Deep Learning and Industrial Internet of Things to Improve Smart City Safety

Abstract

the Internet of Things (IoT) has grown rapidly in recent years, enabling the manufacture of a vast array of devices that may improve different operational components in a wide variety of enterprises. This study is being done to lay the groundwork for the establishment of "smart cities," or metropolitan regions where robots will outnumber humans. Data sensitivity has increased, particularly in the commercial and industrial sectors of the economy. We will also go through the myriad security risks that have occurred because of this company's rapid growth. In this study, deep learning, perceptrons, and convolutional neural networks are highlighted as the deep learning (DL) approaches for IoT applications in smart cities. Deep learning, perceptrons, and convolutional neural networks are only a few of the additional methodologies considered. We also discuss the benefits and drawbacks of various types of safety precautions. Furthermore, we evaluate the lessons learned, the obstacles that remain, and the future trends in the use of DL approach to improve Industrial Internet of Things (IIoT) safety.