Social Networks (SNs), such as Facebook, Twitter, Google+, are becoming more and more popular nowadays. People are now more connected than before. They share information, pictures, videos and news with their family and friends. However, sharing physical phenomena in SNs is still a manual process done by people themselves. For instance, people would like to share current health status, feelings, thoughts, weather or riding information with friends. The sharing of ambient information automatically in SNs can promote independent living. Moreover, it can enhance the autonomy and confidence of elderly people via continuous monitoring and health support. A set of biometric sensors, for example, placed within a patient body can inform a doctor about patient’s health status; hence the doctor can perform a remote diagnosis.

Nowadays people are surrounded by devices like smartphone, sensors, cameras, computers and many other devices known as machines. These devices can automatically collect contextual information from the neighborhood. This thesis proposes an architecture for posting contextual information in SNs to support the automatic sharing of physical phenomena. In the proposed architecture, machines collect the contextual data through an overlay-based gateway to support scalability in terms of number of devices. Considering the resource-constrained devices, the architecture makes use of the Constrained Application Protocol (CoAP), a lightweight standard protocol. An SN processes that data into shareable information and disseminates it as appropriate within the users’ Community of Interests (COIs) (e.g., family, friends).

A proof of concept prototype is developed to verify the feasibility of the proposed architecture and its performance has been partially evaluated.