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Updated: Dec 14, 2025

Quasi-light Storage for Optical Data Packets
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Fog-Based Delay-Sensitive Data Transmission Algorithm for Data Forwarding and Storage in Cloud Environment for

Azath Mubarakali1, Anand Deva Durai2, Mohmmed Alshehri3

  • 1Department of Computer Networks and Communication Engineering, College of Computer Science and King Khalid University, Abha, Saudi Arabia.

Big Data
|July 17, 2020
PubMed
Summary
This summary is machine-generated.

Fog computing accelerates time-critical IoT applications by processing data closer to the source. A new algorithm significantly reduces latency for delay-sensitive applications like traffic monitoring.

Keywords:
data generator nodedelay-sensitive applicationsfile transmissionsfog computinground trip timetracking application

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Area of Science:

  • Computer Science
  • Network Engineering
  • Distributed Systems

Background:

  • Fog computing is crucial for Internet of Things (IoT) data transmission, adding a layer between devices and the cloud to speed up applications.
  • Integrating Software-Defined Networks (SDN) and Network Function Virtualization (NFV) into fog computing presents significant challenges for network monitoring.
  • Current trends necessitate advancements in fog computing to overcome integration hurdles and enhance performance.

Purpose of the Study:

  • To develop a robust, optimal fog-based algorithm for delay-sensitive data transmission.
  • To ensure low and predictable latency in critical applications such as traffic monitoring and vehicle tracking.
  • To address the challenges of network monitoring and integration within fog computing environments.

Main Methods:

  • Proposed a fog-based delay-sensitive data transmission algorithm.
  • Implemented data storage and processing closer to the data source at an optimal network depth.
  • Evaluated the algorithm's performance using datasets of 10 KB, 100 KB, and 1 MB across different Amazon Datacenter Regions.

Main Results:

  • The proposed algorithm reduced round trip time by 15.67 ms.
  • Achieved an average delay reduction of 2 seconds for various data set sizes.
  • Demonstrated effectiveness in India, Singapore, and Japan Amazon Datacenter Regions compared to conventional methods.

Conclusions:

  • The developed fog-based algorithm effectively reduces latency in delay-sensitive applications.
  • Processing data closer to the source with optimal network depth is a viable strategy for improving fog computing performance.
  • The findings support the adoption of advanced algorithms to overcome challenges in fog computing for IoT.