TiHAN-V2X: A Comprehensive Dataset for Dynamic C-V2X Communication within the Indian Context

Documentation (Data Dictionaries and Structure Documentation):

1. V2V (Vehicle-to-Vehicle) Communication:

   • Entries: 10,253
   • Fields: 35
   • Scenarios: 7 (S1-S7) covering urban, rural, highways, and city streets.
   • Key Parameters:
     • Time_Epoch: Time of communication event (epoch format)
     • Transmitted Latitude, Longitude: Location of the transmitting vehicle (degrees)
     • Received Latitude, Longitude: Location of the receiving vehicle (degrees)
     • Speed (km/h): Speed of both transmitting and receiving vehicles
     • Altitude (m): Altitude of both vehicles
     • Heading (degrees): Heading direction of both vehicles
     • Distance (m): Distance between the transmitting and receiving vehicles
     • SNR (dB): Signal-to-noise ratio during communication
     • RSSI (dBm): Received Signal Strength Indicator for two antennas
     • Path Loss (dB): Signal attenuation
     • ERP (dBm): Effective Radiative Power
     • RX Power (dBm): Received power at the receiving vehicle
     • Noise Power (dBm): Power level of background noise
     • Packet Error Rate (PER): Rate of packet loss during transmission

2. V2I (Vehicle-to-Infrastructure) Communication:

   • Entries: 2,957
   • Fields: 30
   • Scenarios: 3 V2I scenarios
   • Key Parameters:
     • Latitude/Longitude (Tx & Rx): Location of the vehicle and RSU (degrees)
     • Heading (degrees): Heading direction of the vehicle
     • Speed (km/h): Speed of the vehicle
     • Altitude (m): Altitude of both the RSU and vehicle
     • Latency (ms): Communication delay between vehicle and RSU
     • Throughput (bits/sec): Data transfer rate
     • Distance (m): Distance between vehicle and RSU
     • Path Loss (dB): Signal path loss
     • ERP (dBm): Effective radiated power
     • RX Power (dBm): Received power at the vehicle
     • Noise Power (dBm): Background noise level
     • SNR (dB): Signal-to-noise ratio
     • Packet Error Rate (PER): Communication error rate
     • Receiver Gain (dB): Gain of the receiving antenna

3. I2V (Infrastructure-to-Vehicle) Communication:

   • Entries: 2,106
   • Fields: 38
   • Scenarios: 1 (conducted at TiHAN - IIT Hyderabad)
   • Key Parameters:
     • Similar to V2I but includes additional weather data (temperature, pressure, precipitation) as broadcasted by RSUs via the 5G sidelink interface.
     • This dataset supports the analysis of I2V communication performance and its reliability under different weather conditions.

4. V2C (Vehicle-to-Cloud) Communication:

   • Entries: 1,971
   • Fields: 22
   • Key Parameters:
     • Transmission/Reception Timestamp: Time of data transmission and reception
     • Latitude/Longitude (Tx): Location of the transmitting vehicle
     • Cloud Coordinates: Location of the receiving cloud server
     • Distance to Cloud (m): Distance between vehicle and cloud
     • Latency (ms): Delay in communication between vehicle and cloud
     • Packet Error Rate (PER): Communication error rate
     • Bandwidth (Mbits/sec): Data transmission capacity
     • Download/Upload Speed (Mbits/sec): Speed of data transfer
     • Total Data Transferred (MBytes): Cumulative amount of data transferred between vehicle and cloud
     • UDP: The dataset employs the User Datagram Protocol (UDP) for time-sensitive, efficient transmission between vehicle and cloud.

Instructions for Utilizing the Dataset:

1. Loading the Data:

   • Each CSV file (V2V, V2I, I2V, V2C) can be loaded into any data analysis platform, such as Python using pandas.
   • Example for loading the V2V data:pythonimport pandas as pd v2v_data = pd.read_csv('FinalV2V_Dataset.csv')

2. Preprocessing:

   • Unit normalization: Convert latencies, throughput, and power levels to consistent units across datasets if needed.
   • Filtering: Filter by scenarios or specific locations to focus on particular communication patterns.
   • Handling missing data: Check for missing or erroneous values and apply appropriate imputation techniques.

3. AI/ML Applications:

   • Predictive Models: Use regression models to predict latency or throughput based on distance, SNR, or other parameters.
   • Classification Models: Build classifiers to detect conditions that lead to high packet error rates or communication failures.
   • Optimization: Machine learning can be used to optimize transmission settings (e.g., frequency, power) for improved V2X performance.
   • Autonomous Driving Simulations: Use this data to model real-world V2X communications in smart city or highway driving scenarios, enhancing decision-making for autonomous vehicles.

4. Analyzing Performance:

   • Latency and Throughput: Use descriptive statistics to evaluate latency and throughput for different communication types.
   • Path Loss and SNR Analysis: Investigate how varying distances, locations, and scenarios impact path loss and SNR.
   • Scenario-based Analysis: Compare V2X performance across the seven V2V scenarios, or evaluate performance under specific urban, rural, and highway conditions.

Size of the Dataset:

• V2V Dataset: 10,253 entries, 35 fields
• V2I Dataset: 2,957 entries, 30 fields
• I2V Dataset: 2,106 entries, 38 fields
• V2C Dataset: 1,971 entries, 22 fields

This large dataset provides sufficient data for in-depth machine learning analysis, simulation studies, and real-world evaluation of V2X systems.