Wireless communications and mobile computing conference, p.p , July 2011.

 Abstract  Introduction  Sampling approaches in measuring Qos parameter  Description of adaptive sampling approach  An overview of traffic parameters  Modeling and simulation  Results and discussions  Conclusions  References

 Real-time audio and video applications generate vast amount of data in form of information packets. The collection and processing of all these packets in real time are not practically feasible. Therefore, an appropriate sampling technique is required in order to reduce the amount of collected data and their processing. In this paper, a statistical adaptive sampling technique to adjust sampling rate based on the traffic's statistics was developed using a Fuzzy Inference System (FIS). The FIS determined the sampling rate by using a set of rules to interpret statistical variations in Quality of Service (QoS) parameters.

 A comparison of adaptive statistical sampling using FIS against systematic, stratified, and random sampling was also carried out. The Network Simulator- 2 (NS-2) was used to evaluate the operation of sampling techniques. The study indicated that biasness values of sampled traffic obtained from the developed adaptive sampling technique were closer to zero than the values obtained using conventional sampling techniques which indicates the effectiveness of the proposed method.

 Multimedia applications such as video and Voice over Internet Protocol (VoIP) are sensitive to Quality of Service (QoS) parameters such as packet transmission delay, jitter, loss, and throughput. For instance, the quality of VoIP can be seriously degraded due to large delay or jitter [1].  In order for time critical multimedia applications to be transmitted with an appropriate quality, the QoS parameters of these applications need to be measured and quantified accurately. However, multimedia applications generate vast amount of data in the form of information packets. The collection and processing of all these packets in real time are not practically feasible. Hence, appropriate traffic sampling techniques are required to reduce the amount of data processed.

 In this paper, a statistical adaptive sampling method to adjust the sampling rate based on traffic's statistics was developed using a fuzzy inference system (FIS). A comparison of the devised method versus non-adaptive sampling techniques was also carried out using a simulated VoIP traffic.  This paper is organized as follows: section II reviews non-adaptive (conventional) and adaptive sampling approaches which are used to measure QoS parameters. Section III describes the proposed adaptive statistical sampling algorithm. Section IV explains the QoS parameters included in the measurements. Section V demonstrates network simulation and traffic models. The results are discussed in section VI. Finally, conclusions are provided in section VII.

 A. Non-adaptive Sampling Approaches:  These approaches can be classified into systematic, random, and stratified. In systematic sampling scheme, a packet is chosen at either a fixed time interval or a fixed packet interval. In random sampling, a packet is chosen from the parent population at a random time interval or in a random order. In tratified sampling a fixed interval of time is chosen and a packet is randomly selected from that interval [4].

 B. Adaptive Sampling Approach :  In this approach, the sampling rate is djusted according to traffic characteristics. In other words, lower sampling interval can be used during the periods of high activity, whereas higher sampling interval can be used during the periods of low activity [5].  Adaptive sampling methods are not only used for traffic analysis, but they also used for a number of other applications. For instance, in [9], a small packet threshold adaptive sampling algorithm was proposed to capture attack packets (i.e. a computer network security application). In this paper, a fuzzy inference system (FIS) was devised by considering traffic's statistics to adaptively adjust the sampling rate. The devised FIS provided a fast response to the variation of traffic. Also, a comparison of the devised method versus non-adaptive sampling techniques (systematic, stratified, and random sampling) was carried out to demonstrate the effectiveness of the proposed method.

 Different types of traffic were transmitted over a simulated network (VoIP, videoconferencing, and data). In this paper, VoIP packets were sampled based on the statistics of throughput. From the sampled packets, the main QoS parameters (delay, jitter, and packet loss ratio) were calculated. A brief description of these parameters is provided below [11].

 A novel statistical adaptive sampling technique to adjust sampling rate based on traffic's statistics was developed using Fuzzy Inference System (FIS). The algorithm’s performance was evaluated using NS-2. A comparison of adaptive statistical sampling using FIS against systematic, stratified, and random sampling was also carried out. The study indicates that the developed adaptive sampling method is more effective than conventional sampling method