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

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.