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.