Dynamic Pricing Strategies Based on Adaptive Terminals for Wireless Service Providers

Low latency, low energy consumption and high security make the mobile edge computing (MEC) receive a lot of attention and good comments from art industry in the past few years. However, with task offloading, pricing of data is always a difficulty in MEC. In this paper, dynamic pricing schemes based on adaptive terminals are studied for wireless service providers. Adaptive terminals (ATs) use computational and machine learning technologies to analyze and induce the historical cosmetology records for self-decision making. In the considered model, there is a wireless marketplace monopolized by two competing operators who offer differentiated wireless service to users and price wireless service in different periods. Each user has a valuation on quality of wireless service. ATs will adjust automatically the valuation and make it most suitable for the owners' psychological expectation, then it can help the users determine when to connect to which base station (BS) for maximum individual benefit. Toward this end, the problem is modeled as a Markov decision process. This paper aims at designing an algorithm for finding the two operators' optimal pricing strategies in a competing version.