Since the appearance of OpenFlow back in 2008, software-defined networking (SDN) has gained momentum. Although there are some discrepancies between the standards developing organizations working with SDN about what SDN is and how it is defined, they all outline traffic engineering (TE) as a key application. One of the most common objectives of TE is the congestion minimization, where techniques such as traffic splitting among multiple paths or advanced reservation systems are used. In such a scenario, this manuscript surveys the role of a comprehensive list of SDN protocols in TE solutions, in order to assess how these protocols can benefit TE. The SDN protocols have been categorized using the SDN architecture proposed by the open networking foundation, which differentiates among data-controller plane interfaces, application-controller plane interfaces, and management interfaces, in order to state how the interface type in which they operate influences TE. In addition, the impact of the SDN protocols on TE has been evaluated by comparing them with the path computation element (PCE)-based architecture. The PCE-based architecture has been selected to measure the impact of SDN on TE because it is the most novel TE architecture until the date, and because it already defines a set of metrics to measure the performance of TE solutions. We conclude that using the three types of interfaces simultaneously will result in more powerful and enhanced TE solutions, since they benefit TE in complementary ways.

Communication networks are in constant evolution to adapt to the ever-changing demands of modern services and applications. On the one hand, technologies within the framework of the fifth generation of communication networks (5G), currently in a steady course towards 6G, are being designed with unprecedented performance and flexibility capabilities to empower the digital transformation of heterogeneous vertical industries. On the other hand, Time-Sensitive Networking (TSN) is being developed to enable converged network infrastructures able to satisfy the most stringent communication requirements in regards to bounded low latency and reliability. In verticals such as manufacturing, a joint usage of 5G and TSN is very promising to enable revolutionary use cases that coexist with the constraints imposed by industrial communications and systems. In this paper, we exhaustively survey the technology landscape concerning the integration between 5G and TSN technologies, including the perspectives of both industry and academia. In the first part of this paper, we provide a comprehensive review of relevant standards and outcomes from industry initiatives. We first examine the capabilities provided by TSN and their application to industrial communications. Likewise, we contextualize the 5G architecture and its key enabling technologies, and later provide a comprehensive review of the architecture and mechanisms to enable the integration between TSN and 5G. In the second part of this paper, we extensively survey the state of the art from academic literature concerning proposals that contribute to enabling, improving, and/or demonstrating different aspects of the integration or interworking between 5G and TSN technologies.

The Industry 4.0 revolution envisions fully interconnected scenarios in the manufacturing industry to improve the efficiency, quality, and performance of the manufacturing processes. In parallel, the consolidation of 5G technology is providing substantial advances in the world of communication and information technologies. Furthermore, 5G also presents itself as a key enabler to fulfill Industry 4.0 requirements. In this article, the authors first propose a 5G-enabled architecture for Industry 4.0. Smart Networks for Industry (SN4I) is introduced, an experimental facility based on two 5G key-enabling technologies—Network Functions Virtualization (NFV) and Software-Defined Networking (SDN)—which connects the University of the Basque Country’s Aeronautics Advanced Manufacturing Center and Faculty of Engineering in Bilbao. Then, the authors present the deployment of a Wireless Sensor Network (WSN) with strong access control mechanisms into such architecture, enabling secure and flexible Industrial Internet of Things (IIoT) applications. Additionally, the authors demonstrate the implementation of a use case consisting in the monitoring of a broaching process that makes use of machine tools located in the manufacturing center, and of services from the proposed architecture. The authors finally highlight the benefits achieved regarding flexibility, efficiency, and security within the presented scenario and to the manufacturing industry overall.

In the era of Big Data and the advancement of the Internet of Things, there is an increasing amount of valuable information. It is important to emphasize that this data is usually sensitive or confidential, so security and privacy are two of the highest priorities for organizations when performing Data Mining. Researchers have explored techniques such as secure multi-party computation (SMPC) in the last decades. Nevertheless, there is still a significant gap between the theory of SMPC and its applicability, especially when working with resource-constrained devices or massive data. This work has been conducted with a systematic literature review, and it intends to analyze the open issues of adapting SMPC to those scenarios, by classifying the studies to answer two research questions: (1) how has the use of SMPC attempted to be adapted to constrained devices? and (2) how have traditional techniques fitted with Big Data? At the end of the process, after analyzing a total of 637 studies, 19 papers were selected. Regarding constrained devices, solutions are grouped into three main techniques: secure outsourcing, hardware-based trusted execution, and intermediate representations. As for Big Data, the selected studies use mixed protocols to change over cleartext and ciphertext, combine different types of SMPC protocols, or modify existing protocols through optimizations.

Industrial networks are introducing Internet of Things (IoT) technologies in their manufacturing processes in order to enhance existing methods and obtain smarter, greener and more effective processes. Global predictions forecast a massive widespread of IoT technology in industrial sectors in the near future. However, these innovations face several challenges, such as achieving short response times in case of time-critical applications. Concepts like in-network computing or edge computing can provide adequate communication quality for these industrial environments, and data plane programming has been proved as a useful mechanism for their implementation. Specifically, P4 language is used for the definition of the behavior of programmable switches and network elements. This paper presents a solution for industrial IoT (IIoT) network communications to reduce response times using in-network computing through data plane programming and P4. Our solution processes Message Queuing Telemetry Transport (MQTT) packets sent by a sensor in the data plane and generates an alarm in case of exceeding a threshold in the measured value. The implementation has been tested in an experimental facility, using a Netronome SmartNIC as a P4 programmable network device. Response times are reduced by 74% while processing, and delay introduced by the P4 network processing is insignificant.