Internet of Things (IoT), AWS & Raspberry Pi3

Introduction

In this article I plan to describe how you can use Raspberry Pi3 as a Single Board Computer (SBC) to read temperature and humidity through DHT11 sensor and use AWS IoT as the MQTT message broker to send the data to AWS for further analysis.

Architectural Overview

In the architectural diagram above the arrows are showing a flow of events with numbers used to explain the order in which these events occur.

Point1:

This is the Raspberry Pi3 circuit that is generating sensor data. I did not use a microcontroller here to make things easy for me.

Point2:

AWS IoT is being used as a Message Broker to collect device data. Although I am using AWS IoT here, other cloud providers like Azure’s IoT Hub or Google’s Cloud IoT will work as well. If you do not want to use a cloud provider and want to have a self-hosted MQTT message broker you can use Eclipse Mosquitto. All these providers support MQTT protocol which is the protocol I plan to use with Raspberry Pi3. Other protocols are also supported by cloud providers but MQTT is widely accepted standard. MQTT is a publisher/subscriber messaging protocol which fits well in a disconnected architecture like IoT. Its bidirectional so you can send information/configuration instructions to devices as well.

Point3:

AWS IoT allows devices to publish to an AWS Topic. In this diagram, the device (Pi3 with DHT11 sensor) uses AWS IoT MQTT broker to send data to the topic.

Point4:

AWS allows you to define rules that can trigger actions based on data that is published by multiple devices to a specific Topic. You can define multiple rules for the same Topic.

Point5:

AWS allows multiple AWS services to be integrated with AWS IoT through the execution of actions that are run by the AWS IoT rules. In the diagram a few of the AWS services are listed, most notably - AWS Lambda, AWS DynamoDB, AWS S3, AWS SNS/SQS, AWS Kinesis (for real time streaming) and ElasticSearch. NOTE: The list is not complete

Point6:

Once the IoT device data is received at the AWS end and the appropriate AWS service is used to store the data, it’s possible to do data analysis on the same. For example: If you save the data in DynamoDB you can do data analytics using AWS QuickSight.

Raspberry Pi3 Circuit Diagram

The above diagram shows a logical circuit diagram of how Raspberry Pi3 is connected to the DHT11 temperature and humidity sensor. I am not showing the actual pin connection to keep this article simple but there is a bread board missing here which is connecting all of these components. A few observation points about the above diagram

• On the MicroSD card of Raspberry Pi3 I have installed Raspbian Stretch Desktop OS (Debian based Linux OS)
• I am using python libraries to communicate with GPIO pins instead of “C” program, just to make life easy for me. Basically I am using the “RPi.GPIO” libraries that come with Raspbian Stretch OS 
• I have installed AWS CLI client on the Raspberry Pi3 to allow it to communicate with AWS IoT
• I have installed the “AWSIoTPythonSDK.MQTTLib” python library to allow Pi3 to communicate with AWS IoT MQTT broker as a MQTT client
• The resistors in the diagram are used to control the current flow.

The logical flow of events is as follows

Point 1:

The pressing of button controls when Pi3 will read sensor data. I am using a button just to control the event flow through a user driven action. If you want you can remove the button and the LED from the above circuit and let the Pi3 read temperature and humidity at periodic intervals directly from DHT11 sensor

Point 2:

Once the button is pressed, I am using a LED as a visual indicator to tell me that Pi3 will be reading sensor data

Point 3:

The button also sends a signal to Pi3 via the GPIO pins as a trigger to read sensor data

Point 4:

Pi3 reads the temperature and humidity data from the DHT11 sensor once it receives the trigger from the button press event.

Point 5:

Pi3 then uses the “AWSIoTPythonSDK.MQTTLib” python library to send data to AWS IoT. Once the data reaches AWS IoT, then data analysis can be done from that point forward using AWS services shown in previous architectural diagram.

A few take away

Things you should consider exploring but is not elaborated in this article are listed below

At the AWS end

• It’s possible to use AWS Athena to do data analysis directly from S3 bucket
• You can do real-time data analytics using Spark and EMR cluster of AWS and use AWS Kinesis as a streaming source which streams data from multiple IoT devices.
• If you want to do any data transformation on the IoT data received from devices, you can consider using AWS Glue/Data pipeline.

At the IoT end

• You can use a Passive infrared sensor (PIR motion sensor) and connect it to Raspberry Pi3 and built your own home security system that sends you email alerts when someone enters your house or you can connect a Piezo buzzer to your Pi3 along with the motion sensor to sound an alarm
• You can also connect a Relay to Pi3 and then use it to control pretty much anything over the web, I suggest using a smart Relay like Adafruit's “Controllable Four Outlet Power Relay Module version 2” that has an inbuilt relay so you can connect low voltage circuit like Pi3 at one end and have the power strips for high voltage at the other end to which you can connect any electrical appliance like (air conditioner, table lamp etc.). This setup will allow you to control pretty much anything in your house from anywhere over the cloud. Using a smart relay will avoid the need to work with high voltage wires and the need to connect them directly to a regular relay, safety first.
• You can measure pressure, temperature and altitude using BMP180 digital pressure sensor that can be connected to Pi3.

Conclusion

I hope this gets you excited with IoT and encourages you to do some DIY projects. I have given some DIY project pointers in the “A few take away” sections in my article for you to get started on. Good luck.