Incentive Mechanisms Design for Crowdsensing in Large Scale Artificial Intelligence of Things

In recent years, the combination of Artificial Intelligence (AI) and Internet of Things (IoT), known as Artificial intelligence of things (AIoT), has driven significant advancements within crowdsensing paradigms. Under crowdsensing paradigms, a large number of AIoT devices providing local data facilitates the superior completion of complex tasks, but also poses challenges in terms of bandwidth. Semantic communication has emerged as a promising solution to alleviate the bandwidth demands associated with AIoT crowdsensing. However, the substantial computational and energy costs associated with semantic communication make AIoT devices reluctant to participate in crowdsensing tasks or provide low-value semantic information. To address these challenges, we propose a novel crowdsensing incentive architecture designed to motivate smart devices to engage in crowdsensing activities. Our framework leverages contract theory and reputation models to ensure incentive compatibility within the information-asymmetry market between distributed AIoT devices and the crowdsensing platform. Furthermore, to enhance the convergence performance of market strategies in large-scale AIoT environments, we employ a diffusion model to generate contract design from complex data distributions within the market, optimizing the learning of the optimal contract. Simulation results confirm the effectiveness of our proposed architecture in improving the performance of AIoT crowdsensing, demonstrating its potential to overcome the identified challenges.