Paasmer, an IOT platform for Developers has released a new enhanced version of value-added features for Edge Analytics and Machine Learning. The features are packaged and delivered the enhanced version of Paasmer docker containers. The Paasmer Edge core now comes with a generic Python library that can be used for many IoT devices to connect to the Paasmer cloud. And these new and enhanced features making it simple for the users to access IoT devices in a much simpler way
Here is the list of features released in Version 2.0 and a brief on the same.
- Paasmer Python Library
- Paasmer Edge Analytics Version 2
- Paasmer Edge Machine Learning Version 1
- Paasmer Edge Docker Version 2
Paasmer Python Library
Paasmer Python Library, a new and simpler approach to connect, subscribe and publish to Paasmer Platform from your IoT devices. It provides simple function calls allowing the user to work with Paasmer platform more easily than before. It can also support Edge Analytics on any feed while publishing. It has separate Callback for each feed and thus making the connection simple.
Find more details on the feature, installation and how to use instructions and download at https://github.com/PaasmerIoT/Paasmer-Python-Library-V-1
Paasmer Edge Analytics Version 2
Paasmer has already got Edge Analytics capability and now the enhanced version of this feature offers more analytics options for the developers. And this feature is packaged and delivered as a Docker that makes it easy for the developers to install, uninstall and upgrade.
The following are the new analytics options added to the feature.
- Aggregate – Calculates the minimum value, maximum value, mean and standard deviation on the last n numbers of feed values in the stream.
- Average – Calculates the average of last n numbers of feed values.
- Feed Monitoring – Continuously monitors the change in the feed value and updates it in the Paasmer platform.
You can analyze your sensor data based on your Analytics condition. The Paasmer-Edge-Analytics-Docker-V-2 also consists of Paasmer Python library and Python SDK to connect to the Paasmer Gateway. This Feature is available as a docker and it can be run on any RPi devices along with our customized Paasmer OS.
Find more details on the feature, installation and how to use instructions and download at https://github.com/PaasmerIoT/Paasmer-Edge-Analytics-Docker-V-2
Paasmer Edge Machine Learning Version 1
This feature adds Machine Learning capability to Paasmer platform. A sample use case in Health care is added to demonstrate the feature. With the Healthcare application in Machine Learning, we can predict the chances of a person falling sick in the next two weeks based on the data collected on Blood Pressure, Blood Sugar and the Breakfast time. This feature is along with a sample Android Application that sends the data in a periodic delay. This Feature is available as a docker and it can be run on any RPi devices along with our customized Paasmer OS.
Find more details on the feature, installation and how to use instructions and download at https://github.com/PaasmerIoT/Paasmer-Edge-ML-Docker-V-1
Paasmer Edge Docker Version 2
The Paasmer has already got a containerized Architecture on Paasmer Edge. The first version of the Paasmer Edge Docker offered Edge analytics along with the Paasmer Edge core. The version 2 offers both the value added features, Edge Analytics and Machine Learning along with the new Paasmer code that includes the Python Library. Using this we can perform all the new features in a single Paasmer OS image running on RPi devices. This makes the tasks simple for users allowing to perform Analytics, Machine Learning and simpler communication with Paasmer IoT platform.
Find more details on the feature, installation and how to use instructions and download at https://github.com/PaasmerIoT/Paasmer-Edge-Docker-V-2