Category: Azure IoT

Azure IoT Hub – OpenSSL – Generate proof of possession

The Azure IoT documentation has guides on setting up certifications for production use. That documentation showcases how to properly setup using certificate authorities to generate proof of possession. For development purposes, you may want to use self signed certificates.

  1. Assuming  the original key and cert were created with the following commands (Azure IoT reports unverified if you upload it):
# Create root key
openssl genrsa -out iotHubRoot.key 2048

# Create root cert
openssl req -new -x509 -key iotHubRoot.key -out iotHubRoot.cer -days 500
  1. Then generate the verification cert (pay attention to fill in common name with verification code):
# Create verification key and csr
openssl genrsa -out verification.key 2048
openssl req -new -key verification.key -out verification.csr

#It will prompt for cert fields. 
#IMPORTANT: The Common Name needs to be your Verification Code (generate and copy that from portal)

# Create verification pem
openssl x509 -req -in -verification.csr -CA iotHubRoot.cer -CAkey iotHubRoot.key -CAcreateserial -out verification.pem -days 500 -sha256
  1. Upload pem file to portal to verify certificate

Authoring for Pluralsight – Azure Machine Learning

Off to start another set of courses for Pluralsight:

  • Sourcing Data in Microsoft Azure
  • Deploying and Managing Models in Microsoft Azure
  • Cleaning and Preparing Data in Microsoft Azure

If you would like to check out any of my other courses, visit my author’s profile.

Sourcing Data in Microsoft Azure

This course is for people looking to move into the data sciences. They can have an existing background in development or IT.

This course will show how to find data in Microsoft Azure, how to move and change that data, and finally how to build workflows around that data.

This course assumes the developer has an understanding of basic computer terminology and the azure portal.

Deploying and Managing Models in Microsoft Azure

This course is for people looking to move into the data sciences. They can have an existing background in development or IT.

This course introduces the audience to the different data preparation steps involved with data projects. This course will show how to clean, transform, and wrangle the data needed for a data project.

This course assumes the developer has an understanding of basic computer terminology and the azure portal.

Cleaning and Preparing Data in Microsoft Azure

This course is for data science practitioners who need to learn more about how to utilize tools for managing the models they create.

The audience will be taken through automation and DevOps to learn more about how to manage their workflows. Everything from versioning, automated deployments, automated hyper-parameter tuning, and more will be discussed.

This course assumes the data scientist has an understanding of machine learning and common terminology and integration in machine learning projects. The course also assumes the data scientist has knowledge of Azure and the Azure portal.

New Pluralsight Courses

I’ve been busy and not able to update that I have new courses available on Pluralsight:

Developing Microsoft Azure Intelligent Edge Solutions

This course targets software developers that are looking to build edge solutions that can process data and make intelligent decisions. This course will showcase how to develop those solutions using Microsoft Azure.

Over time, what was once simply Internet of Things solutions has evolved into Edge solutions. In this course, Developing an Intelligent Edge in Microsoft Azure, you will learn foundational knowledge of edge computing, how it interacts with data and messaging systems, and how to utilize both with Microsoft Azure. First, you will learn the concepts of edge and internet of things computing. Next, you will discover how to process streaming data on hot, warm, and cold paths. Finally, you will explore how real-time and batch processing can be utilized in an edge solution. When you are finished with this course, you will have the skills and knowledge of edge and internet of things in Azure needed to architect your next edge solution. Software required: Microsoft Azure, .NET.

Building Your First Data Science Project in Microsoft Azure

This course targets software developers looking to build data science solutions that can utilize the power of the cloud. The content will also showcase how to create those solutions using Microsoft Azure.

The past five years have shown a boom in the data science field with advancements in hardware and cloud computing. In this course, Building Your First Data Science Project in Microsoft Azure, you will learn about data science and how to get started utilizing it in Microsoft Azure. First, you will learn the data science and the tools surrounding it. Next, you will discover how to create a development environment in Microsoft Azure. Finally, you will explore how to maintain and utilize that development environment. When you are finished with this course, you will have the skills and knowledge of data science to build your first data science project in Microsoft Azure. Software required: Microsoft Azure.

