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Introduction to Azure IoT Hub and Related Services SDKs

In February 2021, I wrote an article exploring the differences between Azure IoT Hub Devices and Modules. It was well-received and remains one of my most-visited blog posts. Now, with more experience in device and Edge development, I’d like to revisit the topic, but expand it to include other types of Azure IoT SDKs. There have been changes to the Azure IoT Hub device landscape, and I’m better equipped to provide useful information about the various SDKs available.

Microsoft has these divided into three basic SDK types.

  • Device SDKs
  • Service SDKs
  • Management SDKs

To make this as simple as possible, I like to think of the SDKs as serving these purposes:

  • Device – this code runs on the device either as bare metal or in a container. It represents either a leaf device or an Edge gateway. If it connects to IoT to feed it data, it needs this SDK.
  • Service – this code enables me to manage my devices with updates or other forms of communication using IoT Hub. This involves administrative tasks, all of which are focused on using the IoT Hub API to interact with the devices.
  • Management is more complex, since these are SDKs designed to manage your IoT Hub or Hubs. I could use this to monitor my IoT Hub, create or delete Hub instances, and keep track of my shared access policies.

This document focuses on the SDKs and their updates to match the latest versions of Azure IoT services. Use this as a starting point for further research. If you spot any mistakes or think I’ve missed something important, please let me know in the comments. I’m always open to feedback from my readers. I’m well aware that I make mistakes, and I’m always learning.

What are Azure IoT Hub Device SDKs?

Azure IoT Hub Device SDKs provide APIs and libraries that allow developers to create applications for IoT devices and connect them to Azure IoT Hub. These SDKs support a wide range of programming languages and platforms, and offer unique sets of features tailored to the language and platform. Common use cases include sending telemetry data, receiving commands, connecting devices using various protocols, authenticating devices, handling messaging, managing device twins, and receiving notifications.

A small code example in Python

import os
from azure.iot.device import IoTHubDeviceClient
# Retrieve the connection string for the device from an environment variable
conn_str = os.getenv("IOTHUB_DEVICE_CONNECTION_STRING")
# Create an IoT Hub client
client = IoTHubDeviceClient.create_from_connection_string(conn_str)
# Send a message to IoT Hub
message = "Hello, I am a device using IoT Hub!"
client.send_message(message)
print("Sent message: {}".format(message))
# Disconnect the client
client.disconnect()

This code imports the necessary modules from the Azure IoT Hub Device SDK and creates an IoT Hub client. It then sends a message to IoT Hub and disconnects the client when finished.

To use this code, you must have the Azure IoT Hub Device SDK installed and have a connection string for a device registered with IoT Hub.

Unlike many vendor devices, the SDKs present you with the opportunity to create your own IoT devices. You may have heard of IoT Edge, so let’s address what that is now.

What is Azure IoT Edge?

Azure IoT Edge is a fully managed service that enables developers to deploy cloud intelligence directly onto IoT devices. It allows them to build, deploy, and manage applications that run on edge devices and leverage cloud-based services and data to perform tasks such as data processing, analytics, machine learning, and more.

Azure IoT Edge consists of three main components:

  1. Edge Modules: Small, lightweight pieces of code that run on edge devices and perform specific tasks. Edge modules can be written in a variety of programming languages, including C#, Java, Python, and more.
  2. IoT Edge Runtime: A lightweight container that runs on edge devices and manages the lifecycle of edge modules. The IoT Edge runtime can be installed on a wide range of operating systems and devices, including Windows, Linux, and various embedded platforms.
  3. Azure IoT Hub: A cloud-based service that acts as the central hub for communication between edge devices and the cloud. IoT Hub provides a secure and scalable platform for managing devices, sending commands and notifications to devices, and receiving telemetry data from devices.

Azure IoT Edge enables developers to create and deploy intelligent edge solutions that bring the power of the cloud to the edge of the network. These solutions can perform tasks such as data processing, analytics, machine learning, and more on edge devices, reducing the amount of data that needs to be sent to the cloud and enabling fast decision-making and action. Azure IoT Edge is especially useful for scenarios where latency or connectivity issues make it difficult to rely solely on the cloud for data processing and analysis.

A small code example

import os
from azure.iot.edge.module_client import ModuleClient
# Create a module client
client = ModuleClient.create_from_edge_environment()
# Connect the module client to IoT Hub
client.connect()
# Send a message to IoT Hub
message = "Hello from IoT Edge!"
client.send_message_to_output("channel1", message)
print("Sent message: {}".format(message))
# Disconnect the module client
client.disconnect()

This code imports the necessary modules from the Azure IoT Edge Python SDK and creates a module client. It then uses the module client to connect to IoT Hub, send a message, and disconnect.

To use this code, you must have the Azure IoT Edge Python SDK installed and an IoT Edge runtime running on your device. Additionally, an IoT Edge module must be deployed to the device with an output binding named “channel1”.

These are basic approaches to the device and modules, but I wanted to give you an idea of how easy they can be to use. Setting up IoT Edge for development involves some complexity, but the ability to run containerized cloud solutions at the edge makes it worthwhile.

What about IoT Central?

Many of the concepts covered in this article are also true or will work with IoT Central. For this article, I’m focusing on IoT Hub. I will have a future article dedicated to IoT Central and it’s SDKs and APIs.

Azure IoT Hub Device SDKs

Azure IoT Hub Device SDKs are software development kits (SDKs) that enable developers to create applications that run on IoT devices and connect them to Azure IoT Hub. These SDKs provide a set of APIs and libraries that allow devices to perform basic and complex IoT device to cloud communications and commands.

Azure IoT Hub supports device SDKs for a wide range of programming languages and platforms, including C, C#, Java, JavaScript, Python, and a few that are platform specific. Each device SDK provides a set of APIs and libraries tailored to the programming language and platform, making it easy for developers to create applications that interact with Azure IoT Hub.

