Telemetri sepeda motor saya

(di mana AAA123 adalah kode pin yang dikembalikan dari ponsel Anda yang terhubung ke USB dalam mode Asosiasi)

Di akhir proses, aplikasi akan dipasang di perangkat Anda dan Anda akan dapat "menyematkan untuk memulai":

AZUR

Sekarang kami siap menjelaskan cara mengonfigurasi layanan Azure. Untuk proyek ini akan diperlukan untuk membuat:

• Aplikasi Web + SQL Azure

• IoTHUB

• Tugas Analisis Aliran

• DeviceId yang akan digunakan untuk mengirim data ke IoTHUB .

Oke pergi! Apakah Anda memiliki akun AZURE?

• Jika TIDAK, buka https://azure.microsoft.com/ dan buat.

• Jika YA... BAIK! Buka https://portal.azure.com

LANGKAH 1 - Buat dan konfigurasikan IoT Hub:

IoTHUB adalah titik akses nilai telemetri ke Cloud.

Buat IoTHub Anda, Klik Baru => Internet Pemikiran => Pusat IoT dan kompilasi bidang Hub dan klik Buat

Selanjutnya, di dasbor Azure, Anda mungkin melihat IoT HUB menerapkan dan setelah beberapa detik IoT HUB Anda akan siap.

Klik ubin baru di dasbor Anda dan buka Semua setelan pedang.

Lihat Nama Inang IotHub Anda dan buat catatan:

Dalam Kebijakan akses bersama bilah, klik pemilik iothub kebijakan, lalu salin dan catat string koneksi di iothubowner pedang

Oke...hub Iot Anda telah berhasil dibuat, Anda hampir siap untuk menggunakannya!

LANGKAH 2 - Buat dan konfigurasikan Analisis Aliran

Aliran Analytics memiliki tujuan membaca dan menulis semua data yang dikirim ke IOTHUB dan menyimpannya ke dalam tabel database.

Buat tugas Analisis Aliran baru:

Klik Baru => Internet Pemikiran => Tugas Analisis Aliran

Kompilasi semua bidang dan klik tombol Buat.

Sekarang kita siap untuk mengonfigurasi Input/Output dan kueri JOB streamAnalityncs.

Siapkan Masukan - Pilih Sumber . Dalam kasus kami, sumbernya adalah IoTHub

Atur Output - Pilih tujuan. Dalam kasus kami, outputnya akan menjadi tabel database SQL Azure (Saya akan menunjukkan cara membuat sql db pada langkah berikutnya)

LANGKAH 3 - Buat WebApp + SQL Azure

Buka dasbor dan pilih WebApp+SQL (Baru => Jaringan => Web + Seluler => Aplikasi Web + Sql )

Sekarang konfigurasikan aplikasi web Anda dengan mengompilasi bidang berikut:

• Nama Aplikasi

• Pilih langganan

• Pilih Grup Sumber Daya ( jika Anda tidak memilikinya, buat Grup Sumber Daya pertama Anda)

• Pilih Paket Layanan Aplikasi dan tingkat harga (untuk contoh ini Anda dapat mempertimbangkan untuk memilih paket gratis F1)

Konfigurasikan SQL Anda, buat yang baru dan pilih nama , tingkat harga , nama server dan tambahkan kredensial Login Admin :

Sekarang kembali ke LANGKAH 2 , kita sekarang siap untuk membuat Kueri StreamAnalitycs. Klik ubin StreamAnalyticJob dan pilih "Kueri". Masukkan kueri Anda di panel kanan.

Seperti yang Anda lihat, kueri menjelaskan alur kerja proses. Catch the data from "MyTelemetryInputHub" and save them into "SqlAzureMyTelemetry" ... easy&wonderful!!

At the end, we have only to start the JOB clicking the START tombol.

Create a DeviceId for HUB

This step is necessary in order to create a connection between my device and the IoTHUB. We need to create a new device identity and add into the ID registry of the IoT HUB.

For more information please visit the follow link

To generate a new one deviceId, you have to create a Console Application in Visual Studio.

In the NuGet Package Manager window, search and install the Microsoft.Azure.Devices package.

Add the below code In your Console application, replace your IoTHub Connection String and chose a deviceId alias (in my case the deviced is MyDucatiMonsterDevice ).

class Program{//Install-Package Microsoft.Azure.Devicesstatic RegistryManager registryManager;// Connection String HUBstatic string connectionString ="xxxxxxxxxxxxxx";static void Main(string[] args){registryManager =RegistryManager.CreateFromConnectionString(connectionString);AddDeviceAsync().Wait();Console.ReadLine();}// creates a new device identity with ID myFirstDeviceprivate async static Task AddDeviceAsync(){ string deviceId ="myDucatiMonsterDevice";Device device;try{device =await registryManager.AddDeviceAsync(new Device(deviceId));}catch (DeviceAlreadyExistsException){device =await registryManager.GetDeviceAsync(deviceId);}Console.WriteLine("Generated device key:{0}", device.Authentication.SymmetricKey.PrimaryKey);}} 

Run the console application (press F5) and generate your device key!

