A real-time pricing mechanism considering data freshness based on non-cooperative game in crowdsensing

Abstract

A fair and reasonable price mechanism is fundamental in improving the efficiency of the crowdsensing market. Being an emerging paradigm that utilizes widely distributed wireless communication network and smart sensing devices to collect sensing data, crowdsensing service offers a plausibility for collecting real-time data. However, previous studies ignored the complex competition among participants in the free data trade market and the fresh data demands of increasingly popular real-time applications. This paper introduces the age of information (AoI) as a criterion to measure the freshness of data geared towards constructing incentive models for the data provider and data requester. Subsequently, considering the opening of the market, we design a price mechanism framework based on non-cooperative games, which can help the crowdsensing platform provide each participant a stable sensing strategy to maximize profit. For the AoI-sensitive case, i.e., when the data quality deteriorates with the time. A co-evolutionary algorithm based on heuristic search is designed to solve the sensing strategy for each provider to achieve Nash equilibrium. Furthermore, for the case where AoI is insensitive. An improved relaxation algorithm is proposed to provide a stable strategy for the data provider. Numerical findings reveal that the developed algorithm can effectively identify stable sensing strategies in a Nash equilibrium.