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.

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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

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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

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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

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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

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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

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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

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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}
}

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Related Work

• MobFogSim: 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