A Model-Driven Framework for Optimum Application Placement in Fog Computing Using a Machine Learning Based Approach

COVID-19 presents an urgent global challenge because of its contagious nature, frequently changing characteristics, and the lack of a vaccine or effective medicines. A model for measuring and preventing the continued spread of COVID-19 is urgently required to provide smart health care services. This requires using advanced intelligent computing such as artificial intelligence, machine learning, deep learning, cognitive computing, cloud computing, fog computing, and edge computing. This paper proposes a model for predicting COVID-19 using the SIR and machine learning for smart health care and the well-being of the citizens of KSA. Knowing the number of susceptible, infected, and recovered cases each day is critical for mathematical modeling to be able to identify the behavioral effects of the pandemic. It forecasts the situation for the upcoming 700 days. The proposed system predicts whether COVID-19 will spread in the population or die out in the long run. Mathematical analysis and simulation results are presented here as a means to forecast the progress of the outbreak and its possible end for three types of scenarios: “no actions,” “lockdown,” and “new medicines.” The effect of interventions like lockdown and new medicines is compared with the “no actions” scenario. The lockdown case delays the peak point by decreasing the infection and affects the area equality rule of the infected curves. On the other side, new medicines have a significant impact on infected curve by decreasing the number of infected people about time. Available forecast data on COVID-19 using simulations predict that the highest level of cases might occur between 15 and 30 November 2020. Simulation data suggest that the virus might be fully under control only after June 2021. The reproductive rate shows that measures such as government lockdowns and isolation of individuals are not enough to stop the pandemic. This study recommends that authorities should, as soon as possible, apply a strict long-term containment strategy to reduce the epidemic size successfully.

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Object Detection in Fog Computing Using Machine Learning Algorithms

Advances in Computer and Electrical Engineering - Architecture and Security Issues in Fog Computing Applications ◽

10.4018/978-1-7998-0194-8.ch006 ◽

2020 ◽

pp. 90-107

Author(s):

Peyakunta Bhargavi ◽

Singaraju Jyothi

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Object Detection ◽

Fog Computing ◽

Machine Learning Algorithms ◽

Sensor Data ◽

Core Network ◽

The Core ◽

The Moment ◽

Algorithmic Approaches

The moment we live in today demands the convergence of the cloud computing, fog computing, machine learning, and IoT to explore new technological solutions. Fog computing is an emerging architecture intended for alleviating the network burdens at the cloud and the core network by moving resource-intensive functionalities such as computation, communication, storage, and analytics closer to the end users. Machine learning is a subfield of computer science and is a type of artificial intelligence (AI) that provides machines with the ability to learn without explicit programming. IoT has the ability to make decisions and take actions autonomously based on algorithmic sensing to acquire sensor data. These embedded capabilities will range across the entire spectrum of algorithmic approaches that is associated with machine learning. Here the authors explore how machine learning methods have been used to deploy the object detection, text detection in an image, and incorporated for better fulfillment of requirements in fog computing.

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A Novel Resource Management Framework for Fog Computing by Using Machine Learning Algorithm

Advances in Computer and Electrical Engineering - Architecture and Security Issues in Fog Computing Applications ◽

10.4018/978-1-7998-0194-8.ch002 ◽

2020 ◽

pp. 42-52

Author(s):

Shanthi Thangam Manukumar ◽

Vijayalakshmi Muthuswamy

Keyword(s):

Machine Learning ◽

Resource Management ◽

Mobile Devices ◽

Service Providers ◽

Learning Algorithm ◽

Fog Computing ◽

Mobile Network ◽

Resource Provisioning ◽

Management Framework ◽

Iot Devices

With the development of edge devices and mobile devices, the authenticated fast access for the networks is necessary and important. To make the edge and mobile devices smart, fast, and for the better quality of service (QoS), fog computing is an efficient way. Fog computing is providing the way for resource provisioning, service providers, high response time, and the best solution for mobile network traffic. In this chapter, the proposed method is for handling the fog resource management using efficient offloading mechanism. Offloading is done based on machine learning prediction technology and also by using the KNN algorithm to identify the nearest fog nodes to offload. The proposed method minimizes the energy consumption, latency and improves the QoS for edge devices, IoT devices, and mobile devices.

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