Incentivizing Crowdsourcing Workers via Social Networks in Edge Computing

Crowdsourcing has proven to be an effective approach for leveraging collective intelligence to solve tasks. However, in edge computing environments, crowdsourcing platforms often face challenges such as insufficient worker participation and weak collaboration. Existing incentive mechanisms focus on individual contributions, which leads to inefficient collaboration and suboptimal resource utilization, particularly on resource-constrained edge devices. Furthermore, limited computational capacity and budget constraints hinder widespread participation, negatively impacting task performance and overall engagement. This paper introduces the Agent Cooperation Model (ACM), which enhances worker collaboration and task completion through a distributed, agent-based approach to task allocation. We also propose the Deep Reinforcement Learning-based Dynamic Worker Incentive Mechanism (DDWM) to optimize incentive distribution using social network data, ensuring more effective incentives and fostering greater participation and collaboration. Cooperation Optimization through Multi-Agent Reinforcement Learning (MARL), allowing ACM to dynamically improve resource allocation and cooperation efficiency. The expansion of high-influence worker incentives through DDWM, driving broader participation via social networks. Experiments conducted on real-world datasets confirm that ACM and DDWM consistently enhance worker collaboration and participation across diverse and complex social networks. Compared to other incentive methods, these models exhibit superior stability and convergence, particularly in larger and denser networks, validating the effectiveness of our approach in fostering efficient crowdsourcing cooperation.