AWS IOT platform will be a new platform for DCIM

A couple of weeks ago I wrote about Implementing DCIM on an IOT Platform. And that the end of DCIM is coming. What I didn’t think about was how quickly things can change and AWS would be at the center of this change.

AWS Re:invent is going on. I have never gone, but have plenty of friends who are there and I can chat with them. What I did do yesterday was watch Andy Jassy’s opening keynote and the best part was the last 15 minutes where Andy covered the AWS IOT Platform. Below are the 8 parts of the platform.

Also I placed a pre-order for an AWS DeepLens

With the AWS Platform the current ways of building DCIM are shown to be in the past. The future is to build DCIM on IOT. All you DCIM vendors get ready to compete against AWS and its partners. This includes Litbit who was trying to do a subset of AWS IOT. Luckily for DCIM vendors AWS IOT is not targeting the DCIM market. AWS IOT is going after the industrial IOT market which is magnitudes bigger. I checked out the vendor list to run their services on at the edge and it is impressive for a start at launch.

AWS IOT is good enough that I am looking to add it to a lab environment to play with it more. 

AWS IoT Services

AWS IoT Core 

AWS IoT Core is a managed cloud platform that lets connected devices easily and securely interact with cloud applications and other devices. IoT Core can support billions of devices and trillions of messages, and can process and route those messages to AWS endpoints and to other devices reliably and securely.

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AWS IoT Device Management 

AWS IoT Device Management is a service that makes it easy to securely onboard, organize, monitor, and remotely manage IoT devices at scale.

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AWS Greengrass

AWS Greengrass is software that lets you run local compute, messaging & data caching for connected devices in a secure way. With AWS Greengrass, connected devices can run AWS Lambda functions, keep device data in sync, and communicate with other devices securely - even when not connected to the Internet.

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AWS IoT Analytics

AWS IoT Analytics is a fully-managed service that makes it easy to run sophisticated analytics on massive volumes of IoT data without having to worry about all the cost and complexity typically required to build your own IoT analytics platform. It is the easiest way to run analytics on IoT data and get insights to make better and more accurate decisions for IoT applications and machine learning use cases.

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Amazon FreeRTOS

Amazon FreeRTOS is an operating system for microcontrollers that makes small, low-power edge devices easy to program, deploy, secure, connect, and manage.

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AWS IoT 1-Click 

AWS IoT 1-Click is a service that makes it easy for simple devices to trigger AWS Lambda functions that execute a specific action. Some examples of possible actions include calling technical support, reordering goods and services, or locking and unlocking doors and windows.

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AWS IoT Button

The AWS IoT Button is a programmable button based on the Amazon Dash Button hardware. This simple Wi-Fi device is easy to configure and designed for developers to get started with AWS IoT CoreAWS LambdaAmazon DynamoDBAmazon SNS, and many other Amazon Web Services without writing device-specific code.

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AWS IoT Device Defender

AWS IoT Device Defender is a fully managed service that helps you secure your fleet of IoT devices. AWS IoT Device Defender continuously audits the security policies associated with your devices to make sure that they aren’t deviating from security best practices. AWS IoT Device Defender also lets you monitor devices for behavior that deviates from what you have defined as appropriate behavior for each device. 

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Implement DCIM with an IOT Platform like open source Kaa Project

I wrote about the DCIM is coming. http://www.greenm3.com/gdcblog/2017/10/31/the-end-of-dcim-is-coming 

And an example of its end is how DCIM can be replaced by a more powerful, lower cost, platform like IOT. 

Here is a YouTube video with an example of KaaIoT platform with edge end points used in the data processing, reducing the data to the back-end system 100X. 

This approach has merit and I want to start investigating what can be done by KaaIoT or equivalent. 

 

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Here is a snapshot of the architecture of KaaIoT from the below video

Why I don’t go for Home IOT, because I haven’t figured out the Home State Management IOT tool

I enjoy listening to my friend’s IOT podcast,  https://staceyoniot.com/. But I have few IOT things in the house. Why? Because having IOT at home I want a state management tool to managed, control, and connect the IOT things.

Curious I decided to search for tools and found these. https://www.how2shout.com/tools/best-opensource-iot-platforms-develop-iot-projects.html

Below is a video of one tool Kaa IOT’s platform.

Off to another Fluke Roadshow 2017 in Austin on May 11

Fluke has a Roadshow event and I was on a panel for the Seattle event on May 2. On May 11 in Austin I'll be presenting a breakout session of the use of Machine Learning. The Seattle event had over a hundred people interested in the smart maintenance and connectivity. What some people may call the Industrial Internet of Things.

The event was well worth my time. One of my data center buddy who used to work a cellular company, Mickey Mouse, and the biggest bookstore was there too and the Fluke engineers were interested is his perspectives on how facility maintenance could be improved with IIOT type of efforts.

I had fun on the panel and tested some ideas I have been working on.  Digital media efforts may be the latest, but I am one of the old school people who believe that presenting in front of a live audience allows you to test your ideas.  Then scale that work on digital media.

Making Sense of Sensor Data Webinar, June 25, 2014

Gigaom Research has a webinar on June 25, 2014 on Making Sense of Sensor data.

Maybe you’ve heard of the Internet of Things, and maybe you’re skeptical.  But this isn’t just about thermostats and personal pedometers.  It’s about fleet optimization, supply chain management, container shipping, manufacturing, sentiment analysis, and fraud prevention, too.

Analysis of streaming data focuses on determining not just the “what and why,” but also the “what’s next.”  By combining sensor data with historical data, even deeper insights can be extracted, equipment breakdowns averted, money saved and efficiencies gained.

I am on the panel along with two others.

FEATURED PANELISTS

Dave Ohara
Jim Haughwout
Jim Haughwout Chief Technology Architect, Building the IoT, Savi