Research On Pricing Strategy Of Iot Service For Telecom Operator

With the rapid development of the Internet of Things technology,the demand for data communication of the IoT(Internet of Things)business has been greatly increasing recently.The charging of the Internet of Things under the existing pricing mechanism has several problems as follows.First,some Io T services have a huge number of terminals and frequently transmit short messages.Although they consume a lot of network resources,they can only contribute very little revenue when use traffic size as charging factor.Second,IoT services have more categories than existing data communications services.These services have great differences in terms of transmission rate requirements,delay requirements,and packet size.The existing pricing mechanism cannot meet the differentiated pricing needs of the IoT business.In response to these problems,some scholars have conducted research on the finegrained traffic operations of telecom operators.However,existing related pricing theory is mostly based on the research results of the flow operation and business model selection.There is still lacking the study of network pricing mechanism based on the characteristics of Io T data communicate.Therefore,this thesis proposes several appropriate network pricing mechanisms based on the traffic characteristics of the Internet of Things,and analyzes their optimal pricing scheme,resource allocation scheme and maximum revenue.The main research contents are as follows:First,based on the characteristics of a wide variety of IoT services,this thesis proposes time-related differential pricing mechanism,which has pricing menus are both time-dependent and flow-dependent.Also,the optimal pricing scheme and resource allocation results of the mechanism are given in article.The thesis separately compares time-related differential pricing with time-shared differential pricing and time-related single pricing.The analysis shows that when the network resources are insufficient,users have a greater degree of pay willing difference during different time interval,and the proportion of users with high willingness to pay is small,time-related differential pricing has the most obvious benefit-increasing effect.Second,for the high complexity of time-ralated differential pricing mechanism,This thesis proposes two kinds of improved pricing strategies: “Scavenger” traffic pricing and partial differential pricing.Research shows that it’s possible using “scavenger” traffic provider services for IoT services that require low transmission rates and almost no latency requirements.When the willingness to pay for such services meets certain conditions,Telecom operators can get the same maximum benefit under time-reated differential pricing as under time-sharing differential pricing mechanism.For partial differential pricing which has lower charging complexity,the thesis designs a polynomial time complexity algorithm to solve the optimal pricing scheme.Under certain conditions,partial differential pricing can achieve a better trade-off between revenue improvement and charging complexity.Finally,this thesis studies the incentive pricing mechanism in an incomplete information environment which is lack of user utility information.This mechanism motivates users to choose their own appropriate price menu by creating a multi-level price menu,so that it helps telecom operators achieve differentiated pricing in an incomplete information environment.This thesis analyzes the pricing conditions that this mechanism can effectively help users to achieve "self discrimination".When these conditions are meet,telecom operators can get higher revenue by differential pricing based on multilevel price menu.