The Social Internet of Things (SIoT) is that part of an IoT which is capable of establishing social relationships with other objects with respect to humans. SIoT, attempts to moderate the challenges of IoT in the areas of scalability, trust, and resource discovery by taking a cue from social computing. Representative architecture is a part of the social Internet of things that provides such as navigation where one device is initialized and through it, we can navigate to other connected devices and linked back to the start device thereby creating autonomous relationships between objects and humans.
Basic components of SIoT
IoT requires many devices to be interoperable for the model to function well. The following are some of the major components that enable SIoT applications to be successful:
ID: This refers to the unique method of object identification that is assigned to objects in a typical system. Examples of an ID include MACID, IPv6ID, universal product, and other custom methods.
Meta-information: This describes the form and operations of a device in a system. It is needed to establish relationships with other devices by placing them appropriately within the universe of IoT devices.
Security controls: This is synonymous with a friend list on Facebook, where an owner of the device puts some restrictions regarding how some devices can connect to them. It is sometimes also known as owner controls.
Service discovery: Similar to a system like a service cloud, dedicated directories are created to store details of devices that provide certain kinds of services. Keeping the directories up to date make it possible for devices tolearn about other devices.
Relationship management: This refers to the relationship between devices and how they are managed. For example, storing the relationship between the light controller and a light sensor.
Service composition: This part of the module in the SIoT provides better-integrated services to users. It allows the system to establish a relationship with an analytics engine where large data that are generated are analyzed to learn about the usage pattern for further improved output or services. Thus it is possible to identify users based on say three categories of heavy, medium, and low-energy consumers in their community or among their Facebook friends.
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