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Provisioning QoS for WiFi-enabled Portable Devices in Home Networks

Cited 3 time in Web of Science Cited 3 time in Scopus
Authors

Park, Eun-Chan; Kwak, Nojun; Lee, Suk Kyu; Kim, Jong-Kook; Kim, Hwangnam

Issue Date
2011-04
Publisher
KSII-KOR SOC INTERNET INFORMATION
Citation
KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS, Vol.5 No.4, pp.720-740
Abstract
Wi-Fi-enabled portable devices have recently been introduced into the consumer electronics market. These devices download or upload content, from or to a host machine, such as a personal computer, a laptop, a home gateway, or a media server. This paper investigates the fairness among multiple Wi-Fi-enabled portable devices in a home network when they are simultaneously communicated with the host machine. First, we present that, a simple IEEE 802.11-based home network suffers from unfairness, and the fairness is exaggerated by the wireless link errors. This unfairness is due to the asymmetric response of the TCP to data-packet loss and to acknowledgment-packet loss, and the wireless link errors that occur in the proximity of any node; the errors affect other wireless devices through the interaction at the interface queue of the home gateway. We propose a QoS-provisioning framework in order to achieve per-device fairness and service differentiation. For this purpose, we introduce the medium access price, which denotes an aggregate value of network-wide traffic load, per-device link usage, and per-device link error rate. We implemented the proposed framework in the ns-2 simulator, and carried out a simulation study to evaluate its performance with respect to fairness, service differentiation, loss and delay. The simulation results indicate that the proposed method enforces the per-device fairness, regardless of the number of devices present and regardless of the level of wireless link errors; furthermore it achieves high link utilization with only a small amount of frame losses.
ISSN
1976-7277
URI
https://hdl.handle.net/10371/207996
DOI
https://doi.org/10.3837/tiis.2011.04.006
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  • Graduate School of Convergence Science & Technology
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Research Area Feature Selection and Extraction, Object Detection, Object Recognition

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