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Intelligent Control in SDN/NFV-Empowered IoT Systems for Smart City Application


Artificial intelligence (AI)-based control approaches for management and orchestration layer are studied in this paper by investigating the applicability of emerging machine learning (ML)/deep learning (DL) modules in software defined networking (SDN) and network functions virtualization (NFV) control systems. The system architecture jointly integrates SDN/NFV empowered Internet of Things (IoT) for smart city applications. This study covers the coarse grained working flows of networking state observation, action configuration, and output metric evaluation in ML/DL-assisted modelling to obtain reliable performance metrics. The comparison between the conventional, SDN/NFV empowered IoT, and extended ML/DL-assisted systems are discussed to illustrate the performance improvement in terms of Quality of Service (QoS) metrics in the intelligent core networks. The simulation tools, such as Mininet, RYU SDN controllers (with FlowManager), and Iperf3, are discussed to deploy the IoT infrastructure topology, configure AI-assisted actions, and capture QoS metrics, respectively, to assist the planning before deployment

2022 Papers

Local and Global Orientation Correction for Oriented Human (Pose) Detection

Preliminary Study on SSCF-derived Polar Coordinate for ASR

Text Recognition on the Khmer Identification Cards and Its Application in Electronic Know Your Customer (e-KYC)

Exploration of Semantic Information of Previous Sentences for Automatic Speech Recognition

Cambodia Distributed Ledger – CamDL

Job Trends Analysis Using Power BI

Students’ Sentiment and Feedback Analysis on Online Learning System during COVID-19

Temperature Forcasting in Pnhom Penh Using Time Series Models

Eveluation of Regularization based Contiual Learning Alogorithm in the Context of Human Activity Recognition

Implementation of Deep Learning for Smart City Application: Lessons Learned

ENI-ETSI Meets the Proactive Network Solutions for Multi-tier Networking


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