A back propagation NN to optimize the IoT network
Animesh Srivastava, Anoop Kumar
Abstract
In this paper, effective network optimization is carried out for the internet of things (IoT) with backpropagation neural we can improve the performance. An application area is the Internet of Things, where a massive amount of sensor data has to be classified. In this paper, we have to use backpropagation to reduce the network error, and the particle optimization network is further used to obtain the optimal weight and threshold of the network. This paper shows better performance in terms of reducing error rate after implementing neural networks for network optimization. Therefore, there is enormous demand for network optimization in the IoT for dynamic resource allocation in any infrastructure. The simulation process and the results demonstrate the effectiveness of the proposed approach in comparison to pieces of the proposed approach for the optimization problem.