WEB application

At this point of the project i thinked about how can i see my telemetry data and how can i show them to my friends and share the results on the social media. to do that I decided to create a responsive web site in ASP .NET

The website consist in a dashboard contains all my telemetries. The data has been read using the Entity framework from SQL Azure.

You can discover the web site by the following url:

Has been created an account for all Hackster Users , SignIn using the following user/psw credentials:

User ID:demoPassword:demo 

The web site is look like this:

And look like this in mobile version:

When you create a web app in azure using the Free tier, the domain name will be something like this:

.azurewebsites.net

To change the domain name you have to buy one and on azure this is very simple! Follow this steps:

• Go to your Azure dashboard and click on you web app Tile.

• Select "Custom domains and SSL " and click on "Buy domains ".

• Fill all fields (about your domain name and your contact informations)

Ducati Monster Wiring

Now is time to Introduce my motorbike, this is my Ducati Monster 695 that for this project has been the cavy.

Below some pictures about my Hardware (control unit) mounted onboard.

to measure the tyre temperature I created a bracket clamped behind the wheel.

One Ultrasonic ranging module HC-SR04 has been fixed under the front headlight. It misures the distance between headlight and front mudguard.

The other one Ultrasonic ranging module has been fixed under the bike seat in order to determined the back shock absorber measuring the distance between seat and rear tyre.

The central unit containing the Arduino, the GPS, the Accelerometer and the external temperature sensor has been fixed behind the seat.

And at the end, after the wiring, my motorbike is look like this

Machine Learning

The last idea of this project has been try to understand if exist a correlation between telemetries data in order to try to understand how to improve my driving style.

My data samples are not relatable because I make different routes every time and the traffic conditions are never the same. I think that a good condition should be use the system in a speedway. I Imagine that in a real race could be usefull to understand by telemetry if you can increase the speed or if you have to decrease the speed according to the output values.

The Machine Learning (ML) embodies the magic of software insights about the data, solves problems by giving information that represents a certain evolution. The ML understands how a given phenomenon will evolve according to the data in its knowledge.

The first approach using the ML is to do Experiments . Using the data, you can play and be able to do the analysis with the aim of investigating the phenomenon that interests us.

Ok let's go to discover the Azure Machine Learning

Go to https://studio.azureml.net and sign in using your Azure account.

On the left of the page click on "Studio"

You will be redirect to your personal Dashboard and now you will be ready to create experiments

• In the experiments blade, Click NEW (on the left button of the page) .

• The first step is to Import data that you would like to use in your ML. In my case I imported the data from SQL Azure.

• Into my Table all data are varchar so i needed to convert it in float and excluding some bad data in order to have only best quality records. Below you can see the query used to import data

select CAST(speed as float) _speed, CAST(roll as float) _roll, CAST(pitch as float) _pitch, CAST(Xg as float) _Xg, CAST(Yg as float) _Yg, CAST(Zg as float) _Zg, CAST(TempExt as float) _TempExt, CAST(TempTyre as float) _TempTyre, CAST(FrontAbsorber as float) _FrontAbsorber, CAST(BackAbsorber as float) _BackAbsorberfrom mytelemetrywhere CAST(speed as float)>3 and CAST(FrontAbsorber as float)>0 and CAST(BackAbsorber as float)>0 and CAST(roll as float)<60 and CAST(roll as float)>-60 

• Then add a SPLIT DATA element in order to use some data to Train your ML and decide the data percentage to use for the configuration.

• A data portion will be used to configure the template and the other portion to see if your model works fine. This will allow me to assess the goodness.

• Add a Train Model and decide what is the field that would you like the model guess and decide which algorithm use for your train.

• Now we verify how he behaved the algorithm giving us a feeling of goodness, to do that we need to use "Score Model ". the SM accept in input 2 sources, the first one is from Train Model and the second one from the SPLIT DATA .

• At the end we ready to estimate the model according to the test data, comparing them with those that have not yet used (Evaluate Model ).

Official Documentation about " Evaluate Model" can be found here

Below there is a sample experiment useful for comparing 2 algorithms using the Evaluation Model , the 2 algorithms are Two-class decision jungle and Two-class decision forest .

