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README.md

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+---
+license: cc-by-4.0
+task_categories:
+  - reinforcement-learning
+  - tabular-classification
+language:
+  - en
+tags:
+  - network-slicing
+  - 5G
+  - IoV
+  - DRL
+  - MEC
+  - URLLC
+  - eMBB
+  - mMTC
+  - vehicular-networks
+  - 3GPP
+  - MobFogSim
+  - SUMO
+size_categories:
+  - 1M<n<10M
+---
+
+# IoV-DynSlice-2026
+
+**Dynamic 5G Network Slicing Dataset for Internet of Vehicles — 7.83 Million Rows**
+
+Generated using **MobFogSim** (fog/edge computing simulator) extended with a **3GPP TR 38.901 Urban Macro (UMa) channel model**, driven by a real-world **SUMO vehicular mobility trace** from Islamabad, Pakistan.
+
+Designed for training Deep Reinforcement Learning (DRL) agents (PPO, SAC, TD3) to perform dynamic bandwidth allocation across three 5G network slices: URLLC, eMBB, and mMTC.
+
+---
+
+## Dataset at a Glance
+
+| Property | Value |
+|---|---|
+| Total rows | 7,834,940 |
+| Columns | 42 |
+| Seeds | 10 |
+| Vehicles per seed | 1,000 |
+| Simulation ticks | 786 (seconds) |
+| Missing values | 0 |
+| File format | CSV |
+| License | CC-BY 4.0 |
+
+---
+
+## Simulation Setup
+
+### Infrastructure
+| Parameter | Value | Standard |
+|---|---|---|
+| Base stations (gNBs) | 225 (15×15 grid) | 3GPP TR 38.901 UMa |
+| Inter-Site Distance (ISD) | 500 m | 3GPP TR 38.901 Table A.1-2 |
+| Coverage area | 56.25 km² (7.5×7.5 km) | Urban core Islamabad |
+| Carrier frequency | 3.5 GHz (n78 band) | PTA Pakistan 5G allocation |
+| Total spectrum | 100 MHz | 3GPP TS 38.104 FR1 max |
+| Tx power | 30 dBm | — |
+| BS height | 25 m | UMa standard |
+| UE height | 1.5 m | UMa standard |
+| Noise figure | 7 dB | — |
+
+### Channel Model
+3GPP TR 38.901 Urban Macro (UMa) path loss:
+
+```
+PL [dB] = 13.54 + 39.08 × log₁₀(d) + 20 × log₁₀(fc) − 0.6 × (hUT − 1.5)
+```
+
+where `d` is distance in metres, `fc` is carrier frequency in GHz, and `hUT` is UE height in metres.
+
+SINR → PLR mapping follows 5-tier lookup table derived from 3GPP link-level curves. Shannon capacity: `BW × log₂(1 + SINR_linear) × (1 − PLR)`.
+
+### Mobility
+- **Trace**: SUMO simulation of Islamabad, Pakistan
+- **Total SUMO vehicles**: 171,140
+- **Selected vehicles per seed**: 1,000 (random selection)
+- **Time steps**: 786 seconds
+- **Coverage**: 271 km² total SUMO area
+
+### 5G Network Slices
+| Slice | SLA Latency | SLA PLR | Base BW | Max BW |
+|---|---|---|---|---|
+| URLLC | ≤ 1 ms | ≤ 0.1% | 20 MHz | 50 MHz |
+| eMBB | ≤ 10 ms | ≤ 5% | 60 MHz | 70 MHz |
+| mMTC | ≤ 500 ms | ≤ 10% | 20 MHz | 25 MHz |
+
+---
+
+## Event Types (DPC — Dynamic Priority Controller)
+
+The DPC detects 6 network conditions from real-time traffic signals and adjusts slice priorities and bandwidth allocation accordingly.
