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.



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.


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.

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.


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.,

References –

Hybride_internet of things_platform

Hybrid IOT: The path forward for Industrial IOT

Sridhar krishnan

Sridhar krishnan

IIOT for manufacturing efficiency

The Industrial Internet of Things can help the manufacturing industries to transform to a smart industry. It enables them to collect data on machines, manufacturing process, the products produced and the quality of the products from the systems like mechatronics, embedded systems, and sensor hardware network. IIOT offers analytical applications to extract information from the raw data collected from various systems. The information helps the industries to improve the efficiency of the manufacturing process that will result in improve their productivity with optimized cost and improved quality, implement automation and predictive maintenance to avoid breakdowns.


Challenges with mix of legacy and modern systems

The challenges and complexities of industries in implementing IIOT applications are various kinds of machines in the industry both legacy and modern. Most of the legacy machines have older sensors, controllers and proprietary systems, whereas the modern manufacturing equipment offers a wide selection of connectivity options, a whole range of data feeds and integrates data into modern data analytics software. The industry is working on to build and implement standard protocols, compatible interfaces and architectures to achieve full interoperability. But bringing old machines into IIOT is continue to be challenging as they require retrofitting of sensors, connectivity and simple compute endpoints to generate any kind of meaningful data at all.

Edge devices can solve the problem

The Edge or Gateway IIOT device which can act as a bridge between the data generating endpoints and IIOT cloud applications can solve the problem of connecting older sensors, controllers and proprietary system by custom implementations.The different kinds of capabilities can be added in the Edge devices to access different kinds of machines and sensors both legacy and modern. These devices can be deployed and IIOT applications can be provisioned to implement machine to machine communications and to connect with IIOT management platform.


Hybrid IIOT development platform for Edge device development and management

We have a challenge in developing different kinds of edge devices to connect both older and modern machines with IIOT management platform to build a complete IIOT implementation for a manufacturing industry.

A hybrid IIOT platform can solve this challenge by offering the different kinds of Edge device development kits both in-premises private cloud with industry-specific support to develop and manage the edge devices for older machines and in public cloud with open standard support to develop and manage the edge devices for modern machines. So, A Hybrid IIOT development and management platform can solve the challenges in implementing a complete IIOT solution for industries.