Some common use cases for Azure IoT Hub Device SDKs include:

  • Sending telemetry data from devices to IoT Hub
  • Receiving commands from IoT Hub and executing them on devices
  • Connecting devices to IoT Hub using various protocols, such as MQTT, AMQP, HTTP, and more
  • Authenticating devices with IoT Hub using various authentication mechanisms, such as shared access signatures (SAS) tokens and X.509 certificates
  • Handling device-to-cloud and cloud-to-device messaging in a reliable and secure manner
  • Managing device twins, which are JSON documents that store metadata about devices in IoT Hub
  • Receiving notifications about device connections, disconnections, and other events from IoT Hub

Overview of the different device SDKs available

  • C: The C Device SDK is a lightweight, portable library that enables C-based devices to connect to Azure IoT Hub and interact with the cloud. It supports various protocols, including MQTT, AMQP, HTTP, and more, and provides APIs for tasks such as sending telemetry data, receiving commands, and authenticating with the IoT Hub.
#include <stdio.h>
#include <stdlib.h>
#include "iothub_client.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"
int main(void)
{
    // Retrieve the connection string for the device from an environment variable
    char* conn_str = getenv("IOTHUB_DEVICE_CONNECTION_STRING");
    // Create an IoT Hub client
    IOTHUB_CLIENT_HANDLE client = IoTHubClient_CreateFromConnectionString(conn_str);
    if (client == NULL)
    {
        (void)printf("ERROR: iothub_client_create failed\\n");
    }
    else
    {
        // Send a message to IoT Hub
        IOTHUB_MESSAGE_HANDLE message = IoTHubMessage_CreateFromString("Hello, Azure IoT Hub!");
        if (IoTHubClient_SendEventAsync(client, message, NULL, NULL) != IOTHUB_CLIENT_OK)
        {
            (void)printf("ERROR: IoTHubClient_SendEventAsync failed\\n");
        }
        else
        {
            (void)printf("Sent message: Hello, Azure IoT Hub!\\n");
        }
        IoTHubMessage_Destroy(message);
    }
    // Disconnect the client
    IoTHubClient_Destroy(client);
    return 0;
}

This code includes the necessary header files from the Azure IoT Hub C Device SDK and uses them to create an IoT Hub client. It then sends a message to IoT Hub using the client, before disconnecting the client when complete.

To use this code, you must have the Azure IoT Hub C Device SDK installed on your machine and have a connection string for a device registered with IoT Hub.

  • C#: The C# Device SDK is a fully-featured library that enables C#-based devices to connect to Azure IoT Hub and interact with the cloud. It supports various protocols, including MQTT, AMQP, HTTP, and more, and provides APIs for tasks such as sending telemetry data, receiving commands, and authenticating with the IoT Hub. It also includes support for advanced features such as device twins and direct method invocations.
using Microsoft.Azure.Devices.Client;
using System;
using System.Text;
using System.Threading.Tasks;
namespace ConsoleApp
{
    class Program
    {
        static void Main(string[] args)
        {
            // Retrieve the connection string for the device from an environment variable
            string conn_str = Environment.GetEnvironmentVariable("IOTHUB_DEVICE_CONNECTION_STRING");
            // Create an IoT Hub client
            DeviceClient client = DeviceClient.CreateFromConnectionString(conn_str);
            // Send a message to IoT Hub
            string message = "Hello, Azure IoT Hub!";
            byte[] message_bytes = Encoding.UTF8.GetBytes(message);
            Message iot_hub_message = new Message(message_bytes);
            client.SendEventAsync(iot_hub_message).Wait();
            Console.WriteLine("Sent message: {0}", message);
            // Disconnect the client
            client.CloseAsync().Wait();
        }
    }
}

This code imports the necessary namespaces from the Azure IoT Hub C# Device SDK and creates an IoT Hub client. It then sends a message to IoT Hub, before disconnecting the client.

To use this code, you must install the Azure IoT Hub C# Device SDK on your machine and obtain a connection string for a device registered with IoT Hub.

  • Java: The Java Device SDK is a fully-featured library that enables Java-based devices to connect to Azure IoT Hub and interact with the cloud. It supports various protocols, including MQTT, AMQP, HTTP, and more, and provides APIs for tasks such as sending telemetry data, receiving commands, and authenticating with the IoT Hub. It also includes support for advanced features such as device twins and direct method invocations.
import com.microsoft.azure.sdk.iot.device.DeviceClient;
import com.microsoft.azure.sdk.iot.device.IotHubClientProtocol;
import com.microsoft.azure.sdk.iot.device.Message;
public class Main
{
    public static void main(String[] args) throws Exception
    {
        // Retrieve the connection string for the device from an environment variable
        String conn_str = System.getenv("IOTHUB_DEVICE_CONNECTION_STRING");
        // Create an IoT Hub client
        IotHubClientProtocol protocol = IotHubClientProtocol.AMQPS;
        DeviceClient client = new DeviceClient(conn_str, protocol);
        client.open();
        // Send a message to IoT Hub
        String message = "Hello!";
        Message iot_hub_message = new Message(message);
        client.sendEventAsync(iot_hub_message);
        System.out.println("Sent message: " + message);
        // Disconnect the client
        client.close();
    }
}

This code includes the necessary namespaces from the Azure IoT Hub Java Device SDK and uses them to create an IoT Hub client. It then uses the client to connect to IoT Hub, send a message to IoT Hub, and disconnect the client when it’s finished.

  • JavaScript: The JavaScript Device SDK is a fully-featured library that enables JavaScript-based devices to connect to Azure IoT Hub and interact with the cloud. It supports various protocols, including MQTT, AMQP, HTTP, and more, and provides APIs for tasks such as sending telemetry data, receiving commands, and authenticating with the IoT Hub. It also includes support for advanced features such as device twins and direct method invocations.
const { Client } = require('azure-iot-device');
// Retrieve the connection string for the device from an environment variable
const conn_str = process.env.IOTHUB_DEVICE_CONNECTION_STRING;
// Create an IoT Hub client
const client = Client.fromConnectionString(conn_str, Client.IotHubTransport);
// Connect the client
client.open((err) => {
    if (err) {
        console.error('Error opening the client: ' + err.message);
    } else {
        // Send a message to IoT Hub
        const message = new Message('Hello, Azure IoT Hub!');
        client.sendEvent(message, (send_err) => {
            if (send_err) {
                console.error('Error sending the message: ' + send_err.message);
            } else {
                console.log('Sent message: ' + message.getData());
            }
        });
    }
});

This code imports the necessary modules from the Azure IoT Hub JavaScript Device SDK, creating an IoT Hub client. It then connects to IoT Hub and sends a message.

