Projects and Research Interests

Goal

• • MEC Design : MEC Server Placement, Capacity Planning

  • Energy Efficient MEC Operation and Management

  • MEC System Capacity Maximization through Efficient Resource Usage while Satisfying Task QoS Requirements

Technical Approaches

• • Federated Learning

  • Multi-Agent Deep Reinforcement Learning

  • Graph Neural Network

Goal

• • Optimize Power Consumption of Wireless Devices to Prolong Their Lifetime

Technical Approach

• • Develop Analytical Model for Standard Energy Saving Method

  • Online Bank-of-Expert Machine Learning

Goal

• • • Minimizing Task Migration Cost for Enhancing System Resource Utilization

Technical Approach

• • • Reinforcement Learning among Cooperative Vehicular Edge Computing Servers

Goal

• • • Data Transfer Interference Management among Autonomous WBANs in a Distributed Manner

Technical Approach

• Bio-Inspired Approaches: Synchronization and De-Synchronization

Goal

• When Massive IoT Devices Contend for Network Access, Resolve the Contention in a Distributed Manner

Technical Approaches

• Develop a Learning Model by using Minority Game Theory for IoT Devices

Goal

• Resolve Mismatch between Distribution of Network Resources and Traffic Demand

Technical Approaches

• Bio-Inspired Algorithm

• Matching with Preference Game Theory

Goal

• Detect the Time when Statistical Characteristics of Internet Traffic Changes in Advance

Technical Approaches

• Online Traffic Sampling

• Non-Parametric Statistical Analysis

Goal

• Mobile Device Handover Prediction for Providing Seamless Services

Technical Approaches

• Time-Series Analysis