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Journal articles on the topic 'Fog manufacturing'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Wang, Junliang, Peng Zheng, Youlong Lv, Jingsong Bao, and Jie Zhang. "Fog-IBDIS: Industrial Big Data Integration and Sharing with Fog Computing for Manufacturing Systems." Engineering 5, no. 4 (August 2019): 662–70. http://dx.doi.org/10.1016/j.eng.2018.12.013.

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2

Wang, Juan, and Di Li. "Task Scheduling Based on a Hybrid Heuristic Algorithm for Smart Production Line with Fog Computing." Sensors 19, no. 5 (February 28, 2019): 1023. http://dx.doi.org/10.3390/s19051023.

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Fog computing provides computation, storage and network services for smart manufacturing. However, in a smart factory, the task requests, terminal devices and fog nodes have very strong heterogeneity, such as the different task characteristics of terminal equipment: fault detection tasks have high real-time demands; production scheduling tasks require a large amount of calculation; inventory management tasks require a vast amount of storage space, and so on. In addition, the fog nodes have different processing abilities, such that strong fog nodes with considerable computing resources can help terminal equipment to complete the complex task processing, such as manufacturing inspection, fault detection, state analysis of devices, and so on. In this setting, a new problem has appeared, that is, determining how to perform task scheduling among the different fog nodes to minimize the delay and energy consumption as well as improve the smart manufacturing performance metrics, such as production efficiency, product quality and equipment utilization rate. Therefore, this paper studies the task scheduling strategy in the fog computing scenario. A task scheduling strategy based on a hybrid heuristic (HH) algorithm is proposed that mainly solves the problem of terminal devices with limited computing resources and high energy consumption and makes the scheme feasible for real-time and efficient processing tasks of terminal devices. Finally, the experimental results show that the proposed strategy achieves superior performance compared to other strategies.

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3

Sherlekar, Riddhiman, Binil Starly, and Paul H. Cohen. "Provisioned Data Distribution for Intelligent Manufacturing via Fog Computing." Procedia Manufacturing 34 (2019): 893–902. http://dx.doi.org/10.1016/j.promfg.2019.06.158.

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4

Chen, Xin. "Big Data Integration Method of Mathematical Modeling and Manufacturing System Based on Fog Calculation." Mathematical Problems in Engineering 2021 (July 9, 2021): 1–9. http://dx.doi.org/10.1155/2021/9987714.

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Using big data to promote economic development, improve social governance, and improve service and regulatory capabilities is becoming a trend. However, the current cloud computing for data processing has been difficult to meet the demand, and the server pressure has increased dramatically, so people pay special attention to the big data integration of fog computing. In order to make the application of big data meet people’s needs, we have established relevant mathematical models based on fog calculation, made system big data integration, collected relevant data, designed experiments, and obtained relevant research data by reviewing relevant literature and interviewing professionals. The research shows that big data integration using fog computing modeling has the characteristics of fast response and stable function. Compared with cloud computing and previous computer algorithms, big data integration has obvious advantages, and the computing speed is nearly 20% faster than cloud computing and about 35% higher than other computing methods. This shows that big data integration built by fog computing can have a huge impact on people’s lives.

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Shrestha, Hewan, Puviyarai T., Sana Sodanapalli, and Chandramohan Dhasarathan. "Evolution of Fog Computing Applications, Opportunities, and Challenges." International Journal of Fog Computing 4, no. 1 (January 2021): 1–17. http://dx.doi.org/10.4018/ijfc.2021010101.

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The emerging trend of internet of things in recent times is a blessing for various industries in the world. With the increasing amount of data generated by these devices, it makes it difficult for proper data flow and computation over the regular cloud architecture. Fog computing is a great alternative for cloud computing as it supports computation in devices over a large distributed geographical area, which is a plus for fog computing. Having applications in various domains including healthcare, logistics, design, marketing, manufacturing, and many more, fog computing is a great boon for the future. Evolving fog computing in various domains with different methods and techniques has shaped a clear future for it. Applicability of fog computing in vehicular communications and storage-as-a-service has made the term more popular these days. It is a review of all the possible fog computing-enabled applications and their future scope. It also prepares a basis for further research into fog computing domain-enabled services with low latency and minimum costs.

