Hierarchical Reinforcement Learning for Integrated Cloud-Fog-Edge Computing in IoT Systems

TL;DR

This paper introduces HIPA, a hierarchical framework that dynamically allocates IoT processing tasks across cloud, fog, and edge layers using machine learning to improve latency, scalability, and data privacy.

Contribution

The paper proposes a novel hierarchical architecture, HIPA, integrating cloud, fog, and edge computing with machine learning for optimized IoT data processing.

Findings

HIPA improves latency and scalability in IoT systems

Dynamic task allocation enhances data privacy and security

Framework synthesizes current research with innovative architecture

Abstract

The Internet of Things (IoT) is transforming industries by connecting billions of devices to collect, process, and share data. However, the massive data volumes and real-time demands of IoT applications strain traditional cloud computing architectures. This paper explores the complementary roles of cloud, fog, and edge computing in enhancing IoT performance, focusing on their ability to reduce latency, improve scalability, and ensure data privacy. We propose a novel framework, the Hierarchical IoT Processing Architecture (HIPA), which dynamically allocates computational tasks across cloud, fog, and edge layers using machine learning. By synthesizing current research and introducing HIPA, this paper highlights how these paradigms can create efficient, secure, and scalable IoT ecosystems.