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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM51707

Real-Time Intelligence at the Network Edge: A Comprehensive Study of 5G-MEC Integration for Enterprise AI Applications

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM51707

Volume:

9 |

8 |

Aug 2025

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM51707

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

VKS

Varinder Kumar Sharma,

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Abstract

Modern enterprises face unprecedented challenges in processing massive data volumes with millisecond-level response requirements. This investigation examines how the synergy between fifth-generation cellular networks and edge computing infrastructures revolutionizes artificial intelligence deployment strategies. Our empirical analysis of production implementations reveals dramatic performance enhancements: response times plummet from traditional cloud latencies of 50-100 milliseconds to sub-millisecond levels, while computational throughput increases by factors exceeding 400%. Field deployments across manufacturing, transportation, and medical sectors demonstrate tangible business value, with return on investment improvements averaging 292%. These findings suggest a fundamental shift in distributed computing architectures, with global economic impact projected to reach $2.3 trillion within this decade. This paper synthesizes technical architectures, operational frameworks, and strategic considerations essential for organizations navigating this technological transformation.

KEYWORDS

5G cellular systems edge intelligence distributed computing enterprise systems network latency real-time processing

Cite This Article

Varinder Kumar Sharma, "Real-Time Intelligence at the Network Edge: A Comprehensive Study of 5G-MEC Integration for Enterprise AI Applications," International Journal of Scientific Research in Engineering and Management, vol. 9, no. 8, Aug 2025. https://doi.org/10.55041/IJSREM51707

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Publication Date

8/27/2025

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