Performance Analysis and Prediction for Mobile Internet-of-Things (IoT) Networks: A CNN Approach

Abstract

With the increasingly mature sensor technology and the increasing popularity of broadband network, “the Internet-of-Everything” era is coming, and the mobile Internet of Things (IoT) is booming around the world. However, the mobile IoT communication networks face serious challenges, which are caused by the complex and variable communication environments. The mobile IoT applications can produce large-scale data, which will consume substantial energy. The transmit antenna selection (TAS) and cooperative communication schemes are commonly used to reduce the complexity and the energy consumption, which directly impact the performance of mobile IoT networks. To evaluate the performance of mobile IoT networks, it is important to analyze outage probability (OP) performance. In this article, we investigate the OP performance analysis of mobile IoT communication networks and propose an OP intelligent prediction algorithm based on an improved convolutional neural network (CNN). First, the mobile OP performance is analyzed by combining the TAS and decode-and-forward cooperative schemes, and the exact OP expressions are derived. Then, an improved CNN is designed to avoid the loss of important information, which contains the input layer, three-convolution layer, one fully connected layer, and output layer. The proposed CNN-based prediction approach is compared with the radial basis function (RBF), generalized regression (GR), Elman, and extreme learning machine (ELM) methods. The simulation results validate that the proposed CNN prediction approach can achieve a better prediction effect than RBF, Elman, GR, and ELM methods. For the CNN approach, it has a 44% increase in the prediction accuracy.