Deep Learning-Based Optimization of IoT Performance in Cloud Environments

Abstract

The rapid emergence of the Internet of Things (IoT) has given rise to immense volumes of data, which require proper processing, storage, and analysis. Cloud computing can scale to meet the needs of the Internet of Things (IoT), but latency, bandwidth usage, and inefficient resource utilization are bottlenecks affecting performance. The capacity of deep learning (DL) to build and optimize connections amidst assets and the capacity to describe complicated associations is turning into a groundbreaking method of advancing the capability of IoT in the cloud-based setting. The article provides a comprehensive summary of the deep learning-based strategies to maximize the functionality of the IoT. It also talks about how it can be used in smart task scheduling and smart energy management, anomaly discovery, smart resource provisioning, and smart latency reduction. Deep neural networks based on convolutional neural networks (CNNs), recurrent neural networks (RNNs), autoencoders, and reinforcement learning are considered as architectures in the framework of IoT-cloud integration. It is demonstrated that DL can significantly increase throughput, reliability, and responsiveness and reduce costs and energy usage. Nevertheless, pressing issues include high computational costs, the interpretability of deep learning systems, data confidentiality, and counterexamples. To offer solutions to them, in addition to federated learning, edge-cloud interaction, and explainable AI, it is proposed to combine them in the future. This paper concludes that IoT systems optimized through the use of deep learning-based cloud-IoT frameworks can be considered as a promising trend that can ensure scalability, resilience, and effectiveness in the next-generation smart environments.