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Patrick Steinert Posts

IoT is dead, long live Edge Computing

In the last 3.5 years, I was responsible for the service development of the Cloud of Things, the IoT platform of Deutsche Telekom. It’s been an incredible adventure, but after almost four years, it’s time for me to move on. Developing a product for the Internet of Things was a fantastic adventure. Learning the various different customer problems and build a full-service stack to solve them with ease, fast and secure. We started from a small base and have grown up to a major pillar in the DT IoT strategy. But, inside Deutsche Telekom, the topic went “big”, too big. I selected my next adventure: low latency edge computing. The Low Latency Computing platform is built to reduce the latency between the computing device and the cloud. The Low Latency Computing platform enables customer solutions like suitable Car-to-X communication. It helps to smooth the end-customer experience with low latency, e.g.…

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Retrospective 11/17

Retropective-11-17 - Chris Lawton

A lot happened in the past months. There was a big change in the company regarding IoT. A re-organization, new goals, a broader approach to the market. With little free time, I couldn’t manage to write about the details, I just tweeted about short stuff. But now, by the end of the year, I find time to write some updates.

What have I done:

  • managed, managed and managed – less real work
  • we released version 1.0 of the Cloud of Things developer SDK
  • I voted in the general election
  • changed my job – more on this in another post
  • starting development of some features on Cloud of Things and
  • start writing a book

Thought:

  • about platform strategy, technology development
  • much about my job and what I want to do
  • (1 try) change it, (2) leave it, love it
  • how to live more healthy and mindful
  • write a book!

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Analyze Strava Training By Zones With Python

Example diagram - title image
Example diagram – title image

Effective training is key to build a strong foundation for competition. That’s why I am using the Strava API with Python to analyze Strava training activities. I started my winter training recently and wanted to make sure I’m training effectively. Especially, I want to see if I stay in the right zones during the training. I have set up my Forerunner alarms appropriate, but I train outdoor, so it goes up and down during the run and I can’t control everything during the ride. Human. Not robot.

I started with the stravalib library, for reading the data from the API. As usual in data science, I need to transform and prepare the data for visualization. I end up storing the generated plots and build a quick & dirty HTML-file to display the stuff. I will now guide you through the example. I will explain the important steps. You can find the complete code on https://github.com/marquies/strava-viz.

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Retrospective Q1/2017

What have I done:

  • The first quarter was much about migration from OpenShift PaaS to IaaS operating platform. We migrated all existing staging and production environments, with all customer data. A simple lesson learned: doing something over and over again trains you and you can perform much better if it counts.
  • In March Cloud of Things Starter Kit was launched.
  • Offsite, Offsite, Offsite. The growing demand of leaving the office building to getting to know each other, re-organize work and getting things done looks like an anti-pattern of work culture.
  • Late start of training for the upcoming MTB season.
  • Started programming an iOS App to refresh my developer skills.
  • Thought about some blog posts, but deleted them again. Some things should be untold.

Thought:

  • SMART defined goals are key.
  • Visualize your work.
  • Stop arguing about tools, start using one of them!
  • If you have more meetings than working hours a day, something is wrong.
  • Do you even have a target picture, bro?

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Retrospective January 2017

Slogan of the month: Stop starting and start finishing.

(Again)
What have I done:

  • Re-created the CoT project portfolio, now in a digital version (experimenting with Excel). It is good for sharing in our digital and distributed working environment, but a bit harder to update.
  • I attended the Bonn Agile Meetup January: “IoT und Bastelprojekte” and talked about my AirQuality Lab project 
  • I attended a lot of Kick-Off-events (unit, department, CoT-team, Service Development team). Some were useful, some had good food.
  • I worked on our first goals for 2017. First results in February 2017.
  • I completed a course on my Coursera data science specialization course (Reproducible research)
  • Watched the NFL playoffs

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The AirQuality Lab: How To Work With IoT Sensors

IoT Sensor: The AirQuality Lab

 

Want to learn about the Internet of Things and how to work with a sensor? I did too! I’ve been experimenting with the “Internet of Things” (IoT) since 2014 and have learned about the challenges with my sensor project: the AirQuality Lab. In the beginning, I just wanted to create something and work with the components. The scope of my side project was to read values from a sensor, transfer it to a thing-backend and then learning from the data. This post starts with the basic setup of the Thing and the following posts will cover further points.

