A Review of Fog Computing and Machine Learning: Concepts, Applications, Challenges, and Open Issues

Abdulkareem, Karrar Hameed; Mohammed, Mazin Abed; Gunasekaran, Saraswathy Shamini; Al-Mhiqani, Mohammed Nasser; Mutlag, Ammar Awad; Mostafa, Salama A.; Ali, Nabeel Salih; Ibrahim, Dheyaa Ahmed

doi:10.1109/access.2019.2947542

Cited by 147 publications

(66 citation statements)

References 87 publications

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“…This is because, in hydrology and hydraulics, which are usually nonlinear, the SVM approach is considered a reasonable choice. Several approaches use the nonlinear kernel function to solve regression problems in a strategy [47].…”

Section: A Machine Learning Modelsmentioning

confidence: 99%

Benchmarking Methodology for Selection of Optimal COVID-19 Diagnostic Model Based on Entropy and TOPSIS Methods

et al. 2020

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Nowadays, coronavirus (COVID-19) is getting international attention due it considered as a life-threatened epidemic disease that hard to control the spread of infection around the world. Machine learning (ML) is one of intelligent technique that able to automatically predict the event with reasonable accuracy based on the experience and learning process. In the meantime, a rapid number of ML models have been proposed for predicate the cases of COVID-19. Thus, there is need for an evaluation and benchmarking of COVID-19 ML models which considered the main challenge of this study. Furthermore, there is no single study have addressed the problem of evaluation and benchmarking of COVID diagnosis models. However, this study proposed an intelligent methodology is to help the health organisations in the selection COVID-19 diagnosis system. The benchmarking and evaluation of diagnostic models for COVID-19 is not a trivial process. There are multiple criteria requires to evaluate and some of the criteria are conflicting with each other. Our study is formulated as a decision matrix (DM) that embedded mix of ten evaluation criteria and twelve diagnostic models for COVID-19. The multi-criteria decision-making (MCDM) method is employed to evaluate and benchmarking the different diagnostic models for COVID19 with respect to the evaluation criteria. An integrated MCDM method are proposed where TOPSIS applied for the benchmarking and ranking purpose while Entropy used to calculate the weights of criteria. The study results revealed that the benchmarking and selection problems associated with COVID19 diagnosis models can be effectively solved using the integration of Entropy and TOPSIS. The SVM (linear) classifier is selected as the best diagnosis model for COVID19 with the closeness coefficient value of 0.9899 for our case study data. Furthermore, the proposed methodology has solved the significant variance for each criterion in terms of ideal best and worst best value, beside issue when specific diagnosis models have same ideal best value. INDEX TERMS COVID19 diagnostic, machine learning, benchmarking methodology, chest X-rays images, entropy, TOPSIS, multi-criteria decision-making. The associate editor coordinating the review of this manuscript and approving it for publication was Zheng Xiao .

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Section: A Machine Learning Modelsmentioning

confidence: 99%

Benchmarking Methodology for Selection of Optimal COVID-19 Diagnostic Model Based on Entropy and TOPSIS Methods

et al. 2020

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“…Beside being the core of the application-oriented solutions discussed so far, machine learning techniques are also commonly used to deal with technical aspects of fog computing, such as efficient resource management, latency and energy consumption reduction [13], as well as modeling network traffic. An exhaustive analysis of machine learning in the context of fog computing is proposed in [14].…”

Section: Related Workmentioning

confidence: 99%

SMCP: a Secure Mobile Crowdsensing Protocol for fog-based applications

Concone

Morana

2020

Hum. Cent. Comput. Inf. Sci.

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In recent years, the availability of an ever increasing number of heterogeneous smart devices has changed everyday life as only few other technologies has been able to do in the past. The diffusion of the Internet of Things (IoT), as well as the spreading of user's personal devices, such as smartphones, tablets, and, more recently, smartwatches or other wristband devices, have enabled new people-centric sensing paradigms in which raw data captured by on board sensors can be analyzed to infer higher-level knowledge. Participatory sensing [1] and mobile crowdsensing [2], for instance, are two common strategies in which the devices owned by a community allow to understand, or even predict, relevant phenomena [3].

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“…Any network entity on the path between data source and the cloud center can act as an edge computing node, which has the capability of computing, storage and resource sharing [36] [43]. In a traditional distribution, the edge computing node is typically provided by the service operator, while more and more user terminals, which is closer to the data source and also has some level of computing capabilities, are used as edge nodes.…”

Section: Device-enhanced Mecmentioning

confidence: 99%

Efficient, Customizable and Edge-Based WebGIS System

He¹,

Zhu

2020

IEEE Access

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Nowadays there is a wide range of applications for WebGIS which can add great value to modern economic, and building WebGIS system for specific scenarios is the common requirement of the industry. While currently separate WebGIS systems are deployed at different sites and operated by different owners, each of which has the whole set of functionalities of WebGIS, and thus introduce high cost of development and maintenance, which is a waste of resources as most of the functionalities are the same or similar. An edge computing based WebGIS architecture is proposed in the paper to meet the need of customization by applying the idea of SaaS. In this distributed architecture, the resource load is reasonably balanced between the server and the browser, which improves the overall performance of the system. Also it utilizes edge computing to reduce the pressure on the server by sharing map tiles among WebGIS clients. The proposed WebGIS system can not only be well customized and personalized as it is edge computing base, but also be well usable for large number of visits due to its distributed feature. The experiments show 5 concurrent requests per second, as well as response speed increases by more than 38.6% against traditional deployment. INDEX TERMS edge computing, customizable, WebGIS, high performance, map tile sharing, deviceenhanced MEC.

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A Review of Fog Computing and Machine Learning: Concepts, Applications, Challenges, and Open Issues

Cited by 147 publications

References 87 publications

Benchmarking Methodology for Selection of Optimal COVID-19 Diagnostic Model Based on Entropy and TOPSIS Methods

Benchmarking Methodology for Selection of Optimal COVID-19 Diagnostic Model Based on Entropy and TOPSIS Methods

SMCP: a Secure Mobile Crowdsensing Protocol for fog-based applications

Efficient, Customizable and Edge-Based WebGIS System

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