IIoT Resource Allocation Optimization Using Hybrid Model Engineering and Multi-Layer Perceptron

IIoT enables innovational opportunities for automation requirements in industrial processes by applying superior resource management concepts. The following research strategies are recommended in this study to enhance IIoT resource allocation by the proposed hybrid Model Engineering (ME) and Multi-Layer Perceptron (MLP) innovation. The proposed methodology combines the resilient deep learning ability and the previously adopted engineering models to schedule tasks and conserve energy in IIoT clouds. Structured in multiple layers, the MLP receives different IoT device signals and determines the best resources for setting up while considering dynamic industrial settings. The hybrid model together with the dynamics of the system guarantees the accurate estimation of operational parameters and achieves optimizations at the same time and as well minimizes computational complexities at various demands of the system. Some of the simulation results we obtained include enhanced resource utilization, energy utilization and, process robustness over a conventional method. This IIoT framework creates a strong base for developing methodologies to implement efficient intelligent decision-making models into the industrial automation and to make better manufacturing processes.