 

Authoring for Pluralsight – Developing Microsoft Azure Intelligent Edge Solutions

Off to start another course for Pluralsight. This time its Developing Microsoft Azure Intelligent Edge Solutions. If you would like to check out any of my other courses, visit my author’s profile. The new course will cover the following topics:

  • Edge
    • IoT Architecture
    • IoT use cases and solutions
    • Edge Architecture
  • Azure IoT Hub
    • Overview of the IoT Ecosystem in Azure
    • IoT Hub message routing
    • Stream processing overview
  • Hot, Warm, and Cold paths
    • Use cases for hot, warm, and cold paths
    • Hot path with event hubs and log app
    • Warm path with Cosmos DB
    • Cold path with Azure Blob Storage
  • Real Time and Batch Processing
    • Overview and Demos of Stream Analytics Service
    • Overview and Demos of Time Series Insights

Pluralsight Course Published – Designing an Intelligent Edge in Microsoft Azure

Designing an Intelligent Edge in Microsoft Azure was just published on Pluralsight! Check it out. Here is a synopsis of what’s in it:

This course targets software developers that are looking to integrate AI solutions in edge scenarios ranging from an edge data center down to secure microcontrollers. This course will showcase how to design solutions using Microsoft Azure.

Cloud computing has moved more and more out of the cloud and onto the edge. In this course, Designing an Intelligent Edge in Microsoft Azure, you will learn foundational knowledge of edge computing, its intersection with AI, and how to utilize both with Microsoft Azure. First, you will learn the concepts of edge computing. Next, you will discover how to create an edge solution utilizing Azure Stack, Azure Data Box Edge, and Azure IoT Edge. Finally, you will explore how to utilize off the shelf AI and build your own for Azure IoT Edge. When you are finished with this course, you will have the skills and knowledge of AI on the edge needed to architect your next edge solution. Software required: Microsoft Azure, .NET

 

iotedge: error while loading shared libraries: libssl.so.1.0.2: cannot open shared object file: No such file or directory – Raspberry Pi

After installing Azure IoT Edge using the guide for Linux ARM32, the following error was presented: “iotedge: error while loading shared libraries: libssl.so.1.0.2: cannot open shared object file: No such file or directory“. 

The fix was simple enough, just install the building libssl1.02 using the following command:

sudo apt-get install libssl1.0.2

Test by running the iotedge command:

iotedge

azureiotedgeCapture.PNG

If that works successfully, restart the iotedge service:

service iotedge edge restart

Verify that it is running by checking the service status:

service iotedge edge status

azureiotedgeCapture

Azure IoT Edge – YOLO, Stream Analytics Service, and Blob Storage

As a continuation of the Izon camera hack //TODO: link to previous article, I wanted to detect if my dog was using the doggy door in the main room. The approach was going to be simple at first, detect the dog in the room and not in the room. When in the room changes (or not in the room), upload 10 seconds worth of images to Azure to see if the dog used the door.

Image Classification

To detect the dog, the first and largest challenge to these types of tasks is getting enough images to train the model. For me, this meant saving images of the dog in a pre-aligned shot. This is easy enough to accomplish; the room the images will be processed in should only have the dog moving in it. Since he is the only moving object, YOLO can be used to detect the position of objects in the room and then the position of these objects can be checked to see if there is any movement. If there is any movement, the images can be saved for later categorization. To accomplish this, there will be four modules:

  • Camera Module – Accesses the camera feeds to save the images
  • Object Detection Module – Uses YOLO to detect object and object positions
  • Motion Detection Module – Uses Stream Analytics Service to detect if object positions are moving.
  • Image Storage Module – Uses Blob Storage so save and delete the images

