Using Multiple Contexts To Detect And Form Opportunistic Groups

We present a new technique that allows mobile devices to opportunistically group with one another, thus improving their ability to facilitate one-time or spontaneous exchanges of information. In our approach, devices share context with each other, and form groups when these readings are found to be similar to one another. Through a formative study, we examine the limitations of using a single type of context to form groups, and show how leveraging multiple contexts improves our ability to detect and form relevant groupings. We then present DIDJA, a robust software toolkit that automatically collects and analyzes contextual information in order to find and form groups. Through two prototypes, we demonstrate how DIDJA enhances existing user experiences, and show how developers can use our toolkit to easily facilitate frictionless collaborations between users and their environment. We then perform an extended experiment and show how DIDJA is able to accurately form groups under realistic conditions.