IoT Network Traffic Intelligence Dataset - Resource Management

Dataset Overview

This specialized dataset targets the growing challenge of intelligent network management in heterogeneous IoT environments. It provides labeled network traffic from multiple device categories enabling researchers to develop adaptive resource allocation algorithms and traffic prediction models.

Device Diversity

Traffic captured from multiple IoT device classes: smart home (security cameras, smart speakers, thermostats, lighting systems), wearables (fitness trackers, smartwatches, health monitors), industrial sensors (temperature, pressure, vibration sensors), smart appliances (refrigerators, washing machines, HVAC systems), and entertainment devices (smart TVs, streaming devices, gaming consoles).

Traffic Characteristics

The dataset captures diverse patterns including periodic transmissions (regular sensor readings), event-driven traffic (alarm triggers, motion detection alerts), bulk transfers (firmware updates, video streaming, backup operations), and interactive sessions (real-time control commands, video calls).

QoS Metrics

Includes bandwidth utilization per device type, latency measurements (min, max, average, jitter), packet loss rates under varying network loads, throughput statistics, and protocol overhead analysis. Structured for immediate use in ML pipelines with device identifiers, temporal features, protocol information, statistical features, and labeled network states.