scholarly journals A Smart Manufacturing Service System Based on Edge Computing, Fog Computing, and Cloud Computing

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 86769-86777 ◽  
Author(s):  
Qinglin Qi ◽  
Fei Tao
Keyword(s):  
Cloud Computing ◽  
Service System ◽  
Fog Computing ◽  
Edge Computing ◽  
Smart Manufacturing

Modeling of Cyber-Physical Systems and Digital Twin Based on Edge Computing, Fog Computing and Cloud Computing Towards Smart Manufacturing

2018 ◽  
Author(s):  
Qinglin Qi ◽  
Dongming Zhao ◽  
T. Warren Liao ◽  
Fei Tao
Keyword(s):  
Cloud Computing ◽  
Fog Computing ◽  
Cyber Physical Systems ◽  
Edge Computing ◽  
System Level ◽  
Smart Manufacturing ◽  
Level System ◽  
Physical Systems ◽  
Unit Level ◽  
Digital Twin

Nowadays, smart manufacturing has attracted more and more interesting and attentions of researchers. As an important prerequisite for smart manufacturing, the cyber-physical integration of manufacturing is becoming more and more important. Cyber-physical systems (CPS) and digital twin (DT) are the preferred means to achieve the interoperability and integration between the physical and cyber worlds. From the perspective of hierarchy, CPS and DT can be divided into unit level, system level, and SoS (system of system) level. To meet the different requirements of each level, the following three complementary technologies, i.e., edge computing, fog computing and cloud computing, are instrumental to accelerate the development of various CPS and DT. In this article, the perspectives of unit-level, system-level, and SoS-level of CPS and DT supported by edge computing, fog computing and cloud computing are discussed.

The Role of Hybrid IoT with Cloud Computing and Fog Computing to Help the Oil and Gas Industry Recover from Covid-19 and Face Future Challenges

2021 ◽  
Author(s):  
Ethar H. K. Alkamil ◽  
Ammar A. Mutlag ◽  
Haider W. Alsaffar ◽  
Mustafa H. Sabah
Keyword(s):  
Cloud Computing ◽  
Oil And Gas ◽  
Fog Computing ◽  
Petroleum Industry ◽  
Oil And Gas Industry ◽  
Edge Computing ◽  
Cloud Services ◽  
Gas Industry ◽  
Comprehensive Review ◽  
Better Than

Abstract Recently, the oil and gas industry faced several crucial challenges affecting the global energy market, including the Covid-19 outbreak, fluctuations in oil prices with considerable uncertainty, dramatically increased environmental regulations, and digital cybersecurity challenges. Therefore, the industrial internet of things (IIoT) may provide needed hybrid cloud and fog computing to analyze huge amounts of sensitive data from sensors and actuators to monitor oil rigs and wells closely, thereby better controlling global oil production. Improved quality of service (QoS) is possible with the fog computing, since it can alleviate challenges that a standard isolated cloud can't handle, an extended cloud located near underlying nodes is being developed. The paradigm of cloud computing is not sufficient to meet the needs of the already extensively utilized IIoT (i.e., edge) applications (e.g., low latency and jitter, context awareness, and mobility support) for a variety of reasons (e.g., health care and sensor networks). Couple of paradigms just like mobile edge computing, fog computing, and mobile cloud computing, have arisen in recently to meet these criteria. Fog computing helps to optimize services and create better user experiences, such as faster responses for critical, time-sensitive needs. At the same time, it also invites problems, such as overload, underload, and disparity in resource usage, including latency, time responses, throughput, etc. The comprehensive review presented in this work shows that fog devices have highly constrained environments and limited hardware capabilities. The existing cloud computing infrastructure is not capable of processing all data in a centralized manner because of the network bandwidth costs and response latency requirements. Therefore, fog computing demonstrated, instead of edge computing, and referred to as "the enabling technologies allowing computation to be performed at the edge of the network, on downstream data on behalf of cloud services and upstream data on behalf of IIoT services" (Shi et al., 2016) is more effective for data processing when data sources are close together. A review of fog and cloud computing literature suggests that fog is better than cloud computing because fog computing performs time-dependent computations better than cloud computing. The cloud is inefficient for latency-sensitive multimedia services and other time-sensitive applications since it is accessible over the internet, like the real-time monitoring, automation, and optimization of petroleum industry operations. As a result, a growing number of IIoT projects are dispersing fog computing capacity throughout the edge network as well as through data centers and the public cloud. A comprehensive review of fog computing features is presented here, with the potential of using it in the petroleum industry. Fog computing can provide a rapid response for applications through preprocess and filter data. Data that has been trimmed can then be transmitted to the cloud for additional analysis and better service delivery.

