PAASMER_Docker

New Dockerized Value Added Features in Paasmer Version 2.0

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.

python_logo

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.

Edge Analytics

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.

Edge_ML

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.

logo

Find more details on the feature, installation and how to use instructions and download at https://github.com/PaasmerIoT/Paasmer-Edge-Docker-V-2

Paasmer Developement Status

Paasmer Development Status – December 2017

ManagementTeamMouli1

Srinidhi Murthy

Hello everyone, we at Paasmer are continuously striving towards making your life as an IoT Developer or Organization or Institution easier. In this blog, we talk about some of the key new features that we have made available on the Paasmer Platform. They are listed below.

Paasmer 2.x Python, Java and C SDK

We have added an auto-download script to download the credentials and the configuration file of the device based on device creation and configurations done on the Paasmer IoT platform Dashboard. We have also added support for BT and Zigbee protocols for the above SDK’s.

Program_sdk
Paasmer CoAP V-1

Bringing the web to constrained IoT devices that lack the capabilities of computers or smartphones requires a special sort of IoT protocol, and CoAP is one such protocol that fits that bill. The Internet Engineering Task Force (IETF) standardized the Constrained Application Protocol or CoAP as RFC 7252 in 2014, essentially as HTTP designed specifically for constrained devices.

Constrained Application Protocol or CoAP is a service layer protocol that is intended for use in resource-constrained internet devices, such as wireless sensor network nodes. CoAP is designed to easily translate to HTTP for simplified integration with the web, while also meeting specialized requirements such as multicast support, very low overhead, and simplicity.

The Constrained Application Protocol was necessary because traditional protocols are considered “too heavy” for IoT applications involving constrained devices. CoAP is a software protocol that enables simple constrained “things” such as low-power sensors and actuators to communicate interactively via the internet. It runs on devices that support the User Datagram Protocol (UDP) and implements a “lightweight” application layer that features small message sizes, message management and lightweight message overhead ideally suited for low-power, low-memory devices.

The IoT realm is widely using CoAP as a protocol for home automation and in numerous industrial applications. The Open Connectivity Foundation and ZigBee are tapping CoAP as a core protocol for their frameworks and product implementations. To keep pace with the Cambrian-like explosion of growth of connected “things” ahead, an IoT protocol designed specifically for constrained devices, such as CoAP, has a critical role to play.

Paasmer IoT Platform always adopts the latest and most cutting-edge technology, drafts and protocols and adding CoAP support was a natural progression. The Paasmer CoAP V-1 is an SDK with ESP8266 Arduino libraries and Gateway Server for SBC’s. The Paasmer CoAP V-1 is a collection of source files that enables you to connect to the Paasmer IoT Platform and uses CoAP a service layer protocol.

arch_view

Paasmer CoAP V-1 SDK is designed for the Gateway and the End-device / Sensor-node and has two separate components.

Paasmer CoAP Gateway establishes communication between the End-devices and Paasmer IoT platform. It will connect and control the End-devices / Sensor-nodes and update the status to the Paasmer IoT platform.

Paasmer CoAP End-Device communicates to the Paasmer Gateway and Control GPIO Pins based on Gateway instructions.

The Readme file for the Paasmer CoAP V-1 is available here which talks about the Pre-requisites, installation, and setup in detail.

Paasmer 3.0 Preview

Paasmer 3.0 makes life easy for IoT Developers, Institutions and Organizations to deploy, update, and maintain code running on field devices. We aim to bring development and deployment workflow to hardware, using well-known tools like git, Docker, and simple toolchains to allow you to seamlessly update all your embedded IoT devices anywhere in the world. We handle all the nitty-gritty stuff so that you can concentrate on your IoT solutions and nothing more.

Paasmer 3.0

The Paasmer 3.0  would encompass the following features.

