Fuzzy Logic and Regression Approaches for Adaptive Sampling of Multimedia Traffic in Wireless Computer Networks †
1
Materials and Engineering Research Institute, Sheffield Hallam University, Howard Street, Sheffield S1 1WB, UK
2
Department of Engineering and Mathematics, Sheffield Hallam University, City Campus, Sheaf Building, Howard Street, Sheffield S1 1WB, UK
3
Sheffield Children’s Hospital, Western Bank, Sheffield S10 2TH, UK
*
Author to whom correspondence should be addressed.
†
This paper is an extended version of our paper published in AAATE2017 Congress Proceedings, Sheffield, UK, 13–14 September 2017, with permission from IOS Press.
Technologies 2018, 6(1), 24; https://doi.org/10.3390/technologies6010024
Received: 22 December 2017 / Revised: 8 February 2018 / Accepted: 9 February 2018 / Published: 13 February 2018
(This article belongs to the Special Issue Selected Papers from AAATE2017 Congress)
Organisations such as hospitals and the public are increasingly relying on large computer networks to access information and to communicate multimedia-type data. To assess the effectiveness of these networks, the traffic parameters need to be analysed. Due to the quantity of the data packets, examining each packet’s transmission parameters is not practical, especially in real time. Sampling techniques allow a subset of packets that accurately represents the original traffic to be examined and they are thus important in evaluating the performance of multimedia networks. In this study, an adaptive sampling technique based on regression and a fuzzy inference system was developed. The technique dynamically updates the number of packets sampled by responding to the traffic’s variations. Its performance was found to be superior to the conventional nonadaptive sampling methods.
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Keywords:
computer network traffic sampling; multimedia transmission; quality of service; network performance evaluation
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MDPI and ACS Style
Salama, A.; Saatchi, R.; Burke, D. Fuzzy Logic and Regression Approaches for Adaptive Sampling of Multimedia Traffic in Wireless Computer Networks. Technologies 2018, 6, 24. https://doi.org/10.3390/technologies6010024
AMA Style
Salama A, Saatchi R, Burke D. Fuzzy Logic and Regression Approaches for Adaptive Sampling of Multimedia Traffic in Wireless Computer Networks. Technologies. 2018; 6(1):24. https://doi.org/10.3390/technologies6010024
Chicago/Turabian StyleSalama, Abdussalam, Reza Saatchi, and Derek Burke. 2018. "Fuzzy Logic and Regression Approaches for Adaptive Sampling of Multimedia Traffic in Wireless Computer Networks" Technologies 6, no. 1: 24. https://doi.org/10.3390/technologies6010024
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