QoS Optimal Location-Based Services Architecture Based on Mobile Cloud Computing and Behavior Prediction

Abstract

With an explosive growth of personalized services based on mobile users' location information, the QoS optimization of Location-based services (LBS) based on mobile cloud computing have become a focus of research. The massive LBS requests and intelligent service associated with the users' movement should be taken into account. This paper proposes a new LBS cloud architecture based on behavior prediction, which processes massive LBS requests with mobile cloud computing and provides intelligent services relying on the behavior prediction. We have analyzed the performance of the new architecture using numerical analysis method. The experimental results show that the proposed architecture can ameliorate the platform processing capacity significantly.