In what reads somewhat like a convoluted detective story, the events unfolding at the Chornobyl Exclusion Zone (CEZ) in Ukraine during late February had the media channels lighting up with chatter about ‘elevated gamma radiation levels’, which showed up on the public CEZ radiation monitoring dashboard for a handful of gamma radiation sensors. This happened right before this reporting system went off-line, leaving outside observers guessing at what was going on. By the time occupying forces had been driven out of the CEZ, the gamma radiation levels were reported as being similar to before the invasion, yet the computer hardware which was part of the monitoring system had vanished along with the occupying forces. After considering many explanations, this left security researchers like [Ruben Santamarta] to consider that the high values had been spoofed.
[RiverTechJess] is in the process of getting a PhD in environmental engineering and has devoted a chapter to creating a turbidity sensor for river network monitoring. Environmental sensing benefits from being able to measure accurately and frequently, so providing low cost devices helps get more data and excuse the occasional device loss that’s bound to happen when deploying electronics out in the wild. Towards this end, [RiverTechJess] has created a low cost turbidity sensor that rivals the more expensive alternatives in cost and accuracy.
The turbidity sensor is designed to be at least partially submerged allowing for the LED and light sensors to be be able to take measurements. [RiverTechJess] has made a 3D printed prototype to test the design, allowing for rapid experimentation and deployment of the sensors to work out issues. The 3D printed enclosure prototype uses rubber o-rings and “vacuum grease” to provide a watertight seal. An ESP32 microcontroller is used to store logged data on an SD card and drive the TSHG6200 850nm infrared LED and the two TSL237S-LF sensors.
The resulting paper on the turbidity sensor, in addition to the blogs of the process, provide a wealth of data that show what goes into developing and calibrating a device that is meant to be used for environmental monitoring. All source code is available on GitHub and development continues on a newer revision of the turbidity sensor with updated electronics and hardware.
Video after the break!
As anyone who has taken care of chickens or other poultry before will tell you, it can be backbreaking work. So why not build a robot to do all the hard work for us? That’s precisely what [Aktar Kutluhan] demonstrated with an AI-powered IoT system that automatically feeds chicks and monitors unhatched eggs.
Make no mistake, hens are adorable, feathered creatures, but they can be quite finicky. An egg’s weight, size, and frequency can determine the overall health of a hen, and they can stop laying eggs altogether if something as simple as their feeding schedule is too sporadic. This is precisely what inspired [Aktar] to create a system that can feed hens at a consistent time every day while keeping track of the eggs laid to ensure the coop is happy and healthy.
What’s so impressive about this build isn’t just the clever automation that scratches off a daily chore, it’s built completely with IoT devices, including the AI. The setup uses Edge Impulse as an object detection model on an OPenMV Cam H7 microcontroller to recognize eggs in the coop. From there, an WizFi360-EVB-Pico board was attached so data could be sent over WiFi, with a DHT22 thrown in to monitor and record the overall temperature of the coop.
This is already an amazing setup, but when it comes to IoT devices, the sky’s the limit. You could control heat lamps in larger coops, automatically refill a water bowl if the hens’ water is low, or even build a hands-off incubator. We’re only just beginning to see the clever ways with which AI can help monitor our pet’s health. Just look at how another hacker used AI to monitor cat poop to make sure their furry friend wasn’t eating plastic. Thanks to [Aktar Kutluhan] for showing us more ways we can use AI to help our pets!
Outwardly, this sleek CO2 monitor designed by [Daniel Gernert] might look like something cooked up in Amazon’s consumer electronics division. But open up that 3D printed case, and you’ll find a surprisingly low parts count that’s been cleverly packed in so as to make the most of the enclosure’s meager internal dimensions.
There are, if you can believe it, just three principle components to this device: a Seeed Studio Seeeduino XIAO microcontroller, a Infineon S2GO PAS CO2 sensor board, and a ring of WS2812B LEDs. You could even delete the ring altogether and replace it with a single addressable LED to accomplish the same goal, but we’d say the full ring is money-well-spent if you’re going to spin up your own copy.
Functionality is very straightforward — the LED ring will indicate the detected CO2 concentration by lighting up green and working its way through yellow and onto red. The sensor has no wireless capability, but if you plug it into your computer, you can get a local readout of current conditions.
We love environmental monitoring solutions here almost as much as we love intricately designed 3D printed enclosures. If you’d like to see another project where those two concepts aligned, check out this printable ESP8266 sensor enclosure.
