Recent advances in Industrial Internet: insights and challenges

The Industrial Internet is a promising technology combining industrial systems with Internet connectivity to significantly improve the product efficiency and reduce production cost by cooperating with intelligent devices, in which the advanced computing, big data analysis and intelligent perception techniques have been involved. This paper comprehensively surveys the recent advances of the Industrial Internet, including reference architectures, key technologies, relative applications and future challenges. Reference architectures which have been proposed for different application scenarios and their corresponding characteristics are summarized. Key technologies, such as cloud computing, mobile edge computing, fog computing, which are classified according to different layers in the architecture, are presented to support a variety of applications in the Industrial Internet. Meanwhile, future challenges and research trends are discussed as well to promote further research of the Industrial Internet.     

Fog computing for next-generation Internet of Things: Fundamental,state-of-the-art and research challenges

In recent times, the Internet of Things (IoT) applications, including smart transportation, smart healthcare, smart grid, smart city, etc. generate a large volume of real-time data for decision making. In the past decades, real-time sensory data have been offloaded to centralized cloud servers for data analysis through a reliable communication channel. However, due to the long communication distance between end-users and centralized cloud servers, the chances of increasing network congestion, data loss, latency, and energy consumption are getting significantly higher. To address the challenges mentioned above, fog computing emerges in a distributed environment that extends the computation and storage facilities at the edge of the network. Compared to centralized cloud infrastructure, a distributed fog framework can support delay-sensitive IoT applications with minimum latency and energy consumption while analyzing the data using a set of resource-constraint fog/edge devices. Thus our survey covers the layered IoT architecture, evaluation metrics, and applications aspects of fog computing and its progress in the last four years. Furthermore, the layered architecture of the standard fog framework and different state-of-the-art techniques for utilizing computing resources of fog networks have been covered in this study. Moreover, we included an IoT use case scenario to demonstrate the fog data offloading and resource provisioning example in heterogeneous vehicular fog networks. Finally, we examine various challenges and potential solutions to establish interoperable communication and computation for next-generation IoT applications in fog networks.     

Task scheduling in fog environment — Challenges,tools & methodologies: A review

Even though cloud computing offers many advantages, it can be a poor choice sometimes because of its slow response to existing requests, leading to the need for fog computing. Scheduling tasks in a fog environment is a major challenge. It is important that IoT clients execute their tasks in a timely manner and obtain lower-cost services; however, they are also looking for tasks to be executed in a secure manner. In this paper, we review the advantages, limitations, and issues associated with scheduling algorithms proposed by a number of different researchers for fog environments. For fog computing developers, we compare different simulation tools to help them choose the product that is most appropriate and flexible for simulating the application they are considering. Finally, open issues and promising research directions associated with task scheduling in fog computing are discussed.     

Deep learning models for cloud,edge, fog,and IoT computing paradigms: Survey,recent advances,and future directions

In recent times, the machine learning (ML) community has recognized the deep learning (DL) computing model as the Gold Standard. DL has gradually become the most widely used computational approach in the field of machine learning, achieving remarkable results in various complex cognitive tasks that are comparable to, or even surpassing human performance. One of the key benefits of DL is its ability to learn from vast amounts of data. In recent years, the DL field has witnessed rapid expansion and has found successful applications in various conventional areas. Significantly, DL has outperformed established ML techniques in multiple domains, such as cloud computing, robotics, cybersecurity, and several others. Nowadays, cloud computing has become crucial owing to the constant growth of the IoT network. It remains the finest approach for putting sophisticated computational applications into use, stressing the huge data processing. Nevertheless, the cloud falls short because of the crucial limitations of cutting-edge IoT applications that produce enormous amounts of data and necessitate a quick reaction time with increased privacy. The latest trend is to adopt a decentralized distributed architecture and transfer processing and storage resources to the network edge. This eliminates the bottleneck of cloud computing as it places data processing and analytics closer to the consumer. Machine learning (ML) is being increasingly utilized at the network edge to strengthen computer programs, specifically by reducing latency and energy consumption while enhancing resource management and security. To achieve optimal outcomes in terms of efficiency, space, reliability, and safety with minimal power usage, intensive research is needed to develop and apply machine learning algorithms. This comprehensive examination of prevalent computing paradigms underscores recent advancements resulting from the integration of machine learning and emerging computing models, while also addressing the underlying open research issues along with potential future directions. Because it is thought to open up new opportunities for both interdisciplinary research and commercial applications, we present a thorough assessment of the most recent works involving the convergence of deep learning with various computing paradigms, including cloud, fog, edge, and IoT, in this contribution. We also draw attention to the main issues and possible future lines of research. We hope this survey will spur additional study and contributions in this exciting area.     

