Home / Internet Of Things / I Scored third on the Azure AI Hackathon With an IoT Sensible Water Meter

I Scored third on the Azure AI Hackathon With an IoT Sensible Water Meter

Representation: © IoT For All
My sensible water meter.

I lately had the excitement of collaborating within the Microsoft Azure AI Hackathon, the place with reference to 1,000 individuals hacked in combination tasks the use of Azure’s Cognitive Products and services.

On this weblog put up, I’ll quilt how I used the Azure Anomaly Detector API and an IoT Starter Kit – that includes a Raspberry Pi and an IoT SIM card – to create an award-winning sensible water meter that measures drastic adjustments in water ranges.

Fabrics You’ll Want

  • IoT Starter Equipment
  • A Soracom Account
  • An Azure Account
  • Sample code
  • *not obligatory* some kind of enclosure for the venture.

First off, let’s quilt what we’ll be doing with the above parts. The primary purpose is to gather readings from the starter equipment’s ultrasonic sensor and ship it by the use of HTTP over mobile to Soracom Beam.

Subsequent, we’ll configure Beam to encrypt that message into HTTPS, after which ship it alongside to our Azure Anomaly Detector endpoint. Azure will go back a reaction to Beam noting whether or not or no longer an anomaly was once detected, and Beam will go back that reaction to the IoT sensible water meter in order that it may possibly then take an motion in accordance with the result.

A timestamp and distance studying is taken from the ultrasonic sensor each and every minute and saved in a CSV record at the tool. As soon as sufficient knowledge issues are saved (Anomaly Detector API lately has at least 12), the knowledge is distributed to the Anomaly Detector API to research the newest price in opposition to the former ones.

When an unusual match happens, the Anomaly Detector API will go back a message to the tool letting it know that an anomaly has been detected. This message can then be used to cause any form of motion that you can imagine. These days, it’s going to make illuminate a purple LED connected to the Raspberry Pi.

Listed here are 4 easy steps for a way I constructed this clever IoT water meter – and the way you’ll construct your individual!

Step 1: Arrange the Azure Anomaly Detector API

Create your Anomaly Detector endpoint throughout the Azure Portal. These days, a unfastened pricing tier is to be had (woohoo!). As soon as created, please remember of the important thing and endpoint, which can be indexed underneath the “Fast Get started” segment of the useful resource.

We’ll wish to plug those values into our Soracom Beam configuration in the next move. Right here’s a hyperlink to the Anomaly Detector API Reference.

Step 2: Arrange Soracom Beam

Subsequent, head over to the Soracom Person Console and configure Soracom Beam in order that it’s able to transform HTTP posts from the tool into HTTPS posts for the Anomaly Detector API endpoint.

Step three: Construct the Soracom Starter Equipment

Now that we’ve got the mobile IoT and cloud plumbing all looked after, let’s construct the tool. Open your Soracom Starter Equipment, and stroll in the course of the following guide for purchasing it arrange.

Stay going till you’ve finished the “Setup the ultrasonic vary finder” segment.

Step four: Accumulate and Transmit Sensor Information to Microsoft Azure

With the tool effectively constructed and able to transmit knowledge, we’ll wish to get the Python scripts loaded onto it which can care for amassing the knowledge and sending it by the use of HTTP to Soracom Beam.

SSH again into the Raspberry Pi and observe the following steps to obtain the code, unzip it and run it:

1) From inside your SSH consultation to the Raspberry Pi, obtain a zipper of this repository to the present listing:

wget https://github.com/RoyCodes/Water-Degree-Detector/archive/grasp.zip

2) Unzip grasp.zip. You must now have a folder referred to as “Water-Degree-Detector-Grasp” which incorporates the entire Python scripts on this repository:

unzip grasp.zip

three) In any case, we’ll blank up by means of deleting the .zip record, as we’ve already extracted the contents:

rm grasp.zip

four) To start out the venture, `cd` into the “Water-Degree-Detector-Grasp” folder and run the next command:

python ultrasonic_beam.py 60

The 60 on the finish we could the script know to take a sensor studying each and every 60 seconds. It is because we now have our Azure Anomaly Detector API granularity set to “minutely”. It additionally helps “hourly”, however then we’d wish to wait no less than 12 hours to look if we broke anything else.

Why I Used Soracom Beam

If we had been to ship our sensor knowledge from the Raspberry Pi without delay to Azure, we’d need to imagine a few additional elements. First, we’d need to retailer each our endpoint and credential without delay at the tool.

Which means that if we had been to ever rotate keys or alternate the endpoint, we’d have to achieve backpedal into the IoT sensible water meter to make the alternate. Secondly, we’d need to ship the knowledge over HTTPS since it might wish to traverse the general public web to be able to achieve Azure. This implies additional overhead at the tool in addition to an build up in bandwidth prices because of the additional header dimension.

Beam lives throughout the Soracom platform and stale of the general public web, so our sensible water meter can ship simple HTTP for the reason that knowledge transmission is already secured via SIM authentication.

The Soracom Person Console we could us arrange our Azure endpoint and credentials in order that the tool can focal point on sending knowledge to Soracom and is unaffected when long term adjustments to endpoints and credentials are made. Those burdens might not be too important in this unmarried IoT smart water meter however can upload up considerably at scale.

Taking This Cell IoT Undertaking Additional

Congratulations! Now that you’ve the IoT sensible water meter sending knowledge to Microsoft Azure and responding to the consequences, listed here are some concepts on what you’ll do to toughen upon this mobile IoT venture:

  • Alter what the Python script does when it detects an anomaly. It might ship an alert, cause another API or perhaps engage with different sensors, motors or relays that you just’ll wish to twine as much as the Raspberry Pi.
  • Support the sample code by means of filing a pull request to the GitHub repository.
  • Construct an enclosure for the venture. Heat up the 3-D printer? Hole out an outdated Furby? (take into account to ship me pictures!)

Come in finding me over on Twitter @Roycodes.

Written by means of Roy Kincaid

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