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Jiang, Yuan, Renjie Xu, Siru Liu, Guilian Liu, and Xiaohong Yan. "Electrostatic fog collection mechanism and design of an electrostatic fog collector with nearly perfect fog collection efficiency." Chemical Engineering Science 247 (January 2022): 117034. http://dx.doi.org/10.1016/j.ces.2021.117034.

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7

Basir, Rabeea, Saad Qaisar, Mudassar Ali, Monther Aldwairi, Muhammad Ikram Ashraf, Aamir Mahmood, and Mikael Gidlund. "Fog Computing Enabling Industrial Internet of Things: State-of-the-Art and Research Challenges." Sensors 19, no. 21 (November 5, 2019): 4807. http://dx.doi.org/10.3390/s19214807.

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Industry is going through a transformation phase, enabling automation and data exchange in manufacturing technologies and processes, and this transformation is called Industry 4.0. Industrial Internet-of-Things (IIoT) applications require real-time processing, near-by storage, ultra-low latency, reliability and high data rate, all of which can be satisfied by fog computing architecture. With smart devices expected to grow exponentially, the need for an optimized fog computing architecture and protocols is crucial. Therein, efficient, intelligent and decentralized solutions are required to ensure real-time connectivity, reliability and green communication. In this paper, we provide a comprehensive review of methods and techniques in fog computing. Our focus is on fog infrastructure and protocols in the context of IIoT applications. This article has two main research areas: In the first half, we discuss the history of industrial revolution, application areas of IIoT followed by key enabling technologies that act as building blocks for industrial transformation. In the second half, we focus on fog computing, providing solutions to critical challenges and as an enabler for IIoT application domains. Finally, open research challenges are discussed to enlighten fog computing aspects in different fields and technologies.

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8

Gupta, Rajni. "Resource Provisioning and Scheduling Techniques of IoT Based Applications in Fog Computing." International Journal of Fog Computing 2, no. 2 (July 2019): 57–70. http://dx.doi.org/10.4018/ijfc.2019070104.

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Internet of Things (IoT) has emerged as a computing paradigm to develop smart applications such e-health care systems, smart city, smart waste management systems, etc. It contains a large number of different devices and heterogeneous networks, which make it difficult to provide secure and fast response to the end user. To provide the faster response services, there is a need to use the concept of Fog computing Recently, the use of fog computing is a rapidly increasing in many industries for the development of applications such as manufacturing, e-health, oil and gas, As more and more users have started to store/process their real-time data in Fog-based Cloud environments, resource provisioning and scheduling of IoT based applications becomes a key element of consideration for efficient execution of these applications. This article will help to select the most suitable technique for processing smart IoT based applications in Fog computing environments.

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9

Barenji, Ali Vatankhah, Hanyang Guo, Yitong Wang, Zhi Li, and Yiming Rong. "Toward blockchain and fog computing collaborative design and manufacturing platform: Support customer view." Robotics and Computer-Integrated Manufacturing 67 (February 2021): 102043. http://dx.doi.org/10.1016/j.rcim.2020.102043.

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10

Wu, Dazhong, Shaopeng Liu, Li Zhang, Janis Terpenny, Robert X. Gao, Thomas Kurfess, and Judith A. Guzzo. "A fog computing-based framework for process monitoring and prognosis in cyber-manufacturing." Journal of Manufacturing Systems 43 (April 2017): 25–34. http://dx.doi.org/10.1016/j.jmsy.2017.02.011.

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11

Yao, Xuanxia, Huafeng Kong, Hong Liu, Tie Qiu, and Huansheng Ning. "An Attribute Credential Based Public Key Scheme for Fog Computing in Digital Manufacturing." IEEE Transactions on Industrial Informatics 15, no. 4 (April 2019): 2297–307. http://dx.doi.org/10.1109/tii.2019.2891079.

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12

Martin, V. R., and P. Sackett. "Focusing through the information fog." Manufacturing Engineer 78, no. 1 (February 1, 1999): 20–23. http://dx.doi.org/10.1049/me:19990113.

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13

Wang, Juan, and Di Li. "Adaptive Computing Optimization in Software-Defined Network-Based Industrial Internet of Things with Fog Computing." Sensors 18, no. 8 (August 1, 2018): 2509. http://dx.doi.org/10.3390/s18082509.