  • Python: The Python Device SDK is a fully-featured library that enables Python-based devices to connect to Azure IoT Hub and interact with the cloud. It supports various protocols, including MQTT, AMQP, HTTP, and more, and provides APIs for tasks such as sending telemetry data, receiving commands, and authenticating with the IoT Hub. It also includes support for advanced features such as device twins and direct method invocations.
import os
from azure.iot.edge.module_client import ModuleClient
# Create a module client
client = ModuleClient.create_from_edge_environment()
# Connect the module client to IoT Hub
client.connect()
# Send a message to IoT Hub
message = "Hello from IoT Edge!"
client.send_message_to_output("output1", message)
print("Sent message: {}".format(message))
# Disconnect the module client
client.disconnect()

Device SDKs for specific programming languages and platforms

Azure RTOS

Azure RTOS (Real-Time Operating System) is a suite of RTOS kernels and middleware components optimized for use with small, resource-constrained devices running on Arm Cortex-M microcontrollers. It provides developers with a robust and scalable platform for building IoT solutions that require real-time performance, low power consumption, and a small footprint.

The Azure RTOS middleware is a set of software components that provide additional functionality and support for developing IoT solutions. These components include:

  • Networking libraries and protocols such as TCP/IP, HTTP, HTTPS, and MQTT, which allow devices to connect to the cloud and other devices over the network.
  • Security libraries and protocols such as SSL/TLS, X.509 certificates, and secure boot, which allow devices to authenticate and encrypt communication with the cloud and other devices.
  • A file system library that allows devices to store and retrieve data on external storage devices such as SD cards.
  • The ThreadX RTOS kernel, which provides a lightweight, scalable, and reliable platform for building real-time applications on Arm Cortex-M microcontrollers.

Azure RTOS is available for a variety of Arm Cortex-M microcontrollers, including those from STMicro.

FreeRTOS

FreeRTOS is an open-source real-time operating system (RTOS) for building small, resource-constrained devices for the Internet of Things (IoT). It is designed to be lightweight, scalable, and reliable, offering a range of features and services that are beneficial for constructing IoT solutions, including:

  • Task scheduling and management
  • Memory management
  • Timers and software timers
  • Inter-task communication and synchronization
  • Networking libraries and protocols, such as TCP/IP, HTTP, and MQTT
  • Security libraries and protocols, such as SSL/TLS, X.509 certificates, and secure boot
  • File system libraries
  • Device drivers and middleware components for various hardware peripherals

FreeRTOS is available for a wide range of microcontrollers and platforms, including those from Atmel, NXP, and STMicroelectronics. It has a large community of developers and users, and is regularly updated and improved.

Azure IoT provides libraries and tools to connect FreeRTOS-based devices to its services, such as the IoT Hub, IoT Central, and IoT Edge. This enables developers to create IoT solutions that benefit from the scalability, security, and reliability of the Azure cloud.

Bare Metal (Azure SDK for Embedded C)

The Azure SDK for Embedded C is a software development kit (SDK) that enables developers to build IoT solutions using the C programming language and Microsoft Azure cloud services. It provides libraries, tools, and documentation to easily connect C-based devices to Azure IoT services such as IoT Hub, IoT Central, and IoT Edge, and construct IoT solutions that benefit from the scalability, security, and reliability of the Azure cloud.

The SDK supports a variety of microcontrollers and platforms from manufacturers like Atmel, NXP, and STMicroelectronics. It offers support for protocols and features like MQTT, AMQP, HTTP, device twins, and direct method invocations, and provides APIs for tasks like sending telemetry data, receiving commands, and authenticating with Azure IoT services.

The Azure SDK for Embedded C is open source and constantly updated and improved by a community of developers and users. It is lightweight, portable, and easy to use, and can be integrated into various development environments and toolchains.

Connecting devices to Azure IoT Hub using the device SDKs

To connect a device to Azure IoT Hub using a device SDK, you need to:

  1. Install the device SDK on your device or development machine. The device SDK provides the necessary libraries and tools to build IoT solutions that can connect to Azure IoT Hub.
  2. Register your device with Azure IoT Hub. This step involves creating a device identity in IoT Hub and obtaining a connection string that uniquely identifies your device. The connection string includes the device’s ID, hub name, and authentication key, and is used to authenticate the device with IoT Hub when it connects.
  3. Use the device SDK to create an IoT Hub client on your device. The IoT Hub client is responsible for establishing and maintaining a connection with IoT Hub, as well as sending and receiving messages.
  4. Use the IoT Hub client to connect to IoT Hub. This typically involves providing the client with the connection string and specifying the protocol to use (such as MQTT or AMQP). The client will then establish a connection with IoT Hub and authenticate using the information in the connection string.
  5. Use the IoT Hub client to send messages to IoT Hub and receive messages from IoT Hub. The device SDK provides APIs for tasks such as sending telemetry data, receiving commands, and interacting with device twins and direct methods.

This article provided a basic overview of the device SDKs for the languages and platforms supported for Azure IoT Hub. The example code was meant to give a glimpse of how it works, but not of real world workloads. In most cases, you’ll use the local device libraries to connect to sensors and collect data to send to IoT Hub. This usually requires developing an asynchronous listener to capture the telemetry data and send it in a message to IoT Hub. This functionality is beyond the scope of this article, but I am working on a newer set of Agriculture code for one of my devices. I will post it here soon, which will provide a more complete solution to device development on a Raspberry Pi.

Azure IoT Hub Service and Management SDKs

The Azure IoT Hub Service SDKs are a set of software development kits (SDKs) that enable developers to build applications that interact with Azure IoT Hub. They provide APIs and libraries in various programming languages and platforms, allowing developers to create, manage, and interact with devices and other resources in IoT Hub. The SDKs are available for .NET, Java, Node.js, Python, and Go, and offer functions such as sending and receiving messages, creating and updating devices, managing device twins and direct methods, and monitoring device health and status.

Overview of the different service SDKs available

The Azure IoT Hub Service SDKs are a set of software development kits (SDKs) that enable developers to build applications that interact with Azure IoT Hub. They provide APIs and libraries in various programming languages and platforms, allowing developers to create, manage, and interact with devices and other resources in IoT Hub. From the choices, it’s clear that these SDKs are designed to supplement or amplify the capabilities of IoT Hub.

These are just a few of the possible uses for the Service SDK:

  • Sending and receiving messages to and from devices
  • Creating, updating, and deleting devices in IoT Hub
  • Managing device twins and direct methods
  • Monitoring the health and status of devices and other resources in IoT Hub

A couple of specific use cases I can imagine are:

  • An integration with an application or an enterprise system that is not available through the standard IoT Hub integration channels.
  • Updating device digital twins is a good use of the SDK, especially for automated processes
  • Device Registry is particularly useful when dealing with a large number of devices. Automating the process is highly recommended. I will go into more detail about this in a future blog post.

