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.

Paasme-machine-learning-iot-platform

Machine Learning and IoT

ManagementTeamMouli1

Srinidhi Murthy

Given all the hype and buzz around machine learning and IoT, it can be difficult to cut through the noise and understand where the actual value lies. In this Blog, we explain how machine learning can be valuable for IoT when it’s appropriate to use, and some machine learning applications and use cases currently out in the world today.

What is Machine Learning?

Machine Learning is not a novelty innovation. As early as 1959, Arthur Samuel defined the concept of machine learning as the ability of computers to learn to function in ways that they were not specifically programmed to do. Of course, the timeline from definition to implementation in everyday life can be a long one. Today, many factors have come together to make machine learning a reality, including large data sources that are great for learning, increased computational power for processing information in split seconds, and algorithms that have become more and more reliable.

What is Data Analytics? How is it different from Machine learning?

Data analytics can help quantify and track goals, enable smarter decision making, and then provide the means for measuring success over time.

Machine learning, on the other hand, is a process of continuous learning, to which the system can make immediate adjustments to improve processes, timelines, decision making etc.

Machine Learning Use Cases in IoT

The data models that are typical of traditional data analytics are often static and of limited use in addressing fast-changing and unstructured data.

When it comes to IoT, it’s often necessary to identify correlations between dozens of sensor inputs and external factors that are rapidly producing millions of data points.

In general, machine learning is valuable when you know what you want but you don’t know the important input variables to make that decision.

Some of the typical use cases of Machine Learning and IoT are given below.

Cost Savings in Industrial Applications

Predictive capabilities are extremely useful in an industrial setting. By drawing data from multiple sensors in or on machines, machine learning algorithms can “learn” what’s typical for the machine and then detect when something abnormal begins to occur.

A Large Equipment Manufacturer has installed Many IoT sensors on its equipment which continuously send data to be learned and any deviation above a threshold be highlighted and immediately triggered as a notification to the concerned person.

Shaping Experiences to Individuals

We’re actually all familiar with machine learning applications in our everyday lives. Both Amazon and Netflix use machine learning to learn our preferences and provide a better experience for the user. That could mean suggesting products that you might like or providing relevant recommendations for movies and TV shows.

Similarly, in IoT machine learning can be extremely valuable in shaping our environment to our personal preferences.

The Nest Thermostat is a great example, it uses machine learning to learn your preferences for heating and cooling, making sure that the house is the right temperature when you get home from work or when you wake up in the morning.

And More

The use cases described above are just a few of the virtually infinite possibilities, but they’re important because they’re useful applications of machine learning in IoT that are happening right now.

But overall …. We’re Just Scratching the Surface. The billions of sensors and devices that will continue to be connected to the internet in the coming years will generate exponentially more data. Not only will we be able to

predict when machines need maintenance, we’ll be able to predict when we need maintenance too.
Machine learning will be applied to the data from our wearable devices to learn our baseline and determine when our

vitals have become abnormal, calling a doctor or ambulance automatically if necessary.

Beyond individuals, we’ll be able to use that health data at scale to see trends across entire populations, predicting outbreaks of disease and proactively addressing health problems.

save-environment-iot-paasmer

Save the environment – IOT is the way to go

Sridhar krishnan

Sridhar krishnan

Saving the environment from pollutants, waste dumps, carbon emissions, contaminated water and contaminated land is very important and must be done to protect the earth for a clean and healthy life. Technology should be used wisely to achieve the same. With advancements in sensor technologies, edge devices, communication protocols, and data analytics, IOT is the perfect solution to save the environment.

Waste collection, segregation, recycling and waste treatment is the standard and established the process in waste management. But the challenge is, executing this process as the amount of waste generated is keep increasing in a faster pace with growing world population. As this is a continuous process, this must be supported by the right government policies, strict measures, auditing, educating people and perfect implementation. A small deviation in the process may cause big damages to the environment. This must be handled with the right technology in all levels, IOT can offer end to end solution to achieve the smart waste management to save the environment.

For smart waste collection, IOT can make the Trace bins as smart Trace bins and connect them to the pickup vehicles and control center. The sensors in the trace bins will notify when the bins are full to the pickup vehicles and the nearest one can collect the waste. This will help to empty the bins as soon as they are full and efficiently allotting pickups to save time and cost. Waste segregation is an important process in waste management. Sensors can be used to automate the segregation process. IR proximity sensors are used in an automation system, Capacitive sensors can be used to segregate wet and solid waste. The segregation process can be connected to waste management IOT system to collect data on diverse types of waste collected from different centers, the data can be used for further analysis to derive useful and meaningful insights. Segregated waste based on the type, can be converted or recycled to other products. The final waste must be treated before dumped in landfills to make sure it will not contaminate land and water. IOT sensors and devices in each level can be efficiently used to collect data and manage the entire waste management process to reduce the damages to the environment.

