Discovering good links between objects in the internet of things

The Internet of Things is an emerging paradigm allowing the control of the physical world via the Internet protocol and both human-to-machine and machine-to-machine communication. In this scenario, one of the most challenging issues is how to choose links among objects in order to guarantee an effective access to services and data. In this paper, we present a new selection criterion that improves the classical approach. To reach this goal, we extract knowledge coming from the social network of humans, as owners of objects, and we exploit a recently proven property called interest assortativity. The preliminary experimental results reported in this paper give a first evidence of the effectiveness of our approach, which performs better than classical strategies. This is achieved by choosing only not redundant links in such a way that network connectivity is preserved and power consumption is reduced.