Azure IoT Hub on a Toradex Colibri VF61 – Part 2: Interfacing sensors and the IoT Car

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CHAIRMANIntroduction

This is the second part in a series of three articles focused on

the development of an IoT application. It goes on about the

reading of sensors and sending of gathered data to the cloud.

The embedded system chosen for this purpose was a Toradex customized

Single Board Computer solution: the Colibri VF61 System on Module + the

Iris Carrier Board. As a retrospect, the Image 1 presents a block diagram that

illustrates the idea of the whole application documented in this series.

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Image 1: Block diagram of the application

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CHAIRMANThe first article of this series holds more detailed information about the project

overview, guiding the user through the configuration of the IoT Hub service and

sending of data from the Toradex embedded system to it: Part 1: sending data to the

cloud

Adding the sensor modules to the Iris board

The sensors used in this project to generate data and the module to connect to the

internet via Wi-Fi are:

• MPU-6050 gyro + accelerometer and temperature

• HC-SR04 ultrasonic ranging module

• IKeyes GPS shield v1.2

• WL250N USB Wi-Fi

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The MPU-6050 already has a Kernel module to interface with Linux applications (

Kernel version 4.4.0 revised for the Toradex Vybrid CoMs). See this document

containing the device tree fragment needed to enable the hardware interface of the

sensor. Since this module isn't added to the Kernel out of the box, there is the need to

include it. In this case, this article has detailed instructions on how to configure,

compile, and update the Kernel. Once the MPU-6050 has the same I²C address of the on-

module RTC (0x68), the easiest solution found to solve the issue was to set (connect to

+3.3V) the AD0 pin of the MPU-6050, which luckily changes its address to 0x69.

The HC-SR04 has a module posted on Github which was forked, modified and compiled

for the Kernel version 4.4.0 to work with the Colibri VF61. The code and also the

compiled module for the Toradex CoM can be found here.

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The GPS module communicates via UART and it is interfaced using the GPSD service.

More information about GPSD can be found here. If you want to build your own image,

there is an OpenEmbedded recipe available for the GPSD service.

In order to configure GPSD, load the HC-SR04 module and start sending the data to the

cloud. Whenever there is a reboot or power is switched on, a service that calls an "init"

script is created. Assuming the Github repository for this article is already cloned into

the board, the service file (car.service) and the "init" script (init.sh) are in the main

folder. For this to work, a few steps must be taken: first of all, the Github repository

must have been cloned into /home/root, which is the default path. Then, the file

car.service must be copied into /lib/systemd/system and the service must be enabled.

The following steps describe the process from the beginning:

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If there is the need to stop the service after boot, i.e. stop sending messages to the IoT

Hub, then it can be stopped by the following command:

The USB Wi-Fi dongle WL250N can be used out of the box, however there is the need

to configure the network to be connected to. Instructions on how to configure the

connection can be found here.

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Regarding the connections between the modules and the Iris Carrier Board, only pins

from the x16 connector – the pin header – were used. The Iris Carrier Board

technical datasheet holds useful information about interfaces, connectors, etc., and

there is also the Colibri VF61 CoM datasheet, for further consulting, if needed. The Table

1 presents the correspondence between the Iris pins and the modules pins. The Image 2

illustrates the connection of the MPU-6050 and the HC-SR04 in an early stage of

development.

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Table 1: Connection between sensors and the Iris carrier board

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Image 2: Connecting the MPU-6050 and the HC-SR04 to the Iris

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The IoT Car

After all of the modules were interfaced to the Toradex embedded system, the next step

was to embed it in a remote controlled car, as displayed in the Image 3. It is a

straightforward process, but some considerations about it may be useful.

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Image 3: The IoT Car prototype

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CHAIRMANSince the GPS module has an active antenna, it was placed under the Toradex system

and then the antenna was attached to the outside of the car roof. To accommodate the

ultrasonic ranging module, a pair of holes was added to the bumper, as described in the

Image 3. The accelerometer module was firmly attached to a printed circuit board of

the car, since the PCB orientation was parallel to the ground, allowing the use of the

module data without further calibration (though it may be desired for a real

application).

