Adaptive sampling technique using regression modelling and fuzzy inference system for network traffic

SALAMA, Abdussalam, SAATCHI, Reza and BURKE, Derek (2017). Adaptive sampling technique using regression modelling and fuzzy inference system for network traffic. In: CUDD, Peter and DE WITTE, Luc, (eds.) Harnessing the power of technology to improve lives. Studies in Health Technology and Informatics (242). IOS Press, 592-599.

This is the latest version of this item.

AAATE Salama 23 05 2017 Final_0.pdf - Accepted Version
All rights reserved.

Download (745kB) | Preview
Official URL:
Link to published version::
Related URLs:


Electronic-health relies on extensive computer networks to facilitate access and to communicate various types of information in the form of data packets. To examine the effectiveness of these networks, the traffic parameters need to be analysed. Due to quantity of packets, examining their transmission parameters individually is not practical, especially when performed in real time. Sampling allows a subset of packets that accurately represents the original traffic to be chosen. In this study an adaptive sampling method based on regression and fuzzy inference system was developed. It dynamically updates the sampling by responding to the traffic changes. Its performance was found to be superior to the conventional non-adaptive sampling methods.

Item Type: Book Section
Additional Information: Paper presented at the AAATE2017 conference Series ISSN: 0926-9630 PMID: 28873858
Uncontrolled Keywords: e-health, computer network traffic sampling, multimedia transmission, QoS.
Research Institute, Centre or Group - Does NOT include content added after October 2018: Materials and Engineering Research Institute > Centre for Automation and Robotics Research > Systems Modelling and Integration Group
Identification Number:
Page Range: 592-599
Depositing User: Reza Saatchi
Date Deposited: 19 Sep 2017 14:43
Last Modified: 18 Mar 2021 06:01

Available Versions of this Item

Actions (login required)

View Item View Item


Downloads per month over past year

View more statistics