Adaptive sampling for QoS traffic parameters using fuzzy system and regression model

SALAMA, Abdussalam, SAATCHI, Reza and BURKE, Derek (2017). Adaptive sampling for QoS traffic parameters using fuzzy system and regression model. Mathematical Models and Methods in Applied Sciences.

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Abstract

Quality of service evaluation of wired and wireless networks for multimedia communication requires transmission parameters of packets making up the traffic through the medium to be analysed. Sampling methods play an important role in this process. Sampling provides a representative subset of the traffic thus reducing the time and resources needed for packet analysis. In an adaptive sampling, unlike fixed rate sampling, the sample rate changes over time in accordance with transmission rate or other traffic characteristics and thus could be more optimal than fixed parameter sampling. In this study an adaptive sampling technique that combined regression modelling and a fuzzy inference system was developed. The method adaptively determined the optimum number of packets to be selected by considering the changes in the traffic transmission characteristics. The method's operation was assessed using a computer network simulated in the NS-2 package. The adaptive sampling evaluated against a number of non-adaptive sampling methods gave an improved performance.

Item Type: Article
Additional Information: Substantially extended version of the paper “Adaptive Sampling Technique for Computer Network Traffic Parameters Using a Combination of Fuzzy System and Regression Model” presented at the 4th International Conference on Mathematics and Computers in Sciences and Industry in August-24-26 2017 in Corfu Greece.
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
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Depositing User: Reza Saatchi
Date Deposited: 27 Oct 2017 14:53
Last Modified: 27 Oct 2017 15:00
URI: http://shura.shu.ac.uk/id/eprint/17161

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