IOT can also be used for Wastewater treatment. It offers a cost-effective, energy-efficient and environmentally friendly solution. The various sensors can be used to measure water temperature, conductivity, pH, turbidity and dissolved oxygen content, as well as atmospheric conditions such as pressure, humidity and solar radiation. After collecting the relevant data, the system can communicate with IOT Gateway to upload the sensor data to the cloud for viewing and analytics. For more details refer
http://www.engineering.com/IOT/ArticleID/14925/Wastewater-Treatment-with-the-Internet-of-Things.aspx

Carbon emission is another major spoiler of the environment. IOT can be used to capture real-time emission data from the sensors and feed it to a cloud storage built for Big Data ingestion, Analyze the data in real time and put in place rules that automate actions when limits are exceeded. Apps can be developed to offer visualization of CO2 emissions so that both the culprits and the government can keep a tab on the emission levels, and appropriate remedial measures can be taken.

IOT based smart solutions can be used to save energy in the home, industries, agriculture, transport, city management to save the environment. So IOT can play a key role in the resolution of these global environmental issues. Cheaper bandwidth, greater availability of computing power and reduced storage costs are all driving the adoption of IOT technologies to combat pollution in more and more innovative ways. With information coming in from so many sensors everywhere, IOT can provide more insight into how we use our world’s resources and how we can conserve them in a way that makes sense.

Paasmer_platform_sdk

Build Your IoT Solution using PAASMER SDK

ManagementTeamMouli1

Srinidhi Murthy

An IOT device is a hardware that connects with various sensors in the enterprises or industries, collects data, preprocess, send them to cloud hosted IOT platform or an IOT application for further analysis and interpretation to present them for decision making or feed them for process efficiency and production improvement. The IOT devices should be developed with the support for IOT communication standards to communicate with different kinds of sensors in the industry and IOT devices should be communicating with each other.

An industry who wants to implement the IOT solution in their company may need to develop different kinds of IOT devices to collect data from various sensors from various locations. It is a time consuming for any IOT solution provider or IOT device developers to develop a new IOT device/solution quickly with support for various kinds of connectivity standards from the scratch.

Once the devices are developed and deployed, the IOT solution should provide a management console to view their devices, the sensors connected to devices and the data collected from them in an easily understandable format. The user who has the authority to manage the IOT devices to view the data and control the devices should be provided with easy to use user interfaces in web UI or smartphone Apps to control the devices. Developing such a management console from the scratch is time-consuming.

An IOT development platform with software development kits to provide APIs to develop IOT devices using commonly used open source IOT hardware can enable IOT solution developers, IOT device developers, and IOT consultants to quickly develop and implement IOT solutions with different kinds of IOT devices.

The IOT platform can also provide a management console to manage all the IOT devices, sensors and their data presented in a user editable formats with control options. The platform can be hosted in in-premises and(or) in the cloud and can connect with the various IOT applications analytical and management applications. This can enable developers to develop value-added IOT solutions quickly.

The software development kits provided in IOT development platform can also provide APIs to quickly develop security layer of IOT solution like TOR and Blockchain.

PAASMER SDK
It enables you to do Design, Connect and Analyze your data using PAASMER’s IOT platform.

Design:
PAASMER’s SDK allows you to connect any sensors to existing IOT boards in the market like Raspberry Pi, Node MCU, and many other IOT boards. The PAASMER platform is hardware agnostic. The trial build will provide support for connecting sensors to RPi and Node MCU and supports languages like C and Python.

Connect:
PAASMER SDK enables easy connectivity of your IOT devices to PAASMER cloud. The SDK allows communication of all the IOT devices with the PAASMER cloud. It works with all the popular platforms and supports multiple communication protocols. The trial version will support the Wi-Fi method of communication using MQTT protocol and RESTFUL APIs. The SDK allows managing all the devices connected to the PAASMER platform.

Analyze:
The PAASMER Developer Login enables the users to Analyze the data that is being sent from the devices and draw a meaningful conclusion out of it. There is also an option for Manual control of sensors based on inputs received.

Paasmer_sdk_platform

Overview of PAASMER IOT platform

SDK:
The PAASMER SDK for Embedded C, Python and ESP Open RTOS is a collection of source files that enables you to connect to the PAASMER IOT Platform. It includes the transport client for MQTT with TLS support. It is distributed in source form and intended to be built into customer firmware along with application code, other libraries, and RTOS.

