DroidOppPathFinder: A context and social-aware path recommender system based on opportunistic sensing

Abstract

In this paper we present DroidOppPathFinder, a Mobile Social Network application designed to generate and share contents about paths for fitness activity in a city. The application is able to recommend the best path in a specific area by analyzing the user's preferences and real-time environmental characteristics collected by heterogeneous sensing devices and services through opportunistic sensing mechanisms. To this aim, DroidOppPathFinder is developed on top of our middleware CAMEO, which provides context- and social-aware functionalities to improve both the application's performances and the user experience. This work represents a real example of opportunistic sensing service as additional support to the development of MSN applications. In addition, it demonstrates an efficient management of heterogeneous sensing data and services on mobile devices in order to further enrich the context of both local and remote nodes.