  • Control and Monitor Field Devices.
    • Reach your devices anywhere.
    • Choose your own flavour of OS.
    • Take control of networking.
    • Heartbeat and Status monitoring.
  • Provision devices with simple Wizards.
    • UUID for each device.
    • Zero Config support.
    • Add preconfigured credentials.
  • Manage many … many devices all at once.
    • Set environment variables for your devices.
    • Access devices via Web address.
  • Security built from the ground up.
    • All communication between Paasmer and Field devices is encrypted with rotating keys.
    • Continuous Updates.
    • Latest web-based authentication like OAuth 2 and OTP for dashboards.

The Paasmer 3.0  would be released in parts, starting with the Edge Docker. The Paasmer Edge Docker Version 1, which is built for Raspberry-pi running Paasmer OS is a collection of Docker containers that enables you to do analytics on edge and to connect to the Paasmer IoT Platform. This Paasmer Edge Docker Version 1 is equipped with Zigbee support along with Board GPIO’s. This is the first step in building the Paasmer 3.0.

The Paasmer Edge Docker Version 1 consists of two key submodules

Paasmer OS is an attempt to make container-based services available for embedded IoT devices. Currently, we support the Raspberry-Pi. Support for other devices coming soon.

Paasmer Edge Analytics is the key feature in Paasmer-Docker which provides you to do analytics on the sensor data. Presently we are providing filter algorithm, where you can filter your sensor data based on your filter condition. Support for More algorithms on analytics coming soon.

The Readme file for the Paasmer Edge Docker Version 1 is available here which talks about the Pre-requisites, installation, and setup in detail.

 

Edge-Analytics-IoT-Paasmer_Platform

Why Edge Analytics should be part of the IOT strategy of an Organization

Sridhar krishnan

Sridhar krishnan

The organizations in today’s ever changing competitive world should be capable of adopting to changes quickly and seamlessly. The organizations should carefully invest in right technologies to align with its strategy to attain maximum benefit. With growing and increasingly disbursed sources of information and the pace of organizational change accelerating rapidly, the ability to filter and analyze only the time-sensitive data both in real-time and historical in edge side and non-time sensitive data in the cloud is invaluable. It is cost effective approach to have central data analytics infrastructure only for non-time sensitive analysis and moving analytics to Edge gives an opportunity to monitor and running stream and batch analytics to get insights to take decisions quickly with speed and simplicity.

Three central value propositions of Edge Analytics:

Real-time response – There are many critical systems that can’t depend on a cloud connection for a decision. A few seconds of latency can make a significant difference for an operation with massive power consumption from multiple sources. And sometimes connections fail entirely, which is far more damaging than simple latency.

Cost of data transmission – Rule-based engines can filter out the noise and send only the interesting information back to the central repositories. Gateways can also batch information into packets with smaller footprints that are more optimal for a given means of transmission.

Information management – Edge processing can make a significant impact by cleaning and be harmonizing data before sending it off to central information management systems. this type of edge processing improves central data mining and analytics capabilities.

Edge Analytics is the game changer in all the industries including industrial IOT, retail, manufacturing, finance, energy and agriculture sectors.

Core benefits of edge analytics and industries where it makes more sense are,

  • Remote Monitoring in Oil & Gas Drilling, Oil & Gas Refineries, and Wind Turbines industries.
  • Preventive Maintenance in Factory Robots, Airplane Tires, and Energy Grid industries.
  • Personnel Safety in Refinery Gas Leaks and Contamination Containment.
  • Real-time Quality Assessment in Oil Drilling, Manufacturing Cell, Train Repair
  • Asset Health in Readiness Assessment.
  • Efficiency Through Digitization & Automation in Smart Meters, Utility Billing, etc.
  • Cost Reduction Through Better Facilities Management in Energy Management & Reduction.

Business use case examples

Manufacturing
Manufacturing organizations must run data aggregation, data preparation and analytic workflows at the source, where the data is generated. When linked to sensors along the path of production, analytic models running on gateway devices at the network edge evaluate and score manufacturing output by parameters such as size, temperature, pressure, color, vibration, and weight. When variances are detected, the embedded sensor, smart device or gateway immediately sends alerts or even stops production to help limit waste. The manufacturing process benefits from the flexibility to monitor and update the model at the point where data is generated and from the network efficiency to send only valuable information like state changes back to the cloud for deeper analysis.