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In recent years, cloud computing and fog computing have appeared one after the other, as promising technologies for augmenting the computing capability of devices locally. By offloading computational tasks to fog servers or cloud servers, the time for task processing decreases greatly. Thus, to guarantee the Quality of Service (QoS) of smart manufacturing systems, fog servers are deployed at network edge to provide fog computing services. In this paper, we study the following problems in a mixed computing system: (1) which computing mode should be chosen for a task in local computing, fog computing or cloud computing? (2) In the fog computing mode, what is the execution sequence for the tasks cached in a task queue? Thus, to solve the problems above, we design a Software-Defined Network (SDN) framework in a smart factory based on an Industrial Internet of Things (IIoT) system. A method based on Computing Mode Selection (CMS) and execution sequences based on the task priority (ASTP) is proposed in this paper. First, a CMS module is designed in the SDN controller and then, after operating the CMS algorithm, each task obtains an optimal computing mode. Second, the task priorities can be calculated according to their real-time performance and calculated amount. According to the task priority, the SDN controller sends a flow table to the SDN switch to complete the task transmission. In other words, the higher the task priority is, the earlier the fog computing service is obtained. Finally, a series of experiments and simulations are performed to evaluate the performance of the proposed method. The results show that our method can achieve real-time performance and high reliability in IIoT.

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Zhu, Pingan, Rifei Chen, Chunmei Zhou, Ye Tian, and Liqiu Wang. "Asymmetric fibers for efficient fog harvesting." Chemical Engineering Journal 415 (July 2021): 128944. http://dx.doi.org/10.1016/j.cej.2021.128944.

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15

Yin, Luxiu, Juan Luo, and Haibo Luo. "Tasks Scheduling and Resource Allocation in Fog Computing Based on Containers for Smart Manufacturing." IEEE Transactions on Industrial Informatics 14, no. 10 (October 2018): 4712–21. http://dx.doi.org/10.1109/tii.2018.2851241.

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16

Qi, Qinglin, and Fei Tao. "A Smart Manufacturing Service System Based on Edge Computing, Fog Computing, and Cloud Computing." IEEE Access 7 (2019): 86769–77. http://dx.doi.org/10.1109/access.2019.2923610.

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17

Lin, Shih-Yang, Yun Du, Po-Chang Ko, Tzu-Jung Wu, Ping-Tsan Ho, V. Sivakumar, and Rama subbareddy. "Fog Computing Based Hybrid Deep Learning Framework in effective inspection system for smart manufacturing." Computer Communications 160 (July 2020): 636–42. http://dx.doi.org/10.1016/j.comcom.2020.05.044.

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18

Schotte, William. "Fog formation of hydrogen fluoride in air." Industrial & Engineering Chemistry Research 26, no. 2 (February 1987): 300–306. http://dx.doi.org/10.1021/ie00062a020.

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19

Ahammad, Ishtiaq, Ashikur Rahman Khan, and Zayed Us Salehin. "A Review on Cloud, Fog, Roof, and Dew Computing." International Journal of Cloud Applications and Computing 11, no. 4 (October 2021): 14–41. http://dx.doi.org/10.4018/ijcac.2021100102.

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The internet of things (IoT) offers a range of benefits for its users, ranging from quicker and more precise perception of our ecosystem to more cost-effective monitoring of manufacturing applications, by taking internet access to the things. Due to the ubiquitous existence of the internet, there's been an increasing pace in the IoT. Such a growing pace has brought about the term of IoT ecosystem. This exponential growing IoT ecosystem will encounter several challenges in its path. Computing domains were used from very initial stage to assist the IoT ecosystem and mitigate those challenges. To understand the impact of computing domains in IoT ecosystem, this paper performs the elaborative study on cloud, fog, roof, and dew computing including their interaction, benefits, and limitations in IoT ecosystem. The brief comparative analysis on these four computing domains are then performed. The impact of internet and offline computing on these computing domains are then analyzed in depth. Finally, this paper presents the suggestions of potential appropriate computing domain strategies for IoT ecosystems.

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20

Hasnain, Sheikh Shamim. "A Critical Comparison between Manufacturing and Service Firms’ Mission Statements: Manage Your Firms’ Mission Statements with Care Using Readability and Lexical Techniques." Business Management and Strategy 11, no. 1 (June 12, 2020): 216. http://dx.doi.org/10.5296/bms.v11i1.17171.

