User activity detection for massive Internet of things with an improved residual convolutional neural network

Abstract

Massive user activity detection is a challenging task for massive Internet of things (mIoT). In this paper, we propose a new deep neural network, named concentrated layers convolutional neural network (CLCNN), for user activity detection in mIoT. We firstly propose three basic rules in the design of residual network specifically for mIoT scenarios. Secondly, with the rules above we develop a new improved residual network block which includes integrated convolutional layers with activation functions, by which the residual convolutional network is constructed. Moreover, the regularization and its corresponding hyperparameter for the proposed network are also investigated against overfitting. Simulation results show that the proposed CLCNN network outperforms the existing deep learning algorithm and conventional compressive sensing solutions in user activity detection and corresponding channel estimation.

IoT‐assisted irregular environmental event determination for health analysis of pregnant females

“…In this manner, IoT-assisted sensing devices have made it possible to observe both macroscopic and microscopic health and environmental events. [18][19][20] Inspired from the above-discussed advantages of IoT, the significant contributions of the proposed research is depicted in Figure 1 and detailed as follows:…”