Smart Implementation of Industrial Internet of Things Using Embedded Mechatronic System

In Industry 4.0, integrating Industrial Internet of Things (IIoT) and smart manufacturing is crucial for high-quality, efficient, and cost-effective production. However, the performance of IIoT systems can be hindered by unevenly distributed edge service providers (ESPs). To tackle this challenge, we propose an optimized embedded system for edge intelligence and smart manufacturing, leveraging digital twin (DT) technology. Our approach employs a DT-assisted alliance game resource optimization strategy to jointly optimize multidimensional resource allocation, including bandwidth, computing, and caching resources, while considering constraints like maximum delay. The optimization problem maximizes edge terminal utility and ESP utility, transformed into a convex optimization problem with linear constraints. An approximate optimal solution is obtained through an alternating iterative method. Simulation results demonstrate significant enhancements in resource utilization efficiency compared to baseline schemes like Nash equilibrium and large coalition. The proposed scheme is ideal for large-scale edge intelligence and smart manufacturing systems, with benefits increasing alongside the number of ESPs.