Posted on Leave a comment

Matter: a Unifying Standard for Home Devices or Hype?

In this short article I want to discuss the release of the recent standard, Matter, formally CHIP (Project Connected Home over IP). Will we have a consumer device communication standard that’s ideal for the smart home or are we looking at yet another standard that will further complicate IoT for the our smart appliances?

Matter was parented by the Connectivity Standards Alliance and release to the world this month. Version 1 is available now for review by manufacturers, developers, and anyone curious about this new means of device communications.

https://csa-iot.org/developer-resource/specifications-download-request/

The goal of this standard is to make interoperability among home devices easier. And because the standard has backing from Samsung, Google, Apple, and a large number of other organizations with interest in the home device market, there appears to be a fair chance the standard will stick.

The promise seems to be less hassle for the user. When you purchase a product that adheres to the Matter standard, you should be able to easily connect and integrate with other devices in your home using that standard.

But what about security? Even at this moment, I can connect to my neighbor’s TV from my iPad if I wanted to. How do we make sure that these devices are safe, even for those who aren’t the most technically skilled?

According to their security white paper, Matter follows five security tenets:

  • Comprehensive – they use a layered approach
  • Strong – they use well-tested, strong cryptography
  • Easy – security should improve ease of use, or decrease it
  • Resilient – protect, detect, recover
  • Agile – able to respond quickly to threats

How easily will this be adopted by the developers? I did take a look at the source code and libraries. It’s written in C++, so it should be fast. And there appear to be libraries for most of your common development boards, chipsets, and MCUs. I think that’s good sign. You can see the repo here:

https://github.com/project-chip/connectedhomeip

So is it a viable standard or is it hype?

Matter was a long time in the making. And it’s creation required manufacturing rivals like Google, Apple, and Samsung to sit across from each other at a table and agree on one direction to take device communication. The standard was just released this month and the official launch is on November 3rd. There are already a large group of early adopters prepared to launch products. Some of your appliances under the Christmas Tree this year might be Matter compatible.

I think it’s safe to say that Matter isn’t just hype. It looks to be a great way for devices to communicate easily on a home network using common IP methods and options like WiFi and a new protocol called Thread. Maybe this time next year we’ll see more IoT innovation in our homes because of better device interoperability.

Posted on Leave a comment

I Read and Summarized the 3rd Edition of IoT Signals So You Don’t Have to

A report built based on survey data taken by the company named, ‘hypothesis’. This is a combined effort between Microsoft and Hypothesis to capture the most current state of IoT from the view of business leaders in certain sectors; manufacturing, energy, mobility, and smart places.

The survey was multi-national and the report includes data captured from in-depth interviews.

Things to know about IoT in 2021

The following are high-level conclusions drawn from the interviews and survey data:

  • IoT continues to drive organizations toward a more productive future
  • COVID-19 has accelerated IoT strategies and fueled business growth
  • AI, Edge Computing, and Digital Twins are essential to advance IoT strategies
  • Although IoT projects are maturing, technological complexity persists
  • Organizations are keeping a close eye on data security

Who they talked to

  • Business decision makers, developers, and IT decision makers who work at enterprise-size companies with greater than 1k employees
    • 71% were familiar with IoT
    • 95% of those familiar, have influence and decision making power on IoT strategies
      • 10% of those familiar were not in adoption of IoT
      • 90% of those familiar were in adoption of IoT

Overall Research Learnings

Big Picture

This year, IoT continues to be widely adopted. 90% of organizations surveyed are IoT adopters. IoT projects can be categorized into four stages:

  • Learn
  • Trial / Proof of Concept
  • Purchase
  • Use

Of the 90%, at least 82% have a project that reached the “use” stage.

The state of most projects overall:

  • 29% in Learn
  • 25% in Trial / POC
  • 22% in Purchase
  • 25% in Use

IoT adoption and value globally (Australia, Italy, and the US lead as primary adopters)

  • 90% of the surveyed leaders in countries fitting criteria are adopters
  • 25% have projects in use
  • Average time to reach “use” is 12 months
  • 66% plan to use IoT more in the next 2 years