scholarly journals An efficient fog computing for comprising approach to avoid data theft attack

2018 ◽  
Vol 7 (2.8) ◽  
pp. 680
Author(s):  
T Pavan Kumar ◽  
B Eswar ◽  
P Ayyappa Reddy ◽  
D Sindhu Bhargavi
Keyword(s):  
Cloud Computing ◽  
Internet Of Things ◽  
Paradigm Shift ◽  
Fog Computing ◽  
Edge Computing ◽  
New Paradigm ◽  
Computing Power ◽  
Data Theft

Cloud computing has become a new paradigm shift in the IT world because of its revolutionary model of computing. It provides flexibility, scalability, and reliability and decreased operational and support expenses for an organization. The Enterprise edition software’s are very costly and maintaining a separate IT team and maintaining their own servers is very expensive and that’s the reason why most of the companies are opting for Cloud computing over enterprise edition of the software. However, few organization cloud customers are not willing to step to cloud computing up on a big scale because of the safety problems present in cloud computing. One more disadvantage of Cloud is it’s not suitable for another revolutionary technology i.e.IoT(Internet of things)In this paper we are going to present the Advantages of Fog Computing and Decoy technology to address the security in cloud computing by extending it into fog computing.Fog Computing is a new paradigm in which the computing power moves to the edge of the network. So, it’s also called as Edge Computing.

scholarly journals Privacy in Cloud-Based Computing

2020 ◽  
pp. 239-252
Author(s):  
Monjur Ahmed ◽  
Nurul I. Sarkar
Keyword(s):  
Cloud Computing ◽  
Internet Of Things ◽  
Data Privacy ◽  
Fog Computing ◽  
Edge Computing ◽  
Third Party ◽  
Research Topics ◽  
Sensitive Data ◽  
Privacy Issues ◽  
Third Party Ownership

Cloud computing, internet of things (IoT), edge computing, and fog computing are gaining attention as emerging research topics and computing approaches in recent years. These computing approaches are rather conceptual and contextual strategies rather than being computing technologies themselves, and in practice, they often overlap. For example, an IoT architecture may incorporate cloud computing and fog computing. Cloud computing is a significant concept in contemporary computing and being adopted in almost every means of computing. All computing architectures incorporating cloud computing are termed as cloud-based computing (CbC) in general. However, cloud computing itself is the basis of CbC because it significantly depends on resources that are remote, and the remote resources are often under third-party ownership where the privacy of sensitive data is a big concern. This chapter investigates various privacy issues associated with CbC. The data privacy issues and possible solutions within the context of cloud computing, IoT, edge computing, and fog computing are also explored.

