Volume 20 No 13 (2022)
Download PDF
Approaches for latency optimization using fog computing in Smart Factory
Saurabh Vaidya , Ganesh Khekare
Abstract
As Smart manufacturing increasing by laps and bond, Industrial automation gains its momentum, an
increase in the number of “Smart” production machines has been never seen before. Reenergize a
traditional distribution value chain, enabling mass customization and a real-time information exchange
between buyers and manufacturers using IoT for drug asset management possible with the help of fog
computing concept. Manufacturers are slowly beginning to transform their manufacturing facility into
an integrated network of Cyber-Physical Systems, also known as "Smart Factory". Additionally,
technological advances in the fields of Cloud Computing, Internet of Things, and Distributed Computers
have also led to produce large amount of data. In contrast, small Manufacturing facilities often fall
behind when it comes to merging downstream production facilities with Cloud due to a lack of IT
infrastructure and associated costs. Besides, manufacturers are concerned about data privacy and
integrity when they view Cloud Manufacturing as a means of distributing and exchanging data. In such
cases Fog computing is a natural alternative of cloud computing, Fog computing characteristics,
compression with cloud computing detail out clearly. For improving latency time various AI method of
scheduling with their benefits compression table mentioned. Fog computing is a data platform that
distributes data generated in the production environment intelligently by resources in various parts of
the cloud network while promoting data privacy and ownership, using the Fog Computing paradigm
Keywords
AI, ML, Fog Computing, Edge computing, Cloud computing, Latency, optimization
Copyright
Copyright © Neuroquantology
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Articles published in the Neuroquantology are available under Creative Commons Attribution Non-Commercial No Derivatives Licence (CC BY-NC-ND 4.0). Authors retain copyright in their work and grant IJECSE right of first publication under CC BY-NC-ND 4.0. Users have the right to read, download, copy, distribute, print, search, or link to the full texts of articles in this journal, and to use them for any other lawful purpose.