Raspberry Pi and ESP32-Based Smart Sensor Network for IoT Platform Integration and Real-Time Environmental Data Monitoring
Thapa, Sulav; K.C., Subash Chandra (2023)
2023
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2023120333739
https://urn.fi/URN:NBN:fi:amk-2023120333739
Tiivistelmä
The aim of this final year project was to develop two sets of identical ESP32 based field devices which would be able to get data from different environmental sensors such as a temperature and humidity sensor, a lux sensor, a barometric pressure and altimeter sensor and an air quality sensor and publish the data to cloud services. A main control unit was also developed which was to subscribe the published data from field devices and store it locally as well as publish them in the IoT cloud. Subsequently the designed system was tested by placing the first ESP32 based field device in a different location from the second ESP32 based field device which then would send data to the main device in a totally different location.
In order to achieve the goals, all of the environmental sensors were connected individually to ESP32, tested for the data and connected to the AWS cloud for transmitting data. The same data was acquired using a Raspberry Pi as a main control unit which would collect data from all the field devices, create a text file for a single day, and append the data. Furthermore, if a great amount of data would arrive at the same time frame from different field devices, all the data would be averaged and appended along the other logged data. Also, the resulting data would be pushed to an IoT cloud platform called Blynk.
The proposed model was tested keeping the two identical field devices in different locations, collecting data sending it to cloud and the main device in a totally different place collecting, analyzing and publishing data. The data was collected for a whole day and analyzed based on all the environmental data collected. It can be concluded that the cheap alternative sensing and monitoring system that was experimented with in this project with a master-slave architecture, where the master can collect data from several slaves or field devices, can be practiced in a real-life scenario.
In order to achieve the goals, all of the environmental sensors were connected individually to ESP32, tested for the data and connected to the AWS cloud for transmitting data. The same data was acquired using a Raspberry Pi as a main control unit which would collect data from all the field devices, create a text file for a single day, and append the data. Furthermore, if a great amount of data would arrive at the same time frame from different field devices, all the data would be averaged and appended along the other logged data. Also, the resulting data would be pushed to an IoT cloud platform called Blynk.
The proposed model was tested keeping the two identical field devices in different locations, collecting data sending it to cloud and the main device in a totally different place collecting, analyzing and publishing data. The data was collected for a whole day and analyzed based on all the environmental data collected. It can be concluded that the cheap alternative sensing and monitoring system that was experimented with in this project with a master-slave architecture, where the master can collect data from several slaves or field devices, can be practiced in a real-life scenario.