Helfi sensor monitoring protocol 🇸🇰

Helfi sensor monitoring protocol 🇸🇰

Technology stack

Java

Spring Boot CLI

Maven

Binary protocol

The client / Slovakia

The client is a Slovak academic entity.

The challenge

The client was seeking an air quality monitoring solution for our academic facility's large classrooms and hallways. Our goal is to maintain a healthy learning environment, and we require a software solution that encompasses our newly acquired hardware components.

The solution should include:

Ensuring the accurate tracking of these metrics was pivotal, accompanied by the implementation of alerts for detecting and addressing abnormal conditions. Ensuring the accurate tracking of these metrics was pivotal, accompanied by the implementation of alerts for detecting and addressing abnormal conditions.

We aimed to utilize a diverse range of sensors designed to monitor essential air quality indicators, including but not limited to PM2.5, PM10, CO2, temperature, humidity, and VOCs (volatile organic compounds). Ensuring the accurate tracking of these metrics was pivotal, accompanied by the implementation of alerts for detecting and addressing abnormal conditions.

Our approach

This project is the very first one our company has ever worked on. We devised a unique binary protocol to facilitate communication between the sensors and the base station, effectively reducing data transfer requirements and consequently extending the sensors' battery lifespan. The CLI aspect of the solution was implemented utilizing Spring Boot CLI in conjunction with Java. A whole simulation suite was create to simulate the communication between fictional sensors and the real base station.

Impact

The solution we've implemented, has had a highly positive impact. One remarkable outcome has been a substantial reduction in data transfer demands, directly leading to an extended battery life for the sensors. This enhancement has not only improved the overall efficiency of the system but has also positively affected our clients' operational costs by minimizing the need for frequent sensor maintenance and replacements.

Team

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