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This paper aims to investigate the Readability and Lexical Density of the mission statements of the large service and manufacturing firms. In respect of mission statements, a comparison between the service and manufacturing firms is drawn. For initial data arrangement, the mission statements of all selected service firms are grouped together, same was also done with the manufacturing firms separately. The mission statements are processed through the software for Readability (Gunning Fog, Flesch-Kincaid, SMOG, Coleman-Liau, Automated, Flesch Reading Ease score) and Lexical Density analysis in two categories, service firms and manufacturing firms. Result show that Service firms’ mission statements are more Lexically dense and possess higher average level than those of the manufacturing firms. This study contributes to the Strategic Management literature and practical implications to the service and manufacturing firms and their stakeholders. Also comes out of the ole ways of data analysis in management studies. Future researchers may carry out similar research in a specific industry.

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Abhijit J, Patil, and Dr G. Syam Prasad. "Trust Based Security Model for IoT and Fog based Applications." International Journal of Engineering & Technology 7, no. 2.7 (March 18, 2018): 691. http://dx.doi.org/10.14419/ijet.v7i2.7.10924.

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Wireless sensor networks (WSN’s) are becoming increasingly popular in the current Era. WSN and Smart Devices are key enablers for the Internet of things (IoT) and Fog Computing based applications and it brings IoT applications with enhanced capabilities for sensing and actuation. IOT is a very growing area and having wide scope for research. Integration of WSNs into IoT is today’s need. Adoptions to cloud computing, increasing use of automation of tools, Integration of internet into most activities of human had reached to the stage where Internet of Things based applications are becoming more popular today. Many new applications are now being developed in the field of IoT. The broad areas of application are Home automation, Smart cities, Retail, Agriculture, Manufacturing, Healthcare, Habitat Monitoring, Security and Surveillance etc. With this wide scope the applications are prone to many security threats. Providing Security for IoT and Fog computing-based applications is a Research Challenge. This paper tries to discuss with the common security attacks, Reasons, their counter measures and research directions. We are also proposing a Trust based Security model for implementing application layer level security in IoT and fog.

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22

Lynn, Roby, Ethan Wescoat, Dongmin Han, and Thomas Kurfess. "Embedded fog computing for high-frequency MTConnect data analytics." Manufacturing Letters 15 (January 2018): 135–38. http://dx.doi.org/10.1016/j.mfglet.2017.11.002.

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23

Cheng, Yan, Songnan Zhang, Shengkai Liu, Jianying Huang, Zhibin Zhang, Xuedong Wang, Zhihua Yu, et al. "Fog catcher brushes with environmental friendly slippery alumina micro-needle structured surface for efficient fog-harvesting." Journal of Cleaner Production 315 (September 2021): 127862. http://dx.doi.org/10.1016/j.jclepro.2021.127862.

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24

Song, Zhengyi, and Young Moon. "Performance analysis of CyberManufacturing Systems." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 233, no. 5 (May 18, 2017): 1362–76. http://dx.doi.org/10.1177/0954405417706996.

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CyberManufacturing System is an advanced vision for future manufacturing where physical components are fully integrated and seamlessly networked with computational processes, forming an on-demand, intelligent, and communicative manufacturing resource and capability repository with optimal and sustainable manufacturing solutions. The CyberManufacturing System utilizes recent developments in Internet of things, cloud computing, fog computing, service-oriented technologies, among others. Manufacturing resources and capabilities can be encapsulated, registered, and connected to each other directly or through the Internet, thus enabling intelligent behaviors of manufacturing components and systems such as self-awareness, self-prediction, self-optimization, and self-configuration. This research presents an introduction to the CyberManufacturing System, establishing the architecture and functions of the CyberManufacturing System, designing the pivotal control strategy, and investigating the performance analysis of the CyberManufacturing System using modeling and simulation techniques. In total, five component-level examples and one system-level case study have been developed and used for illustration and validation of the CyberManufacturing System operations. The results show that the CyberManufacturing System is superior to other types of manufacturing systems in terms of functionality and cooperative performance.

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Tao, Wenjin, Md Al-Amin, Haodong Chen, Ming C. Leu, Zhaozheng Yin, and Ruwen Qin. "Real-Time Assembly Operation Recognition with Fog Computing and Transfer Learning for Human-Centered Intelligent Manufacturing." Procedia Manufacturing 48 (2020): 926–31. http://dx.doi.org/10.1016/j.promfg.2020.05.131.

