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Four and half years ago, Erik Nygren posted a blog at Akamai [1] on the likelihood that IPv6-only mobile networks might drive IPv6 deployment. So far, that speculation has not proven dispositive, but a 5G initiative toward IPv6-only could mark a turning point. Six months ago, Geoff Huston, chief scientist of APNIC, published an in-depth evaluation of the state of IPv6 play in the Internet [2]. Despite

Distributed systems have always presented complex challenges, and technology trends are in many ways making the software designer's job more difficult. In particular, today's systems must successfully handle.

Presents concise summaries of selected papers that are recently published in the field of networking. Each summary describes the paper's main idea, methodology, and technical contributions. The purpose of the column is to bring the state of the art of networking research to readers of IEEE Network. Authors are also welcome to recommend their recently published work to the column, and papers with novel

Next-generation wireless networks (NGWN) aim to support diversified smart applications that require frequent data exchanges and collaborative data processing among multiple stakeholders. Data management (DM), including data collection, storage, sharing, and computation, plays an essential role in empowering NGWN. However, DM for NGWN faces two significant challenges: stakeholders' data cannot be easily

Nowadays the deployment of drones/UAVs is not just limited to military and defense establishments; they are also widely deployed in geo-dispersed applications (environmental monitoring, rescue operation monitoring, road and traffic surveillance, natural disaster monitoring, soil and crop analysis, and consumer product delivery). Drones have become the instigators and enablers of global mechanization

The Internet of Things (IoT) provides billions of Internet-enabled devices connections across the globe. In the IoT era, the healthcare industry has grown up from Healthcare 1.0 to Healthcare 4.0. As in Healthcare 3.0, patients visit hospital for their regular checkups, which increases the overall expenditure on medical treatment. However, with the recent technological advancements such as UAVs and

In recent years, there has been growing popularity of applying drones for medical services. Such applications always involve flying safety, data transmission, and personal privacy; therefore, reliable communication and enhanced information security should be addressed first. The 5G cellular network and blockchain technology emerge as promising candidates for solving these problems. This article first

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

Unmanned aerial vehicles, commonly known as drones, are receiving growing research interest due to their ability to carry a multitude of sensors and to connect to mobile networks. They are also able to move freely across the air, which enables the creation of numerous applications that were until now considered impracticable. However, such applications may require high computational resources, reliable

In order to solve the problem of privacy protection of unmanned aerial vehicle (UAV) big data, it is necessary to design a privacy protection scheme that guarantees safe sharing of UAV big data. In this work, blockchain technology is adopted to solve the privacy protection problem of UAV big data. In particular, the proposed privacy protection scheme uses a number theory research unit cryptosystem

5G-enabled drones have potential applications in a variety of both military and civilian settings (e.g., monitoring and tracking of individuals in demonstrations and/or enforcing of social/ physical distancing during pandemics such as COVID-19). Such applications generally involve the collection and dissemination of (massive) data from the drones to remote data centers for storage and analysis (e.g

In recent years, unmanned aerial vehicle (UAV) technology has developed rapidly and has been widely used in military operations, medical rescue, environmental monitoring, and so on. UAV networks are an important foundation for large-scale UAV collaborative work. Traditional mobile self-or-ganizing network technology is too complicated to be quickly constructed and dynamically adjusted according to

Fifth generation (5G) wireless networks are designed to meet various end-user quality of service (QoS) requirements through high data rates (typically of gigabits per second) and low latencies. Coupled with fog and mobile edge computing, 5G can achieve high data rates, enabling complex autonomous smart city services such as the large deployment of self-driving vehicles and large-scale artificial-intelligence-enabled

An unmanned aerial vehicle ad hoc network (UAANET) is an information sensing, analyzing, and transmitting network formed by multiple coordinated and collaborating UAVs. It is an emerging networking technology with a broad market prospect, but faces the problems of high dynamic topology and lack of trust. To address these issues, this article proposes a blockchain-em-powered trusted networking framework

5G and beyond (B5G) networks significantly promote the popularity and ubiquity of drones by providing high-throughput and low-latency communication. In B5G drone networks, data sharing among drones has great potential to improve and enrich civilian and commercial applications, such as surveillance monitoring. Nevertheless, a series of security challenges arise such as data privacy leakage due to the

The future mobile communication system is expected to provide ubiquitous connectivity and unprecedented services over billions of devices. The flying drone, also known as unmanned aerial vehicle, is prominent in its flexibility and low cost, and has emerged as a significant network entity to realize such ambitious targets. However, the distributed nature makes the operation of a large-scale drone network

Nowadays, drones are not just deployed for defense and military establishments, but they are widely used in many applications such as natural disaster monitoring, soil and crop analysis, road and traffic surveillance, and consumer product delivery. Some information, such as drone identification and flight modes, can be transmitted to other drones. This information can be shared between drones by using

