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A neural-network-based context-aware handoff algorithm for multimedia computing

Published:18 September 2008Publication History
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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.

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          cover image ACM Transactions on Multimedia Computing, Communications, and Applications
          ACM Transactions on Multimedia Computing, Communications, and Applications  Volume 4, Issue 3
          August 2008
          136 pages
          ISSN:1551-6857
          EISSN:1551-6865
          DOI:10.1145/1386109
          Issue’s Table of Contents

          Copyright © 2008 ACM

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          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 18 September 2008
          • Accepted: 1 October 2007
          • Revised: 1 March 2007
          • Received: 1 January 2006
          Published in tomm Volume 4, Issue 3

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