Recommendation-based Smart Indoor Navigation: Poster Abstract

Localization in indoor spaces has to rely on sensing devices (e.g., Radio Frequency Identification (RFID) readers, WiFi routers, bluetooth beacons) rather than GPS devices. On the other side, we could build a smart indoor environment that facilitates all types of spatial services with various sensing devices. In this paper, we focus on the topic of spatial navigation.

Due to the complexity of indoor environments, we believe the indoor navigation strategy should not be limited to the shortest path. Taking shopping centers for example, a navigation path should be not only the shortest path, but also an attractive route to the shopper. We aim to build a smart indoor navigation system, which not only learns the user's behavior through previous sensing data, but also enjoys working with heterogeneous devices. Therefore, we propose a novel recommendation based smart navigation strategy with Recurrent Neural Network (RNN). This strategy provides optimal user experience by: 1) Memorizing the user's historical data; 2) Overlapping the navigation with the user's indoor behavior model; 3) Making recommendations based on real-time detections from sensor devices.