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

Tsungnan Lin*, Chiapin Wang, Po Chiang Lin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

22 Citations (Scopus)


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.

Original languageEnglish
Article number17
JournalACM Transactions on Multimedia Computing, Communications and Applications
Issue number3
Publication statusPublished - 2008 Aug 1


  • Context-aware
  • Handoff
  • Multimedia computing
  • Neural networks

ASJC Scopus subject areas

  • Hardware and Architecture
  • Computer Networks and Communications


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