A neural-network-based context-aware handoff algorithm for multimedia computing
Journal
ACM Transactions on Multimedia Computing, Communications and Applications
Journal Volume
4
Journal Issue
3
Date Issued
2008
Author(s)
Abstract
The access of multimedia computing in wireless networks is concerned with the performance of handoff because of the irretrievable property of real-time data delivery. To lessen throughput degradation incurred by unnecessary handoffs or handoff latencies leading to media disruption perceived by users, this paper presents a link quality based handoff algorithm. Neural networks are used to learn the cross-layer correlation between the link quality estimator such as packet success rate and the corresponding context metric indictors, for example, the transmitting packet length, received signal strength, and signal to noise ratio. Based on a pre-processed learning of link quality profile, neural networks make essential handoff decisions efficiently with the evaluations of link quality instead of the comparisons between relative signal strength. The experiment and simulation results show that the proposed algorithm improves the user perceived qualities in a transmission scenario of VoIP applications by minimizing both the number of lost packets and unnecessary handoffs. © 2008 ACM.
Subjects
Context-aware; Handoff; Multimedia computing; Neural networks
Other Subjects
Neural networks; Signal to noise ratio; Context-Aware; Handoff; Handoff algorithms; Multimedia computing; Received signal strength; Relative signal strengths; Throughput degradation; VoIP applications; Multilayer neural networks
Type
journal article