After finishing a project in late 2014, I played around to learn a bit more about the Internet of Things (IoT) stuff for my next project. So, it was (and is) proposed, that “everything is connected in 2020” and I had to think about a product, product strategy and technical implementation in this area. Our thoughts about a domain model for IoT at this time was, to reduce everything to a source and a drain. Well, as hardware is cheap (but this is not all) I bought a Raspberry PI, a bunch of sensors and a small LCD display. Inspired by the CubeSensors, I wanted to measure air quality with the sensors and work with the derived data. This should help to understand how things process data, how to transfer it, how to analyze and derive information from it. I started the AirQuality Lab.

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Retrospective December 2016

Slogan of the month: The intellectual capital of your business has two legs and walks home every day.

What have I done:

  • Had nice workshops at Fraunhofer Institute for Big Data Analytics
  • Successfully finished hard work on a internal data center migration with the Cloud of Things team
  • Planted the seeds for the hosting quality improvements
  • Worked on the performance of my blog. Improved site performance by 40%. PHP upgrade to v7 was the most gain. Performance could be better but for my kind of hosting it’s very good.
  • Designed strategy, corner milestones and goals for 2017
  • Prepared my talk for Bonn Agile Meetup January: IoT und Bastelprojekte (more on this later in this blog)
  • Enjoyed Christmas and New Year’s Eve vacation

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Books I have read in 2016

As a recap of 2016, I have created a list of the 2016 books I have bought and read. At least a serious bit. Maybe you are also interested in these books. Efficient Learning Machines: Theories, Concepts, and Applications for Engineers and System Designers Mariette Awad, Rahul Khanna This book is more a reference of Machine Learning and I use it this way. The book contains much mathematical stuff to explain the algorithms. So if you like Agile IT Organization Design: For Digital Transformation and Continuous Delivery Sriram Narayan I choose this book as a resource for organizing my teams and make the organization more agile. While carefully selecting the methods to apply for me, I liked one the most: Alignment Maps. This concept helped my team to understand which business goals we have and why they have to do everything. A great way to show the team why their…

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Goal Setting & Time Management

If you are like me preparing the new year between the holidays, maybe these resources help you like me.

Brian Tracy Goal Setting Guide

Brian Tracy has written down a good guide for goal setting. He hasn’t added anything which is not common sense, if you have already read the basics of this topic. You can get his article for 0$ here, you just need to subscribe to the newsletter. Of course you can use trash-mail.com or so, but he is an excellent source for information about personal development and productivity. I just learned he has an app for this process too, but I haven’t tried yet.

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IoT hardware is cheap, but other things are difficult

Internet of Things - Raspberry PiIn the early days of the Internet of Things hype, the industry stated that computing power and accessories are cheap and ubiquitous. Read: available and ready to use. The good part of this statement is, that it is true. Partly. RaspberryPi, Arduino, Seed Studio Groove, Adafruit Feather and so on are cheap and versatile computing devices, available on many online retailers. Several toolkits and online resources help the hackers and makers to prototype and develop products. Also many online platforms are ready for the users to connect and manage the things.

Nonetheless is the Internet of Things not as ready as the industry states. You can prototype fast, but if you would like to adapt the Internet of Things for your business. If you compare the Gartner Hype Cycle for Emerging Technologies from 2015 and 2016 you may see, that the Internet of Things was dropped. While not sure if intentionally or not, it can’t be found on the plateau of productivity. I would place it in the trough of disillusion. For example, here are some topics, the industry is struggling with.

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