The Azure IoT Hub Service SDKs are available for a variety of programming languages and platforms, such as:

  • .NET: The Azure IoT Hub .NET Service SDK
  • Java: The Azure IoT Hub Java Service SDK
  • Node.js: The Azure IoT Hub Node.js Service SDK
  • Python: The Azure IoT Hub Python Service SDK
  • Go: The Azure IoT Hub Go Service SDK

Service SDKs for specific programming languages and platforms

  • .NET: The Azure IoT Hub .NET Service SDK

This is likely the library that most traditional Microsoft developers will gravitate toward. It’s definitely a well-developed, highly supported library. This library has been migrated to

https://github.com/Azure/azure-iot-sdk-csharp

The Azure IoT SDK for C# is a software development kit that enables developers to build IoT solutions using C# and Microsoft Azure cloud services. It provides libraries, tools, and documentation to connect C#-based devices and applications to Azure IoT services, and includes support for a variety of microcontrollers and protocols. The SDK is open source and designed to be lightweight, portable, and easy to use.

I’m not including code sample here, because they tend to be a bit larger. However, I think it’s just as important to work with the service SDKs as the device SDKs. You’ll find they make your administrative work easier.

  • Java: The Azure IoT Hub Java Service SDK

The Java Service SDK offers many of the same functions as the .Net library. It includes the following samples to complete tasks related to device management, data processing and analytics, and integration and orchestration:

  • Device management: Create, update, and delete devices in IoT Hub, manage device twins, and use direct methods.
  • Data processing and analytics: Receive telemetry data from devices in IoT Hub, process and analyze the data, and trigger alerts or create reports.
  • Integration and orchestration: Integrate IoT Hub with other systems and services, such as enterprise applications or cloud platforms, and build complex workflows and processes that span multiple devices and services.

azure-iot-sdk-java/service/iot-service-samples at main · Azure/azure-iot-sdk-java

  • JavaScript: Azure SDK for JS

The IoT Hub Service SDK for Node.js doesn’t appear to have code for the IoT Hub service, yet. However, the JavaScript is still available. It has many of the same functionality as the other SDKs. One thing to note is that many of these are in TypeScript and it appears that there are more samples available here. So if you’re looking for a functionality that doesn’t exists in the other samples, you may want to search through these code examples:

azure-sdk-for-js/sdk/iothub/arm-iothub/samples-dev at main · Azure/azure-sdk-for-js

  • Python: The Azure IoT Hub Python Service SDK

The Azure IoT Hub Python library is here:

https://github.com/Azure/azure-iot-hub-python

It has many of the same functionality as the other library. I would say that the Client library is a bit more robust than the library for IoT Hub, but most of the functionality you’ll want exists. There are also plenty of examples to get you started. And one of the things I love about Python is how much you can accomplish with so little code:

# -------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for
# license information.
# --------------------------------------------------------------------------
import os
import msrest
from azure.iot.hub import DigitalTwinClient
iothub_connection_str = os.getenv("IOTHUB_CONNECTION_STRING")
device_id = os.getenv("IOTHUB_DEVICE_ID")
try:
    # Create DigitalTwinClient
    digital_twin_client = DigitalTwinClient.from_connection_string(iothub_connection_str)
    # Get digital twin and retrieve the modelId from it
    digital_twin = digital_twin_client.get_digital_twin(device_id)
    if digital_twin:
        print(digital_twin)
        print("Model Id: " + digital_twin["$metadata"]["$model"])
    else:
        print("No digital_twin found")
except msrest.exceptions.HttpOperationError as ex:
    print("HttpOperationError error {0}".format(ex.response.text))
except Exception as exc:
    print("Unexpected error {0}".format(exc))
except KeyboardInterrupt:
    print("{} stopped".format(__file__))
finally:
    print("{} finished".format(__file__))
  • Go: Azure SDK for Go

To my knowledge, there is no official Microsoft sponsored IoT Hub or Client library for GO. At the time of this writing, the services supported are:

  • Azure Blob Storage
  • Azure Functions
  • Azure Container Registry
  • Azure Container Instance
  • Azure Kubernetes Service
  • Azure Virtual Machines
  • Azure Key Vault
  • And Azure Cognitive Service

However, the success of Go as a Cloud Native language has prompted an Azure Go SDK. There is a version that appears to have been created here:

https://github.com/amenzhinsky/iothub

There is code for Device and Service here with very few samples, but it’s probably enough to get someone started:

package main
import (
	"context"
	"fmt"
	"log"
	"os"
	"github.com/amenzhinsky/iothub/iotservice"
)
func main() {
	c, err := iotservice.NewFromConnectionString(
		os.Getenv("IOTHUB_SERVICE_CONNECTION_STRING"),
	)
	if err != nil {
		log.Fatal(err)
	}
	// subscribe to device-to-cloud events
	log.Fatal(c.SubscribeEvents(context.Background(), func(msg *iotservice.Event) error {
		fmt.Printf("%q sends %q", msg.ConnectionDeviceID, msg.Payload)
		return nil
	}))
}

Using the management SDKs to communicate with devices and manage IoT Hub resources

Here, I want to discuss some unique ways to use the Azure IoT Hub Management SDKs to work with IoT Hub. The SDKs provide APIs and libraries that enable developers to manage and operate IoT Hub, such as creating and deleting instances, setting and querying properties and quotas, managing shared access policies, monitoring usage and diagnostics, and performing maintenance tasks like repairing or backing up IoT Hub. The SDKs are available for .NET, Python, and Node.js, and can be used on Windows, macOS, and Linux.

I don’t think the average IoT developer will be too concerned with these SDKs. However, if you work in an organization with multiple IoT hubs and millions of devices, these packages could be useful. Additionally, if you want to build products that use IoT Hub as a base service, then you should definitely look into these SDKs.

  • Azure SDK for .Net IoT Management

To get down to more detail, because I think it’s important to look at the detail for this particular SDK, here’s what’s delivered in the primary IoT Hub client for management. This piece of code is what I’m reviewing:

azure-sdk-for-net/IotHubClient.cs at main · Azure/azure-sdk-for-net

The code is a part of the Azure IoT Hub .NET Management SDK, which is a set of libraries and tools for building .NET applications that manage and operate Azure IoT Hub.

The file, IotHubClient.cs, contains the implementation of the IotHubClient class, which is a client object that provides a set of methods and properties for interacting with the Azure IoT Hub service. The IotHubClient class is part of the generated code for the Azure Management Libraries for .NET, which is a set of libraries that provide a consistent, easy-to-use, and high-level API for accessing Azure cloud services.

