Using social network graphs for search space reduction in internet of things

In this paper, we explore reduction of search space in sensor data analytics using social network graph theory. Human centric social network allow graphical connect of individuals either based on familiarity or common intent. This facilitates people centric applications as they now operate on a much smaller data set. Extending this analogy to sensor networks, if sensors can be associated with meaningful social groups, it will reduce sensor data analytics and processing overhead for an application by a huge order. In this paper we explore how in Internet-of-Things sensors can be assigned to human beings who in turn are connected in social networks. Effectively in this way, sensors become part of a social network that results in a reduced data set for sensor data analytics.