Module Arch

The Camera module will send the timestamped images to the Object Detection Module and the Image Storage Module. The Object Detection Module will then use YOLO to detect the objects and their positions in the image. Those detection results will be sent to the Motion Detection Module, which will use Streaming Analytics Service to see if there was motion detected over the last ten seconds. If there is no motion detected over the last ten seconds, then the Motion Detection Module will send a delete command to the Image Storage Module to remove the image without motion from the store. The routing Table will look as so:


"routes": {
"cameraToObjectDetection": "FROM /messages/modules/camera/outputs/imageOutput INTO BrokeredEndpoint(\"/modules/objectDetection/inputs/incomingImages\")",
"cameraToImageStorage": "FROM /messages/modules/camera/outputs/imageOutput INTO BrokeredEndpoint(\"/modules/imageStorage/inputs/incomingImages\")",
"objectDetectionToMotionDetection": "FROM /messages/modules/objectDetection/outputs/objectDetectionOutput INTO BrokeredEndpoint(\"/modules/motionDetection/inputs/incomingObjectDetection\")",
"motionDetectionToDeleteImage": "FROM /messages/modules/motionDetection/outputs/motionDetectionOutput INTO BrokeredEndpoint(\"/modules/imageStorage/inputs/deleteImages\")"
}

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routes.json

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These modules will be broken up into their own articles for readability and searchability. If there is no link to a module article it is because that article is not completed or is not published yet.

Speaking at Orlando Code Camp

I am happy to announce I will speaking at the Orlando Code Camp again this year. I will be presenting AI on the Edge, a look into Microsoft’s Azure IoT Edge.

Title

AI on the Edge

Description

The next evolution in cloud computing is a smarter application not in the cloud. As the cloud has continued to evolve, the applications that utilize it have had more and more capabilities of the cloud. This presentation will show how to push logic and machine learning from the cloud to an edge application. Afterward, creating edge applications which utilize the intelligence of the cloud should become effortless.

Hacking Izon Cameras and using Azure IoT Edge

After Izon announced that they were closing down their services (leaving the cameras I already owned useless), I decided to turn them into something useful using Azure. First let me list some resources:

Use the Will it hack link to get access to the mobileye website and verify that the Izon device is still streaming and still working. If it is working, you are already done with edits to the device unless you would like to change the passwords (which you should).

Our goals are as follows:

  • Process the video feed from the Izon camera (we will cheat this early on and only use the image feed)
    • Check for motion
    • Check for faces
    • Check if faces are white listed
    • Check for my dog
  • Process the audio feed
    • Check for any noise
    • Check for non human noises
    • Check for dog barks
    • Check for my and my wife’s voice

These are all stretch goals that will be referred back to as the project moves forward.

Create the Azure IoT Edge module

For the first module, we will use the C Module base image. We are looking for two things from this module:

  • Download the picture feed and pass it to the Edge Hub
  • Download the audio feed and pass it to the Edge Hub

If you don’t know where to get started with the C module of the Azure IoT Edge platform, there is helpful information on the Azure Documentation page. Once the C module is created and ready for editing, we are going to connect to the image feed from the devices. To make this simple, both feeds will be retrieved using HTTP. For the video feed, its simple enough to grab images from the Izon camera existing camera feed.

Now one thing we need, is to be able to connect to each camera within the local network shared with the Edge. Since we would like to be able to add and remove cameras, we will use the device twin to update and manage the list of IP address. The code for updating the list is as follows:


