Muino Water-sensor Enhancing Robustness and Connectivity

The Muino water approach for water usage sensors aims to simplify installation by reducing complexity. Unlike traditional inductive sensors, the new design is smaller and easier to place, with a more intuitive interface. It also offers higher resolution, allowing for more accurate measurement of water usage, including smaller amounts. connector The muino sensor front and back, the future the pcb will be black

Water meters are used to measure the amount of water that is used in a household. They typically have a spinning wheel with a red half-moon. With the following approach there are three sensors placed around the wheel. When the wheel spins, one of the sensors always passes over the red half-moon. By shining different colored lights green on the wheel, the sensors can detect how much red is present and use this to measure if the spinning read wheel spinned, one full rotation means 1 liter. This setup the resolution is even higher than 1 liter and will be between 0.1-0.3 liter precise.

connector Due to the presence of three sensors, we can achieve a more accurate measurement of water usage, as each sinusoidal period in the raw data output represents the consumption of 1 liter of water.

Why robustness is complex

The algorithm has reached a robust state with an automatic calibration feature, eliminating the need for manual calibration. When devices are restarted or newly deployed, the algorithm can now automatically determine the appropriate maximum and minimum values required for autocorrelation. Although this may appear straightforward, it’s important to note that the algorithm handles non-periodic signals that are not continuously active. This means that the precise moment when the first liter of water is measured remains uncertain, whether it occurs after a few seconds or several hours from the start. Additionally, the minimum and maximum values of the sensor cannot be predetermined.

The provided figure illustrates three different signals, each with its own offset and distinct maximum and minimum values. The horizontal line at zero represents the volume of liters. In the absence of any detected liters, the autocorrelation becomes susceptible to noise interference in the signal. To ensure stability, certain algorithm adjustments have been implemented.

  • At approximately timestep 3000 on the x-axis, the water flow initiated, resulting in a sinusoidal wave movement. Each complete period of the sinusoidal wave corresponds to the consumption of 1 liter of water.
  • Following timestep 5000, there is an increase in the frequency of the sinusoidal wave movement, leading from a higher volume of water consumed.
  • Around timestep 5500, the water usage comes to a halt, causing the sinusoidal movement to end and the signal to remain constant.
  • Upon opening the water valve, a sinusoidal pattern emerges, and the measurements continue to accurately track its progression.

Here’s an example that highlights a situation where the WiFi took excessive time to update the sensor value using MQTT, resulting in the water sensor missing 4 liters of water around step 625K. Although this issue has been resolved, those type of robustness issues needed to be improved.



The following boarod is the first prototype. This board only supports 1 type of water usaged sensor.