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26

Martín-Gómez, Alejandro, María Jesús Ávila-Gutiérrez, and Francisco Aguayo-González. "Holonic Reengineering to Foster Sustainable Cyber-Physical Systems Design in Cognitive Manufacturing." Applied Sciences 11, no. 7 (March 25, 2021): 2941. http://dx.doi.org/10.3390/app11072941.

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Value chain is identified as the generator of the metabolic rift between nature and society. However, the sustainable value chain can mitigate and reverse this rift. In this paper, firstly, a review of the main digital enablers of Industry 4.0 and the current state of cognitive manufacturing is carried out. Secondly, Cyber-Physical Systems are conceived from the holonic paradigm, as an organizational enabler for the whole of enablers. Thirdly, the bijective relationship between holonic paradigm and container-based technology is analyzed. This technology allows mapping the physical and virtual holon as an intelligent agent embodied at the edge, fog and cloud level, with physical and virtual part. Finally, the proposed holonic system based on the cyber-physical holon is developed through multi-agent systems based on container technology. The proposed system allows to model the metabolism of manufacturing systems, from a cell manufacturing to whole value chain, in order to develop, evolve and improve the sustainable value chain.

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27

Almusaied, Zaid, and Bahram Asiabanpour. "A Novel Design of Hybrid Hydrophilic-Superhydrophobic Surfaces for Fog Harvesting." International Journal of Engineering Materials and Manufacture 6, no. 3 (July 15, 2021): 152–62. http://dx.doi.org/10.26776/ijemm.06.03.2021.06.

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The continuous growth in the human population and climate change exacerbates the problems related to water scarcity. Harvesting the atmospheric water can mitigate the water scarcity in many regions around the globe. Fog collection using hybrid hydrophilic-superhydrophobic surfaces has the capacity to achieve a higher water collection rate. In this paper, a new method and materials are introduced to create the hybrid surfaces. The method includes additive manufacturing- to make sheets with holes-, mixing and casting polymeric matrix composite, and a controlled spray coating mechanism. The materials comprised of hydrophobic coating on top of the acrylic printed sheet and hydrophilic composite. The ratios of the pitches to diameters of the hydrophilic regions varied during the experiments to obtain the best water generation. The water collection rate for the sample with diameters of 583 um and a pitch of 1600 um has achieved 57% more than the untreated hydrophilic sample. The contrast in wettability accomplished by this novel method has the potential to be implemented on a large scale for atmospheric water harvesting.

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Sun, Biao, Divyamaan Wadnerkar, Ho-Yen Kim, Ranjeet P. Utikar, and Vishnu K. Pareek. "Investigation on fog formation of LNG ambient air vaporisers." Applied Thermal Engineering 193 (July 2021): 117023. http://dx.doi.org/10.1016/j.applthermaleng.2021.117023.

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29

Henning, Sören, Wilhelm Hasselbring, Heinz Burmester, Armin Möbius, and Maik Wojcieszak. "Goals and measures for analyzing power consumption data in manufacturing enterprises." Journal of Data, Information and Management 3, no. 1 (March 2021): 65–82. http://dx.doi.org/10.1007/s42488-021-00043-5.

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AbstractThe Internet of Things adoption in the manufacturing industry allows enterprises to monitor their electrical power consumption in real time and at machine level. In this paper, we follow up on such emerging opportunities for data acquisition and show that analyzing power consumption in manufacturing enterprises can serve a variety of purposes. In two industrial pilot cases, we discuss how analyzing power consumption data can serve the goals reporting, optimization, fault detection, and predictive maintenance. Accompanied by a literature review, we propose to implement the measures real-time data processing, multi-level monitoring, temporal aggregation, correlation, anomaly detection, forecasting, visualization, and alerting in software to tackle these goals. In a pilot implementation of a power consumption analytics platform, we show how our proposed measures can be implemented with a microservice-based architecture, stream processing techniques, and the fog computing paradigm. We provide the implementations as open source as well as a public show case allowing to reproduce and extend our research.

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Sun, L. X., Z. T. Liu, H. L. Hao, K. Wang, J. Sun, L. X. Leng, X. G. Su, and J. Cai. "Gas turbine air filter elements under fog endurance tests." Filtration + Separation 53, no. 4 (July 2016): 42–45. http://dx.doi.org/10.1016/s0015-1882(16)30171-9.

