Integrated Contractual Computing Resources and Efficiency Searchable Caching for AIoT-Enabled Green Vehicular Supply Chain

The development of Artificial Intelligence-based Internet of Things (AIoT) enables wireless vehicular networks to provide energy-efficient and sustainable solutions for green supply chains through optimized data processing, adaptive resource allocation, and automated control. However, there are still many challenges in resource allocation and network congestion, especially in large-scale AIoT-enabled vehicular networks. Traditional approaches often struggle with scalability due to static resource allocation, lack of incentive mechanisms, and high computational complexity. Therefore, we propose a contract theory and harmony search optimization algorithm(HSO-Con) for AIoT-enabled green supply chain management. This method uses the combination of contract theory and VEC to optimize resource allocation in the AIoT environment. Harmony Search dynamically optimizes task scheduling and data processing and uses a cache mechanism to reduce redundant data transmission. In addition, the graph neural network (GNN) further enhances the adaptability of the AIoT network, enabling adaptive parameter adjustments and efficient processing of data streams. Simulation results show that the HSO-Con algorithm has significant potential in reducing network congestion, and enhancing supply chain efficiency in VEC by AIoT. Specifically, our algorithm improves cache hit rate by 4.27% and 5.05% respectively, and bandwidth utilization by 7.01%.