#include <stdio.h>
#include <stdlib.h>
#include "iothub_module_client_ll.h"
#include "iothub_client_options.h"
#include "iothub_message.h"
#include "azure_c_shared_utility/threadapi.h"
#include "azure_c_shared_utility/crt_abstractions.h"
#include "azure_c_shared_utility/platform.h"
#include "azure_c_shared_utility/shared_util_options.h"
#include "iothubtransportmqtt.h"
#include "iothub.h"
#include "time.h"
#include "parson.h"
typedef struct IP_ADDRESS_NODE
{
const char * address;
struct IP_ADDRESS_NODE * next;
} ip_address_node;
ip_address_node * add_address(const char * address,ip_address_node * previous)
{
ip_address_node * new_node = (ip_address_node *)malloc(sizeof(ip_address_node));
new_node->address = address;
new_node->next = NULL;
if (previous == NULL)
{
return new_node;
}
previous->next = new_node;
return previous;
}
void delete_address(ip_address_node * current)
{
if (current == NULL)
{
return;
}
delete_address(current->next);
//free(current->address);
free(current);
}
ip_address_node * root_node = NULL;
ip_address_node * add_address_to_root(const char * address)
{
if (root_node = NULL)
{
root_node = add_address(address,root_node);
}
ip_address_node * current = root_node;
while(current->next != NULL)
{
current = current->next;
}
add_address(address,current);
}
static void moduleTwinCallback(DEVICE_TWIN_UPDATE_STATE update_state, const unsigned char* payLoad, size_t size, void* userContextCallback)
{
printf("\r\nTwin callback called with (state=%s, size=%zu):\r\n%s\r\n",
ENUM_TO_STRING(DEVICE_TWIN_UPDATE_STATE, update_state), size, payLoad);
JSON_Value *root_value = json_parse_string(payLoad);
JSON_Object *root_object = json_value_get_object(root_value);
JSON_Array * ipaddresses = json_object_dotget_array(root_object, "desired.CameraAddresses");
if (ipaddresses != NULL) {
delete_address(root_node);
for (int i = 0; i < json_array_get_count(ipaddresses); i++) {
add_address_to_root(json_array_get_string(ipaddresses,i));
}
return;
}
ipaddresses = json_object_get_array(root_object, "CameraAddresses");
if (ipaddresses != NULL) {
delete_address(root_node);
for (int i = 0; i < json_array_get_count(ipaddresses); i++) {
add_address_to_root(json_array_get_string(ipaddresses,i));
}
return;
}
}

view raw

main.c

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With that code in place, the list of IP addresses can be updated from the Azure UI and the Azure Service SDKs.

Downloading from the Image feed

The Izon cameras make downloading the image feed trivial. There is an existing endpoint where you can grab the latest image directly from the camera’s web server. The latest image is always at /cgi-bin/img-d1.cgi. (NOTE: if you are checking this image from a browser, be sure to have some cache busting!). To download this image into our module, we will use the Curl library for it’s easy HTTP implementation. To add Curl to our Edge module, we will add the following lines to the Dockerfile.amd64.debug:


FROM ubuntu:xenial AS base
RUN apt-get update && \
apt-get install -y –no-install-recommends software-properties-common gdb && \
add-apt-repository -y ppa:aziotsdklinux/ppa-azureiot && \
apt-get update && \
apt-get install -y azure-iot-sdk-c-dev && \
rm -rf /var/lib/apt/lists/*
FROM base AS build-env
RUN apt-get update && \
apt-get install -y –no-install-recommends cmake gcc g++ make libcurl4-openssl-dev && \
rm -rf /var/lib/apt/lists/*
WORKDIR /app
COPY . ./
RUN cmake -DCMAKE_BUILD_TYPE=Debug .
RUN make
FROM base
WORKDIR /app
COPY –from=build-env /app ./
CMD ["./main"]

