A Tree-Structured Traffic Model of Mobile POS Service Applied in Cellular Network

Abstract:

The user number of mobile point of sale (POS) service is growing rapidly. The market potential is attractive to the mobile network operators, while the traffic challenges caused by heavy mobile POS service is becoming a critical issue to the operators. In order to evaluate the impact on the network and make best use of the existing network, it is important to model the traffic of mobile POS service. A tree-structured modeling method is presented in the paper, which can be used to model the traffic of mobile POS service as well as other interactive M2M services, such as remote medical services. The modeling method is a combination of the layered modeling method used in traditional H2H communications and the transaction tree modeling method used in bank trade. As another main point, a specific traffic model of mobile POS service is given in the paper. There are five parameters in the traffic model, including request packet size, response packet size, the interval between request and response, the interval between two requests, and the transmission probability. After counting the packet length of each message, calculating the average length, analyzing the effect of difference between the actual and average length on the cellular network, we find that all the request type of mobile POS service could be simplified as the average length. Further on, a curve-fitting method is used to obtain the distribution of the intervals. All the data used in the modeling are collected from the traffic of real network. The specific model of mobile POS service can be applied directly in simulation of cellular network to evaluate the impact of mobile POS service on it.

• Periodical: Advanced Materials Research (Volumes 671-674)
• Pages: 3155-3160
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.671-674.3155
• Online since: March 2013
• Authors: Rong Duo Liu, Wei Wu, Jia Dong Du, Wei Xin, Da Cheng Yang
• Keywords: Cellular Network, Machine-to-Machine, Mobile Point of Sales, Traffic Model Method

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