A mobility prediction architecture based on contextual knowledge and spatial conceptual maps

Abstract

User Mobility prediction represents a key component in assisting handoff management, resource reservation, and service preconfiguration. However, most of the existing approaches presume that the user travels in an a priori known pattern with some regularity; an assumption that may not always hold. This paper presents a novel framework for user mobility prediction that can accurately predict the traveling trajectory and destination using knowledge of user's preferences, goals, and analyzed spatial information without imposing any assumptions about the availability of users' movements history. This framework thus incorporates the notion of combining user context and spatial conceptual maps in the prediction process. The main objective of this notion is to circumvent the difficulties that arise in predicting the user's future location when adequate knowledge about the history of user's traveling patterns is not available. Using concepts of evidential reasoning of Dempster-Shafer's theory, the user's navigation behavior is captured by gathering pieces of evidence concerning different groups of candidate future locations. These groups are then refined to predict the user's future location when evidence accumulates using the Dempster rule of combination. Simulation results are presented to demonstrate the performance of the proposed framework.