Adaptive Task Allocation For Iot-Driven Robotics Using NPComplexity 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 decisionmaking
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.