Adaptive Task Allocation for IoT-Driven Robotics Using NP-Complexity Models and Cloud Manufacturing

Abstract

Background Information: The integration of IoT-driven robotics with cloud manufacturing addresses the problem of task allocation in dynamic environments. Using the NP-complexity models, the system will optimize the allocation of tasks between robots for the effective use of resources. This is because it improves the decision-making processes and the performance of smart manufacturing systems. Objectives: Optimize task allocation using NP-complexity models. Improve the Performance of Cloud-based IoT Robotics. Improve resource utilization and efficiency. Methodology: This combines NP-complexity models with real-time data from IoT devices and cloud computing for dynamic task allocation. The approach involves machine learning in the processing of data and scheduling tasks. Empirical Results: In the proposed model, accuracy is achieved at 95.7%, while the execution time reduces to 7.2 seconds and optimizes resource utilization at 80.2%. Conclusion: The new proposed approach surmounts currently existing methods since it offers the efficient solution toward adaptive task allocation in IoT-driven robotics. Hence, it results in high scalability and performance real time.