The Internet of Unmanned Vehicles (IoUV) is regarded as an emerging technology for military applications not only to make surveillance systems and battlefield operations fully coordinated and automated, but also to provide significant strategic advantages. All the UVs form a coordinated network for exchanging information in IoUV, which enhances context awareness and risk analysis, and improves response

Intelligent UAV-based monitoring systems are becoming an essential apparatus for crowd monitoring as they have proven to be viable and cost-effective solutions. Applications of such systems may include detecting antisocial and abnormal behavior among a crowd to ensure public safety and security, especially during periods of pandemic or social unrest when technology is aimed at replacing the human factor

Drones, or unmanned aerial vehicles, can be used for commercial services such as short-dis-tance delivery. In order to ensure quality services, multiple drone-based delivery service providers can be employed in delivery service systems. In this article, we address two very important and unexplored challenges of employing drones in delivery services. First, involving multiple drones from different service

An increasing number of cloud providers now offer mobile edge computing (MEC) services for their customers to support task offloading. This is undertaken to reduce latency associated with forwarding data from IoT devices owned by customers to cloud platforms. However, two challenges remain in existing MEC scenarios: (i) the coverage of MEC services is limited; (ii) there is limited ability to develop

The drone's open and untrusted environment may create problems for authentication and data sharing. To address this issue, we propose a blockchain-enabled efficient and secure data sharing model for 5G flying drones. In this model, blockchain and attribute-based encryption (ABE) are applied to ensure the security of instruction issues and data sharing. The authentication mechanism in the model employs

The recent revival of artificial intelligence (AI) is revolutionizing almost every branch of science and technology. Given the ubiquitous smart mobile terminals and Internet of Things (IoT) devices, it is expected that a majority of intelligent applications will be deployed at the edge networks. Therefore, providing intelligent and sustainable edge computing services will be a hot topic in IoT and

As the basis of Tactile Internet, remote haptic display has been made possible with the development of ultra-reliable low-latency communication in 5G. In this study, edge learning is employed to enable realistic haptic display and stable remote communication. We propose a double-loop control algorithm, which merges decoupling and PID neural network, for magnetic field generation of the electromagnetic

Federated learning is a newly emerged distributed deep learning paradigm, where the clients separately train their local neural network models with private data and then jointly aggregate a global model at the central server. Mobile edge computing is aimed at deploying mobile applications at the edge of wireless networks. Federated learning in mobile edge computing is a prospective distributed framework

The upcoming avenue of IoT, with its massive generated data, makes it really hard to train centralized systems with machine learning in real time. This problem can be addressed with learning-based edge computing systems where the learning is performed in a distributed way on the nodes. In particular, this work focuses on developing multi-agent systems for implementing learning-based edge computing

With the explosive development of mobile Internet and deep learning (DL), intelligent edge computing services based on collaborative learning are widely deployed in various application scenarios. These intelligent services include intelligent applications based on edge computing and DL-based optimization for edge computing (e.g., caching and communicating). However, in a wide variety of domains, DL

Recent years have witnessed the expeditious development of crowdsourced livecast, also referred to as crowdcast, breeding such industry upstarts as Youtube Live, Twitch Tv, Mixer, Douyu, and so on. Unlike traditional TV-based livecast that provides uniform services, in crowd-cast, service providers are racking their brains to satisfy each viewer's personalized QoE demands since different viewers usually

Computer vision tasks such as object detection are crucial for the operations of autonomous vehicles (AVs). Results of many tasks, even those requiring high computational power, can be obtained within a short delay by offloading them to edge clouds. However, although edge clouds are exploited, real-time object detection cannot always be guaranteed due to dynamic channel quality. To mitigate this problem

Edge intelligence (EI), integrated with the merits of both edge computing and artificial intelligence, has been proposed recently to realize intensive computation and low delay inference in the edge of the Internet of Things (IoT). However, the constrained energy and computation ability in edge devices become the main obstacle for EI application in IoT. There is a flexible multi-grade EI deployment

As the fifth generation (5G) network comes to the fore, the realization of 5G-enabled service has attracted much attention from both healthcare academics and practitioners. In particular, 5G-enabled emergency ambulance service allows to connect a patient and an ambulance crew at an accident scene or in transit with the awaiting emergency department team at the destination hospital seamlessly so as

With the rapid increase of edge network scale and the complexity of service interaction, it takes more time for operation staff to analyze anomalies from complex scenarios. To maintain the normal network operation, various key performance indicators, such as link delay, throughput, and memory usage, are monitored for timely anomaly detection and troubleshooting. We introduce artificial intelligence

Collaborative learning with multiple edge devices to build group intelligence is a new trend. Edge artificial intelligence (AI) computing often makes full use of various available data and resources in terminal devices, edge servers, and cloud data centers to achieve collaborative deci-sion-making. However, in order to achieve the goal, it should also guarantee the security of data storage, transmission

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

Anomaly detection/prediction is the first step to secure IoT systems. It usually relies on wide domain knowledge to build up the tools to automatically detect/predict abnormal events or behaviors of an IoT system. However, an IoT system may consist of machines with different capabilities, functionalities and ages. Furthermore, abnormal events or behaviors are usually rare events. It is time-consuming

