Experimental mmWave WiGig-based backhaul network dataset.

The wireless backhaul has emerged as an attractive alternative to traditional fiber backhaul for 5G technology, offering greater flexibility and cost-effectiveness thanks to the availability of high bandwidths capable of achieving fiber-like data rates. However, the millimeter-wave-based (mmWave) protocols, namely IEEE 802.11ad and later IEEE 802.11ay, suffer from a high susceptibility to obstruction, which only allows correct operation under Line-of-Sight conditions (LOS). Any sudden obstructions can significantly reduce the maximum achievable throughput, leading to delays exceeding acceptable limits for critical applications, and may even culminate in link failure in certain circumstances. Therefore, it is essential to assess how different types and durations of obstructions impact different network OSI layers to determine the feasibility of mmWave. WiGig-based technologies for wireless backhaul scenarios. This article describes a dataset collected from an experimental IEEE 802.11ad backhaul network, mmWave-based mesh network at 60 GHz, deployed in an outdoor environment. The data contains multi-layer information, including MAC, PHY, and network data, which provides valuable insights into the WiGig network behavior under three distinct scenarios. These scenarios include normal operation, long-term blocked scenario, and short-term blocked scenario, based on the type and duration of the blockage event crossing the LOS path. The dataset presents an extensive PHY, MAC and transport layer measurement campaign for an outdoor WiGig network, and thus it is a valuable resource for researchers and professionals interested in understanding the behavior and performance of real-life mmWave-based WiGig networks aimed for 5G backhauling.

A dataset of ITS-G5 and cellular vehicular connectivity in urban environment.

Connecting vehicles to the Internet is an emerging challenge of wireless networks. There are two competing methods for achieving this. First, the wireless local area network (WLAN) approach is based on the IEEE 802.11p standard (in its European version called ETSI ITS-G5) created for Cooperative-Intelligent Transportation System applications. Second, the cellular network approach is based on LTE/5G technologies which have been exploited in recent years to support vehicular applications. Advantages such as high bandwidth, high coverage and high reliability make cellular networks a great option for the vehicular environment. This article describes two datasets that support the analysis of WLAN (ETSI ITS-G5) and Cellular (LTE/5G) technologies in a real vehicular and road environment. The two datasets summarize the results obtained in a collection of network performance tests performed in the city of Aveiro, Portugal. In these tests, a set of vehicles (8 On-Board Units) moved randomly around the city, passing near a group of stationary nodes (11 Road-Side Units) uploading data to a server. In the WLAN dataset, data was sent using the ETSI ITS-G5 technology, whereas, in the Cellular dataset, data was sent using LTE/5G technologies. While testing, location, signal quality, and network performance data (achieved throughput, jitter, etc.) were collected. This dataset can support a realistic analysis of WLAN and Cellular performance in an environment that is not only vehicular but also urban, with obstacles and interference.

Time series big data: a survey on data stream frameworks, analysis and algorithms.

Big data has a substantial role nowadays, and its importance has significantly increased over the last decade. Big data's biggest advantages are providing knowledge, supporting the decision-making process, and improving the use of resources, services, and infrastructures. The potential of big data increases when we apply it in real-time by providing real-time analysis, predictions, and forecasts, among many other applications. Our goal with this article is to provide a viewpoint on how to build a system capable of processing big data in real-time, performing analysis, and applying algorithms. A system should be designed to handle vast amounts of data and provide valuable knowledge through analysis and algorithms. This article explores the current approaches and how they can be used for the real-time operations and predictions.

Enabling the Industrial Internet of Things to Cloud Continuum in a Real City Environment.

The Industrial Internet of Things (IIoT) is one of the most demanding IoT applications. The insertion of industries in the context of smart cities and other smart environments, allied with new communication technologies such as 5G, brings a new horizon of possibilities and new requirements. These requirements include low latency, the support of a massive quantity of devices and data, and the need to support horizontal communications between devices at the edge level. To make this feasible, it is necessary to establish an IIoT-to-cloud continuum distributing federated brokers across the infrastructure and providing scalability and interoperability. To attend this type of application, we present the Helix Multi-layered IoT platform and its operating modes. We report and discuss its real-world deployment in the Aveiro Tech City Living Lab in Aveiro, Portugal with functional and performance tests. We tested device-to-device communication across edge and core layers and also interconnected the infrastructure with one in São Paulo, Brazil, replicating the use of a global industry. The successful deployment validates the use of a Helix Multi-layered IoT platform as a suitable backend platform for IIoT applications capable of establishing the IIoT-to-cloud continuum. It also helps for the deployment of other applications in such a domain.

Machine Learning for the Dynamic Positioning of UAVs for Extended Connectivity.

Unmanned Aerial Vehicle (UAV) networks are an emerging technology, useful not only for the military, but also for public and civil purposes. Their versatility provides advantages in situations where an existing network cannot support all requirements of its users, either because of an exceptionally big number of users, or because of the failure of one or more ground base stations. Networks of UAVs can reinforce these cellular networks where needed, redirecting the traffic to available ground stations. Using machine learning algorithms to predict overloaded traffic areas, we propose a UAV positioning algorithm responsible for determining suitable positions for the UAVs, with the objective of a more balanced redistribution of traffic, to avoid saturated base stations and decrease the number of users without a connection. The tests performed with real data of user connections through base stations show that, in less restrictive network conditions, the algorithm to dynamically place the UAVs performs significantly better than in more restrictive conditions, reducing significantly the number of users without a connection. We also conclude that the accuracy of the prediction is a very important factor, not only in the reduction of users without a connection, but also on the number of UAVs deployed.

Radiological Scouting, Monitoring and Inspection Using Drones.