Features:
The SDK’s simplifies access to the Pub/Sub functionality of the PAASMER IOT broker via MQTT. The SDK has been tested to work on the Raspberry Pi 3 running Raspbian Jessie and NodeMCU / Adafruit Huzzah. Support for Other SBC’s running any flavors of Linux would be available shortly. The SBC-SDK provides functionality to create and maintain a mutually authenticated TLS connection over which it runs MQTT. This connection is used for any further publish operations and allow for subscribing to MQTT topics which will call a configurable callback function when these topics are received. More information is available about the SDK’s at https://github.com/PAASMERIOT/

Developer Login:
The PAASMER Developer Login provides the ability for the User to register, Login, View devices, sensor data and the ability to send control messages to the Edge hardware.

Features:
The PAASMER developer Login is available at http://developers.paasmer.co. Here the user can register for the free trial and upon verification of the credentials is allowed to Login.

After login, the user is directed to the various available SDK’s that he can download and edit the same to enable his devices to be connected to the PAASMER IOT platform.

Once the SDK’s are downloaded onto the devices and his credentials entered and the program is running, the device is connected the PAASMER IOT platform and starts sending sensor information.

This sensor information is available on the PAASMER Developer Login for viewing and base on the sensor information the User can take actions using the control feeds to control actuators.

Paasmer_wearable-devices

IOT will be inherent force behind Wearables

Sridhar krishnan

Sridhar krishnan

The capabilities of IOT with the wearable devices like internet connectivity, a device to device communications, lightweight apps, data filtering, local and cloud analytics, etc.., are bringing many opportunities to develop more meaningful solutions for consumers. Here, I would like to talk about few of them.

paasmer_Wearables

The wearable devices with the technological innovation of IOT are widely successful in providing the fitness and healthcare related solutions. We have a lot of scope for environmental monitoring also, but the challenge is cost and power. A very few are successful in low cost and low power sensors in wearable devices used for environmental monitoring. These environmental devices can collect data on Air, Light, and Sound. With continuous innovations in sensors, we can expect that the manufacturing and operation cost of these devices will become more affordable and capable of sensing multiple environmental data. The wearable nature of the device gives constant mobility to the device. This opens up an opportunity to crowdsource the more accurate and real-time environmental data with location details. The data collected by the device can be from the sensors embedded in it and from other sensors in reachable distance based on the supported protocols and standards.

Having more accurate and crowdsourced real-time environmental data in the cloud combined with another cloud accessible real-time and forecasted environmental data from static stations set up by governments and institutes brings tremendous opportunities for IOT device manufacturers and solution providers to build innovative, value added, cost effective consumer devices and solutions.

In health care, the environmental data can be used for the clinical diagnosis and treatment of many human pathologies which are influenced or triggered by environmental stimuli. By analyzing the consumers’ health data like health history, medical records and any other health-related data with the environmental and location data, we can develop customized healthcare solutions to help consumers with health advisories and warnings. It can also be used to provide B2B solutions like connecting consumers with doctors for consultation, Pharmacy to order suggested list of things to order, etc.,

Wearables_healthcare_paasmer

The consumer specific health data and location specific real-time and forecasted environmental data can also be used to provide travel advisories and b2b solutions with a list of things to carry based on the mode of transport and travel locations, and option to buy the recommended list of things online. So, with the capabilities of IOT, we still have lot of opportunities that can be explored with the wearable devices in healthcare, travel, city management, traffic management, waste management, disaster management, etc.,

References – https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4295929/

Blockchain_paasmer_paltform

Basic concepts in a Blockchain transaction

ManagementTeamMouli1

Chandramouli Srinivasan

In this blog, Demir talks about the basic terms used in Blockchain like Hashing, Decentralization, Digital Signature, Share Crypto currency, Minors (Agents), Blockchain (The Ledger).

Using Blockchain technology means

  1. All transaction is made over the Internet using P2P communication, thus removing the need for a central authority.
  2. Users can perform anonymous transactions by utilizing asynchronous cryptography and they are identified only by their private key/public key combination.
  3. You have implemented a validated global ledger of all transactions that has been safely copied to every peer in the network.

These decentralized technology fundamentals can be leveraged by any centralized technology currently being used like IoT, Financial systems, insurance systems etc. The increase in computing power requirement for Blockchain is a potential issue for Blockchain implementations. However, there are many types of researchers underway that could change the way a commercial and viable implementation of Blockchain occurs.

More on this blog from Demir here: https://www.toptal.com/bitcoin/cryptocurrency-for-dummies-bitcoin-and-beyond

raspberry-pi_connect to Paasmer IoT Platform copy-min

How to Connect Raspberry Pi 3 to the PAASMER IoT Platform

ManagementTeamMouli1

Srinidhi Murthy

In this blog series we look at how to connect a Raspberry PI 3 to the PAASMER IoT Platform.

