This blog post gives an overview of how I created a simple IoT project using Arduino, a Temperature Sensor and the excellent Orchestrate.io Database as a Service.
First up, let me explain what the eventual goal is and how this is a first step in that process. The goal is to setup a series of low cost climate/environment modules that capture various types of data like temperature, humidity and more. Then take all this data and put it in the cloud where we can eventually build out dashboards, alerts and more. This blog post explains one single setup where we create a system comprising an Arduino Uno, a temperature sensor, a Python application that can read the data from the Arduino Uno (yes, I did not use an Arduino Internet Shield) and post that data to the cloud.
Towards this goal, chosing Arduino as a microcontroller is a no-brainer though I do plan to look into other controllers in the near future. Once our system is collecting data, ,the important thing is where do I put this data. The Cloud comes up as a rational choice to eventually have all Temperature Sensor stations push their data and we can monitor and build dashboards from a single place. The choice was to build a backend using tools like Google Cloud Platform’s App Engine but that would mean quite a bit of extra work for something that is not immediately central to this project. I went with Orchestrate.io, a Database as a Service provider that makes it dead simple to funnel your data from sources into your database in the cloud, as the post will eventually show.
The Hardware Setup
I used the following:
- Arduino Uno microcontroller
- LM35 Temperature Sensor
- Eventually we will have the Raspberry Pi that interfaces with the Arduino to read and transmit off the values but to validate things for now, the Uno was powered via a laptop/desktop with Python installed on it. The communication between the Uno and the PC is via serial port communication.
Arduino Uno + Temperature Sensor Setup
Here is how the LM35 sensor is connected to the Arduino Uno board.
Ofcourse, we used a breadboard to connect all this together but I am simplifying the diagram here so that you know what is connected to which pin. The LM35 has 3 pins. The first one goes to the 5V power pin on Arduino, the 3rd one is the GND and the middle pin is the VOUT where it emits out the values that we need to capture. We connect this to the Analog Pin (A0) on the Arduino. We can then write our Arduino code to read that value, as is shown next.
The Arduino Code is straight forward as given below:
int tempPin = 0;
temp = analogRead(tempPin);
temp = temp * 0.48828125;
You will notice in the loop that every 10 seconds, we are printing out the temperature value that was read from the Analog Pin (#0)
If you run the Serial Port Monitor that comes with the Arduino IDE and if the Arduino is powered up and connected as per the diagram shown, then you will find the Temperature value being printed on the Serial Monitor as given below:
Once this happens, we know that the Arduino setup is looking good and all we need to do now is to write a client program on the PC that interfaces with this Arduino, read the values via the Serial port and then push them to the Cloud (Database Service).
But first, let us take a look at the Orchestrate.io Database Service setup that we did.
Orchestrate.io is a Database as a Service provider. One of my reasons of going with Orchestrate.io is to eventually make use of their strengths in providing a solid REST API around your data. What this means is that I am just not interested in making sure that it is dead simple to store my data in the cloud. Eventually I will need an API that allows me to do sophisticated searches, time ordered events and more and Orchestrate.io fits that model.
One can get started for free with Orchestrate.io to check out their service and which is what I did. It gives you one application that you can create for free along with a 50K API calls limit / month, which is good enough for my prototype for now.
Once logged into Orchestrate.io, I created the Application by clicking on New Application. It popped up a dialog where we provided the Application name (Temperature_Log) and selected a data center as shown below:
That’s it. The Application is created and the next step was to just define the Collection. A collection is similar to a Table into which all the data records would go. Orchestrate will figure out from the data that you upload and create the schema accordingly for you. So all you need to do for now is to define your collection and which is what I did.
Simply click on New Collection and then I gave my collection name as given below:
That’s all I needed to do to setup my Application and one collection in Orchestrate.io.
If you go to the Orchestrate.io Dashboard, you will see the collection for your application along with the API KEY that we shall use soon. The API KEY will be used when posting data into Orchestrate from your client application and is used to identify yourself to the Orchestrate service.
Now, let us move on to the Python code that interfaces over Serial port to read the temperature values from the Arduino setup and posts that data into Orchestrate.io.
The steps are simple:
1) We initialize 2 things: the Orchestrate.io API Client using our Key. And then we initialize the serial port communication via which we will be reading the Temperature values that the Arduino unit will be emitting every 10 seconds. You need to figure out which Serial Port on your machine is interfaced to the Arduino.
2) Every 10 seconds, the code will read the value from the Serial Port. We can obviously build in more validations in the code, but this is good for now to demonstrate how all the pieces come together.
3) Once we get the data, it will use the Orchestrate.io REST service to post data into your Application data collection. To make things easier, Orchestrate.io has a solid list of client language APIs available that you can use to ease your task. I used the Python client library for Orchestrate.io. To setup that library all one had to do was
pip install porc
The important thing to note in the POST to Orchestrate.io Service is the client.post code. Here, all we are doing is specifying the collection (Temperature_Data) that we had created for our Application in Orchestrate.io and the Data. The Data is specified in JSON format and the fields that I am specifying are:
- Temperature in Centigrade
- Date of the recording
- Time of the recording
- Location Name (Weather Station Name).
Take a look at the Python client program below:
from porc import Client
API_KEY = "Your API Key"
# create an Orchestrate.io client using the default AWS US East host: https://api.orchestrate.io
client = Client(API_KEY)
# make sure our API key works
#Connect to Serial Port for communication
ser = serial.Serial('COM15', 9600, timeout=0)
#Setup a loop to send Temperature values at fixed intervals
fixed_interval = 10
#temperature value obtained from Arduino + LM35 Temp Sensor
temperature_c = ser.readline()
#current time and date
time_hhmmss = time.strftime("%H:%M:%S")
date_mmddyyyy = time.strftime("%d/%m/%Y")
#current location name
temperature_location = "Mumbai-Kandivali"
print temperature_c + ',' + time_hhmmss + ',' + date_mmddyyyy + ',' + temperature_location
response = client.post('Temperature_Data',
"date" : date_mmddyyyy,
"time" : time_hhmmss,
"value" : temperature_c})
print "Record inserted. Key = " + response.key
print('Error! Could not read the Temperature Value from unit')
Checking our data
The final step was to validate if our data was being transmitted successfully and into Orchestrate. All you need to do is to ensure that the Python code executes , you do get the Record inserted message.
A sample run of the Python code is shown below:
Once that is done, come back to Orchestrate Dashboard and visit your collection. A sample snapshot of the Search Query for my collection is shown below:
This validates the end to end working of the project.
Arduino makes electronics prototyping fun. With languages like Python and services like Orchestrate.io, the process of collecting / transmitting / saving the data in the cloud is made simple too. I hope to explore Orchestrate.io in more projects for their other powerful features like Time Events and Graphs.
You can use the steps outlined below to build your own version of a Data Collection IOT Project that uses the Cloud to store and analyze its data.
Till next time.