropolitan areas. We state the problem using a semi-supervised approach and exploit the great performance of different Recurrent Neural Network (RNN) models to learn the temporal context of input sequences. Our proposal processes real traffic traces from the unencrypted LTE Physical Downlink Control Channel (PDCCH) of an operative network, gathered during an extensive measurement campaign in two major cities in Spain. The AD framework is designed to perform: i) a-posteriori analysis to understand users’ behavior and urban environment variations; ii) real-time analysis to automatically and on-the-fly alert urban anomalies; and iii) estimation of the duration of the periods identified as anomalous. Numerical results show the higher performance of our AD framework compared to classic AD algorithms and confirm that the proposed framework predicts anomalous behaviours with high accuracy and regardless of their cause. [ABSTRACT FROM AUTHOR]

This paper presents a processing model for big IoT data. The model includes a continuous delivery scheme based on building blocks for constructing software pipelines from the edge to the cloud. It also includes a data preparation scheme based on parallel patterns for establishing, in an efficient manner, controls over the production and consumption of IoT data. This scheme adds data properties such as cost-efficiency storage, security, and reliability, which are useful to avoid alterations in data and repudiation situations as well as to mitigate risks still arisen in the cloud such as confidentiality violations and service outages. An overlay structure, including planes such as pub/sub, control, and preservation, integrates the proposed schemes into software pipelines. The proposed model was developed in both prototype and simulator of software pipelines. Case studies were conducted based on pipeline services deployed from the edge, passing from the fog to the cloud for processing and managing real climate data repositories, which were produced by three different data sensor sources, such as ground stations deployed on Mexico and Spain, as well as small distributed IoT devices. Information sharing patterns for end-users to retrieve raw and/or processed IoT data were also studied. The experimental evaluation revealed the feasibility of using continuous delivery scheme to create dataflows from the edge to the cloud, the efficacy of the overlay structure to create information sharing patterns, as well as the efficiency of data preparation schemes and parallel patterns to improve the end-user service experience in comparison with traditional state-of-the-art solutions. [ABSTRACT FROM AUTHOR]