A Stackelberg Evolutionary Game Theoretic Framework for Dynamical Data Trading in Artificial Intelligence of Things

Artificial Intelligence of Things (AIoT) aims to build a self-learning, self-adaptive, and self-evolving Internet of Things ecosystem, which has facilitated many promising intelligent services. Data is an important foundational element for many applications. Establishing a well-designed trading mechanism to collect the necessary data from various sources is essential to realize the vision of AIoT. In this article we investigate the data trading incentive mechanism between multiproviders and multibuyers for AIoT. To address the two-sided dilemma, we develop a joint optimization game to maximize the payoff of all market participants. A two-layer Stackelberg evolutionary game theoretic framework is developed to divide the optimization problem into two subproblems: one for data pricing by providers and the other for purchasing decisions by buyers. The subproblem of optimal data pricing for providers is modeled as a noncooperative game. Providers utilize the game's equilibrium solution to dynamically modify their pricing strategies in response to a changing competitive environment and demanding. This is because buyers have limited information, their behaviors are modeled via evolutionary game. By encouraging data providers to take the buyers' evolutionary dynamics into account has the potential to overcome the myopia behaviors. The equilibrium solution is obtained via replicator dynamics. Extensive experiments demonstrate the efficacy and efficiency of the proposed hierarchical interaction framework. Overall, our results show the proposed Stackelberg evolutionary game framework establishes a desired data market and achieves higher long-term revenue for both sides of participants in the market. The hierarchical framework can effectively prompts data trading in the market.