CAPRIO: Context-Aware Path Recommendation Exploiting Indoor and Outdoor Information

During extreme weather conditions and natural disasters caused by meteorological phenomena, it is imperative to enable navigation that minimizes the outdoor section of recommended paths. Existing indoor-outdoor navigation and localization systems have evolved to support queries like the shortest distance, either outdoor or indoor, with additional constraints. However, most of them work in isolation and do not take into consideration the external natural conditions, like the weather, that an individual may experience walking outside during a polar vortex or heatwave. In this paper, we present CAPRIO, a context-aware path recommendation system whose objectives are two-fold: (i) minimizing outdoor exposure; and (ii) minimizing the distance of the recommended path. We propose a novel graph representation that integrates indoor and outdoor information to discover paths that satisfy outdoor exposure and distance constraints. We measure the efficiency of the proposed solution using two real datasets collected from the University of Pittsburgh and University of Cyprus campuses. We show that we can achieve comparable distance to the state-of-the-art in minimizing outdoor exposure.