- Air condition monitoring and scoring across eight parameters
- Visualized historical data of how the air condition changed over time
- Machine learning-based predictions and recommendations
Technologies Used: Espressif ESP32 module, React Native, DeviceHive, Java, openHAB, Apache Kafka, PostgreSQL
Quick Video About the Project
An innovative startup company, enthusiastic about infusing cutting-edge technologies into everyday life. Inspired by possibilities IoT-based systems offer in a today’s connected world, the customer’s prime focus is to deliver robust solutions that will improve user experience within a smart home.
Relying on HQSoftware’s Smart Home development expertise, the customer wanted to develop a solution that will be monitoring the health status of the environment inside a house across such parameters as humidity, temperature, dust particles, CO2, etc.
Specialists at HQSoftware were responsible for designing a cloud-based backend of the solution—to gather and store data for further processing—and a mobile application to enable users to check the health status of their house on the go.
The Concept Behind the Solution
The system under development comprises three major components:
1. Hardware—a physical hub to be installed in any room of a house. There may be more than one hub in different rooms, all gathering data through built-in sensors to further send it to the cloud.
2. A backend with the underlying DeviceHive platform—an open-source IoT toolkit—stores data in the cloud and aggregates it.
3. A mobile application, which allows a user to tune the hub’s settings, while granting access to the statistical information gathered and health status scored.
The sensor-based solution collects data and scores the atmosphere in a room across the following parameters:
- Barometric pressure
- VOC value
- Sound level
- Light level
- Particulate matter
The system scores the health status of the environment against a 100-point grading scale: excellent (95–100), good (83–94), fair (75–82), poor (35–84), bad (1–34), and dangerous (0).
A user is able to tune such core settings as:
- A hub location. With an advanced algorithm behind, the solution analyzes the environment condition in a pre-set location. For instance, if a hub is located in a bedroom, the system will analyze if air condition, noise level, or light intensity are fit for healthy sleep.
- Time interval. A user can set up time intervals to 30 seconds, one minute, 15 minutes, or 30 minutes.
- Notifications. A bunch of alerts are available for a user to customize. For example, one can set up the “night mode” of a hub with notification sounds off. Furthermore, a user can specify the time interval of this mode (e.g., from 10 p.m. to 7 a.m.). One can also set up particular parameters to be displayed (i.e., a total score or temperature/humidity/etc. only). In addition to the above mentioned, users can also configure basic hub settings, such as wi-fi, time zone, etc.
A user also has access to historical data represented by a diagram, which illustrates how the health status changed over time.
To provide even better user experience, it is planned to incorporate machine learning algorithms, thus enabling the solution to make predictions and recommendations.
One of the scenarios to be implemented is to adjust the algorithm to score/predict the environment condition in accordance with the user’s individual needs, for example, in case he/she is allergic to dust.
Further on, it is planned that the system will react to an array of triggers, such as turning on a conditioner if humidity is high and the like. Thus, this IoT hub can serve as the controlling unit of a smart home ecosystem.
Founder at WSA
“HQSoftware have been very flexible with our timesheet and what we specially like is their willingness to not only follow our guidelines, but also to recommend better/existing solutions. Their exceptional communication complemented their professional and supportive management style. Expect a skilled team that is attentive to detail.“
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