IoT Adoption and Value by Industry

  • Manufacturing
    • 91% of the surveyed leaders in countries fitting criteria are adopters
    • 26% have projects in use
    • Average time to reach “use” is 13 months
    • 68% plan to use IoT more in the next 2 years
  • Energy
    • 85% of the surveyed leaders in countries fitting criteria are adopters
    • 22% have projects in use
    • Average time to reach “use” is 15 months
    • 61% plan to use IoT more in the next 2 years
  • Mobility
    • 91% of the surveyed leaders in countries fitting criteria are adopters
    • 23% have projects in use
    • Average time to reach “use” is 14 months
    • 61% plan to use IoT more in the next 2 years
  • Smart Places
    • 94% of the surveyed leaders in countries fitting criteria are adopters
    • 24% have projects in use
    • Average time to reach “use” is 13 months
    • 69% plan to use IoT more in the next 2 years

Why Adopt IoT

Overall Top 5 reasons:

  • Quality Assurance : 43%
  • Cloud Security: 42%
  • Device and Asset Security: 40%
  • Operations Optimization: 40%
  • Employee Productivity: 35%

The report includes evidence that those companies who employ IoT to improve products and service see a higher increase in overall ROI.

Manufacturing Top 5:

  • Quality and compliance
  • Industrial automation
  • Production flow monitoring
  • Production planning and scheduling
  • Supply chain and logistics

Energy (Power and Utilities) Top 5:

  • Smart grid automation
  • Grid asset maintenance
  • Remote infrastructure maintenance
  • Smart metering
  • Workplace safety

Energy (Oil and Gas)

  • Workplace safety
  • Employee safety
  • Remote infrastructure maintenance
  • Emissions monitoring and reduction
  • Asset and predictive maintenance

Mobility

  • Inventory tracking and warehousing
  • Manufacturing operation efficiency
  • Surveillance and safety
  • Remote commands
  • Fleet management

Smart places

  • Productivity enablement and workplace analysis
  • Building safety
  • Predictive maintenance
  • Regulations and compliance management
  • Space management and optimization

Benefits of IoT

Top 5 benefits organizations are reaping from IoT

  • Increases in efficiency of operations
  • Improved safety conditions
  • Allows employees to be more productive
  • Allows for better optimization of tools and equipment
  • Reduces chance for human error

Common measures of success in IoT

  • Quality
  • Security
  • Production Efficiency
  • Reliability
  • Cost efficiency

Less common measures of success

  • More informed decision making
  • Direct impact on increased revenue
  • Sustainability
  • % of project deployed using IoT

Challenges of IoT

Top 5

  • Still implementing our current solution
  • Security risk isn’t worth it
  • Want to work out existing and future challenges before adding or using IoT more
  • Too complex to implement because of technology demands
  • Too complex to implement because of business transformation needed

Top 5 reasons POCs fail

  • High cost of scaling
  • Lack of necessary technology
  • Pilots demonstrate unclear business value or ROI
  • Too many platforms to test
  • Pilot takes too long to deploy

Top 5 security concerns

  • Ensuring data privacy
  • Ensuring network-level security
  • Security endpoints for each IoT device
  • Tracking and managing each IoT device
  • Making sure all existing software is updated

The report includes a section on best practices, and notes that despite security being a big concern, very few are implementing these best practices:

Top 5 best practices

  • Assume breaches at every level of IoT project
  • Analyze dataflows for anomalies and breaches
  • Define trust boundaries between components
  • Implement least privileged access
  • Monitoring 3rd party dependencies for common vulnerabilities

IoT Implementation Strategy

Most of the companies surveyed prefer to work with outsourced resources to implement their IoT strategy. They do prefer bespoke solutions.

Those who outsource see these positive benefits:

  • Increases efficiency of operations
  • Improves safety conditions
  • Reduces changes for human error

Those who do not outsource tend to hit these challenges:

  • Too complex to implement because of business transformation needed
  • Too long to implement
  • No buy-in from senior leadership

Sustainability

Companies with more near-term zero carbon footprint goals are more motivated to implement IoT to help than those who have a longer range target.