The PAASMER IoT Platform makes it simple for Internet of Things companies to build and launch IoT-enabled hardware. PAASMER currently provides an SDK which can be installed on the hardware.

Modifying a few lines of code to provide the correct credentials and sensor information, it is ready to be connected to the PAASMER IoT Platform. The sensor data is then available on the PAASMER Developer Login, which can be utilized and actions taken by using the control fields to control various other sensors connected to the Hardware.

A pre-requisite for using the SBC-SDK is available of the Raspberry Pi 3 hardware running Raspbian Jessie OS. More hardware and software support would be added shortly.

An account created in http://developers.paasmer.co allows for a 30 day trial period to explore the PAASMER IoT platform. After creating and activating the account, the SDK for the appropriate hardware can be downloaded.

Given below are the detailed steps to be followed after downloading the SBC-SDK.

PAASMER IoT SDK for Single Board Computers Running Linux

Overview

The PAASMER SDK for Single Board Computers (SBC) like Raspberry-PI, Intel Edison, Beagle Bone is a collection of source files that enables you to connect to the PAASMER IoT Platform. It includes the transport client for MQTT with TLS support. It is distributed in source form and intended to be built into customer firmware along with application code, other libraries, and RTOS.

Features

The SBC-SDK simplifies access to the Pub/Sub functionality of the PAASMER IoT broker via MQTT. The SDK has been tested to work on the Raspberry Pi 3 running Raspbian Jessie. Support for Other SBC’s running any flavors of Linux would be available shortly.

MQTT Connection

The SBC-SDK provides functionality to create and maintain a mutually authenticated TLS connection over which it runs MQTT. This connection is used for any further publish operations and allow for subscribing to MQTT topics which will call a configurable callback function when these topics are received.

Pre-Requisites

Registration on the portal http://developers.paasmer.co is necessary to connect the devices to the PAASMER IoT Platform.The SDK has been tested on the Raspberry PI 3 with Raspbian Jessie (https://downloads.raspberrypi.org/raspbian_latest)

Installation

  • Download the SDK or clone it using the command below.
    • $ git clone github.com/PaasmerIoT/SBC-SDK.git
    • $ cd SBC-SDK
  • To connect the device to Paasmer IoT Platform, the following steps need to be performed.
    • $ cd external_libs/mbedTLS/
    • $ make
    • $ cd ../../
    • $ sudo ./install.sh
  • Upon successful completion of the above command, the following commands need to be executed.
    • $ sudo su
    • $ source ~/.bashrc
    • $ PAASMER
    • $ sed -i ‘s/alias PAASMER/#alias PAASMER/g’ ~/.bashrc
    • $ exit
  • Go to the directory below.
    • $ cd samples/linux/subscribe_publish_sample/
  • Edit the config.h file to include the username(Email), device name, feed names and GPIO pin details.
    #define UserName "Email Address" //your user name used in developer.paasmer.co for registration
    #define DeviceName "" //your device name
    #define feedname1 "feed1" //feed name used for display in the developer.paasmer.co
    #define sensorpin1 gpio-pin-no-for-sensor-1 //modify with the pin number which you connected the sensor, eg 6 or 7 or 22
    #define feedname2 "feed2" //feed name used for display in the developer.paasmer.co
    #define sensorpin2 gpio-pin-no-for-sensor-2 //modify with the pin number which you connected the sensor, eg 6 or 7 or 22
    #define feedname3 "feed3" //feed name used for display in the developer.paasmer.co
    #define sensorpin3 gpio-pin-no-for-sensor-3 //modify with the pin number which you connected the sensor, eg 6 or 7 or 22
    #define feedname4 "feed4" //feed name used for display in the developer.paasmer.co
    #define sensorpin4 gpio-pin-no-for-sensor-4 //modify with the pin number which you connected the sensor, eg 6 or 7 or 22
    #define controlfeedname1 "controlfeed1" //feed name used for display in the developer.paasmer.co
    #define controlpin1 3 //modify with the pin number which you connected the control device (eg.: motor)
    #define controlfeedname2 "controlfeed2" //feed name used for display in the developer.paasmer.co
    #define controlpin2 4 //modify with the pin number which you connected the control device (eg.: fan)
    #define timePeriod 15000 //change the time delay as you required for sending sensor values to paasmer cloud
  • Compile the code and generate the output file.
    • $ sudo make
  • Run the code using the command below.
    • $ sudo ./subscribe_publish_sample
  • The device would now be connected to the Paasmer IoT Platform and publishing sensor values are specified intervals.

Support

The support forum is hosted on the GitHub, issues can be identified by users and the Team from Paasmer would be taking up requests and resolving them. You could also send a mail to support@paasmer.co with the issue details for a quick resolution.