Internet of Things with Blockchain can accelerate social and economic development

Connected things on Internet with Blockchain as a backbone can be effectively used with other Blockchain based application to establish a true democracy in this world. Democracy is a base for overall development. With a true democracy established with corruption free governments, IoT and Blockchain technologies are offering numerous opportunities to solve the practical issues in transforming low income population of this world into a progressing and developing force.

Today, the challenges involved in producing a product, delivering a service and exchanging values are related to raising capital, access to technology, access to loans, fulfilling the regulatory/environmental requirements, managing resources, handling the supply-chain or logistics, handling other factors like natural or man-made disasters, macro-economic factors and extreme inequalities.

Having smartthings or devices available on the internet and possibilities of all the combinations of interactions like things and things, things and systems and things and humans from IoT along with the features like decentralization, immutability, autonomous, distributed ledger, cryptographic security, private key based access and smart contracts from Blockchain, provide opportunities to build cost effective, secured, trustful, easy to use, simple, meaningful and realistic solutions to handle the challenges effectively.

Corruption free voting system by the combination of smart voting machines and digital identity/ authentication based on Blockchain based solution is the primary achievement to conduct an in-expensive and transparent elections to establish democracy.

IoT and Blockchain based citizen information and identification/authentication system and transparent government welfare execution systems help to create an ecosystem where poor, under developing and developing population get access to information, capital, technology and loans that creates an ecosystem of entrepreneurship for developments.

IoT and Blockchain based investment platform can bring the investors from all part of the world to invest with confidence and trusts. Distributed cloud storage, smart contracts, secured remote access and crypto tokens established on the Blockchain system can help to build a successful entrepreneurial ecosystem that will help economic development which is one of the core base for social development.

As we know that the Internet and Mobile are revolutionary technologies, Today IoT with Blockchain are in the same line and complementing with these technologies contributes to the next level of overall social economic development.

IoT offers to build intelligence in Edge side with Machine Learning, Artificial Intelligence and Analytics. This intelligence helps to build numerous applications that will help to cut cost, bring accuracy, cut wastage, offer quality and build efficiency. The combination of these capabilities of IoT and Blockchain can make it affordable for a under developed and developing population that will help for economic development that will offer social benefits.

As we have seen here IoT and Blockchain based applications can be built in all kinds of areas from public to private, government to non-government, s and Agriculture to Space. This requires a robust platform with all necessary components with expertise consultation and vast use case sample implementations to quickly build any kind of IoT and IoT on Blockchain applications. PAASMER, the platform that offers exactly what is required to build these kind of applications.

PAASMER is a platform offered as a service to build and manage the IoT and Blockchain based prototypes and applications quickly. The platform already offers Machine Learning and Edge Analytics tool kits and soon to offer AI tool kit. It provides device side software and Paasmer agent that help to connect the IoT devices to Paasmer cloud for management. The IoT devices can be prepared, added to Paasmer cloud and configure the device to connect the sensors and actuators. It supports all the most common IoT protocols to connect vast varieties of sensors and actuators. It offers to set the rules to trigger actions for preset conditions on device. All these existing and upcoming features make PAASMER a perfect platform for IoT developers.

The IoT and Blockchain expert team offers consultation and to build any customer specific applications. Register now at developers.paasmer.co and enjoy 3 months free license.

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.

 

SIoT_Paasmer

Smart to Social – Evolution of Social IOT

Sridhar krishnan

Sridhar krishnan

Things, the smart objects turn to social objects to boost the pace of IoT emergency and to make it more universal. The relationships of co-location, co-ownership, co-work and parental among friend objects provide a platform to share services, information, computing, and other resources and output. This modern promising paradigm of technology extension is called Social Internet of Things (SIoT). An inevitable aspect of SIoT is the convergence of smart objects and social media that can introduce new social interactions by enabling the things to have their own social networks and interactions. The smart objects can establish their social relationship based on their activities, interest, and profile.

Here are the three main facets of an SIoT system:

  1. The SIoT is navigable. We can start with one device and navigate through all the devices that are connected to it. It is easy to discover new devices and services using such a social network of IoT devices.
  2. A need of trustworthiness (strength of the relationship) is present between devices (like friends on Facebook).
  3. We can use models like studying human social networks to also study the social networks of IoT devices

Basic Components

In a typical social IoT setting, we treat the devices and services as bots where they can set up relationships between them and modify them over time. This will allow us to seamlessly let the devices cooperate among each other and achieve a complex task.