Impact of COVID-19

When asked if C-19 was an influence in investing:

  • 44% more investment
  • 41% stay the same
  • 7% less
  • 4% too early to tell

Emerging Technologies

Those who are adopting IoT are more likely to adopt other innovative technology associated with IoT:

  • Digital Twins
  • Edge Computing
  • AI at the Edge

This is collectively known as AI Edge

AI Implementation

84% Have a strategy:

  • 31% are implementing
  • 26% developed, but not implemented
  • 26% developing

16% do not have a strategy

  • 11% want to develop
  • 5% have no plans

79% of respondents claim that AI is a core or secondary component of their overall IoT strategy

Top 5 reasons for AI in IoT Adoption:

  • Predictive maintenance
  • Prescriptive maintenance
  • User experience
  • Visual image recognition and interpretation
  • Natural language recognition and processing

Top 5 Barriers to using AI within IoT

  • Too complex to scale
  • Lack of infrastructure
  • Lack of technical knowledge
  • Implementing AI would be too complex
  • Lack of trained personnel

AI Adoption and Value by Industry

Total:

  • 84% – have an AI strategy
  • 31% – Implementing
  • 26% – developed
  • 26% – developing
  • 79% – use AI in IoT solution

Manufacturing:

  • 84% – have an AI strategy
  • 31% – Implementing
  • 23% – developed
  • 30% – developing
  • 83% – use AI in IoT solution

Energy

  • 90% – have an AI strategy
  • 26% – Implementing
  • 28% – developed
  • 36% – developing
  • 89% – use AI in IoT solution

Mobility

  • 81% – have an AI strategy
  • 36% – Implementing
  • 25% – developed
  • 20% – developing
  • 85% – use AI in IoT solution

Smart Places

  • 88% – have an AI strategy
  • 39% – Implementing
  • 28% – developed
  • 21% – developing
  • 75% – use AI in IoT solution

Edge Computing

Edge Computing Implementation Progress

79% have a strategy:

  • 29% implementing
  • 26% developed but not implemented
  • 24% developing

21% do not have a strategy:

  • 15% want to develop
  • 6% have not plans

81% Edge Computing as a core or secondary component:

  • 42% Core
  • 39% Secondary
  • 18% Considering, not yet adopted
  • 1% not considering

Top 5 Reasons to adopt Edge Computing

  • Cloud security
  • Device and asset security
  • Quality assurance
  • Securing the physical environment
  • Operations Optimization

Top 5 barriers to adoption

  • Lack of architectural guidance
  • Lack of trained personnel
  • Lack of infrastructure
  • Difficulty managing security
  • Lack of clarity on edge hardware choices

Edge Computing Adoption and Value by Industry

Total:

  • 79% – Have Edge Computing strategy
  • 29% – implementing
  • 26% – developed
  • 24% – developing
  • 81% – Use Edge Computing in IoT Solutions

Manufacturing:

  • 83% – Have Edge Computing strategy
  • 37% – implementing
  • 28% – developed
  • 18% – developing
  • 77% – Use Edge Computing in IoT Solutions

Energy:

  • 85% – Have Edge Computing strategy
  • 38% – implementing
  • 25% – developed
  • 23% – developing
  • 85% – Use Edge Computing in IoT Solutions

Mobility:

  • 85% – Have Edge Computing strategy
  • 18% – implementing
  • 30% – developed
  • 37% – developing
  • 88% – Use Edge Computing in IoT Solutions

Smart Places:

  • 85% – Have Edge Computing strategy
  • 29% – implementing
  • 26% – developed
  • 30% – developing
  • 83% – Use Edge Computing in IoT Solutions

Digital Twins

77% have a strategy:

  • 24% implementing
  • 29% developed, but not implemented
  • 24% developing

23% do not have a strategy:

  • 14% want to develop
  • 9% have no plans

81% Use and Impact of DT on IoT Solutions

  • 41% feature as core component
  • 40% feature as secondary component
  • 18% considering, but not yet adopted
  • 1% not considering

Top 5 benefits of using DT within IoT:

  • Improve overall quality
  • Increase revenue
  • Reduce operations costs
  • Enhance warranty cost and services
  • Reduce time to market for a new product

Top 5 barriers:

  • Challenges managing the value of data collected
  • Complexity of systems needed to handle digital twins
  • Integration challenges
  • Lack of trained personnel
  • Challenges modeling the environment

Digital Twins Adoption and Value by Industry

Total:

  • 77% – have a DT strategy
  • 24% – implementing
  • 29% – developed
  • 24% – developing
  • 81% – use DT in IoT solution

Manufacturing:

  • 79% – have a DT strategy
  • 31% – implementing
  • 25% – developed
  • 23% – developing
  • 86% – use DT in IoT solution

Energy:

  • 79% – have a DT strategy
  • 26% – implementing
  • 29% – developed
  • 24% – developing
  • 82% – use DT in IoT solution

