Update README.md

README.md

@@ -1,79 +1,88 @@
----
-license: mit
----
-
+# 🚗 MINT-V2X
+### Mobility-Integrated Network Trajectory Dataset for V2X Systems
 
 **MINT-V2X (Mobility-Integrated Network Trajectory Dataset for V2X Systems)** is a large-scale dataset designed for research on **vehicle-to-everything (V2X) communication, mobility-aware networking, and intelligent transportation systems (ITS)**.
 
 The dataset integrates **vehicle trajectory dynamics with wireless network measurements**, enabling predictive modeling of **vehicle mobility, network quality, and RSU resource demand**.
 
-Unlike existing datasets that provide either **vehicle trajectories** or **network statistics** independently, **MINT-V2X provides synchronized mobility and communication data**, allowing researchers to study interactions between **vehicle movement, wireless channel quality, and network load**.
+Unlike existing datasets that provide either **vehicle trajectories** or **network statistics** independently, **MINT-V2X provides synchronized mobility and communication data**, allowing researchers to investigate the interactions between **vehicle movement, wireless channel quality, and network load**.
 
 ---
 
-# Dataset Overview
+## 📊 Dataset Overview
 
 MINT-V2X contains nearly **10 million synchronized records** generated from a realistic **urban V2X simulation environment**.
 
-| Property | Value |
-|--------|------|
-| Vehicles | 1,386 |
-| Total records | 9,873,977 |
-| Simulation duration | 3 hours |
-| Sampling rate | 10 Hz (100 ms) |
-| Features per record | 29 |
-| Spatial coverage | 61.19 km² |
-| RSU deployment | 15 roadside units (5×3 grid) |
+| 📌 Property | 📈 Value |
+|---|---:|
+| 🚙 **Vehicles** | 1,386 |
+| 🗃️ **Total Records** | 9,873,977 |
+| ⏱️ **Simulation Duration** | 3 hours |
+| ⚡ **Sampling Rate** | 10 Hz (100 ms) |
+| 🔢 **Features per Record** | 29 |
+| 🗺️ **Spatial Coverage** | 61.19 km² |
+| 📡 **RSU Deployment** | 15 roadside units (5 × 3 grid) |
 
-Each record corresponds to a **vehicle–timestep observation**, capturing both **vehicle mobility state and wireless network conditions**.
+Each record corresponds to a **vehicle–timestep observation**, capturing both the **vehicle mobility state** and its corresponding **wireless network conditions**.
 
 ---
 
-# Dataset Features
+## 🧩 Dataset Features
 
-Each data sample includes **29 features** spanning four main categories.
+Each data sample contains **29 features** organized into four main categories.
+
+### 🚘 1. Vehicle Trajectory
 
-## 1. Vehicle Trajectory
 - Position `(x, y, z)`
 - Velocity
 - Acceleration
 - Heading angle
 - Lane identifier
 
-## 2. Network State
-- RSU association (cell ID)
+### 📡 2. Network State
+
+- RSU association (Cell ID)
 - Distance to RSU
 - Neighbor vehicle count
-- Signal-to-Interference-plus-Noise Ratio (SINR)
+- Signal-to-Interference-plus-Noise Ratio (**SINR**)
 - Received signal power
 
-## 3. Physical Layer Metrics
-- Channel Quality Indicator (CQI)
-- Modulation and Coding Scheme (MCS)
-- Packet Delivery Ratio (PDR)
-- Channel Busy Ratio (CBR)
+### 📶 3. Physical Layer Metrics
+
+- Channel Quality Indicator (**CQI**)
+- Modulation and Coding Scheme (**MCS**)
+- Packet Delivery Ratio (**PDR**)
+- Channel Busy Ratio (**CBR**)
+
+### 🔄 4. Communication Performance
 
-## 4. Communication Performance
 - Throughput
 - End-to-end latency
 - Handover events
 
-These features enable **joint modeling of vehicle mobility and wireless network performance**.
+> 💡 These features enable **joint modeling of vehicle mobility and wireless network performance**, rather than analyzing mobility and communication as independent processes.
 
 ---
 
-# Dataset Generation Pipeline
+## ⚙️ Dataset Generation Pipeline
+
+MINT-V2X is generated through a **three-layer co-simulation architecture**:
 
-The dataset is generated through a **three-layer co-simulation architecture**.
+### 🚦 Traffic Simulation — SUMO
 
-### Traffic Simulation — SUMO
 Generates realistic vehicle trajectories using urban traffic models.
 
-### Middleware — Veins
-Synchronizes traffic and communication simulators via the TraCI interface.
+**SUMO → Vehicle mobility and traffic dynamics**
+
+### 🔗 Middleware — Veins
+
+Synchronizes the traffic and communication simulators through the **TraCI interface**.
 
