Residential and commercial building sensor data: temperature, humidity, CO₂, occupancy, appliance-level consumption and home-automation event logs.
Real-time sensor data for automated underground drip irrigation of tomato crops, including soil moisture, NPK (N, P, K), temperature, humidity, pressure, wind speed, and solar radiation collected via Edge IoT.[page:2][web:238]
Real in-vehicle CAN bus traffic from a Kia Soul logged via OBD-II port, including normal operation and three types of message injection attacks (DoS, fuzzy, impersonation) for intrusion detection research.[web:224][web:225][web:221]
Over 2 million minute-level measurements of electric power consumption in one household near Paris over 47 months (2006–2010), including global active/reactive power, voltage, current intensity, and sub-metering for kitchen, laundry, and water heater.[page:1][web:212]
Experimental office-room dataset for binary occupancy detection using indoor temperature, relative humidity, light level, CO₂ concentration, and humidity ratio at one-minute intervals.[web:199]
Smart-building dataset from the M5 building, including appliance-level energy usage (watts, voltage, kWh) plus temperature, humidity, and occupancy measurements in offices, labs, kitchen, and other spaces.[web:164][web:167][web:170]
Field data from an automated irrigation setup using capacitive soil moisture sensors and DHT‑11 air sensors, recording soil moisture, air temperature, humidity, and pump on/off status for smart irrigation control.[web:156]
Multimodal body motion and vital sign recordings from 10 volunteers performing 12 physical activities, collected with three body-worn sensor units (chest, wrist, ankle) including 2‑lead ECG.[web:124][web:130][web:148][web:153]
Time-series accelerometer and gyroscope data from smartphones and smartwatches carried by 51 subjects performing 18 activities, suitable for human activity recognition and motion-based biometrics.[web:119][web:123][web:137]
Comprehensive smart energy and environment dataset from three real homes plus a microgrid of 400+ anonymous homes, including electricity usage/generation, environmental conditions, and operational events.[web:97][web:94][web:103]
Real-world data from 54 wireless sensor nodes deployed in the Intel Berkeley Research lab, reporting temperature, humidity, light, and voltage roughly every 31 seconds over more than a month.[web:90][web:93][web:96]
Real smart-home energy consumption data (10-minute resolution) with indoor room sensors and outdoor weather measurements, designed to model appliance energy use in a low-energy house.[web:55][web:61][web:66]
Curated machine-learning-ready dataset from NASA’s Solar Dynamics Observatory (SDO) mission integrating multiple solar instruments for flare prediction and solar activity modeling.
Enhanced smart home energy consumption dataset with minute-resolution monitoring of 13+ appliances and regional weather data. Includes traditional appliances plus new IoT devices like car chargers, water heaters, pool pumps, and outdoor lighting.
Comprehensive smart home dataset generated using OpenSHS simulator with 29 IoT sensors monitoring daily activities across multiple rooms, including labeled data for eating, sleeping, working, and anomaly detection in residential environments.
Real-time environmental dataset from IoT-enabled smart homes focusing on energy consumption optimization and occupant comfort, with 15-minute interval readings of temperature, humidity, lighting, air quality, CO2 levels, and HVAC control data.
Comprehensive energy consumption dataset from a smart home with detailed weather information, containing readings from 17 different appliances and devices with 13 weather parameters for advanced consumption forecasting.
Telemetry from smart plugs monitoring individual household appliances. Used for identifying energy-hungry devices and predictive load balancing.
Real-time traffic telemetry from inductive loop sensors on urban highways. Includes vehicle count, speed, and lane occupancy.
Body motion and vital signs recordings from volunteers performing physical activities, used for human behavior analysis and health monitoring.
Real-world data from a metro train's Air Production Unit (APU), capturing pressure, temperature, and motor current to predict component failures.
Simulated data for analyzing the stability of smart grids under the Decentral Smart Grid Control (DSGC) concept. Classifies grid state as stable or unstable.
Largest publicly available multi-city traffic dataset with 23,541 stationary detectors across 40 cities worldwide. Rich source for traffic dynamics research and urban mobility studies.
High-resolution traffic flow data from 470 sensors across Glasgow covering 4 years (Oct 2019 - Sep 2023). Captures traffic patterns during COVID-19 pandemic with 15-minute intervals.
Industrial equipment sensor data with real-time measurements for predictive maintenance and decision support. Combines temperature, vibration, pressure readings with maintenance costs and failure probability scores.