내 오토바이 원격 측정

(여기서 AAA123 연결 모드에서 USB에 연결된 휴대전화에서 반환된 핀 코드입니다.

프로세스가 끝나면 애플리케이션이 기기에 설치되고 "시작하려면 고정"할 수 있습니다.

하늘빛

이제 Azure 서비스를 구성하는 방법을 설명할 준비가 되었습니다. 이 프로젝트의 경우 다음을 생성해야 합니다.

그래 가자! AZURE 계정이 있습니까?

1단계 - IoT Hub 만들기 및 구성:

IoTHUB는 클라우드에 대한 원격 측정 값의 액세스 지점입니다.

IoTHub를 만들고 새로 만들기 를 클릭합니다. => 생각의 인터넷 => IoT 허브 허브 필드를 컴파일하고 만들기를 클릭합니다.

그런 다음 Azure 대시보드에 IoT HUB가 표시될 수 있습니다. 배포하고 몇 초 후에 IoT HUB가 준비됩니다.

대시보드에서 새 타일을 클릭하고 모든 설정 으로 이동합니다. 블레이드.

IoTHub 호스트 이름 보기 메모:

공유 액세스 정책 블레이드에서 iothubowner를 클릭합니다. 정책을 선택한 다음 iothubowner의 연결 문자열을 복사하여 기록해 둡니다. 블레이드

알겠습니다... IoT 허브가 성공적으로 생성되었으며 거의 ​​사용할 준비가 되었습니다!

2단계 - 스트림 분석 생성 및 구성

스트림 분석은 IoTHUB에 전송된 모든 데이터를 읽고 쓰고 데이터베이스 테이블에 저장하는 목적을 가지고 있습니다.

새 Stream Analytics 작업 만들기:

새로 만들기 를 클릭하십시오. => 생각의 인터넷 => Stream Analytics 작업

모든 필드를 컴파일하고 만들기 버튼을 클릭하십시오.

이제 입력/출력 및 streamAnalityncs JOB 쿼리를 구성할 준비가 되었습니다.

입력 설정 - 소스 선택 . 이 경우 소스는 IoTHub입니다.

출력 설정 - 대상을 선택합니다. 우리의 경우 출력은 SQL Azure 데이터베이스의 테이블이 됩니다(다음 단계에서 sql db를 생성하는 방법을 보여드리겠습니다)

3단계 - WebApp + SQL Azure 만들기

대시보드로 이동하여 WebApp+SQL(새로 만들기 => 네트워킹 => 웹 + 모바일 => 웹 앱 + SQL )

이제 다음 필드를 컴파일하여 웹앱을 구성합니다.

SQL을 구성하고 새 SQL을 만들고 이름을 선택합니다. , 가격 등급 , 서버 이름 관리자 로그인 자격 증명 추가 :

이제 2단계로 돌아갑니다. , 이제 StreamAnalitycs 쿼리를 만들 준비가 되었습니다. StreamAnalyticJob 타일을 클릭하고 "쿼리"를 선택합니다. 오른쪽 패널에 검색어를 입력하세요.

보시다시피 쿼리는 프로세스 워크플로를 설명합니다. 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 버튼.

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:

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

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 

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)

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:

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.

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:

New branch work flow:

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

코드

UWP App C# Code

ARDUINO CODE - Bike Telemetry