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Adaptive wireless thin-client model for mobile computing

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Adaptive wireless thin-client model for mobile computing. / Al-Turkistany, M.; Helal, Sumi; Schmalz, M.

In: Wireless Communications and Mobile Computing, Vol. 9, No. 1, 01.2009, p. 47-59.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Al-Turkistany, M, Helal, S & Schmalz, M 2009, 'Adaptive wireless thin-client model for mobile computing', Wireless Communications and Mobile Computing, vol. 9, no. 1, pp. 47-59. https://doi.org/10.1002/wcm.603

APA

Al-Turkistany, M., Helal, S., & Schmalz, M. (2009). Adaptive wireless thin-client model for mobile computing. Wireless Communications and Mobile Computing, 9(1), 47-59. https://doi.org/10.1002/wcm.603

Vancouver

Al-Turkistany M, Helal S, Schmalz M. Adaptive wireless thin-client model for mobile computing. Wireless Communications and Mobile Computing. 2009 Jan;9(1):47-59. https://doi.org/10.1002/wcm.603

Author

Al-Turkistany, M. ; Helal, Sumi ; Schmalz, M. / Adaptive wireless thin-client model for mobile computing. In: Wireless Communications and Mobile Computing. 2009 ; Vol. 9, No. 1. pp. 47-59.

Bibtex

@article{0b3a278f8a0a49c3827431e0efea08dd,
title = "Adaptive wireless thin-client model for mobile computing",
abstract = "The thin-client computing model has the potential to significantly increase the performance of mobile computing environments. By delivering any application through a single, small-footprint client (called a thin client) implemented on a mobile device, it is possible to optimize application performance without the need for building wireless application gateways. We thus present two significant contributions in the area of wireless thin-client computing. Firstly, a mathematical performance model is derived for wireless thin-client system. This model identifies factors that affect the performance of the system and supports derivation and analysis of adaptation strategies to maintain a user-specified quality of service (QoS). Secondly, a proxy-based adaptation framework is developed for wireless thin-client systems, which dynamically optimizes performance of a wireless thin client via dynamically discovered context. This is implemented with rule-based fuzzy logic that responds to variations in wireless link bandwidth and client processing power. Our fuzzy inference engine uses contextual data to dynamically optimize tradeoffs among different quality of service parameters offered to the end users. Additionally, our adaptation framework uses highly scalable wavelet-based image coding to provide scalable QoS that can degrade gracefully. Our thin-client adaptation framework shields the user from ill effects of highly variable wireless network quality and mobile device resources. This improves performance of active applications, in which the display changes frequently. Further, active application behaviour may produce high transmission latency for screen updates, which can adversely affect user perception of QoS, resulting in poor interactivity. We report measured adaptive performance under realistic mobile device and network conditions for several different clients and servers. Copyright {\textcopyright} 2008 John Wiley & Sons, Ltd.",
keywords = "Mobile computing models, Mobility adaptations, Thin-client model, Applications, Fuzzy inference, Fuzzy logic, Image coding, Mobile devices, Network performance, Object recognition, Portable equipment, Quality control, Quality of service, Telecommunication equipment, Telecommunication systems, Visual communication, Wireless networks, Active applications, Adaptation frameworks, Adaptation strategies, Application performance, Device resources, End users, Fuzzy inference engines, High transmissions, Interactivity, Mathematical performance, Mobile computing environments, Network conditions, Processing power, Quality of Service parameters, Rule-based, Thin-client computing, Thin-client systems, User perceptions, Wavelet-based image coding, Wireless applications, Wireless links, Mobile computing",
author = "M. Al-Turkistany and Sumi Helal and M. Schmalz",
year = "2009",
month = jan,
doi = "10.1002/wcm.603",
language = "English",
volume = "9",
pages = "47--59",
journal = "Wireless Communications and Mobile Computing",
issn = "1530-8669",
publisher = "John Wiley and Sons Ltd",
number = "1",

}

RIS

TY - JOUR

T1 - Adaptive wireless thin-client model for mobile computing

AU - Al-Turkistany, M.

AU - Helal, Sumi

AU - Schmalz, M.

