• With the commercialization of fifth generation networks worldwide, research into sixth generation (6G) networks has been launched to meet the demands for high data rates and low latency for future services. A wireless propagation channel is the transmission medium to transfer information between the transmitter and the receiver. Moreover, channel properties determine the ultimate performance limit of wireless communication systems. Thus, conducting channel research is a prerequisite to designing 6G wireless communication systems. In this paper, we first introduce several emerging technologies and applications for 6G, such as terahertz communication, industrial Internet of Things, space-air-ground integrated network, and machine learning, and point out the developing trends of 6G channel models. Then, we give a review of channel measurements and models for the technologies and applications. Finally, the outlook for 6G channel measurements and models is discussed.
  • As a promising physical layer technique, nonorthogonal multiple access (NOMA) can admit multiple users over the same space-time resource block, and thus improve the spectral efficiency and increase the number of access users. Specifically, NOMA provides a feasible solution to massive Internet of Things (IoT) in 5G and beyond-5G wireless networks over a limited radio spectrum. However, severe co-channel interference and high implementation complexity hinder its application in practical systems. To solve these problems, multiple-antenna techniques have been widely used in NOMA systems by exploiting the benefits of spatial degrees of freedom. This study provides a comprehensive review of various multiple-antenna techniques in NOMA systems, with an emphasis on spatial interference cancellation and complexity reduction. In particular, we provide a detailed investigation on multiple-antenna techniques in two-user, multiuser, massive connectivity, and heterogeneous NOMA systems. Finally, future research directions and challenges are identified.
  • During the past years, a number of concepts have been proposed, such as cloud manufacturing, Industry 4.0, and Industrial Internet. One of their common aims is to optimize the collaborative resource configuration across enterprises by establishing s that aggregate distributed resources. In all of these concepts, a complete manufacturing system consists of distributed physical manufacturing systems and a containing the virtual manufacturing systems mapped from the physical ones. We call such manufacturing systems -based systems (PSMSs). A PSMS can therefore be regarded as a huge cyber-physical system with the cyber part being the and the physical part being the corresponding physical manufacturing system. A significant issue for a PSMS is how to optimally schedule the aggregated resources. technology provides an effective approach for solving this issue. In this paper we propose a architecture for in PSMSs, which consists of a -level system (MAS) and an enterprise- level MAS. Procedures, characteristics, and requirements of in PSMSs are presented. A model for in a PSMS based on the architecture is proposed. A case study is conducted to demonstrate the effectiveness of the proposed architecture and model.
  • Traditional networks face many challenges due to the diversity of applications, such as cloud computing, Internet of Things, and the industrial Internet. Future Internet needs to address these challenges to improve network scalability, security, mobility, and quality of service. In this work, we survey the recently proposed architectures and the emerging technologies that meet these new demands. Some cases for these architectures and technologies are also presented. We propose an integrated framework called the service customized network which combines the strength of current architectures, and discuss some of the open challenges and opportunities for future Internet. We hope that this work can help readers quickly understand the problems and challenges in the current research and serves as a guide and motivation for future network research.
  • Smart  manufacturing is critical in improving the quality of the process industry. In smart manufacturing, there is a trend to incorporate different kinds of new-generation information technologies into process-safety analysis. At present, green manufacturing is facing major obstacles related to safety management, due to the usage of large amounts of hazardous chemicals, resulting in spatial inhomogeneity of chemical industrial processes and increasingly stringent safety and environmental regulations. Emerging information technologies such as artificial intelligence (AI) are quite promising as a means of overcoming these difficulties. Based on state-of-the-art AI methods and the complex safety relations in the process industry, we identify and discuss several technical challenges associated with process safety: ① knowledge acquisition with scarce labels for process safety; ② knowledge-based reasoning for process safety; ③ accurate fusion of heterogeneous data from various sources; and ④ effective learning for dynamic risk assessment and aided decision-making. Current and future works are also discussed in this context.

