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/

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.

Smart_home_protected_paasmer

Smart Home :- A protected Nest or an Open Book

ManagementTeamMouli1

Kavitha Gopalan

IOT is revolutionizing several industries including the consumer industry. Smart home has emerged as one of the top adopters of IOT with some of the cool innovative products. And it’s growing at a phenomenal phase. In 2016, 80 million smart home devices were delivered worldwide, a 64 percent increase from 2015, according to IHS Markit. CES 2017 saw around 190 exhibitors showcasing some of the innovative and futuristic smart home offerings.

The idea of connecting the everyday home devices to the Internet and its ability to be controlled from anywhere was refreshing and everyone jumped on the smart home train. Answering your door even while you are on a vacation, turning on your sprinkler while you are away from home, getting your coffee ready while you still asleep all these looked like a dream from a SCi-FI movie and yet it was affordable and adoptable. This helped in the steep growth of smart home products in the last couple of years. Definitely smart home is taking the concept of home to a new level.

However, the question remains if the privacy and security which is the foundation of the home will get lost as we move into the era of the smart home. Is my private data safe anymore? Am I the only one who knows about me?

Home Smart Home: Domesticating the Internet of Things written by Kent Mundle of toptal discuss this key aspect.

He says “The home is the original security device – the original firewall. But now, as we allow the entire world to float through our walls and into our homes, have we deflated the entire meaning of our home that has stood for millennia? We speak of security and privacy now in the context of technical systems and hardware. But have we forgotten the origin of what privacy meant? In the spaces where we were once the most intimate, by inviting the world in we are becoming the most exposed. To adopt the Smart Home, must we forfeit the home?

Read more here https://www.toptal.com/designers/interactive/smart-home-domestic-internet-of-things

The Smart home devices have become the hub for several security attacks. The recent Mirai security attack used smart home devices as a botnet to create havoc. Hence it becomes imperative to secure the connected devices to prevent any unwarranted usage.

PAASMER IOT platform follows a ground-up implementation to ensure data from the device to cloud and beyond is secure and no data compromise happens. It also ensures that the devices are not exposed to any kind of attack.

Read more about how PAASMER IOT platform help to build secure IOT products and solutions.

http://blogs.paasmer.co/securing-iot-devices-through-paasmer/

iot-and-security-challenges

Real State of IoT and the Security challenges

ManagementTeamMouli1

Chandramouli Srinivasan

In a recent article by our friend Nermin at Toptal says “The Internet of Things (IoT) has been an industry buzzword for years, but sluggish development and limited commercialization have led some industry watchers to start calling it the “Internet of NoThings”. Double puns aside, IoT development is in trouble. Aside from spawning geeky jokes unfit for most social occasions, the hype did not help; and, in fact, I believe it actually caused a lot more harm than good. There are a few problems with IoT, but all the positive coverage and baseless hype are one we could do without. The upside of generating more attention is clear: more investment, more VC funding, more consumer interest”

He also says the top two challenges that continue to haunt IoT as “1) Security – Just not the vulnerable devices that get hacked but also the misuse of the data collected from devices. 2) Hardware pain points – Security needs to be built from hardware and that comes at an additional cost”

While the concerns are genuine on security, we have been talking to a few IoT device manufacturers on the need to increase their budget for hardware and software to secure the devices they use or sell. Most of these cases, we still a lot of reluctance to implement additional security at an additional cost on the consumer side while enterprises are willing to secure the devices at an additional cost. Also the new software paradigms of “IoT on ToR” and “IoT on BlockChain” are also starting to get traction and they also are going to come at additional cost. It appears like it will take many more massive security attacks like Anti-DDoS to shift the mindset to put security first in IoT product designs. The questions remain as “Are we willing to pay the price for what?”

Read full article from Nermin in this link: https://www.toptal.com/it/are-we-creating-an-insecure-internet-of-things

Also check out our article on “IoT on BlockChain” in this link: http://blogs.paasmer.co/a-marriage-made-in-heaven-iot-blockchain/