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Ren, Chunhua, Xiaoming Hu, Poyun Qin, Leilei Li, and Tong He. "Signal filtering for a small-diameter, dual-axis FOG inclinometer." Sensor Review 38, no. 3 (June 18, 2018): 353–59. http://dx.doi.org/10.1108/sr-07-2017-0136.

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PurposeMeasurement-while-drilling (MWD) system has been used to provide trajectory and inclination parameters of the oil and gas well. Fluxgate magnetometer is a traditional choice for one MWD system; however, it cannot obtain effective trajectory parameters in nonmagnetic environments. Fiber-optic-gyroscope (FOG) inclinometer system is a favorable substitute of fluxgate magnetometer, which can avoid the flaws associated with magnetic monitoring devices. However, there are some limitations and increasing surveying errors in this system under high impact conditions. This paper aims to overcome these imperfections of the FOG inclinometer system.Design/methodology/approachTo overcome the imperfections, filtering algorithms are used to improve the precision of the equipment. The authors compare the low-pass filtering algorithm with the wavelet de-noising algorithm applied to real experimental data. Quantitative comparison of the error between the true and processed signal revealed that the wavelet de-noising method outperformed the low-pass filtering method. To achieve optimal positioning effects, the wavelet de-noising algorithm is finally used to inhibit the interference caused by the impact.FindingsThe experimental results show that the method proposed can ensure the azimuth accuracy lower than ±2 degrees and the inclination accuracy lower than ± 0.15 degrees under the condition of interval impact. The method proposed can overcome the interference generated by the impact in the well, which makes the instrument suitable for the measurement of small-diameter casing well.Originality/valueAfter conducting the wavelet threshold filtering on the raw data of accelerometers, the noise generated by the impact is successfully suppressed, which is expected to meet the special requirement of the down-hole survey environment.

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Schotte, William. "Adiabatic expansion of hydrogen chloride and fog formation in air." Industrial & Engineering Chemistry Research 26, no. 1 (January 1987): 134–39. http://dx.doi.org/10.1021/ie00061a026.

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Wende, B., E. Laile, R. Zahoransky, M. Lehner, and K. Schaber. "On-line Measurement of Fog Droplets in Wet Scrubbing Processes." Chemical Engineering & Technology 24, no. 3 (March 2001): 235–38. http://dx.doi.org/10.1002/1521-4125(200103)24:3<235::aid-ceat235>3.0.co;2-g.

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Zenkert, Johannes, Christian Weber, Mareike Dornhöfer, Hasan Abu-Rasheed, and Madjid Fathi. "Knowledge Integration in Smart Factories." Encyclopedia 1, no. 3 (August 16, 2021): 792–811. http://dx.doi.org/10.3390/encyclopedia1030061.

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Knowledge integration is well explained by the human–organization–technology (HOT) approach known from knowledge management. This approach contains the horizontal and vertical interaction and communication between employees, human-to-machine, but also machine-to-machine. Different organizational structures and processes are supported with the help of appropriate technologies and suitable data processing and integration techniques. In a Smart Factory, manufacturing systems act largely autonomously on the basis of continuously collected data. The technical design concerns the networking of machines, their connectivity and the interaction between human and machine as well as machine-to-machine. Within a Smart Factory, machines can be considered as intelligent manufacturing systems. Such manufacturing systems can autonomously adapt to events through the ability to intelligently analyze data and act as adaptive manufacturing systems that consider changes in production, the supply chain and customer requirements. Inter-connected physical devices, sensors, actuators, and controllers form the building block of the Smart Factory, which is called the Internet of Things (IoT). IoT uses different data processing solutions, such as cloud computing, fog computing, or edge computing, to fuse and process data. This is accomplished in an integrated and cross-device manner.

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Yadav, Devendra Kumar, Pragyan Mishra, Singam Jayanthu, Santos Kumar Das, and Sanjay Kumar Sharma. "Application of IoT‐Fog based real‐time monitoring system for open‐cast mines—A survey." IET Wireless Sensor Systems 11, no. 1 (February 2021): 1–21. http://dx.doi.org/10.1049/wss2.12011.

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Feng, Jing, Lieshuang Zhong, and Zhiguang Guo. "Sprayed hieratical biomimetic superhydrophilic-superhydrophobic surface for efficient fog harvesting." Chemical Engineering Journal 388 (May 2020): 124283. http://dx.doi.org/10.1016/j.cej.2020.124283.

