# Diffusion Model-based Incentive Mechanism with Prospect Theory for Edge AIGC Services in 6G IoT

arXiv: https://arxiv.org/abs/2407.10979 · HTML: https://mlsanity.com/paper/2407.10979/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jun 2024.

## Built on by (most cited first, 9 of 9)

- [RadioDiff: An Effective Generative Diffusion Model for Sampling-Free Dynamic Radio Map Construction](https://mlsanity.com/paper/2408.08593/) (2024)
- [Hybrid RAG-empowered Multi-modal LLM for Secure Data Management in Internet of Medical Things: A Diffusion-based Contract Approach](https://mlsanity.com/paper/2407.00978/) (2024)
- [Hybrid-Generative Diffusion Models for Attack-Oriented Twin Migration in Vehicular Metaverses](https://mlsanity.com/paper/2407.11036/) (2024)
- [Diffusion Models for Future Networks and Communications: A Comprehensive Survey](https://mlsanity.com/paper/2508.01586/) (2025)
- [Sustainable Diffusion-based Incentive Mechanism for Generative AI-driven Digital Twins in Industrial Cyber-Physical Systems](https://mlsanity.com/paper/2408.01173/) (2024)
- [LMM-Incentive: Large Multimodal Model-based Incentive Design for User-Generated Content in Web 3.0](https://mlsanity.com/paper/2510.04765/) (2025)
- [Internet of Everything in the 6G Era: Paradigms, Enablers, Potentials and Future Directions](https://mlsanity.com/paper/2604.25018/) (2026)
- [A-MADiff: Attention-Guided Multi-Agent DRL with Diffusion Policies for Memory-Aware Task Orchestration in Mobile AIGC Networks](https://mlsanity.com/paper/2608.29255/) (2026)
- [BeamRMX: Radiation-Pattern-Driven Learning for Generalizable Beam Radio Map Prediction and Beam Management](https://mlsanity.com/paper/2609.00615/) (2026)

## Abstract (arXiv)

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.