Regarding the embedded system power supply, a 2 cell (2S – 7.4V) LiPo RC battery with

1200mAh of capacity was chosen. Considering that the system current is around

200mA and that a practical rule to LiPo discharging is that it shouldn't be more than

80% of the capacity, then the system can work for about 4.8 hours. Image 4 displays the

final prototype version.

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Image 4: The final IoT Car version

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CHAIRMANReading sensors and sending to the cloud

The data from the MPU-6050 and the HC-SR04 can be read by accessing the filesystem,

while to retrieve the GPS data there is a node module named Bancroft that

communicates with GPSD and returns the parsed data.

From the HC-SR04 module, the returned value from the Kernel module is the time in

microseconds between sending a pulse and receiving it back. To convert it to meters, this

value must be multiplied by the speed of sound in the air (approximately 340m/s) and

then divided by 2E6, that converts to seconds and divides by two, because half of the

time corresponds to the ultrasound emitting and the other half to the echo:

                               distance = (value*sound_speed)/2000000

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CHAIRMANThe data from the MPU-6050 has scale values, once the resolution of the

acquisitions can be configured. Also, the temperature sensor has an offset that is

inherent to every different sensor. For the purpose of this article, the default

configuration of the MPU-6050 will be adopted and, to convert the readings, the

general approach can be used:

                                   value = (raw_value+offset)*scale

The converted values units are: acceleration - m/s²; gyro - °/s and; temperature -

°C.

The node GPS module returns an object with values such as those presented in the

below example:

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The application that does the reading of this data and sends it to the IoT Hub is a

modified version of the part 1 application, send_data.js. The file that corresponds to the

current application is the send_data_from_sensors.js and can be found in the Github

repository that was cloned into the board earlier.

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The main considerations about the modified code are that the Bancroft module emits

events whenever it reads new data; also the paths to access the sensors modules

are /sys/bus/iio/devices/iio:device2/ for the MPU-6050 and /sys/class/hcsr04/ for the

HC-SR04. Below, some parts of the code are going to be explained.

First of all, the offset and scale constants from the MPU-6050 are read synchronously,

preventing the code from reading a value and trying to calculate it before the

offset/scale values are set. Also, the variables that will hold the data to be sent to the

cloud are declared:

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Then, the GPS events that are important to the application are handled. Whenever the

coordinates are updated, they are saved in a variable and, if the connection to the module

is lost, it tries to reconnect:

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The last part of the code to be executed is the call to the functions that read the sensors

and that send data to the IoT Hub in a loop. Separate loops were used for each function

to make the code more flexible, for instance, if there is the need to log the sensor

readings to a backup file more frequently than to send to the cloud:

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The function getAllSensors called above will be omitted, nonetheless it is very simple: it

updates the embedded system current time stored and then calls the function

readSensor() for each possible measurement - distance, temperature, acceleration on 3

axis and gyro on 3 axis. readSensor() itself just reads from a file and prints error to the

console, if it is the case; otherwise it is pretty much the same as readFile().

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The function sendToIotHub() transforms the latest data read to a JSON encoded string,

encapsulates the string in a message, logs a feedback to the console and sends it to the

IoT Hub. It is also the last part of code to be commented in the article, and can be seen

below:

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Considerations and what comes next

So far, in the first part of this series of articles, the project goals and an overview about

the Internet of Things were given. Then, the Azure IoT Hub service was configured to

receive messages from the Toradex embedded system (and also send messages,

though it is not explored in this project), some considerations about sending data from

the embedded system were made and, finally, a way to get the data from the cloud was

presented, in order to verify that everything was working fine.

Then, the current article focused on the embedded system part of the project. It

started with the steps taken to interface the sensors/modules to the Colibri VF61 +

Iris Carrier Board and went to the specifics about the Node application.

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CHAIRMANWith all of this set, the points to be explored in the next article are the use of the Azure

Stream Analytics and the Microsoft Power BI to filter data and display easily

understandable outputs, in order to ease the job of extracting useful insights and

generating business intelligence out of it. I hope it was a helpful article and also, I

would like to thank the Grupo Viceri team from Brazil for their expertise regarding

Azure and Business Intelligence, that led to the partnership that which resulted in the

IoT Car project. See you soon in part 3!

This blog post was originally featured on Embarcados.com in Portuguese. See here.

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Thank you!