  The fusion of the Internet of Things (IoT) with Sixth-Generation (6G)
technology has significant potential to revolutionize the IoT landscape. With
the ultra-reliable and low-latency communication capabilities of 6G, 6G-IoT
networks can transmit high-quality and diverse data to enhance edge learning.
Artificial Intelligence-Generated Content (AIGC) harnesses advanced AI
algorithms to automatically generate various types of content. The emergence of
edge AIGC integrates with edge networks, facilitating real-time provision of
customized AIGC services by deploying AIGC models on edge devices. However, the
current practice of edge devices as AIGC Service Providers (ASPs) lacks
incentives, hindering the sustainable provision of high-quality edge AIGC
services amidst information asymmetry. In this paper, we develop a user-centric
incentive mechanism framework for edge AIGC services in 6G-IoT networks.
Specifically, we first propose a contract theory model for incentivizing ASPs
to provide AIGC services to clients. Recognizing the irrationality of clients
towards personalized AIGC services, we utilize Prospect Theory (PT) to capture
their subjective utility better. Furthermore, we adopt the diffusion-based soft
actor-critic algorithm to generate the optimal contract design under PT,
outperforming traditional deep reinforcement learning algorithms. Our numerical
results demonstrate the effectiveness of the proposed scheme.
