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.
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.



How to Use the IonBloc SDK


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.


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.


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 for any information or customization.


How to use the IonToR SDK


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.


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 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.


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 for any information or customization.


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 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.

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.

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.


Machine Learning and IoT


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 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

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.


Smart Transportation and IoT


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:

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:


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:



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 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 for more details.

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.


Build Your IoT Solution using PAASMER SDK


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.

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

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.

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.

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.


Overview of PAASMER IOT platform

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.

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

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.

The PAASMER developer Login is available at 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.


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.


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.,


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.,

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