The IotHubClient class is used to perform various tasks related to managing and operating an IoT Hub instance, such as:

  • Creating and deleting IoT Hub instances
  • Setting and querying IoT Hub properties and quotas
  • Managing IoT Hub shared access policies
  • Monitoring IoT Hub usage and diagnostics
  • Performing maintenance tasks such as repairing or backing up IoT Hub

The IotHubClient class exposes a set of methods that correspond to the different operations that can be performed on an IoT Hub instance. For example, the IotHubClient.CreateOrUpdateAsync method is used to create or update an IoT Hub instance, and the IotHubClient.GetAsync method is used to retrieve the properties of an IoT Hub instance.

  • Azure SDK for Java – Azure Resource Manager IotHub client library for Java

I believe this is a better library than the .Net version. I provides more capabilities and samples.

azure-sdk-for-java/sdk/iothub/azure-resourcemanager-iothub at main · Azure/azure-sdk-for-java

The Java library has many of the same features as other libraries. It is based on the Azure Resource Manager Java library, so if you are already familiar with it, using the IoT Hub Management Library should be straightforward.

  • Azure iotHub client library for JavaScript

This is the same library as the Service library, which makes sense. It explains why the Service library seems larger than the other service libraries. This is also built on top of the Azure Resource Management.

  • Azure SDK for Python – Azure Management IoT Hbu

This library is a bit more like the .Net library. You enter with an IoT Hub Client and access you capabilities that way. It offers most of the same capabilities of the other libraries.

Overall, these libraries offer similar capabilities. Java and JavaScript have had more development, but the .Net and Python versions are still useful. Additionally, the API provides many management capabilities. If the SDKs don’t meet your needs, you can use Azure Resource Manager to manage IoT Hub.

Azure IoT Services SDKs

Azure IoT SDKs provide a range of tools and services for building secure, connected, and location-aware IoT applications and solutions. This section will cover the basics of these products.

Azure Maps, Azure Sphere, Azure Digital Twins, Azure Object Anchors, and Azure Remote Rendering are all included in the Azure IoT resources. Each of these offers unique features and capabilities to enable the development of IoT applications and solutions.

Overview of the Azure IoT services SDKs available

Azure Maps

Azure Maps is a cloud-based mapping and location platform that provides a range of tools and services for building location-based applications and solutions. It is part of the Azure IoT resources, as it can be used to build location-aware IoT applications and solutions that incorporate geospatial data and functionality.

Some of its key features and capabilities include:

  1. Map rendering: Azure Maps offers 2D and 3D maps, street maps, satellite imagery, and terrain maps, which can be rendered and displayed in web-based, mobile, and embedded formats.
  2. Geocoding and reverse geocoding: Convert addresses and location coordinates into geographic locations, and vice versa.
  3. Routing and navigation: Calculate routes and directions between two or more locations.
  4. Location services: Geofencing, location search, and real-time traffic data for building location-based applications.

Overall, Azure Maps is a powerful and flexible platform for building location-based applications and solutions, especially for IoT scenarios.

Azure maps provides four separate SDKs to help developers:

  • REST SDK
  • Web SDK (JavaScript)
  • Android SDK (Java or Kotlin)
  • iOS (Swift)

Azure Maps Documentation

Azure Sphere

Azure Sphere is a secure and connected microcontroller platform designed for building Internet of Things (IoT) applications and solutions. It is part of the Azure IoT resources, providing a range of tools and technologies to build and deploy secure, connected IoT devices.

Key features and capabilities of Azure Sphere include:

  1. Security: Azure Sphere is designed to provide a high level of security for IoT devices. It features a custom-built Linux operating system and a hardware-based security module that provides secure boot, trusted execution, and hardware-level isolation.
  2. Connectivity: Azure Sphere supports various connectivity options, including Wi-Fi and cellular, allowing devices to connect to the internet and communicate with other devices and systems.
  3. Development tools: Azure Sphere includes a software development kit (SDK), libraries and APIs, and integration with Azure IoT Hub and other Azure services, making it easier to build and deploy IoT applications and solutions.

Overall, Azure Sphere is an excellent platform for building and deploying secure, connected IoT devices, especially in scenarios where security and connectivity are paramount. Azure Sphere comes with a complete development kit. There are two basic SDKs, a Windows and a Linux version. The only supported programming language for Sphere appears to be C, which makes sense considering it’s designed for embedded development.

Digital Twins

Azure Digital Twins is different from the device digital twin discussed in this article. It is a cloud-based platform that enables you to construct digital twin models of physical spaces, systems, and assets. A digital twin is a digital representation of a physical object or environment that reflects its real-world characteristics and behavior.

Azure Digital Twins is part of the Azure IoT resources, providing tools and capabilities to build and deploy IoT applications and solutions that incorporate digital twin models. Key features and capabilities of Azure Digital Twins include:

  1. Data modeling and integration: Create a digital twin model by defining objects and properties that represent physical objects and features in the environment, as well as the relationships between them. Integrate data from external sources, such as sensors, devices, and systems, into the digital twin model.
  2. Data storage and querying: Store and query data within the digital twin model. Use various data types and formats to represent data in the digital twin, and use SQL-like queries to retrieve and analyze the data.
  3. Behaviors and logic: Define behaviors and logic in the digital twin model, using tools such as Azure Functions and Azure Stream Analytics. This enables the digital twin to respond to events and conditions in real-time, and perform actions or calculations based on the data in the digital twin.
  4. Visualization and exploration: Use the Azure Digital Twins portal, Azure Grafana, or the Azure Digital Twins REST API to view and interact with the digital twin.

Overall, Azure Digital Twins is a powerful and flexible platform for creating and managing digital twin models, and enabling a wide range of use cases and scenarios in the IoT space.

Azure Digital Twins comes with control plane APIs, data plane APIs, and SDKs for managing instances and its elements. The SDKs available are for .NET (C#), Java, JavaScript, Python, and Go.

I plan to cover Azure Digital Twins in much greater detail in the future. I’m working on a small book on the subject for Solution Architects. I think it’s one of the most interesting technologies available today. I don’t think we’ve scratched the surface of its potential.

Object Anchors

Azure Object Anchors is a service that allows you to create, manage, and interact with digital representations of physical objects in mixed reality applications. It is included in the Azure IoT resources as it can be used to build IoT applications and solutions with mixed reality features.

