arrow_back Data Simulation as a means to intuitively grasp Statistics and it's direct application to prediction problems
Four horsemen of the IoT
Submitted by Makerville Systems (@mkrv) on Sunday, 1 May 2016
MQTT brokers have been around for quite a bit. But never before has there been so much active development for IoT cloud providers. Silicon is cheaper than ever. IoT, especially industrial, is now feasible for even small and medium sized enterprises with lower margins.
I will be splitting the talk in 4 parts.
- Cloud backend
- Hardware (yay!)
Security - How to ensure that your devices don’t turn up on Shodan
You not only have to worry about using SSL/TLS (which is not always possible on low power silicon), but you also need to ensure that your device firmware stays secure. Non-standard practices, planned and unplanned obsolescence are causing consumers to be extremely cautious.
Running the backend like GOOG/AAPL
What if you were Apple or Google and had to manage millions of devices ?? With IoT you will have to manage devices like the big guys, no matter how small you are.
For example MSFT now has device queries in Azure IoT which is a neat buil tool to manage your fleet.
From 8 bit microcontrollers, to FPGAs. Everything is an option as prices drop and tools become open source. The hobbyist and maker movement is surely accelerating things with the rise of ESP and Pi.
I’ll be going through this as briefly as possible using some tables and charts.
This will also cover a quick comparison of the wireless protocols.
Data -The currency of our times
You might have heard how Foursquare is accurately predicting revenue reports using the sensors in your phones. Tools like Amazon’s Rules Engine let you pipe data, like you would on the CLI, on the cloud. I’ll be showing a few examples of going from a single device to a stream of thousands using simple point-and-click configurations.
Tools like these don’t just let you collate and quantify your data, but also let you create ML models on the fly. AWS and IBM Watson are two such examples who are providing tight integrations between data gathere from things and predictions made by complex algorithms.
Anuj runs his own little company called Makerville, where they build hardware and the software to go with it. Some of their projects. He is working on a project called Knit which he is super excited about!