No abstract

No abstract

No abstract

No abstract

Along with the recent and ongoing advances in the wireless and mobile access technologies, a wide plethora of mobile multimedia services have emerged. Ensuring an acceptable level of Quality of Service (QoS) is a crucial requirement to allow users enjoy these mobile multimedia services. One means to ensure QoS is to minimize the frequency and magnitude of fluctuations in the mobile multimedia streaming rates during the multimedia service and while users are on the move. For this purpose, there is need for tools to predict a user's long-term movement. In this vein, this paper proposes a Path Prediction Model (PPM) to predict a user's movement path. PPM is based on historical movement trace, current movement data and spatial conceptual maps; it assumes a priori knowledge of the destination. At each road intersection, the probability of selecting the next road segment is evaluated, based on historical data, towards the destination. These probabilities are computed via (a) filtering historical data according to the day of the week (e.g., weekend, holiday) and the time of the day; and (b) applying conditional probability rules taking into account the path used between the origin of movement, current position, and the destination. Simulations are conducted using real-life data to evaluate the performance of the proposed model. Encouraging results are obtained in terms of average prediction accuracy and mitigation of the impact of learning period and the remaining distance to reach the destination on the path prediction performance.