Some of the key features and capabilities of Azure Object Anchors include:

  1. Object recognition: Azure Object Anchors can recognize and track physical objects in real-time using machine learning algorithms and computer vision techniques. This enables you to create digital representations of physical objects that can be displayed in mixed reality applications.
  2. Digital twin creation: Azure Object Anchors allows you to create digital twin models of physical objects, which can reflect the real-world characteristics and behavior of the objects. You can define the properties and relationships of the objects in the digital twin model, and integrate data from external sources such as sensors and devices.
  3. Mixed reality integration: Azure Object Anchors can be integrated with mixed reality platforms and tools, such as HoloLens and Unity, allowing you to build mixed reality applications that incorporate the digital twin models created with Azure Object Anchors.

Azure Object Anchors is a powerful platform for building mixed reality applications and solutions that incorporate digital representations of physical objects, particularly in IoT scenarios. Development is built around the Unity game development platform and the Hololens device, though not necessarily restricted to them. The SDKs available are a Conversion SDK for .NET designed to turn existing 3D model assets into Unity Digital Twin objects, as well as Runtime SDKs for Unity and Hololens. If you plan to include Augmented Reality into your IoT solution, Azure Object Anchors is a great option.

Remote Rendering

Azure Remote Rendering is a cloud-based service that enables high-quality 3D graphics and visualization to be rendered in real-time, using the power and scale of the cloud. It is included in the Azure IoT resources because it can be used to build IoT applications and solutions that incorporate 3D graphics and visualization, and that require real-time rendering at scale.

Key features and capabilities of Azure Remote Rendering include:

  1. 3D graphics rendering: Azure Remote Rendering offers a range of tools and technologies for rendering high-quality 3D graphics, such as support for various 3D file formats, realistic lighting and materials, and advanced rendering techniques like ray tracing.
  2. Real-time rendering: Azure Remote Rendering is designed to support real-time rendering of 3D graphics, even at high resolutions and frame rates. This is useful for applications and solutions that need to display dynamic, interactive 3D graphics in real-time.
  3. Cloud-based rendering: Azure Remote Rendering leverages the power and scale of the cloud to enable high-performance rendering of 3D graphics. This is beneficial for applications and solutions that need to support large numbers of users or devices, or that need to process large volumes of data.
  4. Integration with other Azure services: Azure Remote Rendering can be integrated with other Azure services, such as Azure IoT Hub and Azure Stream Analytics, which allows for the building of IoT applications and solutions that incorporate 3D graphics and visualization.

Overall, Azure Remote Rendering is an effective platform for building applications and solutions that incorporate 3D graphics and visualization, and that require real-time rendering at scale. It is particularly suitable for use in IoT scenarios where 3D graphics and visualization are important.

Developing for this service requires making use of the existing REST APIs. Those are:

  • Azure Mixed Reality Resource Management REST API
  • Remote Rendering REST API

There is an extensive Microsoft.Azure.RemoteRendering API library available for C#.

Spatial Anchors

Azure Spatial Anchors is a service that enables you to create and manage digital anchors in physical spaces. It can be used to build IoT applications and solutions that incorporate location-based functionality and support mixed reality scenarios. I think one of the best examples of this technology that many people are probably familiar with is the mobile game Pokemon Go. This massive multi-player platform allows gamers to track, capture, train, and battle fantasy creatures in a mixed-reality environment.

Its key features and capabilities include:

  1. Digital anchor creation: You can define the properties and relationships of the anchors in the digital model, and integrate data from external sources such as sensors and devices.
  2. Mixed reality integration: Azure Spatial Anchors can be integrated with mixed reality platforms and tools, such as HoloLens and Unity.
  3. Real-time anchor tracking: It supports real-time tracking of the digital anchors, allowing you to update the location and orientation of the anchors in the mixed reality application in real-time.
  4. Cloud-based anchor management: It provides a cloud-based platform for managing the digital anchors, which is useful for applications and solutions that need to support large numbers of users or devices, or handle large volumes of data.

Overall, Azure Spatial Anchors is a powerful platform for building mixed reality applications and solutions that incorporate location-based functionality. It is especially suitable for IoT scenarios where mixed reality and location-based functionality are important.

There are multiple Spatial Anchors SDKs:

  • SDK for Unity
  • SDK for iOS Objective-C
  • SDK for Android Java
  • SDK for Android NDK
  • SDK for HoloLens C++/WinRT
  • Azure CLI
  • Azure Powershell

There are multiple samples to work through to learn this resource. Again, this is another one of the family of resources that helps bring real world data to life in the digital world.

Time Series Insights

Azure Time Series Insights is a cloud-based platform that allows you to store, visualize, and analyze time series data in real-time. It is included in the Azure IoT resources because it can be used to build IoT applications and solutions that need to process, analyze, and visualize large volumes of time series data in real-time.

Some of its key features and capabilities include:

  1. Time series data storage: Azure Time Series Insights provides a scalable and highly available data store for storing time series data. It can handle large volumes of data and supports a variety of data types and formats.
  2. Real-time data visualization: Azure Time Series Insights offers a range of visualization tools and interfaces that allow you to view and analyze time series data in real-time. You can use the Azure Time Series Insights portal, Azure Grafana, or the Azure Time Series Insights REST API to visualize and explore the data.
  3. Query and analysis: Azure Time Series Insights provides a query language and a range of analytical functions that allow you to perform complex queries and analysis on the time series data. This can be useful for applications and solutions that need advanced analysis.
  4. Integration with other Azure services: Azure Time Series Insights can be integrated with other Azure services, such as Azure IoT Hub and Azure Stream Analytics, to build IoT applications and solutions that incorporate time series data and analysis.

Overall, Azure Time Series Insights is a powerful platform for building IoT applications and solutions that need to process, analyze, and visualize large volumes of time series data in real-time. It is particularly well-suited for scenarios where real-time data analysis and visualization are important.

Azure Time Series Insights and Azure Data Explorer are both cloud-based platforms designed to store, process, and analyze large volumes of data in real-time. However, there are key differences between the two.

Data types and formats: Azure Time Series Insights is designed for time series data, which is data collected and recorded over time at regular intervals. Azure Data Explorer is more general-purpose, and can handle structured and unstructured data.

Query language: Azure Time Series Insights uses Kusto Query Language (KQL) for time series data. Azure Data Explorer also uses KQL, but it is more flexible and can query a wider variety of data types and formats.

Visualization: Both platforms offer visualization tools and interfaces for viewing and exploring the data. Azure Time Series Insights is more specialized for time series data, and provides visualization options specifically designed for it.

Integration with other Azure services: Both platforms can integrate with Azure IoT Hub and Azure Stream Analytics, but the specific integration options and capabilities may vary.