EdgeCloud

2021 ◽  
pp. 2684-2700
Author(s):  
Jamuna S. Murthy
Keyword(s):  
Cloud Computing ◽  
Traffic Management ◽  
Fog Computing ◽  
Research Work ◽  
Edge Computing ◽  
Smart Devices ◽  
Computing Environment ◽  
Time Data ◽  
Cloud Computing Environment ◽  
Real Time Data Processing

In the recent years, edge/fog computing is gaining greater importance and has led to the deployment of many smart devices and application frameworks which support real-time data processing. Edge computing is an extension to existing cloud computing environment and focuses on improving the reliability, scalability, and resource efficiency of cloud by abolishing the need for processing all the data at one time and thus increasing the bandwidth of a network. Edge computing can complement cloud computing in a way leading to a novel architecture which can benefit from both edge and cloud resources. This kind of resource architecture may require resource continuity provided that the selection of resources for executing a service in cloud is independent of physical location. Hence, this research work proposes a novel architecture called “EdgeCloud,” which is a distributed management system for resource continuity in edge to cloud computing environment. The performance of the system is evaluated by considering a traffic management service example mapped into the proposed layered framework.

scholarly journals A review report on challenges and opportunities of edge, fog and cloud computing by employing IoT technology

2018 ◽  
Vol 7 (3.29) ◽  
pp. 263
Author(s):  
Sk. Wasim Akram ◽  
Dr P. Rajesh ◽  
SK. Shama
Keyword(s):  
Cloud Computing ◽  
Fog Computing ◽  
Physical Distance ◽  
Edge Computing ◽  
Distributed Data ◽  
Security Issues ◽  
Computing Platform ◽  
Cloud Models ◽  
Challenges And Opportunities ◽  
Potential Applications

In the future, various information and things will be connected to the network. People can now live more convenient and comfortable life where the things and information coordinated together. A world where things are connected to network is referred as IOT (Internet of Things). A huge amount of incomplete data is generated by IOT need to process and responded to very short time. This pose challenge of dealing with big data from many geometrically distributed data sources which are to be managed and processed. To achieve this objective, cloud computing is a treated as one of the popular choice due to its scalability, storage, computational and other capabilities. However current cloud models are not intended to handle the essentials of IOT– volume, variety, and velocity of data. Moreover, as the physical distance between cloud and user increases, transmission latency increases with it, increasing response time and stressing of the user. In addition to that, the processing speed in this environment is largely dependent on the performance of user device. The viable solution to these problems is identified as Edge Computing. The Edge Computing platform works by allowing some application processing to be performed by a small edge server position between the cloud and user, and crucially in a location physically closed to the user. This paper comprehensively presents various research trends that are available in Edge, Fog computing along with a comparison is made among Cloud. Particularly the architecture, characteristics, key technologies, potential applications, security issues and challenges of Edge, Fog and Cloud Computing are discussed and summarized.  

EdgeCloud

2019 ◽  
pp. 108-128 ◽  
Author(s):  
Jamuna S. Murthy
Keyword(s):  
Cloud Computing ◽  
Traffic Management ◽  
Fog Computing ◽  
Research Work ◽  
Edge Computing ◽  
Smart Devices ◽  
Computing Environment ◽  
Time Data ◽  
Cloud Computing Environment ◽  
Real Time Data Processing

In the recent years, edge/fog computing is gaining greater importance and has led to the deployment of many smart devices and application frameworks which support real-time data processing. Edge computing is an extension to existing cloud computing environment and focuses on improving the reliability, scalability, and resource efficiency of cloud by abolishing the need for processing all the data at one time and thus increasing the bandwidth of a network. Edge computing can complement cloud computing in a way leading to a novel architecture which can benefit from both edge and cloud resources. This kind of resource architecture may require resource continuity provided that the selection of resources for executing a service in cloud is independent of physical location. Hence, this research work proposes a novel architecture called “EdgeCloud,” which is a distributed management system for resource continuity in edge to cloud computing environment. The performance of the system is evaluated by considering a traffic management service example mapped into the proposed layered framework.

What Is Cloud Computing?

