Fuzzy Logic and Regression Approaches for Adaptive Sampling of Multimedia Traffic in Wireless Computer Networks

SALAMA, Abussalam, SAATCHI, Reza and BURKE, Derek (2018). Fuzzy Logic and Regression Approaches for Adaptive Sampling of Multimedia Traffic in Wireless Computer Networks. Technologies, 6(1) (24), 1-17.

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Official URL: http://www.mdpi.com/2227-7080/6/1/24
Link to published version:: 10.3390/technologies6010024


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. Keywords: computer network traffic sampling; multimedia transmission; quality of service; network performance evaluation

Item Type: Article
Additional Information: Please note the paper is on-line. The submitted form is the one attached to the SHURA, i.e. with the journal logo etc. shown. The slightly changed some figures before publishing it, so a Microsoft version of the final version in publication is not currently available but if this is essential, it can be produced.
Research Institute, Centre or Group: Materials and Engineering Research Institute > Centre for Automation and Robotics Research > Mobile Machine and Vision Laboratory
Departments: Arts, Computing, Engineering and Sciences > Engineering and Mathematics
Identification Number: 10.3390/technologies6010024
Related URLs:
Depositing User: Reza Saatchi
Date Deposited: 16 Feb 2018 11:08
Last Modified: 16 Feb 2018 11:13
URI: http://shura.shu.ac.uk/id/eprint/18668

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