When you pass on a scored model for a "two classes" classification algorithm, the evaluation model generates metrics shown below:

Classification Models

The above metrics are reported for evaluating classification models.

(All metrics are reported but the models are ranked by the metric you select for evaluation)

• Accuracy measures the goodness of a classification model as the proportion of true results to total cases. Accuracy =(TP + TN) / (TP + TN + FP + FN)

• Precision is the proportion of true results over all positive results. Precision =TP / (TP + FP)

• Recall is the fraction of all correct results returned by the model. Recall =TP / (TP + TN)

• F-score is computed as the weighted average of precision and recall between 0 and 1, where the ideal F-score value is 1. F1 =2TP / (2TP + FP + FN)

• AUC measures the area under the curve plotted with true positives on the y axis and false positives on the x axis. This metric is useful because it provides a single number that lets you compare models of different types.

As you can see, the Two-class decision forest Algorithm have an Accuracy , Precision ,Recall and F1 Score near the value equal to 1, so i suppose that my model is good described, GREAT !!!

For more details about Evaluate Model please visit the following LINK

Predictive Experiment:

It's time to move our attention on the predictive functionality of the machine learning.

The Training experiment will be convert to a predictive experiment, this feature allows to integrate in others applications the opportunity to use your model and based on your model have a Prediction.

For do that, has been created a new one Experiment, the data source has been the SQL Azure but this time the table used has been a new one. I tried to classify data based on telemetry values.

Now my data look like this and as you can see there is a new column named Scored that represents my classification:

Create the experiment like below, select the Train Model and add a Multiclass algorithm:

• RUN the experiment and see the results:

• Select the Train Model and click on SET UP WEB SERVICE => Predictive Web Service

• Wait some seconds and your predictive service will be ready. Below you can see my Predictive Experiment layout:

• Run this model and then deploy the web service by clicking on the DEPLOY WEB SERVICE tombol

• Your service will be immediately ready to be used and a dashboard will appear, contain a "TEST" button where you can manually enter data and test your service

• Click TEST button and compile all fields, use some fake values and see the Predictive result:

As you can see the result is equal to "SAFE DRIVING ", this mean that my Predictive service has predicted that my driving style is Safe and no action is needed.

If the Result has been for example "HIGH STRESS HIGH SPEED " that means that I have to reduce speed!

Conclusions about ML:

in this section we understand some things.

• How configure an experiment in ML azure environment

• we found that Two-class decision forest Algorithm well describe my motor bike telemetry and we train the ML for the predictive experiment using the Multiclass decision forest Algorithm

• How to convert our Model in a Web Service creating a predictive experiment

• we found some usefull information about my style of guide. Infact as you can see from the below graphs:

I prefer lean on the right and my lean angle doesn't depend for tyre temperature and from the speed. That mean that my tyres are high quality and in add I undestood that my motorbike prefers Wheelie when the tyre temperature is more than 35°C

Final Conclusions

In this project we covered many technological areas.

We discovered How is possible to create a funny Hardware using an Arduino and some sensor spending less then 60$ and using many services provided by microsoft.

We learned how is easy use Azure and how is interesting use Machine Learning Studio suite.

Has been realized a prototype that certainly needs improvements but i think is a good starting point for future developments from each people enthusiast about C#, Azure, Arduino and MotorBike.

Possible future Developments

To Improve this prototype i think will be usefull implements some things:

• About the mobile app (UWP), in case that no 3G/4G network is available, the data are lost because i'm not able to send the data to azure. Would be usefull develop a buffering logic that in case of "no networking" available, stored the data and send them at a later time.

• Create an APP for mobile platform using Xamarin, would be cool have an app who can run in Android OS and iOS also.

• Use more precise sensors and increase performance/accuracy.

• Make the central unit smaller, maybe using an Arduino MKR1000 .

• Create a social media Webapp to share your motorbike travel and telemetries in order to suggest to biker users what is the dangerous curves and increase the safety.

• Modify the source of the Machine Learning using the Steam Analytics. In my case i prefered use a sql AZURE datasource because it was not possible ride the bike and develop the ML in the same time :)

New branch work flow:

• The new branch consists to sending back to device, through IoTHub , the information about the ML predictive service.

• When the speed value is greater than 100 km/h a new one stream analyticsJob will send the telemetry data to Event Hub.

• A cloud service reads this data from EventHub and send a request to the ML predictive service in order to have a score about the Driving Style .

• The Cloud Service will send back the score to IoTHub , and IoTHub will send it to the device back (Windows Phone in this case)

... and now ENJOY !! :-) feel free to contact me for any questions and tips.

Skema

circuit diagrams of the Telemetry system. On board scheda MotorbikeProject.fzz