Overall, Azure Time Series Insights is specialized for time series data, while Azure Data Explorer is more general-purpose.

Other Services

There are additional services that don’t fit into the IoT resources category, but are often used with Azure IoT Hub to fulfill the backend data processing needs of an IoT platform. These include:

  • Azure Functions
  • Azure Event Hubs
  • Azure Synapse
  • Azure Storage
  • Azure Databricks

Choosing the Right Azure IoT Hub SDK

Azure IoT Hub SDKs are available for multiple languages, platforms, protocols, and devices, and should be chosen based on the specific needs of the IoT solution. Best practices for using the SDKs include using the most recent version, following the recommended architecture, using the async programming model, and using secure communication protocols. Message routing, device twins, and device identities and keys should also be used to optimize the performance and security of the solution.

Factors to consider when choosing an Azure IoT Hub SDK

Consider several factors when selecting the right Azure IoT Hub SDK:

  1. Language support: Choose an SDK that supports the programming language you are using for your IoT solution. Azure IoT Hub SDKs are available for C, C#, Java, Node.js, and Python.
  2. Platform support: Consider the platform you are using for your IoT solution. Azure IoT Hub SDKs are available for Windows, Linux, and MacOS.
  3. Protocol support: Choose an SDK that supports the protocol you are using for communication between your IoT devices and the cloud. Azure IoT Hub supports MQTT, AMQP, and HTTP.
  4. Device support: Consider the type of device you are using and choose an SDK that supports the necessary features and capabilities. For example, if you need secure communication, make sure the SDK you choose supports secure communication protocols.
  5. Ecosystem integration: If you are building an IoT solution that integrates with other Azure services or third-party tools, choose an SDK with the necessary integration capabilities.
  6. Ease of use: Consider the learning curve and documentation associated with the SDK. Choose an SDK that is easy to use and has clear documentation to help you get started quickly.
  7. Development environment: Consider the development environment you are using, and choose an SDK that is compatible with your environment.
  8. Performance: Consider the performance needs of your IoT solution and choose an SDK that can meet those needs.
  9. Cost: Consider the cost of the SDK and whether it fits within your budget.

Best practices for using Azure IoT Hub SDKs in different scenarios

Make sure to use the most recent version of the Azure IoT Hub SDKs to take advantage of new features and improvements. Follow the recommended architecture to ensure that your solution is scalable, reliable, and secure. Use the async programming model to use non-blocking code and optimize performance. Secure communication protocols, such as TLS, should be used to protect the data transmitted between devices and the cloud. Device-to-cloud messages should be used for critical data that needs to be stored and analyzed. Cloud-to-device messages can be used to control devices or send commands. Message routing can be used to filter and route messages based on the content of the message or the device that sent it, helping to scale the IoT solution and process messages more efficiently. Device twins allow for managing the state of devices and synchronizing their state with the cloud. Finally, device identities and keys should be used securely and protected from unauthorized access.

Conclusion and Future Directions

The potential of Azure IoT Hub SDKs to drive business value

Azure IoT Hub SDKs offer several potentials to drive business value:

  1. Improve efficiency and productivity: Automate processes, gather real-time data, and enable remote monitoring and control with IoT solutions built using Azure IoT Hub.
  2. Increase customer satisfaction: Better understand and meet customer needs, leading to increased satisfaction and loyalty.
  3. Enhance decision making: Collect and analyze real-time data from IoT devices to make more informed decisions and improve operations.
  4. Improve asset management: Track and monitor the performance and maintenance of assets, leading to improved asset utilization and reduced downtime.
  5. Generate new revenue streams: Offer new products and services, create new business models, and open up new revenue streams with IoT solutions.
  6. Reduce costs: Optimize resource usage, improve maintenance schedules, and automate processes with IoT solutions.
  7. Increase competitiveness: Give businesses a competitive edge by providing them with a way to differentiate themselves and offer unique value to their customers.

Edge computing involves processing data closer to the source of the data, reducing latency, improving performance, and reducing the amount of data sent to the cloud. 5G networks are enabling faster and more reliable IoT connectivity, enabling new use cases and applications. Artificial intelligence and machine learning are being integrated into IoT solutions, making them more intelligent and autonomous, with the ability to learn and adapt. Blockchain technology is being used to improve security, traceability, and trust. The IoT is being applied in healthcare for remote monitoring, diagnostics, and treatment (IoMT). It is also being used in industrial settings, improving efficiency, maintenance, and safety (IIoT). Smart cities are being created with the IoT, enabling improved public services, transportation, and environmental sustainability. Augmented reality and virtual reality are being integrated into IoT applications, creating immersive and interactive experiences.

Closing

Azure IoT Hub Device SDKs are software development kits that enable developers to create applications that run on IoT devices and connect them to Azure IoT Hub. These SDKs provide a set of APIs and libraries that allow devices to perform basic and complex IoT device-to-cloud communications and commands, and support a wide range of programming languages and platforms. Common use cases include sending telemetry data, receiving commands, connecting devices using various protocols, authenticating devices, handling messaging, managing device twins, and receiving notifications.

Azure IoT Edge is a fully managed service that enables developers to deploy cloud intelligence directly onto IoT devices. It consists of edge modules, an IoT Edge runtime, and Azure IoT Hub. This allows developers to create and deploy intelligent edge solutions that bring the power of the cloud to the edge of the network.

This article has presented some of the major SDKs related to IoT and some of the related Azure IoT resources. It brings these resources together to show the array of options available to the Azure IoT developer. If a favorite was left out, please leave a comment. It could be a subject of a future post.

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Personal Post on My Continuing Journey with IoT Edge Computing

shawn deggans personal blog post

I made the biggest career change of my life recently.

I left the consulting firm I had been employed with for the past four years. It wasn’t an easy decision. I gave up a technical leadership role, I left a lot of people who I loved working with, and I gave up the security of a regular paycheck.

What was behind my decision?

Focus.

Focus was my primary reason for leaving. Two years ago I began a journey to learn and apply everything I would need to know to be a competent IoT Edge Architect. I began that journey with the hopes that my career would be heavily focused on helping organizations solve interesting problems using modern data analytics, IoT systems, and containerized machine learning on the edge.

That never really happened. I had the occasional opportunity to work with machine learning, Kubernetes, and some advanced analytics, but the bulk of interesting data work was done by other people while I focused on platform development.

I didn’t allow those IoT skills to go static though, because I did the occasional side work with partners focused on IoT, but my day job always came first. It reached the point that the day job wouldn’t allow time for anything other than the day job. I didn’t want those IoT skills to go stale, so I had to make the difficult decision. Do I stay where I am and try to be happy or do I pursue the career working with the technology I know actually moves the needle for organizations?