+
+| Event | Count | % | Description |
+|---|---|---|---|
+| VEHICLE_SURGE | 1,909,960 | 24.4% | Rapid increase in vehicle density |
+| HANDOVER_STORM | 1,879,990 | 24.0% | High handover rate (>5 per tick) |
+| CONGESTION | 1,527,550 | 19.5% | Slow traffic, moderate density |
+| EMERGENCY | 1,779,900 | 22.7% | Peak density, URLLC breach |
+| NORMAL | 327,570 | 4.2% | Free-flow, low density, no events |
+| ACCIDENT | 409,970 | 5.2% | High stopped-vehicle fraction (≥92%) |
+
+### Bandwidth Allocation per Event (MHz)
+
+| Event | URLLC | eMBB | mMTC | Total |
+|---|---|---|---|---|
+| NORMAL | 20 | 60 | 20 | 100 |
+| CONGESTION | 30 | 55 | 15 | 100 |
+| VEHICLE_SURGE | 30 | 55 | 15 | 100 |
+| HANDOVER_STORM | 35 | 50 | 15 | 100 |
+| EMERGENCY | 40 | 45 | 15 | 100 |
+| ACCIDENT | 50 | 35 | 15 | 100 |
+
+---
+
+## Column Schema (42 columns)
+
+### Identification
+| Column | Type | Description |
+|---|---|---|
+| `vehicle_id` | int | Unique vehicle identifier |
+| `timestamp` | int | SUMO simulation second (0–785) |
+| `seed` | int | Random seed used for this run |
+
+### Vehicle State
+| Column | Type | Description |
+|---|---|---|
+| `vehicle_x_m` | float | Vehicle x-coordinate (metres) |
+| `vehicle_y_m` | float | Vehicle y-coordinate (metres) |
+| `vehicle_speed_mps` | float | Instantaneous speed (m/s) |
+| `dist_to_nearest_ap_m` | float | Distance to nearest gNB (metres) |
+| `nearest_ap_id` | int | ID of nearest gNB |
+| `vehicle_handover` | int | 1 if handover occurred this tick |
+| `vehicle_migration` | int | 1 if VM migration occurred |
+
+### Traffic Context
+| Column | Type | Description |
+|---|---|---|
+| `traffic_density_veh_per_km2` | float | Global vehicle density (all SUMO vehicles / 271 km²) |
+| `vehicle_count` | int | Total SUMO vehicles active at this tick |
+| `event_flag` | int | Numeric encoding of event_type |
+| `event_type` | str | NORMAL / CONGESTION / VEHICLE_SURGE / HANDOVER_STORM / EMERGENCY / ACCIDENT |
+| `root_cause` | str | DPC root cause classification |
+| `congestion_severity` | int | 0=NORMAL, 1=CONGESTION, 2=SURGE/HO-STORM, 3=EMERGENCY/ACCIDENT |
+
+### DRL Reward Signal
+| Column | Type | Range | Description |
+|---|---|---|---|
+| `reward` | float | [−0.44, +0.39] | Composite DRL reward |
+
+Reward formula:
+```
+r = 0.35×latComp + 0.20×lossComp + 0.15×netComp − 0.10×migPenalty − 0.20×slaPenalty
+```
+
+### URLLC Slice Metrics
+| Column | Type | Description |
+|---|---|---|
+| `urllc_latency_ms` | float | URLLC slice latency (ms) |
+| `urllc_packet_loss_rate` | float | URLLC PLR |
+| `urllc_bandwidth_alloc_mhz` | float | Allocated bandwidth (MHz) |
+| `urllc_throughput_mbps` | float | Achieved throughput (Mbps) |
+| `urllc_jitter_ms` | float | Latency jitter (ms) |
+| `urllc_slice_priority` | int | Current priority level |
+
+### eMBB Slice Metrics
+| Column | Type | Description |
+|---|---|---|
+| `embb_latency_ms` | float | eMBB slice latency (ms) |
+| `embb_packet_loss_rate` | float | eMBB PLR |
+| `embb_bandwidth_alloc_mhz` | float | Allocated bandwidth (MHz) |
+| `embb_throughput_mbps` | float | Achieved throughput (Mbps) |
+| `embb_jitter_ms` | float | Latency jitter (ms) |
+| `embb_slice_priority` | int | Current priority level |
+
+### mMTC Slice Metrics
+| Column | Type | Description |
+|---|---|---|
+| `mmtc_latency_ms` | float | mMTC slice latency (ms) |
+| `mmtc_packet_loss_rate` | float | mMTC PLR |
+| `mmtc_bandwidth_alloc_mhz` | float | Allocated bandwidth (MHz) |
+| `mmtc_throughput_mbps` | float | Achieved throughput (Mbps) |
+| `mmtc_jitter_ms` | float | Latency jitter (ms) |
