Multivariate Dataset on IoT and AI - Cross-Domain Integration

Dataset Overview

This multivariate IoT and AI dataset represents a unique cross-domain resource that combines raw IoT sensor measurements with AI algorithm performance metrics. It enables research into hybrid intelligent systems where IoT data collection is tightly coupled with AI-driven decision making.

Dataset Structure

IoT Sensor Variables

Multiple sensor types capturing diverse physical phenomena:

• Environmental Sensors: Temperature, humidity, atmospheric pressure, air quality (CO2, VOCs, PM2.5)
• Motion and Position: Accelerometer data (X, Y, Z axes), gyroscope readings, GPS coordinates
• Energy Monitoring: Voltage, current, power consumption, battery levels
• Light and Sound: Illuminance (Lux), ambient noise (dB), spectral analysis

AI Performance Metrics

Quantitative measures of AI algorithm effectiveness on IoT data:

• Model Accuracy: Classification or regression performance scores
• Inference Latency (ms): Real-time processing speed critical for edge deployment
• Energy Consumption (mW): Power requirements for AI inference on IoT devices
• Memory Footprint (KB): Model size constraints for resource-limited hardware

Contextual Variables

• Device Type: Hardware platform (Arduino, Raspberry Pi, ESP32, industrial gateways)
• Deployment Location: Indoor/outdoor, industrial/residential, urban/rural
• Data Transmission Protocol: MQTT, CoAP, HTTP, LoRaWAN, NB-IoT
• Sampling Rate (Hz): Temporal resolution of sensor readings

Cross-Domain Research Applications

Edge AI Optimization

Study trade-offs between AI model complexity and IoT device constraints. Identify optimal model architectures balancing accuracy, latency, and energy consumption for specific hardware platforms.

Sensor Fusion

Develop multivariate models combining diverse sensor types for enhanced situational awareness. Investigate how AI algorithms can intelligently merge complementary sensor modalities.

Adaptive IoT Systems

Research dynamic systems that adjust sensor sampling rates, AI model complexity, or data transmission frequency based on environmental conditions and performance requirements.

Machine Learning Use Cases

• Multi-Task Learning: Simultaneous prediction of environmental conditions and AI performance metrics
• Dimensionality Reduction: Identify most informative sensor combinations for specific AI tasks
• Transfer Learning: Evaluate how AI models trained on one sensor type generalize to others
• Meta-Learning: Learn optimal AI algorithm selection based on sensor characteristics and deployment context

Practical Value

This dataset bridges the gap between pure IoT sensor research and AI algorithm development, providing insights into real-world integrated system design. It supports development of intelligent IoT platforms where sensing and decision-making are co-optimized.