ESP32 provides a great platform for sensors around the house but by the time you've added a USB power brick, cable and enclosure it's quite messy. I wanted a device that I could just plug in with no exposed wires and no mounting screws needed so I designed one in OpenSCAD and printed it on my Ender 3 Pro.
First I went in search of a suitable USB power brick. I wanted something small, with downward facing USB ports and some way to clip into an enclosure.
I found these that had a dimple on the side and set about creating a 3D printed case that would fit around them and clip into the dimple. One trick you need to be aware of is that most mass manufactured plastic cases like this are narrower at the enclosed end because they were designed to be injection moulded.
Here's how I create the dimples: a cut off sphere, translated and hull'd and then mirror imaged.
After a couple of revisions I now have a case that works. The power brick snaps into place, a bare, male USB-A connector fits in and wires connect to an ESP32 that sits in the compartment. There's room for some sensors too but the heat from the ESP32 is a problem so I'm fixing that with an external bump for a DHT22 or BME680.
The ESP32 has enough memory for a cutdown version of my Bluetooth sniffer reporting to a Raspberry Pi and MQTT sender for sensor data.
I've been working on home automation for over 15 years and I'm close to achieving my goal which is a house that understands where everyone is at all times, can predict where you are going next and can control lighting, heating and other systems without you having to do or say anything. That's a true "smart home".
My year long Bluetooth project that won the $20,000 HCI and Microsoft competition during lockdown has continued to grow and now reliably tracks how many people are in the house and outside and can locate any device down to room level.
Digital Twin are an online representation of a real world object, a copy of its properties in the digital world and a way to send updated and commands to it. In effect I've been making them for years but now they have a trendy name.
An overview of the many sensors I've experimented with for home automation including my favorite under-floor strain gauge, through all the usual PIR, beam and contact sensors to some more esoteric devices like an 8x8 thermal camera.
Why automated learning is hard for a smart home. The perils of over-fitting, under-fitting and how the general unpredictable nature of life makes it hard to build a system that learns your behavior.
One way to reduce the volume of sensor data is to remove redundant points. In a system with timestamped data recorded on an irregular interval we can achieve this by removing co-linear points.
Bluetooth sensing for home automation is a great proxy for people counting as it can detect and locate each cellphone in the house. iBeacons attached to tools, cars and pets can provide a 'find my anything' feature too.
Having at least one light sensor is critical for any home automation system that controls lightng. Lights need to be turned on when it's dark not at specific times of day, especially here in Seattle when it can be dark and cloudy at any time of day.
Microwave doppler sensors can be found in some alarm sensors but there are also available very cheaply as a separate component. They offer exceptional range but suffer from false triggers requiring a probailistic approach to people sensing.
Optical-beam sensors are reliable and can cover a long-distance such as across a garage or aisle-way. When they include multiple-beams they have good false-trigger rejection.
Home automation systems need to respond to events in the real world. Sometimes it's an analog value, sometimes it's binary, rarely is it clean and not susceptible to problems. Let's discuss some of the ways to convert these inputs into actions.
Another super useful function for handling sensor data and converting to probabilities is the logistic function 1/(1+e^-x). Using this you can easily map values onto a 0.0-1.0 probability range.
In a home automation system we often want to convert a measurement into a probability. The ATAN curve is one of my favorite curves for this as it's easy to map overything onto a 0.0-1.0 range.
An if-this-then-that style rules machine is insufficient for lighting control. This state machine accomplishes 90% of the correct behavior for a light that is controlled automatically and manually in a home automation system.