Utilizing Deep Learning for Optimizing Onthophagus Efficiency in IoT Networks Based ont Edge Computing

Abstract

The rapid development of Internet of Things (IoT) technology has encouraged the optimization of computing systems to improve network efficiency. One approach that can be applied is the use of deep learning in optimizing the efficiency of Onthophagus in an IoT network based on edge computing. Edge computing allows data processing to be carried out closer to the source, thereby reducing latency and load on central servers. This study aims to analyze how deep learning can be used to improve the efficiency of the Onthophagus system in the context of edge computing-based IoT networks. The method used in this study is library research, by examining various academic references related to the concepts of deep learning, Onthophagus efficiency, IoT, and edge computing. An analysis was carried out on previous research to understand the implementation of this technology in optimizing IoT network performance. The results of the study show that the integration of deep learning in edge computing systems can improve the efficiency of data management and real-time decision-making. In addition, deep learning models can reduce energy consumption as well as improve response speed in IoT networks. The conclusion of this study emphasizes that the use of deep learning in edge computing-based IoT networks has great potential in improving system efficiency, especially in the aspects of data processing, resource management, and network responsiveness. Further research is needed to explore the practical implementation of this model as well as the challenges that may arise in its implementation.