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Area of Science:

  • Internet of Things (IoT)
  • Environmental Sensing
  • Data Science

Background:

  • The proliferation of Internet of Things (IoT) devices necessitates comprehensive datasets for developing and validating algorithms.
  • Environmental monitoring in research laboratories provides valuable data for understanding microclimates and device performance.
  • Existing datasets may lack the granularity or specific sensor types required for advanced anomaly or occupation detection.

Purpose of the Study:

  • To present a novel dataset comprising multi-sensor measurements from IoT devices deployed in a real-world research laboratory setting.
  • To provide a valuable resource for researchers working on drift detection, anomaly detection, and smart building applications.
  • To facilitate the development and benchmarking of algorithms for analyzing time-series sensor data.

Main Methods:

  • Collected data from 8 Nordic nRF52840 DK IoT devices over a six-month period (February-September 2022).
  • Recorded environmental parameters including temperature, humidity, pressure, gas, light intensity, and accelerometer data every 10 seconds.
  • Included measurement quality indicators and Received Signal Strength Indicator (RSSI) for each device.
  • Organized data into timestamped, compressed CSV files within weekly folders for efficient storage and retrieval.
  • Provided a README.txt file detailing dataset structure and usage instructions.

Main Results:

  • A comprehensive dataset of discrete sensor measurements was successfully collected and curated.
  • The dataset spans six months, offering a rich time-series of environmental and connectivity data.
  • Data is readily accessible in CSV format, compatible with standard data analysis software libraries.

Conclusions:

  • The presented IoT sensor dataset is suitable for a range of applications, including drift and anomaly detection.
  • The data can be leveraged for smart building applications, such as occupation detection and environmental control.
  • This resource supports further research in IoT data analysis, sensor network performance, and machine learning applications.