Paper Info
Title
A neural-network-based context-aware handoff algorithm for multimedia computing
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.
Year
DOI
Venue
2005
10.1145/1386109.1386110
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Keywords
DocType
Volume
link quality,handoff,neural network,relative signal strength,essential handoff decision,handoff algorithm,link quality estimator,context-aware,multimedia computing,handoff latency,link quality profile,signal strength,neural-network-based context-aware handoff algorithm,neural networks,unnecessary handoffs,real time data,degradation,neural nets,wireless network,videoconference,wireless networks,hysteresis,throughput,bandwidth
Conference
4
Issue
ISSN
Citations 
3
1551-6857
13
PageRank 
References 
Authors
0.73
35
3
Name
Order
Citations
PageRank
T. Lin172467.09
Chiapin Wang211710.51
Po-Chiang Lin313910.26