To make such a model work, we need to have many interoperating components. Let us look at some of the major components of such a system.

ID: we need a unique method of object identification. An ID can be assigned to an object based on traditional parameters such as the MAC ID, IPv6 ID, a universal product code, or some other custom method.

Meta information: along with an ID, we need some meta information about the device that describes its form and operation. This is required to establish appropriate relationships with the device and appropriately place it in the universe of IoT devices.

Security controls: this is like “friend list” settings on Facebook. An owner of a device might place restrictions on the kinds of devices that can connect to it. These are typically referred to as owner controls.

Service discovery: such kind of a system is like a service cloud, where we need to have dedicated directories that store details of devices providing certain kinds of services. It becomes very important to keep these directories up to date such that devices can learn about other devices.

Relationship management: this module manages relationships with other devices. It also stores the types of devices that a given device should try to connect with based on the type of services provided. For example, it makes sense for a light controller to make a relationship with a light sensor.

Service composition: this module takes the social IoT model to a new level.
Smart-Social_1
With SIoT, things can publish information and services, find information and services and get environment characteristics that can be used to achieve the following,

Communal sharing – Behavior of objects with collective relevance

Equality matching – Objects operate as equals and requests/provide information among them in the perspective of providing IOT services to users while maintaining their individuality

Authority ranking – Established between objects of different complexity and hierarchical levels

Market pricing – Working together with the view of achieving mutual benefit. Participation in this relationship only when it is worth the while to do so.

The goal of having such a system is to provide better-integrated services to users. For example, if a person has a power sensor with her air conditioner and this device establishes a relationship with an analytics engine, then it is possible for the ensemble to yield a lot of data about the usage patterns of the air conditioner. If the social model is more expensive, and there are many more devices, then it is possible to compare the data with the usage patterns of other users and come up with even more meaningful data. For example, users can be told that they are the largest energy consumers in their community or among their Facebook friends.

References
https://www.hindawi.com/journals/jece/2017/9324035/
https://www.linkedin.com/pulse/social-internet-things-future-smart-objects-michael-kamleitner
https://www.journals.elsevier.com/future-generation-computer-systems/call-for-papers/enabling-technologies-for-social-internet-of-things
https://www.slideshare.net/LuigiAtzori/social-io-tsito-siot?ref=http://www.social-iot.org/

IonBlock-Sdk-Paasmer

How to Use the IonBloc SDK

ManagementTeamMouli1

Srinidhi Murthy

What is Block Chain?

A blockchain – originally block chain – is a distributed database that is used to maintain a continuously growing list of records, called blocks. Each block contains a timestamp and a link to a previous block. A blockchain is typically managed by a peer-to-peer network collectively adhering to a protocol for validating new blocks. By design, blockchains are inherently resistant to modification of the data. Once recorded, the data in any given block cannot be altered retroactively without the alteration of all subsequent blocks and a collusion of the network majority. Functionally, a blockchain can serve as “an open, distributed ledger that can record transactions between two parties efficiently and in a verifiable and permanent way. The ledger itself can also be programmed to trigger transactions automatically.”

Blockchains are secure by design and are an example of a distributed computing system with high Byzantine fault tolerance. Decentralized consensus has therefore been achieved with a blockchain. This makes blockchains potentially suitable for the recording of events, medical records, and other records management activities, such as identity management, transaction processing, and documenting provenance.

IonBloc

ionBlocIonBloc Data flow and usage

IonBloc is our version of Open source Ethereum Blockchain network that can be setup as a public production implementation or a private test implementation depending on client needs.

In an IoT network, the blockchain can keep an immutable record of the history of smart devices. This feature enables the autonomous functioning of smart devices without the need for centralized authority. Thus, blockchain opens the door to a series of IoT scenarios that were remarkably difficult, or even impossible to implement without it.

By leveraging blockchain, IoT solutions can enable secure, trust less messaging between devices in an IoT network. In this model, the blockchain will treat message exchanges between devices like financial transactions in a Bitcoin network. To enable message exchanges, devices will leverage smart contracts which then model the agreement between the two parties.

Using blockchain will enable true autonomous smart devices that can exchange data, or even execute financial transactions, without the need of a centralized broker. This type of autonomy is possible because the nodes in the blockchain network will verify the validity of the transaction without relying on a centralized authority.