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Dockerfile.amd64.debug

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FROM ubuntu:xenial AS base
RUN apt-get update && \
apt-get install -y –no-install-recommends software-properties-common gdb && \
add-apt-repository -y ppa:aziotsdklinux/ppa-azureiot && \
apt-get update && \
apt-get install -y azure-iot-sdk-c-dev && \
rm -rf /var/lib/apt/lists/*
FROM base AS build-env
RUN apt-get update && \
apt-get install -y –no-install-recommends cmake gcc g++ make && \
rm -rf /var/lib/apt/lists/*
WORKDIR /app
COPY . ./
RUN cmake -DCMAKE_BUILD_TYPE=Debug .
RUN make
FROM base
WORKDIR /app
COPY –from=build-env /app ./
CMD ["./main"]

view raw

Dockerfile.amd64.original

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With curl now added to the image, it can be utilized in code by adding it to the method invoked in our main loop. The code will download the file for each entry in the IP address list. Once the image is downloaded, it will send it as a message to the Edge Hub and add the IP address of the camera to the message header. Here is that code:


struct MemoryStruct {
char *memory;
size_t size;
};
static size_t
WriteMemoryCallback(void *contents, size_t size, size_t nmemb, void *userp)
{
size_t realsize = size * nmemb;
struct MemoryStruct *mem = (struct MemoryStruct *)userp;
char *ptr = realloc(mem->memory, mem->size + realsize + 1);
if(ptr == NULL) {
/* out of memory! */
printf("not enough memory (realloc returned NULL)\n");
return 0;
}
mem->memory = ptr;
memcpy(&(mem->memory[mem->size]), contents, realsize);
mem->size += realsize;
mem->memory[mem->size] = 0;
return realsize;
}
struct MemoryStruct download_file(const char * address)
{
struct MemoryStruct chunk;
chunk.memory = malloc(1); /* will be grown as needed by the realloc above */
chunk.size = 0; /* no data at this point */
/* init the curl session */
CURL * curl_handle = curl_easy_init();
/* specify URL to get */
curl_easy_setopt(curl_handle, CURLOPT_URL, address);
/* send all data to this function */
curl_easy_setopt(curl_handle, CURLOPT_WRITEFUNCTION, WriteMemoryCallback);
/* we pass our 'chunk' struct to the callback function */
curl_easy_setopt(curl_handle, CURLOPT_WRITEDATA, (void *)&chunk);
/* some servers don't like requests that are made without a user-agent
field, so we provide one */
curl_easy_setopt(curl_handle, CURLOPT_USERAGENT, "libcurl-agent/1.0");
/* get it! */
CURLcode res = curl_easy_perform(curl_handle);
/* check for errors */
if(res != CURLE_OK) {
fprintf(stderr, "curl_easy_perform() failed: %s\n",
curl_easy_strerror(res));
}
/* cleanup curl stuff */
curl_easy_cleanup(curl_handle);
return chunk;
}
void download_image(IOTHUB_MODULE_CLIENT_LL_HANDLE iotHubModuleClientHandle)
{
ip_address_node * address_node = root_node;
do
{
struct MemoryStruct image = download_file(address_node->address);
if (image.size > 1)
{
IOTHUB_MESSAGE_HANDLE message_handle = IoTHubMessage_CreateFromByteArray((char*)image.memory, image.size);
MAP_HANDLE propMap = IoTHubMessage_Properties(message_handle);
Map_AddOrUpdate(propMap, "IpAddress", address_node->address);
IOTHUB_CLIENT_RESULT clientResult = IoTHubModuleClient_LL_SendEventToOutputAsync(iotHubModuleClientHandle, message_handle, "IncomingImage", NULL,NULL);
if (clientResult != IOTHUB_CLIENT_OK)
{
IoTHubMessage_Destroy(message_handle);
printf("IoTHubModuleClient_LL_SendEventToOutputAsync failed on sending to output IncomingImage, err=%d\n", clientResult);
}
}
free(image.memory);
address_node = address_node->next;
} while(address_node != NULL);
}

view raw

main.c

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Downloading from the Audio feed

Now that the image feed is being published to the Edge Hub, its time to connect the audio feed. The audio feed is trickier since the Izon camera doesn’t have an easy to use endpoint (that I know of) for downloading the audio samples like we can with the image feed. In the next entry in this series, an Audio feed will be derived from an RSTP stream.