Air quality monitoring concept presentation
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The document describes an air quality monitoring system using Internet of Things devices. It uses sensors to measure toxic gases, temperature, and humidity, and sends the data via LoRa wireless protocol to The Things Network and then to an InfluxDB database. Tableau software visualizes the timestamped sensor measurements to allow for trend analysis of air quality over time.
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Air quality monitoring concept presentation
- 1. AIR QUALITY MONITORING Internet Of Things
- 2. Goals • Cost-effective sensors • Accurate Measurements • Trend Visualization and Analysis • Power Saving hardware • Scalable architecture • Ready for Smart Grid applications
- 3. Toxic gas sensor • P-Nucleo-IKA02A17 for periodic gas measurements with temperature compensation • Suitable for outdoor and indoor environments
- 4. STM32 LoRa • ARM Cortex-M0+ core coupled with a SX176 transceiver • Enables the board to use LoRa long-range protocol • Used to send the measurements in the cloud for further analysis.
- 5. STEVAL-MKI141V2 with HTS221 • Relative humidity and temperature sensor • Provides further information about the air quality.
- 6. uTensor • Extremely lightweight ML interface framework built on Mbed and Tensorflow • Used to execute an onboard classification of the measured data.
- 7. TheThingsNetwork • Open Network infrastructures to develop scalable IoT applications. • Used to manage data flow from the devices to the cloud platform
- 8. InfluxDB • Open source Real-Time Time Series database used to aggregate data from several devices • Data is identified as a timestamp-value pair
- 9. Tableau • Interactive Data Visualization software • Cross-platform, including a web interface • Provides tools to visualize and gain insight by interacting directly with the data
- 10. Workflow • Measurements collection with the described sensors • Data filtering with uTensor • Transmission of the filtered values over LoRa and TheThingsNetwork to InfluxDB • Extraction and visualization of the data with Tableau
Editor's Notes
- The goal of our proof of concept is the creation of an air quality sensor which can give accurate measurements and requires little intervention. We use inexpensive hardware to make our product deployable with little effort. Thanks to wireless communication and the hardware cost-effectiveness we can easily place sensors in different places, creating a scalable distributed architecture which provides data to a cloud platform and can be used in smart grid applications, giving information about the environment state.
- The gas sensor (Figaro TGS5141) can detect four types of carbon footprints and uses a temperature sensor (STLM20) for temperature compensation, the low-power design of the sensors can guarantee more than 10 years of life time. This sensor is used to
- Humidity accuracy is between +/- 3.5% and 80%, temperature accuracy +/- 0.5 Celsius degrees Operates between –40 C and 120 C, humidity range from 0% to 100%. The sensor is mounted on a MEMS (Micro Electro Mechanical System)
- Given a trained model in Tensorflow it gives a C++ header and source file to execute classification on directly on the board
- We use existing network infrastructure (Gateway and applications) to get the data from the board via an IoT-friendly protocol and send them via HTTP to the database