Mobility:

  • 76% – have a DT strategy
  • 15% – implementing
  • 39% – developed
  • 23% – developing
  • 77% – use DT in IoT solution

Smart Places

  • 82% – have a DT strategy
  • 27% – implementing
  • 35% – developed
  • 22% – developing
  • 85% – use DT in IoT solution

By Industry

Smart Places

94% IoT Adopters

  • 27% – learn
  • 25% – POC
  • 25% – Purchase
  • 24% – Use

Top Benefits:

  • Increase the efficiency of operations
  • Improves safety conditions
  • Allows for better optimization of tools and equipment

Top 5 reasons for adoption:

  • Productivity enablement
  • Building safety
  • Predictive maintenance
  • Space management and optimization
  • Regulation and compliance management

Top 5 challenges

  • Still implementing current solution
  • Security risk isn’t worth it
  • Too complex to implement because of the need for business transformation
  • Want to work out exiting and future challenges before adding or using
  • Too complex to implement because of technology demands

Manufacturing

91% IoT Adopters

  • 27% – Learn
  • 26% – POC
  • 21% – Purchase
  • 26% – Use

Top Benefits

  • Increases the efficiency of operations
  • Increases production capacity
  • Reduces chance for human error

Top 5 Reasons for Adoption:

  • Quality and compliance
  • Industrial automation
  • Production flow monitoring
  • Production planning and scheduling
  • Supply chain and logistics

Top 5 Challenges to using IoT more

  • Still implementing current solution
  • Too complex to implement because of technology demands
  • Security risk isn’t worth it
  • Want to work out challenges before adding or using IoT more
  • Don’t have human resources to implement or manage

Mobility

91% IoT Adopters:

  • 30% – Learn
  • 26% – POC
  • 21% – Purchase
  • 23% – Use

Top benefits of IoT:

  • Increase efficiency of operations
  • Allows employees to be more productive
  • Improves safety conditions and increases production capacity

Top 5 Reasons for Adoption

  • Inventory tracking and warehousing
  • Manufacturing operations efficiency
  • Surveillance and safety
  • Remote commands
  • Fleet management

Top 5 challenges to using IoT More:

  • Want to work out challenges before adding or using IoT more
  • Too complex to implement because of technology demands
  • Still implementing our current solutions
  • Security risk isn’t worth it
  • Too complex to implement because of business transformation needed

Energy

80% IoT Adopters (Power and Utilities)

  • 28% – Learn
  • 26% – POC
  • 23% – Purchase
  • 23% – Use

Top Benefits

  • Increase the efficiency of operations
  • Increases production capacity
  • Allows employees to be more productive

Top 5 reasons for adoption:

  • Smart grid automation
  • Grid asset maintenance
  • Remote infrastructure maintenance
  • Smart metering
  • Workplace safety

Top Challenges:

  • Too complex because of technology demands
  • Security risk isn’t worth it
  • don’t have human resources to implement and manage

94% IoT Adopters (Oil & Gas)

  • 28% – Learn
  • 27% – POC
  • 24% – Purchase
  • 20% – Use

Top Benefits

  • Increase customer satisfaction
  • Improves business decision-making
  • Increases production capacity and the efficiency of operations

Top 5 reasons for adoption:

  • Workplace safety
  • Employee satisfaction
  • Remote infrastructure maintenance
  • Emissions monitoring and reduciton
  • Asset and predictive maintenance

Top challenges:

  • Lack of technical knowledge
  • Don’t know enough
  • Too complex to implement because of business transformation needed

Final Thoughts

Things worth noting:

  • IoT is not going away, in fact more money, time, and investment goes into it each year
  • Most organizations are looking to add AI, Edge Computing, and Digital Twins to their solutions
  • May organizations are outsourcing their IoT work and seeing more benefits because of this
  • Top challenges are around knowledge, skill, security, and implementation at scale

The original report

Posted on 1 Comment

Why IoT Central?

My first experience with IoT Central

Microsoft has multiple tools and services designed for IoT.

After Ignite this year it’s clear that Microsoft has dedicated a sizable investment of time and people to expanding its IoT reach. I’ve worked quite a bit with IoT Hub, Event Hub, and Azure Functions to capture telemetry data, so when I first heard about IoT Central I thought, why would I want to play with a toy version of all the bigger tools when there’s already so much on offer in Azure?

