Object Detection in Fog Computing Using Machine Learning Algorithms

Object Detection in Fog Computing Using Machine Learning Algorithms

Peyakunta Bhargavi, Singaraju Jyothi
Copyright: © 2020 |Pages: 18
DOI: 10.4018/978-1-7998-0194-8.ch006
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Abstract

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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Fog Computing

Fog computing is that the thought of a network stuff that stretches from the outer edges of wherever information is made to wherever it’ll eventually be hold on, whether or not that is in the cloud or in a customer’s data center.

Fog is another layer of a distributed network location and is closely related to cloud computing and also the internet of things (IoT). Public infrastructure as a service (IaaS) cloud vendors will be thought of as a high-level, global endpoint for data; the edge of the network is where data from IoT devices is created.

Fog computing is that the plan of a distributed network that connects these two environments. “Fog provides the primitive link for what information must be pushed to the cloud, and what can be analyzed locally, at the edge,” explains Mung Chiang, dean of Purdue University’s College of Engineering and one in all the nation’s prime researchers on fog and edge computing.

Fog computing will be perceived each in hefty cloud systems and big data structures, making reference to the growing difficulties in accessing information objectively. This leads to an absence of quality of the obtained content. The things of fog computing on cloud computing and big data system might vary. However, a common aspect is a limitation in accurate content distribution, an issue that has been tackled with the creation of metrics that attempt to improve accuracy.

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