基于重复数据删除技术的雾存储数据去冗余方案

雾计算作为云中心在网络边缘的延伸,将不需要放在云端的数据直接进行存储和处理,从而可以快速响应底端设备的需求。为了解决现有方案中频繁的磁盘输入和输出(I/O),针对雾节点中存储数据的冗余问题,提出重复数据删除方案(DeFog)。利用红黑树的快速查找机制,在内存中构建数据指纹表,通过二次Hash获得索引表。固定时刻刷新内存中的指纹表保存在磁盘中,日志文件记录每次数据更新,这样在系统发生崩溃机器重启时,磁盘中的指纹表会与日志文件合并构建更新后的指纹表。通过在标准数据集中的实验与其他方案进行对比,证明了DeFog在查询效率上提高了54.1%,运行时间降低了42.1%。     

FogQSYM: An Industry 4.0 Analytical Model for Fog Applications

Industry 4.0 refers to the fourth evolution of technology development, which strives to connect people to various industries in terms of achieving their expected outcomes efficiently. However, resource management in an Industry 4.0 network is very complex and challenging. To manage and provide suitable resources to each service, we propose a FogQSYM (Fog–-Queuing system) model; it is an analytical model for Fog Applications that helps divide the application into several layers, then enables the sharing of the resources in an effective way according to the availability of memory, bandwidth, and network services. It follows the Markovian queuing model that helps identify the service rates of the devices, the availability of the system, and the number of jobs in the Industry 4.0 systems, which helps applications process data with a reasonable response time. An experiment is conducted using a Cloud Analyst simulator with multiple segments of datacenters in a fog application, which shows that the model helps efficiently provide the arrival resources to the appropriate services with a low response time. After implementing the proposed model with different sizes of fog services in Industry 4.0 applications, FogQSYM provides a lower response time than the existing optimized response time model. It should also be noted that the average response time increases when the arrival rate increases.     

Wind tunnel experiment for predicting a visible plume region from a wet cooling tower

We developed a new method for a wind tunnel experiment to predict a visible plume region from a wet cooling tower. The diffusion of water vapor and heat emitted from a cooling tower in a wind tunnel is estimated using a tracer gas. The instantaneous concentration of the tracer gas is measured using high-response flame ionization detectors. A moist plume-induced fog is assumed to be generated whenever the instantaneous specific humidity predicted from the concentration of the tracer gas at measured points is larger than the inferred saturation specific humidity. To confirm the validity of the present method, the results in the wind tunnel experiments are roughly compared with the observations obtained at the mechanical-draft cooling tower of the Benning Road plant. The results show that the visible plume length and height are nearly in agreement with observations and the present method has the capability to predict the visible plume region from the cooling tower.     

Forming Climatic Change

The Architectural Association in London is renowned for its unique unit structure in which avant-garde research and design strategies are incubated and hatched. In a new series edited by Michael Weinstock, Academic Head at the AA, the activities of the units are brought under the spotlight. ‘Unit Factor’ kicks off with an account by Steve Hardy and Werner Gaiser of the Environments, Ecology and Sustainability research cluster they lead. Copyright © 2007 John Wiley & Sons, Ltd.     

Numerical simulation of duct flow with fog droplets

Evaporative cooling is a widely used air cooling technique. In this method, evaporation of a liquid in the surrounding air cools the air in contact with it. In the current investigation, numerical simulations are carded out to visualize the evaporation and dynamics of tiny water droplets of different diameters in a long air duct. The effect of initial droplet size on the temperature and relative humidity distribution of the air stream in the duct is investigated. Three different initial conditions of air are considered to verify the influence of ambient conditions. Droplet spray patterns are also analyzed to identify the suitable locations for the spray nozzles within the duct. The resuits obtained are displayed in a series of plots to provide a clear understanding of the evaporative cooling process as well as the droplet dynamics within the ducts.     

Age-related declines in car following performance under simulated fog conditions

The present study examined age-related differences in car following performance when contrast of the driving scene was reduced by simulated fog. Older (mean age of 72.6) and younger (mean age of 21.1) drivers were presented with a car following scenario in a simulator in which a lead vehicle (LV) varied speed according to a sum of three sine wave functions. Drivers were shown an initial following distance of 18 m and were asked to maintain headway distance by controlling speed to match changes in LV speed. Five simulated fog conditions were examined ranging from a no fog condition (contrast of 0.55) to a high fog condition (contrast of 0.03). Average LV speed varied across trials (40, 60, or 80 km/h). The results indicated age-related declines in car following performance for both headway distance and RMS (root mean square) error in matching speed. The greatest decline occurred at moderate speeds under the highest fog density condition, with older drivers maintaining a headway distance that was 21% closer than younger drivers. At higher speeds older drivers maintained a greater headway distance than younger drivers. These results suggest that older drivers may be at greater risk for a collision under high fog density and moderate speeds.