Well, I was wrong. IoT Central is far from a toy. It’s an amazing platform perfectly designed as an entry into IoT application development. I had an opportunity to work with IoT Central for our Catapult Modern Data Culture Summit and I came away with insights that I hope will show you the potential of this powerful platform.

It’s clear that no-code and low-code solutions are rising in popularity. The Microsoft Power Platform has risen to the status of the fourth cloud platform for Microsoft along with Azure, 365, and Dynamics. IoT Central is to IoT development as Power Platform is to application development. IoT Central features the same type of ease to entry and offers many of the same UI goodies developers come to expect from the Power Platform.

I’ll dive into the various pieces of IoT Central and discuss my experiences. My goal was to develop an adequate demonstration aimed at the small to midsize business interested in applying IoT to their operations. I wanted to focus on smaller organizations because I believe there’s a misconception that IoT is really only for large companies with huge budgets and a small army of IoT and data specialists. That’s not to say that IoT Central doesn’t require some technical knowledge, but the barrier to entry and the operational overhead is much smaller. If I could prove to small and midsize organization leaders that IoT Central could give them the same type of insights and observability that larger organizations achieve, but on a much smaller budget, I saw that as a great way to bring more IoT work to Catapult. It’s my opinion that almost any organization can use IoT and AI to strengthen operations, gain insights, and continuously implement total quality improvement. Could IoT Central help me prove this?

What is IoT Central?

Microsoft describes IoT Central as, “IoT Central is an IoT application platform that reduces the burden and cost of developing, managing, and maintaining enterprise-grade IoT solutions.” Unlike Microsoft’s other IoT tools, IoT Central is a web application. You log in through your IoT Central portal and create apps. These apps represent IoT solutions. By solutions, I mean a single app provides everything you need to achieve a robust IoT implementation. From the devices to the backend data, you can use a single IoT Central app to provide enterprise level products to market.

Users & Roles

IoT Central’s user management is built around a few specific roles. The documentation refers to these as personas. When planning out your implementation it’s helpful to keep in mind the type of people you’ll need to help you perform the various tasks required by IoT Central to be truly operational. You won’t need an army of specialists, but you will need a team of individuals willing to fulfill certain responsibilities.

The solution builder is primarily responsible for creating the app, configuring the rules and actions, setting up integrations with other services, and customizing the application for the rest of the various users.

The operator is your device manager. This person manages the devices attached to the application.

The administrator is responsible for adding users and assigning roles and permissions.

The device developer is responsible for writing the code that runs on the device or IoT Edge module connected to your app.

As a solution builder I found I could do everything I needed to build, deploy, and manage my IoT Central app. I could see this as a lone wolf development position at a very small company. In fact, if you’re considering an IoT solo-entrepreneur effort, IoT Central is probably a great platform to start with.

Creating the App

To get started, you need to log into the IoT Central portal. In the latest version I’m working in you are greeted with a landing page describing the service. There’s also a menu with three basic items:

  • Home
  • Build
  • My apps

Use build if you want to start an app. One thing I found most interesting were the number of templates. You can select certain solution templates based on the industries of Retail, Energy, Government, and Healthcare. Each of these presents patterns fitting for common IoT solutions. You also have the option of creating a custom app, which is where I started.

I had an idea to use a bakery vending machine as my example. My fictional company has four bakery vending machines scattered around Texas. One in Houston, Dallas, Austin, and Fort Worth. The idea was to use IoT Central is a management platform for my various vending machines. I wanted to see the following basic data:

  • Number of sales during the telemetry time period (every 15 minutes)
  • High and low temperature of the machine
  • If the machine had been serviced that day
  • Where the machine was located

One thing this demo taught me is that it’s a lot of work to prepare for a fascinating IoT demonstration. I not only needed to create simulation data and IoT device simulators for this demo, but I also needed to plan out business operations. They were actually much more complex than what I went with. I had intended to add trucks, schedule stops by fictional employees to refill the vending machines, oh lots of different things were in the works. However, time got the best of me and I had to settle for generating data for the machines and pretending some additional elements were in place.

One day in the near future I’ll spend more time creating a fictional IoT business, but what I created served my needs well enough for the demo.