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Feng, Rui, Fei Song, Chen Xu, Xiu-Li Wang, and Yu-Zhong Wang. "A Quadruple-Biomimetic surface for spontaneous and efficient fog harvesting." Chemical Engineering Journal 422 (October 2021): 130119. http://dx.doi.org/10.1016/j.cej.2021.130119.

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Wende, B., and K. Schaber. "Fog Formation in Flue Gases Containing Ammonia, Hydrogen Chloride, and Water." Chemie Ingenieur Technik 73, no. 6 (June 2001): 601. http://dx.doi.org/10.1002/1522-2640(200106)73:6<601::aid-cite6012222>3.0.co;2-b.

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Sun, Biao, Joshua Wong, Divyamaan Wadnerkar, Ranjeet P. Utikar, Vishnu K. Pareek, and Kaihua Guo. "Multiphase simulation of LNG vapour dispersion with effect of fog formation." Applied Thermal Engineering 166 (February 2020): 114671. http://dx.doi.org/10.1016/j.applthermaleng.2019.114671.

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Sodanapalli, Sana, Hewan Shrestha, Chandramohan Dhasarathan, Puviyarasi T., and Sam Goundar. "Recent Advances in Edge Computing Paradigms." International Journal of Fog Computing 4, no. 1 (January 2021): 37–51. http://dx.doi.org/10.4018/ijfc.2021010103.

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Edge computing is an exciting new approach to network architecture that helps organizations break beyond the limitations imposed by traditional cloud-based networks. It has emerged as a viable and important architecture that supports distributed computing to deploy compute and storage resources closer to the data source. Edge and fog computing addresses three principles of network limitations of bandwidth, latency, congestion, and reliability. The research community sees edge computing at manufacturing, farming, network optimization, workplace safety, improved healthcare, transportation, etc. The promise of this technology will be realized through addressing new research challenges in the IoT paradigm and the design of highly-efficient communication technology with minimum cost and effort.

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Ghosh, Ritwick, Chandrima Patra, Priya Singh, Ranjan Ganguly, Rakesh P. Sahu, Igor Zhitomirsky, and Ishwar K. Puri. "Influence of metal mesh wettability on fog harvesting in industrial cooling towers." Applied Thermal Engineering 181 (November 2020): 115963. http://dx.doi.org/10.1016/j.applthermaleng.2020.115963.

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Shah, Surendra H., Kalash R. Pai, Sachin R. Shinde, and Bhaskar N. Thorat. "Analysis of a vapor compression refrigeration system using a fog-cooled condenser." Applied Thermal Engineering 196 (September 2021): 117299. http://dx.doi.org/10.1016/j.applthermaleng.2021.117299.

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43

Pei, Wenle, Jinghui Li, Zhenyu Guo, Yufang Liu, Chunlei Gao, Lieshuang Zhong, Shaomin Wang, Yongping Hou, and Yongmei Zheng. "Excellent fog harvesting performance of liquid-infused nano-textured 3D frame." Chemical Engineering Journal 409 (April 2021): 128180. http://dx.doi.org/10.1016/j.cej.2020.128180.

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Mecheva, Teodora, and Nikolay Kakanakov. "Cybersecurity in Intelligent Transportation Systems." Computers 9, no. 4 (October 13, 2020): 83. http://dx.doi.org/10.3390/computers9040083.

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Intelligent Transportation Systems (ITS) are emerging field characterized by complex data model, dynamics and strict time requirements. Ensuring cybersecurity in ITS is a complex task on which the safety and efficiency of transportation depends. The imposition of standards for a comprehensive architecture, as well as specific security standards, is one of the key steps in the evolution of ITS. The article examines the general outlines of the ITS architecture and security issues. The main focus of security approaches is: configuration and initialization of the devices during manufacturing at perception layer; anonymous authentication of nodes in VANET at network layer; defense of fog-based structures at support layer and description and standardization of the complex model of data and metadata and defense of systems, based on AI at application layer. The article oversees some conventional methods as network segmentation and cryptography that should be adapted in order to be applied in ITS cybersecurity. The focus is on innovative approaches that have recently been trying to find their place in ITS security strategies. These approaches includes blockchain, bloom filter, fog computing, artificial intelligence, game theory and ontologies. In conclusion, a correlation is made between the commented methods, the problems they solve and the architectural layers in which they are applied.