By using machine learning, data mining and advanced analytics at the source of the data to examine thousands of steps per process, manufacturers can catch small, bad batches before they become large, seriously bad batches. One pharmaceutical company discovered that minimizing scrap and wasted resources saved them several hundred thousand dollars. Their Edge analytics implementation combined with a program of statistical process control featuring audit trails and role-based security, allowed them to recover their investment in edge analytics in the first quarter after implementation.

semiconductor manufacturers automatically analyze and classify patterns of failures such as scratches or defects around the edges of silicon wafers. They identify possible root causes, and the specific processing steps and respective tools and machines that require inspection or maintenance.

Retail customer behavior analysis
Near instant edge analytics on sales data, images, coupons used, traffics patterns, and videos are created – provides unprecedented insights into customer behavior. This intelligence can help retailers better target merchandise, sales, and promotions and help redesign store layouts and product placement to improve the customer experience. One way this is accomplished is through the use of edge devices such as beacons, which can collect information such as transaction history from a customer’s smartphone, then target promotions and sales items as customers walk through the store.

Banking
Edge Analytics helps banks to understand their customers better by providing insights such as location-based suggestions and customer recommendations. Embedded in the bank’s customer channels – online banking or mobile banking, edge analytics delivers transactional behavior and location-based suggestions in real time.

Agriculture
We believe Edge Analytics can come into play in sectors such as farming and agriculture largely wherein regardless of the network, analytics can point out equipment failure or irrigation leaks.

IoT Energy

IOT in Renewable Energy – Challenges and Solutions

Sridhar krishnan

Sridhar krishnan

Renewable energy business is continuing to grow all over the world as the entire world is looking for clean and alternative energy source to control the pollution.

IOT solutions are helping this business to improve operational excellence, efficiency, cut cost, fix issues proactively, increase productivity and profitability. The effective use of IOT is the differentiator among the competitors. Real-time analytics can make a significant difference to act quickly to identify and fix issues and improve operational excellence. The challenge in the system is the amount of data being collected from the power sources, it is huge. This challenge is common for both wind and solar energy systems where the wind turbines and solar panels are many in number and are spread across various places. As the amount of data being generated is huge from these sources, the cost of sending these data to the cloud and using big data infrastructure are increasing the overall cost of operations. And it impacts the network latency and performance.

Wind energy
In a large Wind energy farm, the IOT system is collecting data from sensors that includes Acceleration, Temperature and Vibration data from turbines. These data need to be collected and analyzed in real-time or near real-time for quick analysis and actionable insights for performance optimization to increase productivity and predictive maintenance to avoid downtime.

The challenge with this IOT system is transferring huge data from various locations in real-time to a central location to run queries on the data and perform analytics. This makes the cost of data transfer much higher than the benefits it is giving.

Solar Energy
In solar energy infrastructures, the essential data that can be collected from the sensors by IOT system includes Irradiance (Solar panel tilt angle), wind factors. Ambient temperature and location. This data can be used for performing analytics, compute predictive algorithm, calculate the energy that is being generated in all the connected solar energy system, analysis of energy generation pattern, Fault/ Problem detection and Real-time visualization of the solar systems. The data need to be collected and analyzed in real-time

Like IOT system for wind energy, the challenge of transferring huge data in real-time to cloud exists in this IOT system also as the size of the data from each Solar unit is huge.

Decentralization – Edge analytics

Decentralization of data storage, processing and analytics can solve the problem of sending huge data to the centralized cloud. Ability to run queries at any given time can be achieved by Edge analytics. The data generated from the solar panels or wind turbines can be filtered at the edge and send only the meaningful data to the cloud to run queries for analytics, this will help to reduce the data transfer cost. Real-time and post-mortem analytics can also be done completely in Edge gateway or with cloud analytics whichever is the best suitable and cost effective for the specific renewable energy use case.

So, the existing challenges in IOT systems of renewable energy can be handled by implementing a decentralized IOT solution with data filtering, processing, and analytics done in Edge side of the IOT solution.