Creating Devices

Unlike IoT Hub where you add a device and then assign that device ID to an actual physical device, IoT Central gives you a number of options that start at the device template creation stage. From here you can:

  • Design the device template in IoT Central and then implement its device model in the device code.
  • Create a device model using VS Code and publish the model to a repository. Implement your device code from the model and connect your device to your IoT Central app. IoT central will find the device model from the repository and create a single device template for you.
  • Create a device model using VS Code. Implement the device code from the model. Manually import the device model into your IoT Central app and then add any cloud properties, customizations, and dashboard your IoT Central app needs.

I went with the first option and created a template manually in the portal. The second choice looks like the better way to do it, and I might try that in the future. I did create a number of customizations. For instance, I ended up adding the latitude and longitude values as cloud parameters so that I could see those on a device dashboard.

Simulating the devices

I had initially wanted to use my MXChip as a device, but setting that up became non-trivial. I was just learning about the system and didn’t really have a firm understanding of how to do it. And once I started, I thought it would be a better demonstration if I had more than one device. Here’s what my code looked like:

from dotenv import load_dotenv
import sys, asyncio, os, json, time
from iotc import (
    IOTCConnectType,
    IOTCLogLevel,
    IOTCEvents,
    Command,
    CredentialsCache,
    Storage,
)
from iotc.aio import IoTCClient
from random import randint, uniform

load_dotenv()

def getPurchaseAmount():
    return randint(1,5)

def getTemperature():
    return round(uniform(8.88, 16.66))

# Load the device connection values
DEVICE_ID_AUSTIN = os.environ.get("DEVICE_ID_AUSTIN")
ID_SCOPE_AUSTIN = os.environ.get("ID_SCOPE_AUSTIN")
PRIMARY_KEY_AUSTIN = os.environ.get("PRIMARY_KEY_AUSTIN")


austin_client = IoTCClient(
    DEVICE_ID_AUSTIN,
    ID_SCOPE_AUSTIN,
    IOTCConnectType.IOTC_CONNECT_DEVICE_KEY,
    PRIMARY_KEY_AUSTIN,
    None,
    None
)
        
async def sendAustinTelemetry():
    await austin_client.connect()
    while austin_client.is_connected():
        print("client connected {}".format(austin_client._device_client.connected))
        await austin_client.send_telemetry(
            {
                "CakeInventoryFilled": 0,
                "CakeInventoryPurchased": getPurchaseAmount(),
                "FridgeTemperature": getTemperature(),
                "LocationLatitude": 30.26715,
                "LocationLongitude": -95.36327
            }
        )
        await asyncio.sleep(900)

async def main():
    await sendAustinTelemetry()

asyncio.run(main())

I used the IoTC library to make this easy. And it was. Creating the code for an IoT Central app for basic telemetry is trivial. However, I didn’t dive into cloud-to-device messaging. It would have been nice to have the ability to send a message to the machine to tell it to reset its internal inventory counter. I have more learning to do around integrating the operations side of IoT Central with a more sophisticated IoT Device. But I can definitely see the potential here.

Customization, Dashboards, Rules, and Telemetry Data

IoT Central includes a lot of tools you would need to develop yourself if you went with the traditional Azure IoT suite. You might still want to build these tools, but there was enough in IoT Central that I think you could cover the majority of your operations needs from the portal.

I explored a number of different dashboard options from device specific dashboards to sales and inventory dashboards. I was able to drag and drop tiles to represent specific telemetry or cloud parameters. If you are familiar with Power BI tiles or Azure portal tiles, you’ll recognize these UI elements. Understanding how they work is fairly intuitive. There’s not a lot of flexibility here, but if you need more than the standard set of time, you can always route data to Power BI.

You can track telemetry and add rules around the inbound data. I practiced by setting alerts based on high or low temperatures. This worked ok, but it’s by no means the sort of thing I would need to predictive analytics or something more sophisticated. If you want something more than visuals built around incoming data or custom parameters, you’ll want to integrate IoT Central with other services.

And that’s probably some of the best news. Integrating with other services is relatively easy. I connected my app to Event Hubs and captured data to send to a Stream Analytics Job. This allowed me to build a Power BI dashboard. But with a Stream Analytics Job I would perform much more elaborate rules and queries on the incoming data. You could also use the IoT Central API to build mobile apps, custom integration with lines of business like an inventory system or a telematics solution, or perform more sophisticated device management. However, I want to be clear that you don’t necessarily need to do these things. For my fictional scenario, there was really no need to create any sort of integration. I could have managed my vending machines and the surrounding services from IoT Central alone.

