scholarly journals Fog Computing, Edge Computing and a return to privacy and personal autonomy

2020 ◽  
Vol 171 ◽  
pp. 1750-1759 ◽  
Author(s):  
Michael Losavio
Keyword(s):  
Personal Autonomy ◽  
Fog Computing ◽  
Edge Computing

scholarly journals Implementation analysis of IoT-based offloading frameworks on cloud/edge computing for sensor generated big data

2021 ◽  
Author(s):  
Karan Bajaj ◽  
Bhisham Sharma ◽  
Raman Singh
Keyword(s):  
High Speed ◽  
Smart Cities ◽  
Fog Computing ◽  
Edge Computing ◽  
Computation Offloading ◽  
Performance Parameters ◽  
Iot Applications ◽  
Time Sensitivity ◽  
Implementation Analysis ◽  
Increasing Demand

AbstractThe Internet of Things (IoT) applications and services are increasingly becoming a part of daily life; from smart homes to smart cities, industry, agriculture, it is penetrating practically in every domain. Data collected over the IoT applications, mostly through the sensors connected over the devices, and with the increasing demand, it is not possible to process all the data on the devices itself. The data collected by the device sensors are in vast amount and require high-speed computation and processing, which demand advanced resources. Various applications and services that are crucial require meeting multiple performance parameters like time-sensitivity and energy efficiency, computation offloading framework comes into play to meet these performance parameters and extreme computation requirements. Computation or data offloading tasks to nearby devices or the fog or cloud structure can aid in achieving the resource requirements of IoT applications. In this paper, the role of context or situation to perform the offloading is studied and drawn to a conclusion, that to meet the performance requirements of IoT enabled services, context-based offloading can play a crucial role. Some of the existing frameworks EMCO, MobiCOP-IoT, Autonomic Management Framework, CSOS, Fog Computing Framework, based on their novelty and optimum performance are taken for implementation analysis and compared with the MAUI, AnyRun Computing (ARC), AutoScaler, Edge computing and Context-Sensitive Model for Offloading System (CoSMOS) frameworks. Based on the study of drawn results and limitations of the existing frameworks, future directions under offloading scenarios are discussed.

scholarly journals A Data-driven Platform for Simulating Vehicular Fog Computing Environment

2022 ◽  
Author(s):  
Ozgur Umut Akgul ◽  
Wencan Mao ◽  
Byungjin Cho ◽  
Yu Xiao
Keyword(s):  
Capacity Planning ◽  
Fog Computing ◽  
Deep Understanding ◽  
Temporal Variations ◽  
Edge Computing ◽  
Business Strategies ◽  
Open Platform ◽  
Trade Offs ◽  
Spatio Temporal ◽  
Computing Capacity

<div>Edge/fog computing is a key enabling technology in 5G and beyond for fulfilling the tight latency requirements of compute-intensive vehicular applications such as cooperative driving. Concerning the spatio-temporal variation in the vehicular traffic flows and the demand for edge computing capacity generated by connected vehicles, vehicular fog computing (VFC) has been proposed as a cost-efficient deployment model that complements stationary fog nodes with mobile ones carried by moving vehicles. Accessing the feasibility and the applicability of such hybrid topology, and further planning and managing the networking and computing resources at the edge, require deep understanding of the spatio-temporal variations in the demand and the supply of edge computing capacity as well as the trade-offs between achievable Quality-of-Services and potential deployment and operating costs. To meet such requirements, we propose in this paper an open platform for simulating the VFC environment and for evaluating the performance and cost efficiency of capacity planning and resource allocation strategies under diverse physical conditions and business strategies. Compared with the existing edge/fog computing simulators, our platform supports the mobility of fog nodes and provides a realistic modeling of vehicular networking with the 5G and beyond network in the urban environment. We demonstrate the functionality of the platform using city-scale VFC capacity planning as example. The simulation results provide insights on the feasibility of different deployment strategies from both technical and financial perspectives.</div>

scholarly journals All one needs to know about fog computing and related edge computing paradigms: A complete survey

2019 ◽  
Vol 98 ◽  
pp. 289-330 ◽  
Author(s):  
Ashkan Yousefpour ◽  
Caleb Fung ◽  
Tam Nguyen ◽  
Krishna Kadiyala ◽  
Fatemeh Jalali ◽  
...  
Keyword(s):  
Fog Computing ◽  
Edge Computing ◽  
Complete Survey

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 Deployment of IoT Edge and Fog Computing Technologies to Develop Smart Building Services

2018 ◽  
Vol 10 (11) ◽  
pp. 3832 ◽  
Author(s):  
Francisco-Javier Ferrández-Pastor ◽  
Higinio Mora ◽  
Antonio Jimeno-Morenilla ◽  
Bruno Volckaert
Keyword(s):  
Fog Computing ◽  
Edge Computing ◽  
Climate Control ◽  
Embedded Devices ◽  
Computing Systems ◽  
Smart Building ◽  
Computing Paradigm ◽  
Novel Approach ◽  
Edge And Fog Computing ◽  
Building Designs

Advances in embedded systems, based on System-on-a-Chip (SoC) architectures, have enabled the development of many commercial devices that are powerful enough to run operating systems and complex algorithms. These devices integrate a set of different sensors with connectivity, computing capacities and cost reduction. In this context, the Internet of Things (IoT) potential increases and introduces other development possibilities: “Things” can now increase computation near the source of the data; consequently, different IoT services can be deployed on local systems. This paradigm is known as “edge computing” and it integrates IoT technologies and cloud computing systems. Edge computing reduces the communications’ bandwidth needed between sensors and the central data centre. Management of sensors, actuators, embedded devices and other resources that may not be continuously connected to a network (such as smartphones) are required for this method. This trend is very attractive for smart building designs, where different subsystems (energy, climate control, security, comfort, user services, maintenance, and operating costs) must be integrated to develop intelligent facilities. In this work, a method to design smart services based on the edge computing paradigm is analysed and proposed. This novel approach overcomes some drawbacks of existing designs related to interoperability and scalability of services. An experimental architecture based on embedded devices is described. Energy management, security system, climate control and information services are the subsystems on which new smart facilities are implemented.

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.

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