In 5G and beyond networks, the increasing inclusion of heterogeneous smart devices and the rising privacy and security concerns, are two crucial challenges in terms of computation complexity and privacy preservation for Artificial Intelligence (AI)-based solutions. In this regard, federated learning emerges as a new technique, which enlarges the scale of training data, and protects the privacy of user

As network data keeps getting bigger, deep learning is coming to play a key role in network design and management. Meanwhile, accurate network traffic prediction is of critical importance for network management that is implemented to improve the quality of service (QoS) for users. However, the performance of existing network traffic prediction methods is still poor due to three challenges: complicated

Federated learning has been widely studied and applied to various scenarios, such as financial credit, medical identification, and so on. Under these settings, federated learning protects users from exposing their private data, while cooperatively training a shared machine learning algorithm model (i.e., the global model) for a variety of realworld applications. The only data exchanged is the gradient

A seamless interworking between network function virtualization (NFV) and software defined networking (SDN) to orchestrate network services for the 5G systems is very fundamental for network slice creation. The orchestration of large scale network slices across multiple administrative as well as technological domains with heterogeneous resources and a distributed form of slice management can benefit

Along with the unprecedented development of artificial intelligence (AI), a considerable number of intelligent applications are universally recognized to significantly facilitate the evolution of anthropogenic activities. The abundant AI computing power is one of the main pillars to fuel the booming of ubiquitous AI applications. As the computing power proliferates to a multitude of network edges,

Recently, the industrial Internet of Things (IoT) has quickly become a disruptive force reshaping how we live and work. Compared to the consumer Internet, the industrial IoT puts forward much higher performance requirements in terms of network and computing capacity. The industrial IoT system needs to process tons of data generated by millions of IoT sensors in real-time. Recently, with the advent

Energy Internet (also referred to as Smart Grid 2.0) is another promising application of the Industrial Internet of Things (IIoT), for example, in the way energy is being produced, traded, distributed, and consumed. This is partly due to the lowering of barriers (e.g., costs and Internet connectivity) and advances in the underlying technologies, such as smart meters, electric vehicles, and actuators

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The deployment of 5G mobile networks has begun in earnest. The 5G network is built on a service-based architecture (SBA) which enables programmability of the control plane of 5G Core (5GC) and supports network slicing (NS) in both core and access networks. NS enables the creation of multiple, isolated network slices tailored for specific services with diverse KPI objectives [1]. SBA of 5GC facilitates

Presents the front cover for this issue of the publication.

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Presents the table of contents for this issue of the publication.

The deployment of 5G mobile networks has begun in earnest. The 5G network is built on a service-based architecture (SBA) which enables programmability of the control plane of 5G Core (5GC) and supports network slicing (NS) in both core and access networks. NS enables the creation of multiple, isolated network slices tailored for specific services with diverse KPI objectives. SBA of 5GC facilitates

Presents concise summaries of selected papers that are recently published in the field of networking. Each summary describes the paper's main idea, methodology, and technical contributions. The purpose of the column is to bring the state of the art of networking research to readers of IEEE Network. Authors are also welcome to recommend their recently published work to the column, and papers with novel

CANs promise to apply cognition to overcome shortcomings of self-organizing networks, such as limited flexibility and adaptability to changing environments. in CAN, machine-learning-based network automation functions, called CFs, learn context-specific policies for automating network operations. For this, CFs need a common abstract description of the network states to which they respond. This article

While the application of artificial intelligence (Ai) to 5G networks has raised strong interest, standard solutions to bring Ai into 5G systems are still in their infancy and have a long way to go before they can be used to build an operational system. in this article, we contribute to bridging the gap between standards and a working solution by defining a framework that brings together the relevant

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The six articles in this special section focus on blockchain and artificial intelligence (AI) for future 5G networks. 6G mobile communications is now emerging to support a massive number of users’ connectivity and multi-gigabits transmission rate. The B5G networks should be intelligent enough to adapt to very dynamic topologies, intensive computation and storage applications, and diverse QoS requirements

Due to the rapid development of the internet of Things (ioT), a massive number of devices are connected to the internet. For these distributed devices in ioT networks, how to ensure their security and privacy becomes a significant challenge. Blockchain technology provides a promising solution to protect the data integrity, provenance, privacy, and consistency for ioT networks. in blockchains, communication

The sixth generation (6G) networks are expected to provide a fully connected world with terrestrial wireless and satellite communications integration. The design concept of 6G networks is to leverage artificial intelligence (Ai) to promote the intelligent and agile development of network services. intelligent services inevitably involve the processing of large amounts of data, such as storage, computing

As the next-generation network, beyond fifth generation (B5G) provides transmission capability up to terabits and processes hundreds of exabytes of content data per day from the internet of Everything. From 5G to B5G, the information-centric network (iCN) is expected to play a vital role due to the strong capabilities of content distribution, caching, and processing. As security is a major concern