What about IoT Edge and AI?

I chose to stick with simple devices for my demonstration, but IoT Central definitely supports IoT Edge deployment. IoT Central treats these devices as Edge Gateways and as IoT Edge devices. IoT Central models an IoT Edge device as follows:

  • Every IoT Edge device template has a capability model
  • For each custom module in the deployment manifest, a module capability model is generated
  • A relationship is established between each module capability model and device model
  • A module capability model implements one or more module interfaces
  • Each module interface contains telemetry, properties, and commands

I like this approach to creating a device shadow that represents the IoT Edge device. It’s a good reason to implement IoT Edge devices on IoT Central.

Closing thoughts

When I first learned of IoT Central I didn’t get it. Why create a suite of Azure IoT services, and then turn around and create a little version of all these things crammed into a web app? Well, I get it now. IoT Central is far from a toy and doesn’t diminish any of the other IoT tools and services that Microsoft has to offer. If anything, It adds to that collection. I plan to explore more applications in IoT Central in the future. I’m considering it for my backyard garden’s automated irrigation system, because with so few devices and so little data, it basically cots me nothing.

Posted on Leave a comment

AI Edge Engineering at University of Oxford

This week I’m wrapping up a continuing education course from Oxford focused on the approaches and tools of the AI Edge Engineering discipline. I’m overwhelmed with how much I’ve learned and how much I still have to learn. Without a doubt, this course will help set the direction of my career moving forward.

I wasn’t new to the concept of AI Edge Engineering. Before entering the course I had already passed my AZ-220 and AI-100. Two certifications I saw as fundamental to practicing AEE on the Azure platform. These certifications focus on Azure’s IoT tools with the AZ-220 and Azure’s AI services and Machine Learning tools with AI-100. It’s important to note that the AI-100 primarily focuses on applying existing AI in solutions. It only covers Data Science from a high level; you might be expected to understand what clustering, categorizing, and regression are, but you aren’t expected to build them from scratch or use any DS tools to build them for the certification test. That’s appropriate for a 100 level certification. Despite having these certifications, I wasn’t ready for the depth we would take on in the course in such a short period.

Luckily, the course used cohort learning as a mechanism to complete some of the more challenging projects. Our group efforts afforded us the opportunity to trade opinions, approaches, and skills to achieve project deliverables. This is also a skill in the AEE field. Few people will have all the skills needed to apply artificial intelligence at the edge. This means that organizations who wish to use AEE will need to team members who have varied specialized skills and knowledge of the bigger picture of AEE. Our projects made this very clear to me.

I won’t go into detail on what we learned and how we learned it, because much of that is the IP of the course and Oxford, but I will say that we dived deep in the following general areas:

  • The basic concepts of delivering AI to the edge using IoT and Edge platforms
  • Cloud- all the clouds (Azure, AWS, GCS)
  • Cloud concepts like PaaS, SaaS, and Cloud Native Services
  • All the big pieces of Machine Learning and Machine Learning concepts
  • 5G networks
  • Device design and development
  • DevOps, MLOPS, and even AIOPs

That doesn’t even cover all the guest lectures and insights into AI and AEE application demonstrated. And without the fantastic instruction of course director Ajit Jaokar and his amazing team of tutors and instructors, we wouldn’t have been able to learn so much in such a short period of time. Ajit’s passion for this specialty was clear in every class, which made it a joy to attend. It was definitely worth waking up at 3 AM to attend a class remotely just to spend the time with others who have such a strong appreciation for this burgeoning discipline. This course succeeds because of the people behind it. I have to include the choice of students as well in that success. My study group was full of passionate, knowledgeable, curious, and delightful professionals. We plan to stay in contact well after the course ends. I expect to see amazing accomplishments from their careers.

We wrap up the course on Tuesday and submit all our final homework projects over the next couple of months. I won’t be officially done with the course until May. However, I won’t ever consider myself officially done with AEE, from a learning perspective. I’m taking what I’ve learned from Oxford and building a continuing learning track to master most of the concepts covered in the course.

It’s even clearer to me now that the engineering skills of delivering greater levels of intelligence closer to the source of events and data, so that they may act upon those events and data is a skillset that will be in higher demand in the coming years. I believe this course has helped me to begin building a better roadmap toward mastery of AI Edge Engineering.