Port container terminals with their logistic infrastructure are essential nodes for a functional economic activity in the developed world. Hence, innovation in efficiency of the port-container industry is a fundamental issue. This paper presents a Next Generation IoT architecture inspired by the merging of commercial terminal-oriented ICT solutions with the results of H2020 research. The architecture is designed to overcome the constraints and meet requirements of a real scenario in maritime ports where different Container Handling Equipment must share heterogeneous data in a decentralized way, leveraging edge computing. This allows for their easier cooperation and avoiding unproductive movements and central communication, while reducing communication latency related to terminal operations. The paper describes a usage example, accompanied with evidence of the first stages of deployment.

In practical realizations of a Federated Learning ecosystems, the parties cooperating during the training process, and that later use the trained/global model may consist of competing institutions. This can result in incentives for malicious behavior, which can infringe on the safety and data privacy of other participants. Additionally, even in cases devoid of foul play, the format of the data stored locally, and the equipment available for training, may differ between participating institutions. This necessitates creation of a flexible and adaptable preprocessing pipeline, including a comprehensive registration and data preparation process. Among others, it should identify the affiliation of the joining device(s), maintain appropriate data privacy mechanisms, and compensate for the heterogeneity of the devices that are to participate in model training. In this context, the practical aspects of deploying federated learning solutions, in real-life production environments, are discussed.

The recent crises, such as COVID-19 or the Ukrainian war, have reinforced the need for the logistics industry to permanently optimize its operational processes for remaining competitive at the global level. In that sense, maritime logistics play a vital role, making them essential for modern society. The overall objective of this work is framed within the optimisation of maritime terminal yard operations through the application of computer vision services under the auspicious of Industry 4.0. In particular, the work focuses on object detection of containers in a maritime port terminal environment. Our study shows that a trained ResNet50 convolutional neural network on container detection within a maritime port obtains a total loss (a validation metric for object detection models) lower than one, as recommended for reliability in the posterior detection phase. This result testifies to the potential use of this algorithm for container detection in the context of maritime terminals, helping on automating certain manual processes for port operators.

The Internet of things (IoT) ecosystem provides a platform for the connectivity of interrelated smart devices to automate manual processes and reduce labor costs. IoT has brought significant benefits to all industries, including maritime, as various objects (e.g., ports, ships, agents, etc.) are connected to gather and share information within the maritime ecosystem. The innovative technological aspects of IoT are promoting the effective collaboration between the research community and the maritime industry, for enhancing the performance of maritime transportation systems. Therefore, this study discusses recent advances delivered by the IoT and other emerging technologies, like machine learning (ML) and computer vision (CV), for smart maritime transportation systems (SMTSs). In particular, this paper presents two specific use cases of SMTSs, namely, predictive maintenance and container damage/seal inspection. Moreover, the key benefits of integrating IoT with ML and CV are highlighted for the above-mentioned use cases. Finally, a discussion is presented to highlight key opportunities along with foreseeable future challenges in adopting these new technologies by the maritime industry.

The maritime industry has recently seen a boom of Internet of Things (loT) technologies, which are successfully digitalizing maritime transportation. The loT-enabled maritime transportation has led to the introduction of the Internet of Ships (loS) paradigm, where maritime objects are interconnected with the goal of boosting the maritime industry. Therefore, this study discusses loS, its architecture, and its elements, in combination with other emerging technologies, such as machine learning and computer vision, which lead to the creation of smart maritime container terminals (SMCTs). In particular, two specific use cases for SMCTs are presented, namely predictive maintenance and container damage/seal inspection. In addition, specific architectures are proposed and the key benefits of integrating IoT with ML and CV for the above use cases are highlighted.

Federated learning (FL) was proposed to train models in distributed environments. It facilitates data privacy and uses local resources for model training. Until now, the majority of research has been devoted to the "core issues", such as adaptation of machine learning algorithms to FL, data privacy protection, or dealing with effects of unbalanced data distribution. This contribution is anchored in a practical use case, where FL is to be actually deployed within an Internet of Things ecosystem. Hence, different issues that need to be considered are identified. Moreover, an architecture that enables the building of flexible, and adaptable, FL solutions is introduced.

New requirements, posed by the Next Generation IoT, demand design of novel reference architectures, providing foundation for implementation of Internet of Things (IoT) ecosystems. Building on cloud-native concepts (e.g. microservices, virtualisation, and containerization), a flexible architecture that answers requirements present in recent IoT deployments is introduced. A general description of components of the architecture (grouped in horizontal planes and vertical capabilities) is provided, together with formal definition of architectural views. Moreover, ground is laid for upcoming validation in real-world-anchored scenarios. Functional, node, deployment and data views are presented, each of them addressing concerns of different stakeholder groups, typically involved in an IoT deployments.

