The concept of the Internet of Things (IoT) has been thriving over the last couple years. As computing systems have become more ubiquitous due to the reduced prices of chips and sensors, the proliferation of mobile devices and sensors has created opportunities for context-aware applications. Previously, we have built Cacophony, a network of peer-to-peer nodes where each Cacophony node (C-Node) is capable of monitoring, reasoning about, and providing real-time prediction of a specified set of sensors in the wild. However, making meaning out of data from sensors and aggregating data from other C-Nodes can be difficult. A robust system is necessary for C-Nodes to deal with unavailable nodes and communicate with other online nodes in a peer-to-peer network.

In this thesis, I propose a new communication model for sensors on the basis of sociological theories. I have developed a group status system to examine people's mental process of building awareness of their friends' daily trends of life and have conducted two pilot studies to gain a deeper understanding of computer-mediated communication behaviors. In the pilot studies, participants are required to report their statuses and guess their friends' via their smartphones every three hours from 9 A.M. to 9 P.M. Through follow-up semi-structured interviews with the participants, we uncover their rationale for the status they reported and their behaviors in computer-mediated communication by drawing on social exchange theory and social penetration theory. The results give new insights into communication behaviors between humans and help us establish a different communication model for C-Nodes, as human communication in a (social) network can provide a template for how C-Nodes interact in a peer-to-peer computer network.