Sangheon Pack

Professor

Korea University

South Korea

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Professor Sangheon Pack is a faculty member at Korea University, South Korea. His research focuses on advanced topics in network telemetry, split computing, and the design of 6G networks, with recent publications addressing energy efficiency, real-time data processing, and AI/ML optimization. He is also involved in developing frameworks for enhancing the performance of mobile devices and Internet of Things systems.

Sangheon Pack

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Articles (28)

FAT-INT: Frequency-Aware and Item-Wise In-band Network Telemetry for Low-Overhead and Accurate Measurement

Split Computing for Mobile Devices: Energy and Latency Perspective

An Overview for Designing 6G Networks: Technologies, Spectrum Management, Enhanced Air Interface, and AI/ML Optimization

Fast and fair split computing for accelerating deep neural network (DNN) inference

An Efficient Winner and Payment Determination Algorithm in Reverse Auction for Edge Federation

Cooperative and Adaptive Load-Aware Handover Optimization in Heterogeneous Networks

Cost-Aware Neural Adaptive Scaling for vRAN Resource Allocation

Demand-Aware Distributed Scheduling With Adaptive Buffer Control in Reconfigurable Data Center Networks

Dynamic Split Computing Framework for Multi-Task Learning Models: A Deep Reinforcement Learning Approach

Mobility-aware personalized handover function provisioning system in B5G networks

Two-Phase Split Computing Framework in Edge–Cloud Continuum

Routing attack induced anomaly detection in IoT network using RBM-LSTM

Dynamic Split Computing Framework in Distributed Serverless Edge Clouds

Hierarchical Network Data Analytics Framework for 6G Network Automation: Design and Implementation

CREDIT: A Credible Trust Framework for Dynamic Mobile Data Pricing Enforcement

CheckBullet: A Lightweight Checkpointing System for Robust Model Training on Mobile Networks

Divide and Cache: Design and Implementation of Control Plane Framework for Private 5G

Restoration-Aware Sleep Scheduling Framework in Energy Harvesting Internet of Things: A Deep Reinforcement Learning Approach

Data Distribution-Aware Online Client Selection Algorithm for Federated Learning in Heterogeneous Networks

Joint Client Selection and Bandwidth Allocation Algorithm for Federated Learning

Situation-Aware Cluster and Quantization Level Selection Algorithm for Fast Federated Learning

Utility-based sampling rate allocation in in-band network telemetry for high network visibility

Sensing Quality-Aware Task Allocation for Multidimensional Vehicular Urban Sensing

Deep Q-Network-Based Cloud-Native Network Function Placement in Edge Cloud-Enabled Non-Public Networks

Virtual Experience-Based Mobile Device Selection Algorithm for Federated Learning

Performance Optimization of Serverless Computing for Latency-Guaranteed and Energy-Efficient Task Offloading in Energy-Harvesting Industrial IoT

Function-Aware Resource Management Framework for Serverless Edge Computing

A Belief-Based Task Offloading Algorithm in Vehicular Edge Computing

Collaborators (2)

Daeyoung Jung

Sangwon Seo

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