FAQ

What problem does it solve?

YDrip primarily addresses the issue of costly water leaks and provides real-time usage information to help homeowners identify ways to conserve water. It can also assist municipalities in reducing consumption when deployed at scale, as population growth and changing climates continue to put a demand on water reserves. For example, Arizona recently implemented restrictions on new home construction as they face challenges related to declining groundwater levels.

How do most people deal with leaks and monitor usage today?

Many municipalities still rely on manual reading of meters by human operators. This means homeowners are charged based on estimates from past bills and only discover the actual cost 1-2 months later. By that time, a water leak could have already caused significant financial losses.

The minority of municipalities that have installed smart meters often lack effective leak notification systems. Their websites can be difficult to use, and reporting delays of hours or days are common. Additionally, profit incentives often don't align with conservation efforts, which is why some municipalities have started to decouple the profit motive.

Beyond leak detection, it is challenging to experiment with habit changes to reduce consumption without near real-time data.

What are the problems with existing solutions?

Existing commercial devices have various limitations:

How does YDrip solve these limitations?

Requirements

YDrip shall:

Current System Design

Hardware Design Decisions

The main limitations on the hardware designs are affordability and battery life. Many IoT devices require a hub to bridge the low power RF like LoRa to WiFi/Ethernet. This increase cost and complexity for an initial prototype. However, using WiFi presents challenges with power consumption. An ultra low power reprogrammable digital logic chip was chosen to offset this. It allows for maximum flexibility while using less power than general purpose CPU for meter reading.

An ultra sensitive tunneling magnetoresistance (TMR) sensor was chosen for maximum measurement distance, which reduces the need for excessive signal amplification.

Software Design Decisions

The choice of ESPHome software was driven by its compatibility with Home Assistant and a large community of existing users. YAML files simplifies the process of modifying settings and developing new features, even for non-technical users.