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Digitalization is chewing the world with strong economic and social impacts. Recently, the management of the COVID 19 crisis highlighted the power of digital tools and their impact on stakes such as privacy, surveillance, transparency, and censorship. How nations deal with massive digitalization and master the technologies, and applications that are deployed and used on their soils by their companies

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

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Tremendous efforts have been devoted to 5G and Internet of Things (IoT) over the last decade due to their potential applications, such as smart city, home robotics, and Augmented Reality (AR). 5G represents a complete revolution in mobile networks for accommodating the ever-growing demands of users, services and applications. These applications have drawn the attention and increased aspirations of

This article highlights the importance of Internet of Things (IoT) and 5G networks in sustainable cities, focusing on disaster management systems. It also discusses suitable 5G communication technologies in disaster affected and communications outage areas. The article surveys various potential emerging communication technologies such as IoT, device-to-device communications, vehicular networks, cloud

With the explosive growth of multimedia contents in vehicular networks and the development of 5G technology come great challenges to the Internet of Vehicles system. By utilizing the storage capability of roadside units, popular contents can be cached in advance during nonpeak periods, which can bring better quality of experience to users. However, it is difficult for the existing content caching schemes

The 5G network brings about significant convenience in deploying neural network models to edge devices. However, the flexibility challenges the current generation of neural network architectures that seldom involve the target platform as the bounds during the structure searching. The difficult deployment is rooted in the hidden resource provision of the target devices during the SW/HW joint tuning

The past few years have witnessed the compelling applications of the Internet of Things (IoT) in our daily life. Meanwhile, with the explosion of IoT devices and various applications, the expectations for the performance, reliability, and security of networks are greater than ever. The current end-host-based or centralized control framework incurs too much communication and computation overhead, therefore

Beyond 5G (B5G) has the potential of realizing all three pillars of 5G, which are massive type communication, ultra-reliable low latency communication, and enhanced mobile broadband. Currently, a COVID-19-like pandemic can utilize B5G to combat the pandemic by using real-time processing of massive volumes of test data at the edge of hospitals and by leveraging seamless communication between the edge

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Recent advances in blockchain technology have led to a significant interest in developing blockchain-based applications. While data can be retained in a blockchain, the stored values can be deleted or updated. From a user viewpoint that searches for data, it is unclear whether the discovered data from the blockchain storage is relevant for real-time decision-making processes for block-chain-based applications

The integration of unmanned aerial vehicle (UAV) networks and device-to-device (D2D) communications is expected to provide ubiquitous connectivity and high-speed rates for sensitive information transmission in future wireless networks. However, the traditional cryptography and physical layer security techniques still cannot prevent adversaries from knowing the existence of information transmission

Wireless mesh network (WMN) is one of the most promising technologies for Internet of Things (IoT) applications because of its self-adaptive and self-organization nature. To meet different performance requirements on communications in WMNs, traditional approaches always have to program flow control strategies in an explicit way. In this case, the performance of WMNs will be significantly affected by

Software-Defined Vehicular Networks (SDVNs) have been a vital component to various radio access technologies that supports massive data loads in various security and infotainment applications. SDVNs elevate the constraints of static hardware network devices to programmable units and provide a global view of the network's status and a common interface between varied radio access technologies. However

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5G system and beyond targets a large number of emerging applications and services that will create extra overhead on network traffic. These industrial verticals have aggressive, contentious, and conflicting requirements that make the network have an arduous mission for achieving the desired objectives. It is expected to get requirements with close to zero time latency, high data rate, and network reliability

Fifth Generation (5G) phase 2 rollouts are around the corner to make mobile ultra-reliable and low-latency services a reality. However, to realize that scenario, besides the new 5G built-in Ultra-Reliable Low-Latency Communication (URLLC) capabilities, it is required to provide a substrate network with deterministic Qual-ity-of-Service support for interconnecting the different 5G network functions

The emerging advances in personal devices and privacy concerns have given the rise to the concept of Federated Learning. Federated Learning proves its effectiveness and privacy preservation through collaborative local training and updating a shared machine learning model while protecting the individual data-sets. This article investigates a new type of vehicular network concept, namely a Federated

There is an ever-increasing demand for network technologies supporting Ultra-Reliable Low Latency Communications (URLLC) services and their co-existence with best-effort traffic. By way of example, reference can be made to the emerging 5G mobile networks. In this vein, this article investigates the Software-Defined Networking (SDN) technology capabilities for providing Quality of Service (QoS) guarantees

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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