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Angelopoulos, Angelos, Emmanouel T. Michailidis, Nikolaos Nomikos, Panagiotis Trakadas, Antonis Hatziefremidis, Stamatis Voliotis, and Theodore Zahariadis. "Tackling Faults in the Industry 4.0 Era—A Survey of Machine-Learning Solutions and Key Aspects." Sensors 20, no. 1 (December 23, 2019): 109. http://dx.doi.org/10.3390/s20010109.

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The recent advancements in the fields of artificial intelligence (AI) and machine learning (ML) have affected several research fields, leading to improvements that could not have been possible with conventional optimization techniques. Among the sectors where AI/ML enables a plethora of opportunities, industrial manufacturing can expect significant gains from the increased process automation. At the same time, the introduction of the Industrial Internet of Things (IIoT), providing improved wireless connectivity for real-time manufacturing data collection and processing, has resulted in the culmination of the fourth industrial revolution, also known as Industry 4.0. In this survey, we focus on the vital processes of fault detection, prediction and prevention in Industry 4.0 and present recent developments in ML-based solutions. We start by examining various proposed cloud/fog/edge architectures, highlighting their importance for acquiring manufacturing data in order to train the ML algorithms. In addition, as faults might also occur from sources beyond machine degradation, the potential of ML in safeguarding cyber-security is thoroughly discussed. Moreover, a major concern in the Industry 4.0 ecosystem is the role of human operators and workers. Towards this end, a detailed overview of ML-based human–machine interaction techniques is provided, allowing humans to be in-the-loop of the manufacturing processes in a symbiotic manner with minimal errors. Finally, open issues in these relevant fields are given, stimulating further research.

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Duan, Qiang, Hongliang Liang, Jiuyun Xu, Xiaoting Sun, and Ruru Zhang. "Fog-cloud task scheduling of energy consumption optimisation with deadline consideration." International Journal of Internet Manufacturing and Services 7, no. 4 (2020): 375. http://dx.doi.org/10.1504/ijims.2020.10028654.

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Xu, Jiuyun, Xiaoting Sun, Ruru Zhang, Hongliang Liang, and Qiang Duan. "Fog-cloud task scheduling of energy consumption optimisation with deadline consideration." International Journal of Internet Manufacturing and Services 7, no. 4 (2020): 375. http://dx.doi.org/10.1504/ijims.2020.110228.

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Yang, Yongming, Manuel AG Silva, and R. J. C. Silva. "Material degradation of cfrp-to-steel joints subjected to salt fog." Composites Part B: Engineering 173 (September 2019): 106884. http://dx.doi.org/10.1016/j.compositesb.2019.05.095.

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Li, Deke, Yufeng Fan, Guocai Han, and Zhiguang Guo. "Multibioinspired Janus membranes with superwettable performance for unidirectional transportation and fog collection." Chemical Engineering Journal 404 (January 2021): 126515. http://dx.doi.org/10.1016/j.cej.2020.126515.

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Melo, Aurelio G., Milena F. Pinto, Andre L. M. Marcato, Leonardo M. Honório, and Fabrício O. Coelho. "Dynamic Optimization and Heuristics Based Online Coverage Path Planning in 3D Environment for UAVs." Sensors 21, no. 4 (February 5, 2021): 1108. http://dx.doi.org/10.3390/s21041108.

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Path planning is one of the most important issues in the robotics field, being applied in many domains ranging from aerospace technology and military tasks to manufacturing and agriculture. Path planning is a branch of autonomous navigation. In autonomous navigation, dynamic decisions about the path have to be taken while the robot moves towards its goal. Among the navigation area, an important class of problems is Coverage Path Planning (CPP). The CPP technique is associated with determining a collision-free path that passes through all viewpoints in a specific area. This paper presents a method to perform CPP in 3D environment for Unmanned Aerial Vehicles (UAVs) applications, namely 3D dynamic for CPP applications (3DD-CPP). The proposed method can be deployed in an unknown environment through a combination of linear optimization and heuristics. A model to estimate cost matrices accounting for UAV power usage is proposed and evaluated for a few different flight speeds. As linear optimization methods can be computationally demanding to be used on-board a UAV, this work also proposes a distributed execution of the algorithm through fog-edge computing. Results showed that 3DD-CPP had a good performance in both local execution and fog-edge for different simulated scenarios. The proposed heuristic is capable of re-optimization, enabling execution in environments with local knowledge of the environments.

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