I am interested in mobile edge computing, task offloading, resource allocation, edge intelligence, AIoT, and machine learning.
My current work focuses on reproducible experiments and intelligent decision-making for mobile edge computing systems.
- Mobile Edge Computing
- Task Offloading and Resource Allocation
- Edge Intelligence and AIoT
- QoE- and Fairness-Aware Optimization
- Machine Learning for IoT Systems
- Reproducible Algorithm Experiments
A reproducible research repository for QoE- and fairness-aware task offloading and resource allocation in mobile edge computing. It includes algorithm implementations, baseline comparisons, repeated experiments, statistical analysis, ablation studies, sensitivity analysis, tables, and figures.
A lightweight Python simulation and visualization framework for comparing local, edge, and cloud task-offloading strategies.
A structured machine learning study project covering regression, classification, model evaluation, overfitting, data leakage, and an IoT predictive-maintenance case study.
- MEC task offloading and resource allocation
- Reproducible optimization experiments
- Machine learning and reinforcement learning
- Learning-based decision-making for dynamic edge systems
- Languages: Python, Java, C
- Machine Learning: scikit-learn, regression, classification, model evaluation, PCA, and K-means
- Research: simulation, statistical analysis, visualization, and reproducible experiments
- Tools: Git, GitHub, VS Code, PyCharm, IntelliJ IDEA, Codex, Arduino, RISC-V, and LTspice
Email: ryan.zhoujiangyi@gmail.com