Integration of IoT & cloud computing in mobile communication to breach limitation

The evolution of cloud computing has brought great progress in data processing capabilities but advanced issues still hinder the performance of mobile applications, especially those that require fast interaction and delicate data management. Traditional cloud data centers are geographically distant...

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Bibliographic Details
Published in:International journal of information technology (Singapore. Online) Vol. 17; no. 1; pp. 539 - 545
Main Authors: Kanchan, Pradeep, Selvakumar, V., Lavanya, P., Kumar, Ravi, Mishra, Awakash, Haripriya, V., Ahluwalia, Gunveen
Format: Journal Article
Language:English
Published: Singapore Springer Nature Singapore 01.01.2025
Springer Nature B.V
Subjects:
Applications programs
Artificial Intelligence
Cloud computing
Communication
Comparative analysis
Computer centers
Computer Imaging
Computer Science
Data centers
Data management
Data processing
Data transfer (computers)
End users
Image Processing and Computer Vision
Internet of Things
Machine Learning
Mobile computing
Original Research
Pattern Recognition and Graphics
Real time
Root-mean-square errors
Smartphones
Software
Software Engineering
Vision
Cloud computing
Computation
Mobile agent (M-agent)s
IoT
Latency
ISSN:2511-2104, 2511-2112
Online Access:Get full text
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Summary:The evolution of cloud computing has brought great progress in data processing capabilities but advanced issues still hinder the performance of mobile applications, especially those that require fast interaction and delicate data management. Traditional cloud data centers are geographically distant from end users, which slows data transfer and limits real-time interactions. In response to these challenges, the given paper proposes an Internet of things (IoT) based mobile cloud computing framework for enabling affordable and space-based computing for mobile users. The proposed framework comprises mobile users, controllers and IoT devices. When the mobile client moves across different locations, the data automatically transfers to nearby IoT devices thereby ensuring efficient processing without relying on remote isolated data centres. Lastly, the performance of the proposed framework is validated and compared with existing recent studies based on evaluation metrics such as root mean square error (RMSE), mean absolute error (MAE), computation time (mins) and accuracy (%).
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ISSN:2511-2104
2511-2112
DOI:10.1007/s41870-024-02286-y