The recently introduced 5G New Radio is the first wireless standard natively designed to support critical and massive machine type communications (MTC). However, it is already becoming evident that some of the more demanding requirements for MTC cannot be fully supported by 5G networks. Alongside, emerging use cases and applications towards 2030 will give rise to new and more stringent requirements on wireless connectivity in general and MTC in particular. Next generation wireless networks, namely 6G, should therefore be an agile and efficient convergent network designed to meet the diverse and challenging requirements anticipated by 2030. This paper explores the main drivers and requirements of MTC towards 6G, and discusses a wide variety of enabling technologies. More specifically, we first explore the emerging key performance indicators for MTC in 6G. Thereafter, we present a vision for an MTC-optimized holistic end-to-end network architecture. Finally, key enablers towards (1) ultra-low power MTC, (2) massively scalable global connectivity, (3) critical and dependable MTC, and (4) security and privacy preserving schemes for MTC are detailed. Our main objective is to present a set of research directions considering different aspects for an MTC-optimized 6G network in the 2030-era.

Current Internet of Things (IoT) stacks are frequently focused on handling an increasing volume of data that require a sophisticated interpretation through analytics to improve decision making and thus generate business value. In this paper, a cognitive IoT architecture based on FIWARE IoT principles is presented. The architecture incorporates a new cognitive component that enables the incorporation of intelligent services to the FIWARE framework, allowing to modernize IoT infrastructures with Artificial Intelligence (AI) technologies. This allows to extend the effective life of the legacy system, using existing assets and reducing costs. Using the architecture, a cognitive service capable of predicting with high accuracy the vessel port arrival is developed and integrated in a legacy sea traffic management solution. The cognitive service uses automatic identification system (AIS) and maritime oceanographic data to predict time of arrival of ships. The validation has been carried out using the port of Valencia. The results indicate that the incorporation of AI into the legacy system allows to predict the arrival time with higher accuracy, thus improving the efficiency of port operations. Moreover, the architecture is generic, allowing an easy integration of the cognitive services in other domains.

The latest generation of mobile communications (5G) has brought a revolution in the world of communications, bringing with it a series of advantages that are affecting different business areas, such as industry and communications. One of the topics with a wide interest in the world of the audiovisual industry is Broadcast/Multicast. In that sense, 5G New Radio (NR) specifications in Release 15 have not included the capabilities for Multicast/Broadcast for 5G. In the following release of the 3rd Generation Partnership Project (3GPP), Release 16, just the latest Broadcast capabilities specified under LTE in Release 14 were improved, thus leaving for the incoming Release 17 the Multicast Broadcast specifications for NR, known as 5G Multicast Broadcast Services (5G MBS). In this article we will perform a deep analysis of 5G MBS performance, comparing with LTE Broadcast capabilities, based on simulations using a system Level Simulator.

The 2020 Scott Helt Memorial Award was awarded to Manuel Fuentes, Hongzhi Chen, Eduardo Garro, Jose Luis Carcel, David Vargas, Belkacem Mouhouche, David Gomez-Barquero for their paper, “Physical Layer Performance Evaluation of LTE-Advanced Pro Broadcast and ATSC 3.0 Systems.” The papers appeared in the IEEE Transactions on Broadcasting, vol. 65, no. 3, pp. 477–488, September 2019. The purpose of the IEEE Scott Helt Memorial Award is to recognize exceptional publications in the field and to stimulate interest in and encourage contributions to the fields of interest of the Society.

The society as a whole, and many vertical sectors in particular, is becoming increasingly digitalized. Machine Type Communication (MTC), encompassing its massive and critical aspects, and ubiquitous wireless connectivity are among the main enablers of such digitization at large. The recently introduced 5G New Radio is natively designed to support both aspects of MTC to promote the digital transformation of the society. However, it is evident that some of the more demanding requirements cannot be fully supported by 5G networks. Alongside, further development of the society towards 2030 will give rise to new and more stringent requirements on wireless connectivity in general, and MTC in particular. Driven by the societal trends towards 2030, the next generation (6G) will be an agile and efficient convergent network serving a set of diverse service classes and a wide range of key performance indicators (KPI). This white paper explores the main drivers and requirements of an MTC-optimized 6G network, and discusses the following six key research questions: - Will the main KPIs of 5G continue to be the dominant KPIs in 6G; or will there emerge new key metrics? - How to deliver different E2E service mandates with different KPI requirements considering joint-optimization at the physical up to the application layer? - What are the key enablers towards designing ultra-low power receivers and highly efficient sleep modes? - How to tackle a disruptive rather than incremental joint design of a massively scalable waveform and medium access policy for global MTC connectivity? - How to support new service classes characterizing mission-critical and dependable MTC in 6G? - What are the potential enablers of long term, lightweight and flexible privacy and security schemes considering MTC device requirements?

