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Datasets:
axakhan
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IOV

Tasks:
Reinforcement Learning
Tabular Classification
Modalities:
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Formats:
csv
Languages:
English
Size:
1M - 10M
Tags:
network-slicing
5G
IoV
DRL
MEC
URLLC
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metadata 
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) 

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 

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:

@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