The Augmented Reality (AR) technology enhances the human perception of the world by combining the real environment with the virtual space. With the explosive growth of powerful, less expensive mobile devices, and the emergence of sophisticated communication infrastructure, Mobile Augmented Reality (MAR) applications are gaining increased popularity. MAR allows users to run AR applications on mobile devices with greater mobility and at a lower cost. The emerging 5G communication technologies act as critical enablers for future MAR applications to achieve ultra-low latency and extremely high data rates while Multi-access Edge Computing (MEC) brings enhanced computational power closer to the users to complement MAR. This paper extensively discusses the landscape of MAR through the past and its future prospects with respect to the 5G systems and complementary technology MEC. The paper especially provides an informative analysis of the network formation of current and future MAR systems in terms of cloud, edge, localized, and hybrid architectural options. The paper discusses key application areas for MAR and their future with the advent of 5G technologies. The paper also discusses the requirements and limitations of MAR technical aspects such as communication, mobility management, energy management, service offloading and migration, security, and privacy and analyzes the role of 5G technologies.

/pdf/a-survey-on-mobile-augmented-reality-with-5g-mobile-edge-11f7i8j1c6.pdf

A Survey on Mobile Augmented Reality With 5G Mobile Edge Computing: Architectures, Applications, and Technical Aspects

Space–air–ground-integrated power Internet of Things (SAG-PIoT) can provide ubiquitous communication and computing services for PIoT devices deployed in remote areas In SAG-PIoT, the tasks can be either processed locally by PIoT devices, offloaded to edge servers through unmanned aerial vehicles (UAVs), or offloaded to cloud servers through satellites However, the joint optimization of task offloading and computational resource allocation faces several challenges, such as incomplete information, dimensionality curse, and coupling between long-term constraints of queuing delay and short-term decision making In this article, we propose a learning-based queue-aware task offloading and resource allocation algorithm (QUARTER) Specifically, the joint optimization problem is decomposed into three deterministic subproblems: 1) device-side task splitting and resource allocation; 2) task offloading; and 3) server-side resource allocation The first subproblem is solved by the Lagrange dual decomposition For the second subproblem, we propose a queue-aware actor–critic-based task offloading algorithm to cope with dimensionality curse A greedy-based low-complexity algorithm is developed to solve the third subproblem Compared with existing algorithms, simulation results demonstrate that QUARTER has superior performances in energy consumption, queuing delay, and convergence

Learning-Based Queue-Aware Task Offloading and Resource Allocation for Space–Air–Ground-Integrated Power IoT

6G-enabled Edge Intelligence for Ultra -Reliable Low Latency Applications: Vision and Mission

Network slicing has been widely agreed as a promising technique to accommodate diverse services for the Industrial Internet of Things (IIoT). Smart transportation, smart energy, and smart factory/manufacturing are the three key services to form the backbone of IIoT. Network slicing management is of paramount importance in the face of IIoT services with diversified requirements. It is important to have a comprehensive survey on intelligent network slicing management to provide guidance for future research in this field. In this paper, we provide a thorough investigation and analysis of network slicing management in its general use cases as well as specific IIoT services including smart transportation, smart energy and smart factory, and highlight the advantages and drawbacks across many existing works/surveys and this current survey in terms of a set of important criteria. In addition, we present an architecture for intelligent network slicing management for IIoT focusing on the above three IIoT services. For each service, we provide a detailed analysis of the application requirements and network slicing architecture, as well as the associated enabling technologies. Further, we present a deep understanding of network slicing orchestration and management for each service, in terms of orchestration architecture, AI-assisted management and operation, edge computing empowered network slicing, reliability, and security. For the presented architecture for intelligent network slicing management and its application in each IIoT service, we identify the corresponding key challenges and open issues that can guide future research. To facilitate the understanding of the implementation, we provide a case study of the intelligent network slicing management for integrated smart transportation, smart energy, and smart factory. Some lessons learnt include: 1) For smart transportation, it is necessary to explicitly identify service function chains (SFCs) for specific applications along with the orchestration of underlying VNFs/PNFs for supporting such SFCs; 2) For smart energy, it is crucial to guarantee both ultra-low latency and extremely high reliability; 3) For smart factory, resource management across heterogeneous network domains is of paramount importance. We hope that this survey is useful for both researchers and engineers on the innovation and deployment of intelligent network slicing management for IIoT.

/pdf/a-survey-of-intelligent-network-slicing-management-for-3vftte0e.pdf

A Survey of Intelligent Network Slicing Management for Industrial IoT: Integrated Approaches for Smart Transportation, Smart Energy, and Smart Factory

Smart grid is a new vision of the conventional power grid to integrate green and renewable technologies. Smart grid (SG) has become a hot research topic with the development of new technologies, such as IoT, edge computing, artificial intelligence, big data, 5G, and so on. The efficiency of SG will be increased by smart embedded devices that have intelligent decision-making ability. Various types of sensors and data sources will collect data of high resolution. One of the vital challenges for IoT is to manage a large amount of data produced by sensors. Sending this massive amount of data directly to the cloud will create problems of latency, security, privacy, and high bandwidth utilization. This issue is addressed by edge computing (EC). In EC, the data are processed at the edge of the network that is near the embedded gadgets. This paper provides a comprehensive review of the smart grid systems, based on IoT and EC. The development in the rising technologies, the framework for EC-IoT-based SG, and requirements to implement the EC-IoT-based SG system are highlighted in the paper. Framework for EC-IoT-based SG is examined, and important requirements to implement the EC-IoT-based SG system are outlined. Finally, some critical issues and challenges faced in the implementation of EC-IoT-based SG systems are identified. Some important open research issues are also identified.

/pdf/edge-computing-for-iot-enabled-smart-grid-58rqbdy099.pdf

Edge Computing for IoT-Enabled Smart Grid

To satisfy the increasing demand of mobile data traffic and meet the stringent requirements of the emerging Internet-of-Things (IoT) applications such as smart city, healthcare, and augmented/virtual reality (AR/VR), the fifth-generation (5G) enabling technologies are proposed and utilized in networks As an emerging key technology of 5G and a key enabler of IoT, multiaccess edge computing (MEC), which integrates telecommunication and IT services, offers cloud computing capabilities at the edge of the radio access network (RAN) By providing computational and storage resources at the edge, MEC can reduce latency for end users Hence, this article investigates MEC for 5G and IoT comprehensively It analyzes the main features of MEC in the context of 5G and IoT and presents several fundamental key technologies which enable MEC to be applied in 5G and IoT, such as cloud computing, software-defined networking/network function virtualization, information-centric networks, virtual machine (VM) and containers, smart devices, network slicing, and computation offloading In addition, this article provides an overview of the role of MEC in 5G and IoT, bringing light into the different MEC-enabled 5G and IoT applications as well as the promising future directions of integrating MEC with 5G and IoT Moreover, this article further elaborates research challenges and open issues of MEC for 5G and IoT Last but not least, we propose a use case that utilizes MEC to achieve edge intelligence in IoT scenarios

Yudong Chen

Papers

Toward Edge Intelligence: Multiaccess Edge Computing for 5G and Internet of Things

Residual Dense Attention Network for Pavement Crack Detection

Clustering Heterogeneous Financial Networks

Steel Industry Intruding on Sediment Pollution by Heavy Metals of Urban Water System within the Yangtze River Basin