Real-Time Data Delivery Using Prediction Mechanism in Mobile Environments

Abstract

Mobile data delivery is a critical issue in the mobile computing area. One of the most important problems is the efficient access to data. A proposed solution to this problem is the prefetching technique which consists in putting in reserving the information before the users need it. Low bandwidth, unreliable wireless links, and frequent disconnections of mobile environments make it difficult to satisfy the timing requirements of traditional strategies. This paper investigates broadcast scheduling strategies for push-based broadcast with timing constraints in the form of deadlines ,and proposes a prediction algorithm based on Kalman filter theory for this study. The proposed dissemination policy and adaptive bandwidth allocation scheme obtain sufficient conditions such that all the time-bounded traffic sources satisfy their timing constraints to provide various quality of service guarantees in the broadcast period. Our goal is to identify scheduling algorithms for broadcast systems that ensure requests meeting their deadlines. Our approach examines the performance of traditional real-time strategies and mobile broadcasting strategies, and demonstrates that traditional real-time algorithms do not always perform the best in mobile environments. The proposed design indeed achieves good performance in mobile environments.