The most recent standard for broadcast services, Advanced Television System Committee-Third Generation (ATSC 3.0), has adopted co-located multi-antenna schemes and Layered Division Multiplexing (LDM) in order to increase the capacity and reliability compared to former Digital Terrestrial Television (DTT) systems. ATSC 3.0 has adopted both technologies separately, but no combination of them is planned yet. Compared to baseline LDM case, use of several antennas allows for diverse parametrization for each layer. This paper analyzes the potential combination of co-located Multiple-Input-Multiple-Output (MIMO) schemes with LDM. A trade-off analysis between complexity constraints and performance benefits is evaluated.

This work presents a potential solution for enabling the use of multicast in the 5G New Radio Release 17, called 5G NR Mixed Mode. The proposed multicast/broadcast mode follows one of the two approaches envisaged in 3GPP, which enables a dynamic and seamless switching between unicast and multicast, both in the downlink and the uplink. This paper also provides a performance evaluation of several IMT-2020 KPIs, including available data rate and spectral efficiency, user and control plane latencies, energy efficiency, and mobility, highlighting the potential advantages of this solution over unicast in relevant scenarios. Finally, other multipoint-based KPIs such as coverage or packet loss rate are also evaluated by means of system level simulations.

The fifth generation (5G) of mobile radio technologies has been defined as a new delivery model where services are tailored to specific vertical industries. 5G supports three types of services with different and heterogeneous requirements, i.e. enhanced Mobile Broadband (eMBB), Ultra-Reliable Low-Latency Communications (URLLC) and massive Machine-Type Communications (mMTC). These services are directly related to exemplary verticals such as media, vehicular communications or the Industry 4.0. This work provides a detailed analysis and performance evaluation of 5G New Radio (NR) against a set of Key Performance Indicators (KPI), as defined in the International Mobile Telecommunications 2020 (IMT-2020) guidelines, and provides an overview about the fulfillment of their associated requirements. The objective of this work is to provide an independent evaluation, complementing the Third Generation Partnership Project (3GPP) contribution. From the original group of sixteen KPIs, eleven of them have been carefully selected, paying special attention to eMBB services. Results show that 5G NR achieves all considered requirements, therefore fulfilling the specific market's needs for years to come.

The next generation mobile technology aims at revolutionizing the world of connectivity. 5G will support data rates in excess of several tons of megabytes per second, expand network coverage, and reduce latency. Among this new ecosystem, 5G could be essential in the media industry, such as broadcast network operators, broadcast service providers or content producers. Nevertheless, 5G spectral efficiency can be further enhanced with new signal processing algorithms that are being used in other terrestrial television standards. One of the most efficient broadcast standards, ATSC 3.0, adopted Layered Division Multiplexing (LDM) which can simultaneously deliver services with different robustness capabilities in a more efficient way than traditional orthogonal multiple access modes like Time/Frequency Division Multiplexing (TDM/FDM). This paper evaluates the integration of the ATSC 3.0 LDM mode into the 5G New Radio (NR) physical air interface. Different use cases are devised and a performance evaluation by means of physical layer simulations is carried out, where it is demonstrated the potential benefits of LDM with respect to TDM/FDM.

This work presents a potential solution for enabling the use of multicast in the 3GPP Release 15 air interface, called 5G New Radio (NR). The proposed multicast mode, denoted as 5G Mixed Mode follows one of the two approaches envisaged in 3GPP. which enables a dynamic and seamless switching between unicast and multicast, both in the downlink and the uplink. This paper also provides a performance evaluation in Single Frequency Networks (SFN) and mobility scenarios, showcasing the potential advantages of this solution over unicast in relevant scenarios.

3GPP LTE eMBMS release (Rel-) 14, also referred to as further evolved multimedia broadcast multicast service (FeMBMS) or enhanced TV (EnTV), is the first mobile broadband technology standard to incorporate a transmission mode designed to deliver terrestrial broadcast services from conventional high power high tower (HPHT) broadcast infrastructure. With respect to the physical layer, the main improvements in FeMBMS are the support of larger inter-site distance for single frequency networks (SFNs) and the ability to allocate 100% of a carrier's resources to the broadcast payload, with self-contained signaling in the downlink. From the system architecture perspective, a receive-only mode enables free-to-air (FTA) reception with no need for an uplink or SIM card, thus receiving content without user equipment registration with a network. These functionalities are only available in the LTE advanced pro specifications as 5G new radio (NR), standardized in 3GPP from Rel-15, has so far focused entirely on unicast. This paper outlines a physical layer design for NR-MBMS, a system derived, with minor modifications, from the 5G-NR specifications, and suitable for the transmission of linear TV and radio services in either single-cell or SFN operation. This paper evaluates the NR-MBMS proposition and compares it to LTE-based FeMBMS in terms of flexibility, performance, capacity, and coverage.