Resource-Efficient Spectrum-Based Traffic Classification on Constrained Devices
Author:
Affiliation:
1. Department of Computer Science, University of Antwerp—imec, Antwerp, Belgium
2. Departamento de Ingeniería Electrónica, Universidad de Antioquia, Medellín, Colombia
Funder
Antwerpen IDLab Group; in part by the Universidad de Antioquia GITA Group
European Union’s Horizon 2020 Research and Innovation Program
Colombian Ministry of Science Technology and Innovation
Communications Regulation Commission
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
http://xplorestaging.ieee.org/ielx7/8782661/10362961/10517277.pdf?arnumber=10517277
Reference60 articles.
1. AI-Enabled Reliable QoS in Multi-RAT Wireless IoT Networks: Prospects, Challenges, and Future Directions
2. 6G Wireless Communication Systems: Applications, Requirements, Technologies, Challenges, and Research Directions
3. Towards the Deployment of Machine Learning Solutions in Network Traffic Classification: A Systematic Survey
4. Multi-Task Learning at the Mobile Edge: An Effective Way to Combine Traffic Classification and Prediction
5. Mobile Encrypted Traffic Classification Using Deep Learning: Experimental Evaluation, Lessons Learned, and Challenges
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