-### Network Simulation — OMNeT++ / Simu5G
-Computes wireless metrics such as:
+**Veins → Mobility–network synchronization**
+
+### 📡 Network Simulation — OMNeT++ / Simu5G
+
+Simulates V2X wireless communication and computes network metrics including:
 
 - SINR
 - CQI
@@ -81,57 +90,70 @@ Computes wireless metrics such as:
 - Throughput
 - Latency
 
-All data is recorded at **10 Hz temporal resolution**, ensuring precise synchronization between mobility and network states.
+**OMNeT++ / Simu5G → Wireless communication and network performance**
+
+### 🔄 Overall Pipeline
+
+`SUMO` ➜ `Veins / TraCI` ➜ `OMNeT++ / Simu5G` ➜ `MINT-V2X Dataset`
+
+All measurements are recorded at a **10 Hz temporal resolution**, providing precise synchronization between vehicle mobility and network states.
 
 ---
 
-# Validation Framework
+## ✅ Validation Framework
 
-The dataset was validated using a **14-point validation framework** referencing:
+MINT-V2X was evaluated using a **14-point validation framework** based on established communication standards and theoretical principles:
 
-- **3GPP C-V2X standards**
-- **ETSI congestion control specifications**
-- **Shannon information theory**
+- 📘 **3GPP C-V2X standards**
+- 📡 **ETSI congestion control specifications**
+- 📐 **Shannon information theory**
 
-Key validations include:
+### Key Validation Results
 
-- SINR range: **−5 dB to +25 dB**
-- CQI–SINR correlation: **0.993**
-- SINR–PDR correlation: **0.946**
-- Connectivity ratio: **99.78%**
+| Validation Metric | Result |
+|---|---:|
+| 📶 **SINR Range** | −5 dB to +25 dB |
+| 🔗 **CQI–SINR Correlation** | **0.993** |
+| 🔗 **SINR–PDR Correlation** | **0.946** |
+| 🌐 **Connectivity Ratio** | **99.78%** |
 
-These tests confirm that the simulated network metrics follow **physically consistent wireless communication behavior**.
+> ✅ These results confirm that the simulated network measurements exhibit **physically consistent wireless communication behavior**.
 
 ---
 
-# Research Applications
+## 🔬 Research Applications
 
-MINT-V2X supports research in several areas of **vehicular networking and intelligent transportation systems**, including:
+MINT-V2X supports research across **vehicular networking, V2X communication, machine learning, and intelligent transportation systems**.
 
-- Vehicle trajectory prediction
-- Mobility-aware network traffic prediction
-- RSU load forecasting
-- Handover optimization
-- Proactive resource allocation
-- Multi-agent V2X communication modeling
-- Mobility-communication co-learning models
+Potential applications include:
 
-The dataset particularly enables **mobility-aware RSU resource prediction**, where future network demand is estimated from predicted vehicle trajectories.
+- 🚗 **Vehicle trajectory prediction**
+- 📈 **Mobility-aware network traffic prediction**
+- 📡 **RSU load forecasting**
+- 🔄 **Handover optimization**
+- ⚡ **Proactive resource allocation**
+- 🤖 **Multi-agent V2X communication modeling**
+- 🧠 **Mobility–communication co-learning models**
+
+### 🎯 Mobility-Aware Resource Prediction
+
+A key application of MINT-V2X is **mobility-aware RSU resource prediction**, where future network demand can be estimated from predicted vehicle trajectories.
+
+This enables models to move from **reactive resource management** toward **predictive and proactive V2X resource allocation**.
 
 ---
 
-> ### 📚 Citation
->
-> If you use **MINT-V2X** in your research, please cite our study:
->
-> ```bibtex
-> @misc{anjum2026mintv2xmobilityintegratednetworktrajectory,
->       title={MINT-V2X: A Mobility-Integrated Network Trajectory Dataset for Predictive Resource Management},
->       author={Abdullah Anjum and Abdolazim Rezaei and Mehdi Sookhak},
->       year={2026},
->       eprint={2607.22654},
->       archivePrefix={arXiv},
->       primaryClass={cs.AI},
->       url={https://arxiv.org/abs/2607.22654}
-> }
-> ```
+## 📚 Citation
+
+If you use **MINT-V2X** in your research, please cite:
+
+```bibtex
+@misc{anjum2026mintv2xmobilityintegratednetworktrajectory,
+      title={MINT-V2X: A Mobility-Integrated Network Trajectory Dataset for Predictive Resource Management},
+      author={Abdullah Anjum and Abdolazim Rezaei and Mehdi Sookhak},
+      year={2026},
+      eprint={2607.22654},
+      archivePrefix={arXiv},
+      primaryClass={cs.AI},
+      url={https://arxiv.org/abs/2607.22654}
+}