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]
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]
City-scale environmental IoT dataset with more than 24 million temperature measurements from SmartSantander sensors deployed across Santander, Spain, including spatial, temporal, and device metadata.[web:113][web:115][web:118]
Collection of real-world smart city IoT datasets from the CityPulse project, including vehicle traffic, parking occupancy, and weather data from Aarhus (Denmark) and other cities.[web:111][web:114][web:116]
Real-time network traffic dataset from diverse IoT devices including normal behavior and various attacks (DDoS, brute-force, scans) for developing intrusion detection systems.
Real-world hourly air quality measurements from an array of chemical gas sensors deployed at road level in a polluted Italian city, collected from March 2004 to February 2005.[web:46][web:53][web:77]
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]
Specialized dataset containing features influencing vehicle collisions in Internet of Vehicles (IoV) networks. Includes V2V communication data, sensor readings, traffic conditions, and collision indicators for developing intelligent collision detection and prevention systems.
Large-scale real-time air quality monitoring dataset from Dhaka, Bangladesh with 155,406 records. Captures CO, NO2, SO2, O3, PM2.5, and PM10 using IoT sensors with Arduino integration. Ideal for environmental analytics, pollution prediction, and smart city air quality management.
Two years of continuous IoT-based smart parking lot usage data collected via ThingSpeak platform. Features IR sensors and ESP32 boards monitoring slot availability, occupancy patterns, peak hours, and parking duration for urban parking management optimization.
Multimodal smart city dataset combining environmental sensors (temperature, humidity, gas, vibration, noise, motion) and surveillance images with binary anomaly labels. Designed for edge computing, urban anomaly detection, and real-time city monitoring research.