Installation:

The IonBloc SDK allows for a seamless connection to our private Blockchain. Here’s how you can do it. You need to have a Raspberry PI 3 with a 16GB SD card and running the latest Raspbian OS. Also, you need to have a stable high speed Internet connection (500 kbps at a minimum)

  • Download the Zip file from Github or execute a git clone command on our repository.
  • Extract and Install the SDK using the Installation script.
  • Some of the necessary support libraries are installed and you are taken to a GETH console.

Once you are in the GETH console,

  • Create a new account. (Noted down the password)
  • Provide our Admin Enode address and Pair
  • That’s it you are connected to our private Block Chain.

Now the possibilities are endless, but to get you started we have given a sample code and procedure to create contracts to blink an LED connected to a GPIO pin on the R-PI.

You can create contracts which have more features, which can read sensors, turn on actuators, monitor sensors etc.
IonBloc SDK is just the starting point … the tip of the iceberg. Some of the more advanced use cases can be found in

IonBloc SDK is just the starting point … the tip of the iceberg. Some of the more advanced use cases can be found in

  • Industrial and manufacturing for improving monitoring and efficient “Just in Time” (JIT) processes.
  • Connected and Driverless vehicles where every vehicle becomes a node and there can be an efficient vehicle to vehicle communication.
  • Public infrastructure and smart cities: Smart devices are already being used to track the health of bridges, roads, power grids etc. Blockchains can be used to interconnect these to share efficiencies and to conduct maintenance, forecast usage trends for power usage, pollution etc.

We have given a limited set of features to create a working POC that can be later developed into fully fledged modules. There are many more features that can be added as a part of customization for specific requirements.
We can do a lot more with IonBloc (some of the features are present in the data flow diagram).

Please feel free to contact us at the mailto:support@paasmer.co for any information or customization.

Paasmer-Ion-Tor_2

How to use the IonToR SDK

ManagementTeamMouli1

Srinidhi Murthy

First Up, What is this TOR Network? TOR or The Onion Router funnels all the data traffic from the device to its end user or master update servers via a Tor-Kind connection, instead of using the public Internet.

The software is run to turn on a Tor configuration, which, in a simplified explanation, sets up a special Onion site on the device. Remote users who want to access the IoT device will need to know the Onion link to the software first, which will then relay the connection to the actual IoT device, working as a proxy. The advantages of using such a system are palpable, for both users and IoT vendors, who might be interested in embedding such technology into their devices by default.

First off, there’s no need to complicate software development with setting up complex SSL/TLS certificates for supporting HTTPS connections, since all Tor connections are encrypted by default, with several layers of encryption (Onion protocol).

Secondly, users don’t need to uselessly open firewall ports or use VPNs to access their IoT devices.

Here’s a simple illustration of how a traditional TOR network Works.Ion-Tor_paasmerOverview

The IonToR SDK provides the ability to connect your things or Devices to the Internet and the ability to control them across a TOR Network via a TOR Browser.

The IonToR SDK for Single Board Computers (SBC) like Raspberry-PI, Intel Edison, and Beagle Bone is a collection of source files that enables you to connect to the IonTor service. It includes the tor libraries to connect to TOR network. It is distributed in the application form and intended to be built into customer solution along with other libraries. The below Image represents how IonTor works.

Ion-Tor-Paasmer1Features

The IonToR SDK simplifies access to the TOR network and automatically configures an .onion DNS name along with a hidden service for accessing a UI on a TOR browser. The SDK installs all necessary software and creates a simple web UI through which sensor data can be viewed and actuators controlled. 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.

Installation
Installation of the IonTor SDK is a matter of a few simple steps and viola! You are ready to control and read sensor information from anywhere in the across a TOR network. The TOR SDK can be installed from the GitHub location. Following the steps in the installation guide (Readme file) will complete the installation.

Installation includes the following modules.

  • HostAPD: to provide an access point for Wireless sensors to connect.
  • LAMP Serve: Facilitate the being up of the UI on the SBC.
  • TOR Installation : Configures Hidden service and provides the “dot onion” DNS address.
  • Configuration files need to be edited to give proper names to Sensors and Actuators

Running the given script enables the data gathering from sensors after configures interval and is stored in the DB.

Tor Client Access Setup

The TOR Browser allows you to access your PAASMER-IonToR instance over Tor from your laptop or mobile device, using Tor Browser

A Hidden Service Authentication credentials must be added to the TOR browser to allow the access of the Hidden Service configured on the SBC. Once connected to the “dot onion” site, you are presented with a graphical representation of your sensor data and Actuator control. This Sensor data being displayed is live data and you can turn on and Off Actuators.

