Data processing with time constraint using prediction algorithm in mobile environment

This paper describes data processing with real-time property based on prediction mechanism in wireless multichannel environments. Low bandwidth, unreliable wireless links, and frequent disconnections of mobile environments make it difficult to satisfy the timing requirements of supported strategies. This study 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 (QoS) guarantees in the broadcast period. Our goal is to identify scheduling algorithms for broadcast systems that ensure requests meet their deadlines. This study 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 a mobile environment. The proposed design indeed achieves good performance in mobile environments.