2020 ◽  
pp. 1-27
Author(s):  
Saadia Karim ◽  
Tariq Rahim Soomro
Keyword(s):  
Cloud Computing ◽  
Fog Computing ◽  
Edge Computing ◽  
Distributed Environment ◽  
Technical Staff ◽  
Different Types ◽  
Successful Approach ◽  
High Scalability ◽  
The Cost ◽  
Data Centres

Cloud computing is a distributed environment for multiple organizations to use remotely and get high scalability, reliability on anytime, anywhere, and pay-as-you-go concepts. An organization has to create data centres to store, manage, and process the information to achieve benefits from data and make decisions. Cloud gives organizations a successful approach that leads to profit without maintaining the cost of data centres and technical staff to manage the services. Cloud has different types of architectures, types of clouds, and cost packages for using the cloud. These services can be scaled up or down when required by an organization. Cloud has unbeatable future because IT world is acquiring it and giving a boost to their businesses. Many cloud providers are using it and the remaining are moving to cloud. Cloud computing also gives birth to edge computing, fog computing, and many more zero downtime solutions.

scholarly journals A New Edge Computing based Cloud System for IoT Applications

2019 ◽  
Vol 8 (2) ◽  
pp. 4289-4293
Keyword(s):  
Cloud Computing ◽  
Spectral Efficiency ◽  
Telecommunication Network ◽  
Fog Computing ◽  
Mobile Internet ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Novel Technology ◽  
Different Characteristics ◽  
The Internet Of Things

The mobile internet and the internet of things (IoT) has emerged out with various applications were centralized cloud computing has faced several challenges over the past years. Challenges include high latency and low Spectral Efficiency. Nevertheless, these challenges can be faced using a novel technology which is now emerging out as a major trending technology that supersedes centralized cloud computing with edge devices of networks. Well, this technology will reduce the latency and will enhance spectral efficiency and will also support massive machine types of communication. A detailed description of this trending technology deals with mobile edge computing, cloudlets and fog computing. In addition, the functioning process of each computing technology is also included. The different characteristics of mobile edge computing and fog computing have been focused. However the most significant part of how these technologies work under the discussion of telecommunication network is also briefly explained.

scholarly journals Fog Computing Application of Cyber-Physical Models of IoT Devices with Symbolic Approximation Algorithms

2021 ◽  
Author(s):  
Deok-Kee Choi
Keyword(s):  
Cloud Computing ◽  
Manufacturing Systems ◽  
Fog Computing ◽  
Streaming Data ◽  
Physical Models ◽  
Sensor Data ◽  
Smart Manufacturing ◽  
Symbolic Approximation ◽  
Iot Devices ◽  
Smart Manufacturing Systems

Abstract Smart manufacturing systems transmit out streaming data from IoT devices to cloud computing; however, this could bring about several disadvantages such as high latency, immobility, and high bandwidth usage, etc. As for streaming data generated in many IoT devices, to avoid a long path from the devices to cloud computing, Fog computing has drawn in manufacturing recently much attention. This may allow IoT devices to utilize the closer resource without heavily depending on cloud computing. In this research, we set up a three-blade fan as IoT device used in manufacturing system with an accelerometer installed and analyzed the sensor data through cyber-physical models based on machine learning and streaming data analytics at Fog computing. Most of the previous studies on the similar subject are of pre-processed data open to public on the Internet, not with real-world data. Thus, studies using real-world sensor data are rarely found. A symbolic approximation algorithm is a combination of the dictionary-based algorithm of symbolic approximation algorithms and term-frequency inverse document frequency algorithm to approximate the time-series signal of sensors. We closely followed the Bayesian approach to clarify the whole procedure in a logical order. In order to monitor a fan's state in real time, we employed five different cyber-physical models, among which the symbolic approximation algorithm resulted in about 98% accuracy at a 95% confidence level with correctly classifying the current state of the fan. Furthermore, we have run statistical rigor tests on both experimental data and the simulation results through executing the post-hoc analysis. By implementing micro-intelligence with a trained cyber-physical model unto an individual IoT device through Fog computing we may alienate significant amount of load on cloud computing; thus, with saving cost on managing cloud computing facility. We would expect that this framework to be utilized for various IoT devices of smart manufacturing systems.

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