+| `mmtc_slice_priority` | int | Current priority level |
+
+### Network State
+| Column | Type | Description |
+|---|---|---|
+| `network_utilization_percent` | float | Total spectrum utilisation (always 100% — full allocation) |
+| `edge_cpu_utilization_percent` | float | Edge server CPU utilisation |
+| `global_migration_count` | int | Cumulative VM migrations |
+| `global_handover_count` | int | Cumulative handovers |
+| `handover_rate_per_tick` | float | Handovers per second at this tick |
+| `isci_value` | float | Inter-Slice Contention Index |
+| `dynamic_priority_shift_flag` | int | 1 if DPC shifted priorities this tick |
+| `sla_violation_count` | int | Number of slices violating SLA (0–3) |
+| `packet_delivery_ratio` | float | Network-wide packet delivery ratio |
+| `fog_latency_ms` | float | Fog node RTT (ms) from 3GPP physics model |
+
+---
+
+## DRL Environment
+
+### Observation Space (9 features)
+```python
+obs = [
+    urllc_latency_ms,           # URLLC slice latency
+    embb_latency_ms,            # eMBB slice latency
+    mmtc_latency_ms,            # mMTC slice latency
+    urllc_bandwidth_alloc_mhz,  # current URLLC BW allocation
+    embb_bandwidth_alloc_mhz,   # current eMBB BW allocation
+    mmtc_bandwidth_alloc_mhz,   # current mMTC BW allocation
+    traffic_density_veh_per_km2,# traffic density
+    vehicle_speed_mps,          # mean vehicle speed
+    isci_value                  # inter-slice contention index
+]
+```
+
+### Action Space (3 continuous values)
+Bandwidth allocation in MHz for each slice. Constraint: sum ≤ 100 MHz.
+```
+action = [urllc_bw, embb_bw, mmtc_bw]   ∈ [0, 100]³
+```
+
+### Reward
+```
+range: [−0.44, +0.39]
+mean:  −0.291  (std: 0.133)
+```
+| Event | Mean Reward |
+|---|---|
+| NORMAL | +0.255 |
+| VEHICLE_SURGE | −0.251 |
+| CONGESTION | −0.282 |
+| HANDOVER_STORM | −0.313 |
+| ACCIDENT | −0.358 |
+| EMERGENCY | −0.403 |
+
+---
+
+## Loading the Dataset
+
+```python
+import pandas as pd
+from datasets import load_dataset
+
+# Option 1: pandas (recommended for large datasets)
+df = pd.read_csv("hf://datasets/axakhan/IOV/combined_dataset_v6.csv")
+
+# Option 2: HuggingFace datasets library
+dataset = load_dataset("axakhan/IOV")
+df = dataset["train"].to_pandas()
+
+# DRL state columns
+STATE_COLS = [
+    "urllc_latency_ms", "embb_latency_ms", "mmtc_latency_ms",
+    "urllc_bandwidth_alloc_mhz", "embb_bandwidth_alloc_mhz", "mmtc_bandwidth_alloc_mhz",
+    "traffic_density_veh_per_km2", "vehicle_speed_mps", "isci_value"
+]
+
+ACTION_COLS = [
+    "urllc_bandwidth_alloc_mhz",
+    "embb_bandwidth_alloc_mhz",
+    "mmtc_bandwidth_alloc_mhz"
+]
+
+X = df[STATE_COLS].values          # observations
+a = df[ACTION_COLS].values         # actions taken by DPC
+r = df["reward"].values            # rewards
+```
+
+---
+
+## Citation
+
+If you use this dataset in your research, please cite:
+
+```bibtex
+@dataset{khan2026iovdynslice,
+  author    = {Khan, Abubakar},
+  title     = {IoV-DynSlice-2026: Dynamic 5G Network Slicing Dataset for Internet of Vehicles},
+  year      = {2026},
+  publisher = {Hugging Face},
+  url       = {https://huggingface.co/datasets/axakhan/IOV},
+  note      = {7.83M rows, MobFogSim + 3GPP TR 38.901 UMa + SUMO Islamabad trace,
+               225 gNBs, ISD=500m, fc=3.5GHz, BW=100MHz, 1000 vehicles × 10 seeds}
+}
+```
+
+---
+
+## Related Work
+
+- MobFogSim: [github.com/diogomg/MobFogSim](https://github.com/diogomg/MobFogSim)
+- 3GPP TR 38.901: Study on channel model for frequencies from 0.5 to 100 GHz
+- 3GPP TS 22.261: Service requirements for the 5G system
+- SUMO: [sumo.dlr.de](https://sumo.dlr.de)