So here I am, completely independent. Ready to focus.

I got pretty good with infrastructure deployment and DevOps, so that’s the majority of the work the firm put me on. And they put me on a lot of it. Systems architecture and design became my everything for a while. Let me be clear that there’s absolutely nothing wrong with systems design work. It can be incredibly rewarding. It’s actually a big part of IoT Edge development. It just became clear to me that I was never going to have the opportunity to help build anything interesting on the infrastructure I was creating.

During my time with the firm, I went from a senior application developer to a cloud systems architect. It took me four years to make it happen, but it was definitely a necessary milestone for the next stage of my journey.

What is the next stage of my journey?

I’m returning my focus to IoT Edge computing.

What I want to do for my career is implement edge and IoT systems from multiple types of systems to multiple cloud and server solutions using modern communication systems, analytics, and security. I mean, for something that’s supposed to be a focus, that’s pretty broad. However, it all fits together nicely for certain, specialized use cases.

I have a lot of experience and a few certifications in Azure, so I have no plans to walk away from Azure any time soon, but I’ve had the opportunity to work with a few other products like InfluxDB, Milesight, Chirpstack, Eclipse Mosquitto, and I don’t want to limit myself to one cloud or one set of solutions. Much of my focus moving forward will be more on the IoT Edge System Design. The theory, the best practices, the understanding of why certain technologies are used over other technologies.

Basically, in order to improve my IoT Edge expertise, I need to say no to a lot of other types of work. Am I capable of helping an organization migrate all their SQL data systems from on-premises to the cloud? Yes, it’s completely within my wheelhouse. Could I build a distributed e-commerce solution using .Net applications in Docker containers for high availability using the best security Azure has to offer? Yes, also within my skillset. Will I take on any of that work? No. I won’t. And that’s the point I’ve reached in my career. I’m going to be very selective about the type of work I take on, and the type of clients who I work with.

That’s my goal. Focus. Be one of the best.

What can you expect from this blog?

It won’t change too much, but I will create more content. I do like doing technical deep dives, so you will see more of these. I also like to explore use cases. You’ll see more of that, especially in the Controlled Environment Agriculture industry. I believe this is an area that will need more help as our environment undergoes more changes in the coming years. If we want food in the future, we will need to know how to control our environments in a way that is economical and sustainable.

I will also write more about IoT architectures for multiple clouds and scenarios. sensors, endpoints, and power management. I want to look at how Claude Shannon’s Information Theory shapes the way we communicate between the cloud and devices. I will probably write far more about networking than you want to read, but it’s an area that I used to hate that I’ve now grown unreasonably in love with. Obviously, lots of discussion around Edge Computing, protocols, Fog computing, Augmented Reality, Machine Learning, MLOPs, DevOps, EdgeOps, and of course security.

That’s the technical side, but I also want to start working more with the community. DFW, Texas has quite the community of IoT organizations and engineers. I hope to connect with these individuals and organizations and capture some of ways I can help them, or they help me and record those outcomes here.

What about money?

Ah, yes. I do actually have bills to pay. So I will need to make money. Luckily, I’m in the financial position that I don’t necessarily need to earn a fulltime income immediately. I’m also fortunate enough to have work from one of my business partners that fits directly within my goals. We’re building an InfluxDB for some time series data and using Pandas to build some forecasting. I’ve also had my own LLC for a few years now, so the business side of things won’t be a hurdle.

But I do have additional plans. Next month I’m presenting a few ways that we can partner together, if you are interesting in working on an IoT Edge project. I’m opening my calendar to a small group of people for bookings through my company called, “Green Nanny LLC.” That’s a name you’ll see me mention more in the future as I build it out to its full intention.

Here are just a few of the services I’ll offer:

  • Assessments – are you ready for IoT Edge? Do you know what it is and how it applies to modern, distributed solutions? What does it look like, operationally? This helps you make better decisions and pick a direction for next steps.
  • Architecture design sessions – let’s think through the art of the possible using machine learning, IoT, and modern data analytics. What does your future system need to look like to support edge workloads?
  • Briefings – if we were to implement a project, what would it take? Do you need a proof-of-value to start or are you ready for a well-architected IoT solution?
  • Implementations- how can I help your team implement an IoT Edge solution?
  • POV – let’s see if we can create a proof of value in a very short period of time?
  • Team training – how can I help your team learn how to implement IoT Edge?
  • Well-architected review and recommendations- can we improve your existing solution? Do you need help with reliability, cost optimization, security, operational excellence, or performance?
  • Managed Service – what are some options for managing your IoT products using a managed services provider? I don’t provide this service myself, but I still have many connections who can help make this a reality.
  • Retainer – Do you just need someone with a lot of IoT Edge knowledge to bounce questions off of? Not sure where you want to start on your own IoT Edge journey, but would like a guide?

I’m excited for the future

I think and feel that I made the right decision for this move at the right time. My skills as an Azure Architect have put me in an excellent position to transition into something more specialized. The consulting work I did with the firm clarified the type of work I like to do, and the type of work that I’m good at. I see a lot of promise in my area of focus and a lot of financial opportunity for myself, my partners, and the clients who I will serve.

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Cloud, Fog, and Ambient Computing

This won’t be a long post. I will likely start posting a few more shorter posts that are a bit more personal. This one is about taking that next step. The one that I hope helps with my career growth. Not long ago I passed two major certifications that are within my niche goals. Yes, I have a goal of focusing on a particular career niche and building around it. The niche is AI Edge Engineering. That’s a discipline that combines machine learning, IoT, and DevOps. One of the reasons I’ve tackled this particular area of the solution development world has to do with information flow.

You see, I’ve become convinced that even though we are living in the information age, we still think like people living and working in the industrial age. Our brains are stilled wired for thinking of business as creating a thing of value and selling that thing of value. Though that is still a point of concern, it’s not the way we express modern busines. Modern business information systems isn’t about static data as information. Modern systems should be about information flow, communication flow, and bringing actionable events closer to the source. For that paradigm, we need a new type of information system.

We need more than cloud, and more than fog computing. We need near-realtime ambiant systems. I think we are still a few years from seeing that come into being, but we are definitely on the road, and we’re making great drive time.

What will this new approach to computing do for us? It brings the power of AI to devices that are closer to you. That device could be a wearable, it could be a car, it could be an artificial organ. IoT Edge Engineering is just one discipline on the way toward that new way of working with devices.