Human populations and natural ecosystems are bound to be exposed to ionizing radiation from the deposition of artificial radionuclides resulting from nuclear accidents, nuclear devices or radiological dispersive devices ("dirty bombs"). On the other hand, Naturally Occurring Radioactive Material industries such as phosphate production or uranium mining, contribute to the on site storage of residuals with enhanced concentrations of natural radionuclides. Therefore, in the context of the European agreements concerning nuclear energy, namely the European Atomic Energy Community Treaty, monitoring is an essential feature of the environmental radiological surveillance. In this work, we obtain 3D maps from outdoor scenarios, and complete such maps with measured radiation levels and with its radionuclide signature. In such scenarios, we face challenges such as unknown and rough terrain, limited number of sampled locations and the need for different sensors and therefore different tasks. We propose a radiological solution for scouting, monitoring and inspecting an area of interest, using a fleet of drones and a controlling ground station. First, we scout an area with a Light Detection and Ranging sensor onboard a drone to accurately 3D-map the area. Then, we monitor that area with a Geiger-Müller Counter at a low-vertical distance from the ground to produce a radiological (heat)map that is overlaid on the 3D map of the scenario. Next, we identify the hotspots of radiation, and inspect them in detail using a drone by landing on them, to reveal its radionuclide signature using a Cadmium-Zinc-Telluride detector. We present the algorithms used to implement such tasks both at the ground station and on the drones. The three mission phases were validated using actual experiments in three different outdoor scenarios. We conclude that drones can not only perform the mission efficiently, but in general they are faster and as reliable as personnel on the ground.

Using Aerial and Vehicular NFV Infrastructures to Agilely Create Vertical Services.

5G communications have become an enabler for the creation of new and more complex networking scenarios, bringing together different vertical ecosystems. Such behavior has been fostered by the network function virtualization (NFV) concept, where the orchestration and virtualization capabilities allow the possibility of dynamically supplying network resources according to its needs. Nevertheless, the integration and performance of heterogeneous network environments, each one supported by a different provider, and with specific characteristics and requirements, in a single NFV framework is not straightforward. In this work we propose an NFV-based framework capable of supporting the flexible, cost-effective deployment of vertical services, through the integration of two distinguished mobile environments and their networks: small sized unmanned aerial vehicles (SUAVs), supporting a flying ad hoc network (FANET) and vehicles, promoting a vehicular ad hoc network (VANET). In this context, a use case involving the public safety vertical will be used as an illustrative example to showcase the potential of this framework. This work also includes the technical implementation details of the framework proposed, allowing to analyse and discuss the delays on the network services deployment process. The results show that the deployment times can be significantly reduced through a distributed VNF configuration function based on the publish-subscribe model.

A Platform of Unmanned Surface Vehicle Swarms for Real Time Monitoring in Aquaculture Environments.

The Internet of Things (IoT) is a rapidly evolving technology that is changing almost every business, and aquaculture is no exception. In this work we present an integrated IoT platform for the acquisition of environmental data and the monitoring of aquaculture environments, supported by a real-time communication and processing network. The complete monitoring platform consists of environmental sensors equipped in a swarm of mobile Unmanned Surface Vehicles (USVs) and Buoys, capable of collecting aquatic and outside information, and sending it to a central station where it will be stored and processed. The sensing platform, formed by the USVs and Buoys, are equipped with multi-communication technology: IEEE 802.11n (Wi-Fi) and Bluetooth for short range communication, for mission delegation and the transmission of data collection, and LoRa for periodic report. On the back-end side, supported by FIWARE technology, an interactive web-based platform can be used to define sensing missions and for data visualization. Results on the sensing platform lifetime, mission control and delay processing time are presented to assess the performance of the aquatic monitoring system.

An Aquatic Mobile Sensing USV Swarm with a Link Quality-Based Delay Tolerant Network.

The Smart City concept is starting to extend into maritime environments alongside with the increase of Unmanned Surface Vehicles (USV) models on the market. Consequently, by joining both Smart City and USV technologies, a set of platforms and applications for aquatic environments are emerging. This work proposes a low-cost aquatic mobile sensing platform for data gathering with a swarm of USVs communicating through a Delay-Tolerant Network (DTN). A set of DTN link quality-based routing strategies select the best quality path in a dynamic approach so the sensed information is able to reach the mobile gateway in a reliable way. A Link Quality Estimation (LQE) approach is proposed and its accuracy is evaluated through real experimentation. An aquatic simulation environment, considering both navigation and communication layers, was also proposed and used to evaluate the performance of the proposed routing strategies, and complement real environment performance studies.

A Multi-Technology Communication Platform for Urban Mobile Sensing.

A common concern in smart cities is the focus on sensing procedures to provide city-wide information to city managers and citizens. To meet the growing demands of smart cities, the network must provide the ability to handle a large number of mobile sensors/devices, with high heterogeneity and unpredictable mobility, by collecting and delivering the sensed information for future treatment. This work proposes a multi-wireless technology communication platform for opportunistic data gathering and data exchange with respect to smart cities. Through the implementation of a proprietary long-range (LoRa) network and an urban sensor network, our platform addresses the heterogeneity of Internet of Things (IoT) devices while conferring communications in an opportunistic manner, increasing the interoperability of our platform. It implements and evaluates a medium access communication (MAC) protocol for LoRa networks with multiple gateways. It also implements mobile Opportunistic VEhicular (mOVE), a delay-tolerant network (DTN)-based architecture to address the mobility dimension. The platform provides vehicle-to-everything (V2X) communication with support for highly reliable and actionable information flows. Moreover, taking into account the high mobility pattern that a smart city scenario presents, we propose and evaluate two forwarding strategies for the opportunistic sensor network.