The plethora of wireless technologies has made internet-connected devices the norm, but it’s not always necessary if you don’t need real-time updates. Whether it’s due to battery life, or location and range constraints, downloading data directly from the device whenever possible might be a viable solution. [Malcolm Mackay] demonstrates an elegant solution on the open source cuplTag temperature/humidity logger, using any NFC-enabled smartphone, without requiring a custom app.
The cuplTag utilizes the feature on NFC-enabled smartphones to automatically open a URL provided by the cuplTag. It encodes the sensor data from the sensor unit as a circular buffer in a ~1 kB URL, which automatically uploads to a web frontend that plots the data. (You can use their server or run your own.)
This means that data can be collected by anyone with the appropriate phone with zero setup. The data is displayed on the web app and can be downloaded as a CSV. To deter spoofing, each tag ships with a secret key which is used to generate a unique HMAC every time the circular buffer changes.
Battery life is a priority on the cuplTag, and it’s theoretically capable of running seven years on a single CR1220 coin cell using the current-sipping Texas Instruments MSP430 microcontroller. The hardware, firmware, and server-side frontend and backend code are all open source and available on GitHub.
Earlier this year, we held a data logging contest, and featured submissions that monitored everything from your garden’s moisture levels to your caffeine intake.
The votes for Hackaday’s Data Loggin’ Contest have been received, saved to SD, pushed out to MQTT, and graphed. Now it’s time to announce the three projects that made the most sense out of life’s random data and earned themselves a $100 gift certificate for Tindie, the Internet’s foremost purveyor of fine hand-crafted artisanal electronics.
First up, and winner of the Data Wizard category, is this whole-garden soil moisture monitor by [Joseph Eoff]. You might not realize it from the picture at the top of the page, but lurking underneath the mulch of that lovely garden is more than 20 Bluetooth soil sensors arranged in a grid pattern. All of the data is sucked up by a series of solar powered ESP32 access points, and ultimately ends up on a Raspberry Pi by way of MQTT. Here, custom Python software generates a heatmap that indicates possible trouble spots in the garden. With its easy to understand visualization of what’s happening under the surface, this project perfectly captured the spirit of the category.
Next up is the Nespresso Shield from [Steadman]. This clever gadget literally listens for the telltale sounds of the eponymous coffee maker doing its business to not only estimate your daily consumption, but warn you when the machine is running low on water. The clever non-invasive method of pulling data from a household appliance made this a strong entry for the Creative Genius category.
Last but certainly not least is this comprehensive IoT weather station that uses machine learning to predict rainfall. With crops and livestock at risk from sudden intense storms, [kutluhan_aktar] envisions this device as an early warning for farmers. The documentation on this project, from setting up the GPRS-enabled ESP8266 weather station to creating the web interface and importing all the data into TensorFlow, is absolutely phenomenal. This project serves as a invaluable framework for similar DIY weather detection and prediction systems, which made it the perfect choice for our World Changer category.
There may have only been three winners this time around, but the legendary skill and creativity of the Hackaday community was on full display for this contest. A browse through the rest of the submissions is highly recommended, and we’re sure the creators would love to hear your feedback and suggestions in the comments.
If we’ve learned anything over the years, it’s that hackers love to know what the temperature is. Seriously. A stroll through the archives here at Hackaday uncovers an overwhelming number of bespoke gadgets for recording, displaying, and transmitting the current conditions. From outdoor weather stations to an ESP8266 with a DHT11 soldered on, there’s no shortage of prior art should you want to start collecting your own environmental data.
Now obviously we’re big fans of DIY it here, that’s sort of the point of the whole website. But there’s no denying that it can be hard to compete with the economies of scale, especially when dealing with imported goods. Even the most experienced hardware hacker would have trouble building something like the Xiaomi LYWSD03MMC. For as little as $4 USD each, you’ve got a slick energy efficient sensor with an integrated LCD that broadcasts the current temperature and humidity over Bluetooth Low Energy.
It’s pretty much the ideal platform for setting up a whole-house environmental monitoring system except for one detail: it’s designed to work as part of Xiaomi’s home automation system, and not necessarily the hacked-together setups that folks like us have going on at home. But that was before Aaron Christophel got on the case.
We first brought news of his ambitious project to create an open source firmware for these low-cost sensors last month, and unsurprisingly it generated quite a bit of interest. After all, folks taking existing pieces of hardware, making them better, and sharing how they did it with the world is a core tenet of this community.
Believing that such a well crafted projected deserved a second look, and frankly because I wanted to start monitoring the conditions in my own home on the cheap, I decided to order a pack of Xiaomi thermometers and dive in.