  • We outline the smart manufacturing challenges for formulated products, which are typically multicomponent, structured, and multiphase. These challenges predominate in the food, pharmaceuticals, agricultural and specialty chemicals, energy storage and energetic materials, and consumer goods industries, and are driven by fast-changing customer demand and, in some cases, a tight regulatory framework. This paper discusses progress in smart manufacturing—namely, digitalization and the use of large datasets with predictive models and solution-finding algorithms—in these industries. While some progress has been achieved, there is a strong need for more demonstration of model-based tools on realistic problems in order to demonstrate their benefits and highlight any systemic weaknesses.

  • Safe, efficient, and sustainable operations and control are primary objectives in industrial manufacturing processes. State-of-the-art technologies heavily rely on human intervention, thereby showing apparent limitations in practice. The burgeoning era of big data is influencing the process industries tremendously, providing unprecedented opportunities to achieve smart manufacturing. This kind of manufacturing requires machines to not only be capable of relieving humans from intensive physical work, but also be effective in taking on intellectual labor and even producing innovations on their own. To attain this goal, data analytics and machine learning are indispensable. In this paper, we review recent advances in data analytics and machine learning applied to the monitoring, control, and optimization of industrial processes, paying particular attention to the interpretability and functionality of machine learning models. By analyzing the gap between practical requirements and the current research status, promising future research directions are identified.

  • Based on the analysis of the present situation and problems in application of Internet of Things(IoT) technology in Chinese aquaculture, this paper puts forward some key technical issues and policy suggestions for the development of the IoT. At the moment, IoT technology has been used in the aquaculture industry for water quality monitoring, breeding area monitoring and management, livestock germination monitoring, aquaculture product storage and transport, and supervision of processing procedures. During this early development stage, China is faced with a number of challenges in the application of this technology, including: Backward extensive aquaculture practice and poor infrastructures; lack of advanced equipment and unified standards; and the need of large sum investment and high costs. A number of key technological issues should be solved in order to improve IoT application in this industry, including: precise aquaculture sensor technology for precise breeding simulation technology, precise intelligent control technology for aquaculture equipment, precise aquaculture management technology, and precise integrated production technology for large-scale aquaculture. Finally, this paper makes suggestions to relevant government agencies on the formulation of an comprehensive development plan for China aquaculture IoT technology to enhance industrial informatization and optimize the application facilities for IoT, initiate a research and development program for precise aquaculture environmental sensor technology, advance the implementation of aquaculture IoT demonstration projects, and upgrade the government’s capacity for promoting the commercialization of aquaculture IoT.

  • With the world energy crisis and environmental protection issues, countries around the world are actively engaged in exploring the new way of the future energy development. In recent years, Jeremy Rifkin, the famous American economist, proposed the idea of the third industrial revolution and the energy internet which caused widespread concern. Following this trend, countries put forward their own development strategies of industrial technology which are driven by internet respectively, such as: the third industrial revolution and industrial Internet of American, industry 4.0 of Germany, smart grid and “Internet + energy” of China. Based on energy efficiency theory in the system, the ubiquitous energy Internet has a stereoscopic structure which consists of three levels: information network, energy network and physical network. By using the optimization control based on the coupling between information and energy to adjust measures to local conditions and fully exploit the multiple grade energy, the ubiquitous energy Internet can give the solution to the integration stability of the renewable energy and the dynamic matching between demand and supply, and become a representative internet of the new energy.

  • The internet of things (IoT) enabling things connects things, is very hot and provides technical support and development opportunities to China´s growing transportation industry. This paper puts forward the concept and structure of the intelligent transportation system(ITS) under IoT; discusses the difference and changes of traditional ITS with ITS under IoT; analysis of our country´s basic condition and the breakthrough focus to develop ITS under IoT; put forward our country´s development strategy of ITS under IoT. So as to promote China´s rapid development of intelligent transportation industry and IoT industry.

    Deng Aimin , Mao Lang et al.
Related research