Ion-Tor-Paasmer_dashbord

The IonToR SDK can be used to create Proof of Concept projects that can be later developed into fully fledged modules. There are many more features that can be added as a part of customization for specific requirements.

The IonToR SDK provides a completely anonymous way of accessing your devices, things, sensors and it protects the users and the devices from attacks like DOS, Bot-nets etc.

All connections will go through the Tor hidden network, and nobody will know to what you’re connecting. It could be your IoT baby cam at home or a drug marketplace. It’s anyone’s guess.

Scanning Tor-protected IoT devices are technically impossible. This means no more searching for vulnerable IoT devices via Shodan and blindly stumbling upon vulnerable equipment.

Please feel free to contact us at the support@paasmer.co for any information or customization.

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.

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Smart Transportation and IoT

ManagementTeamMouli1

Srinidhi Murthy

Thanks to laptops and smartphones, human beings are able to stay connected to the Internet more often that ever before, but yet there are still some notable dead spots, particularly in cars, buses, and trains. Anyone with a daily commute can speak to lapses in coverage on the subway or when going through a tunnel.

But the Internet of Things is looking to change all that and keep people connected at every moment of every day. Connected cars, buses, trains, and even planes will allow people to have a stable Internet connection at almost all times.

And the transformation won’t stop there, as the IoT will make transportation itself more efficient and help us get from place to place more quickly.

A new term called as “Internet of Transportation” has been coined and it will create the new era of connected transportation and change how we travel.

With Internet of Transportation assets we can:

  • Reduce congestion by monitoring and controlling traffic lights.
  • Send alerts to drivers and emergency responders on trip conditions, then offer alternative routes.
  • Reduce fuel consumption and vehicle emissions.
  • Provide smart parking solutions that identify and communicate available spaces.
  • Improve safety for motorists, pedestrians, and bicyclists.
  • Identify structural issues of bridges, roadways, and tunnels.
  • Provide smart lighting and security monitoring for city streets.

Let’s look at some of the use cases of Smart Transportation and how they are implemented.

Fleet Management:
Fleet telematics solutions help businesses, transportation carriers, and governments improve economics, safety, and compliance by intelligently monitoring and controlling their vehicles.

Specific or Generic Smart Applications gather and analyze data from on-board instrumentation and GPS sensors to track vehicle status and location, optimize routing, and monitor driver and equipment performance and productivity.

Connected Cars:

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In the last few years, connected cars or smart cars have surged in popularity thanks to the IoT. Today, car companies are connecting their vehicles in two manners: embedded and tethered. Embedded cars employ a built-in antenna and chipset, while tethered connections make use of hardware to let drivers connect to their cars through their smartphones.

On top of this, app integration is becoming more and more standard in the car of today. Google Maps and other navigation systems have started to replace built-in GPS systems in dashboards.

Apps such as GasBuddy show the driver where he or she can locate the cheapest fuel in their area. And music apps such as Spotify have started to away the need for traditional and satellite radio.

Transport Logistics:

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Intelligent transport logistics solutions help long-haul cargo operators and last-mile delivery providers efficiently manage the transportation and distribution of freight and merchandise.

Smart applications gather and analyze data from onboard sensors to track containers and packages, and to monitor environmental conditions, ensuring goods arrive on time, at the right place, intact.

Traffic and Parking Management:

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Integrated Traffic and Parking Management Systems can reduce congestion and save fuel in Business Districts and City Centers. Sensors built into Parking Meters can indicate to a server when a parking slot becomes available. A car User with a Smart Phone App can request to find a parking space within a designated radius based on the GPS location and the server responds with the available parking spaces. This saves fuel and congestion caused by Simply using visual parking search methods.

Traffic Management systems can use sensors, cameras, to find out intersection that has become congested and Use smart algorithms to determine which direction the traffic needs to be moving too quickly clear congestion. It can also act as an Emergency response systems in case of accidents or emergencies.

PAASMER:
Paasmer is an IoT Platform As A Service which can connect any of your IoT devices to the the Paasmer Platform allow for control, visualization and Analytics of Data that are received from the Sensors.

What can PAASMER do for Smart Transportation:
PAASMER is a flexible, economical and easy way to connect your IoT devices and it works on a various range of Hardware devices that are available off the shelf. Using Paasmer Building POC for Smart Transportation would be most reliable, fast and economical. Please do visit us at paasmer.co for more details.