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Welcome to the IoT Wiki

Introduction

You will find detailed instructions for reproducing the end to end IoT proof of concept, built on Microsoft Azure Cloud Platform and a Texas Instruments LaunchPad development board.

View Working Example

You can view a working example at the following links.

Website home page - http://www.azurelaunchpad.net/
Send fake telemetry from webpage using AMQP - http://www.azurelaunchpad.net/eventhub/eventhubamqp
Send fake telemetry from webpage using REST API - http://www.azurelaunchpad.net/eventhub/eventhubrest
View processed telemetry - http://www.azurelaunchpad.net/sqldatabase/processedtelemetry
View averaged telemetry - http://www.azurelaunchpad.net/sqldatabase/averagetelemetry
Machine learning model - http://www.azurelaunchpad.net/machinelearning/model

Process Description

The Texas Instruments LaunchPad sends telemetry including simulated temperature and humidity readings, alternatively telemetry can be produced via an Azure Web Site webpage using a fake telemetry generator. The telemetry is then ingested by Azure Event Hub and processed by Azure Stream Analytics in near real time, the processed telemetry is then stored in an Azure SQL Server Table. One table stores telemetry that has just been processed, where as another table stores averaged temperature and humidity data, along with the data count for a 5 minute tumbling window.

Alerts can be generated via a Stream Analytics query that compares values stored in a SQL Database Table, these alert details are sent to Event Hub and can be used to generate an SMS, email or phone alert using an Azure API App and an External System like Twilio. Alternatively notification can be sent to Mobile Apps and be viewed as a Smart Watch Alert.

Live data can be viewed in a moving graph as Stream Analytics send processed data to Power BI. Newly stored processed telemetry can also be viewed as it arrives via an Azure Web App webpage as can averages for time periods.

A predictive model is created using Azure Machine Learning and made available as a web service. The model can be queried from an Azure Website webpage to determine whether a system requires maintenance. Azure Stream Analytics can also query the predictive models via the Azure Machine Learning Web Service and hence provide near real-time predictions for many different scenarios.

Solution Architecture

The following image outline the solution architecture of the end to end IoT proof of concept. Some parts are yet to be detailed in this step by step example, however all the main components from the LaunchPad through to Event Hubs and then to Stream Analytics, with data stored in SQL Database and viewed from a Website are complete. Details on mobile services and machine learning are yet to be included.

end to end Iot proof of concept solution architecture

Assumptions

These instructions provide the details required to setup a reproduction of the end to end IoT example. However it is assumed that you are familiar with Microsoft Azure and the Texas Instruments LaunchPad you have selected.

Setting Up The Examples

There are 2 main technologies to setup to get this example working

Azure Setup Step - https://github.com/remixed123/IoT/wiki/Azure-Setup
LaunchPad Setup Steps - Where you will select one or more LaunchPad you wish to connect to Azure, your choices are:-

CC3200 LaunchPad - https://github.com/remixed123/IoT/wiki/CC3200-LaunchPad-Setup
MSP432 LaunchPad - https://github.com/remixed123/IoT/wiki/MSP432-LaunchPad-Setup

If you are only interested in the LaunchPad side of the example, you can use the default settings provided in the source code. This will then communicate with the Azure Cloud I have setup and you can then view the telemetry you send here to determine if your have everything working. There is also a binary provided, which you can flash the LaunchPad with.