Edge Computing: Decentralizing the Future of Data Processing

Authors

  • Dana Martin
  • Drew Wright
  • Jesse Wilson

Keywords:

edge computing, latency, bandwidth, real-time, analytics

Abstract

The research presented in this article explores edge computing as a solution for reducing latency and bandwidth usage in cloud computing systems. By processing data closer to the source, edge computing enables faster decision-making and improves real-time analytics. The study analyzes various deployment strategies across industries such as smart cities and autonomous vehicles, offering insights into how edge computing can reshape data processing paradigms.

Author Biographies

Dana Martin

PhD in Electrical Engineering
California Institute of Technology
1200 E California Blvd, Pasadena, CA 91125, USA

Drew Wright

PhD in Computer Science
Delft University of Technology
Mekelweg 5, 2628 CD Delft, Netherlands

Jesse Wilson

PhD in Telecommunications
National Technical University of Ukraine 'Igor Sikorsky Kyiv Polytechnic Institute'
Peremohy Ave, 37, Kyiv, Ukraine, 03056

References

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Suleymanov, T. I., Mamedov, S. E., Ragimov, E. R., & Rahimov, J. R. (2024). Wind influence on the pollutants emitted spread by motor vehicles. Synchroinfo Journal, 10(4), 14-20.

Mamedov, S. E., & Rahimov, E. R. (2024). Information model of vehicle telematics data cluster collection using UAV. Synchroinfo Journal, 10(2), 21-27.

Рагимов, Э. Р. О. (2011). Метрология элементов безопасности программных комплексов, реализующих систему защиты информации корпоративных сетей. Вопросы защиты информации, (2), 36-41.

Published

2024-07-31

Issue

Section

Articles