PY - 2009/1

Y1 - 2009/1

N2 - The thin-client computing model has the potential to significantly increase the performance of mobile computing environments. By delivering any application through a single, small-footprint client (called a thin client) implemented on a mobile device, it is possible to optimize application performance without the need for building wireless application gateways. We thus present two significant contributions in the area of wireless thin-client computing. Firstly, a mathematical performance model is derived for wireless thin-client system. This model identifies factors that affect the performance of the system and supports derivation and analysis of adaptation strategies to maintain a user-specified quality of service (QoS). Secondly, a proxy-based adaptation framework is developed for wireless thin-client systems, which dynamically optimizes performance of a wireless thin client via dynamically discovered context. This is implemented with rule-based fuzzy logic that responds to variations in wireless link bandwidth and client processing power. Our fuzzy inference engine uses contextual data to dynamically optimize tradeoffs among different quality of service parameters offered to the end users. Additionally, our adaptation framework uses highly scalable wavelet-based image coding to provide scalable QoS that can degrade gracefully. Our thin-client adaptation framework shields the user from ill effects of highly variable wireless network quality and mobile device resources. This improves performance of active applications, in which the display changes frequently. Further, active application behaviour may produce high transmission latency for screen updates, which can adversely affect user perception of QoS, resulting in poor interactivity. We report measured adaptive performance under realistic mobile device and network conditions for several different clients and servers. Copyright © 2008 John Wiley & Sons, Ltd.

AB - The thin-client computing model has the potential to significantly increase the performance of mobile computing environments. By delivering any application through a single, small-footprint client (called a thin client) implemented on a mobile device, it is possible to optimize application performance without the need for building wireless application gateways. We thus present two significant contributions in the area of wireless thin-client computing. Firstly, a mathematical performance model is derived for wireless thin-client system. This model identifies factors that affect the performance of the system and supports derivation and analysis of adaptation strategies to maintain a user-specified quality of service (QoS). Secondly, a proxy-based adaptation framework is developed for wireless thin-client systems, which dynamically optimizes performance of a wireless thin client via dynamically discovered context. This is implemented with rule-based fuzzy logic that responds to variations in wireless link bandwidth and client processing power. Our fuzzy inference engine uses contextual data to dynamically optimize tradeoffs among different quality of service parameters offered to the end users. Additionally, our adaptation framework uses highly scalable wavelet-based image coding to provide scalable QoS that can degrade gracefully. Our thin-client adaptation framework shields the user from ill effects of highly variable wireless network quality and mobile device resources. This improves performance of active applications, in which the display changes frequently. Further, active application behaviour may produce high transmission latency for screen updates, which can adversely affect user perception of QoS, resulting in poor interactivity. We report measured adaptive performance under realistic mobile device and network conditions for several different clients and servers. Copyright © 2008 John Wiley & Sons, Ltd.

KW - Mobile computing models

KW - Mobility adaptations

KW - Thin-client model

KW - Applications

KW - Fuzzy inference

KW - Fuzzy logic

KW - Image coding

KW - Mobile devices

KW - Network performance

KW - Object recognition

KW - Portable equipment

KW - Quality control

KW - Quality of service

KW - Telecommunication equipment

KW - Telecommunication systems

KW - Visual communication

KW - Wireless networks

KW - Active applications

KW - Adaptation frameworks

KW - Adaptation strategies

KW - Application performance

KW - Device resources

KW - End users

KW - Fuzzy inference engines

KW - High transmissions

KW - Interactivity

KW - Mathematical performance

KW - Mobile computing environments

KW - Network conditions

KW - Processing power

KW - Quality of Service parameters

KW - Rule-based

KW - Thin-client computing

KW - Thin-client systems

KW - User perceptions

KW - Wavelet-based image coding

KW - Wireless applications

KW - Wireless links

KW - Mobile computing

U2 - 10.1002/wcm.603

DO - 10.1002/wcm.603

M3 - Journal article

VL - 9

SP - 47

EP - 59

JO - Wireless Communications and Mobile Computing

JF - Wireless Communications and Mobile Computing

SN - 1530-8669

IS - 1

ER -