A new generation of social discovery and crowd-sourced location-based services promise to integrate mobile computing into our lives more than ever. An essential component in these services is the neighbor or peer selection, that is also needed for peer-to-peer (P2P) mobile applications and opportunistic networking. One major challenge is posed by the interpretation of the information collected by the smart-phones (about nearby devices, locations) to bring the user closer to context-awareness and informed peer selection . In this work, we introduce a framework for mobile peer rating using a multi-dimensional metric scheme based on encounter and location sensing; called ConnectEnc.

As a main contribution, this study investigates the interpretation of the collected information and its meaning to the users through actual deployment of our mobile app. It reveals that in applications like social discovery, getting a perfect match is no longer the goal. Instead, providing the user with a balance between acquaintances and new matches is a more useful and realistic measure.

ConnectEnc maintains a history of discovered nearby devices and locations, and rates them using an array of metrics ranging in complexity from simple (encounter frequency) to complex and novel (location vector and matrix similarity). It is evaluated via trace driven analysis, implementation deployment of social discovery and large-scale simulations of filtered DTN routing. Our analysis shows that the metrics can provide stable rating and facilitate cooperative networking. Results from the deployment shows that statistically high correlation exists between ConnectEnc recommendations and user selections.

Our framework is distributed, modular and can serve as a peer selection platform for other mobile protocols based on specific requirements. It runs on individual devices and does not require data sharing or interaction; and hence can boot-strap recommendation, cooperation or reputation systems.