EconPapers    
Economics at your fingertips  
 

Multi-Task Learning at The Mobile Edge: An Effective Way to Combine Traffic Classification and Prediction

S. N. Nisha Rani, A. Asiya Mariam, M. Roseline Juliana and K. R. Ramela
Additional contact information
S. N. Nisha Rani: Fatima Michael College of Engineering and Technology, Madurai, India.
A. Asiya Mariam: Fatima Michael College of Engineering and Technology, Madurai, India.
M. Roseline Juliana: Fatima Michael College of Engineering and Technology, Madurai, India.
K. R. Ramela: Fatima Michael College of Engineering and Technology, Madurai, India.

International Journal of Latest Technology in Engineering, Management & Applied Science, 2025, vol. 14, issue 7, 828-839

Abstract: The rapid growth of mobile data traffic, fueled by a variety of user applications and diverse service needs, has created significant challenges for managing traffic efficiently in next-generation wireless networks. Typically, traditional models for traffic classification and prediction function as separate tasks, which leads to a heavier computational load and less efficient inference. This paper introduces a cohesive solution that employs multi-task learning (MTL) at the mobile edge, allowing for simultaneous traffic classification and prediction in a way that is both resource-efficient and context-aware. The proposed edge-based MTL architecture utilizes shared representations through deep neural networks, facilitating the joint learning of related tasks. This approach not only boosts task performance by leveraging the connections between tasks but also greatly reduces latency and bandwidth usage by processing data closer to its source. By implementing the model on edge servers situated near base stations, we effectively eliminate the need to send data to centralized clouds, achieving real-time intelligence. Comprehensive experiments conducted on real-world mobile traffic datasets show that our model achieves impressive classification accuracy while keeping prediction error rates low. Additionally, when compared to traditional single-task learning models, our edge-based MTL method enhances generalization, shortens training time, and supports adaptive learning in dynamic mobile environments. This research lays the groundwork for a promising framework for intelligent traffic management in 5G and beyond, fostering efficient resource allocation, network slicing, and quality of service (QoS) guarantees across various traffic scenarios.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.ijltemas.in/DigitalLibrary/Vol.14Issue7/828-839.pdf (application/pdf)
https://www.ijltemas.in/papers/volume-14-issue-7/828-839.html (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bjb:journl:v:14:y:2025:i:7:p:828-839

Access Statistics for this article

International Journal of Latest Technology in Engineering, Management & Applied Science is currently edited by Dr. Pawan Verma

More articles in International Journal of Latest Technology in Engineering, Management & Applied Science from International Journal of Latest Technology in Engineering, Management & Applied Science (IJLTEMAS)
Bibliographic data for series maintained by Dr. Pawan Verma ().

 
Page updated 2025-10-09
Handle: RePEc:bjb:journl:v:14:y:2025:i:7:p:828-839