Advancements in Edge Computing: Architectures, Challenges, and Opportunities

Abstract

Edge computing has emerged as a transformative paradigm for processing data closer to the source of generation, enabling real-time analytics, low-latency responses, and reduced bandwidth usage. This abstract presents an overview of recent advancements in edge computing, focusing on architectures, challenges, and opportunities. We delve into the evolving landscape of edge computing architectures, including fog computing, mobile edge computing (MEC), and distributed edge computing models. Furthermore, we identify key challenges facing edge computing deployments, such as resource constraints, security and privacy concerns, network connectivity issues, and heterogeneity of edge devices.

We discuss innovative solutions and approaches to address these challenges, including edge intelligence, federated learning, and blockchain-based security mechanisms. Moreover, we highlight emerging opportunities in edge computing, such as enabling edge AI applications, supporting Internet of Things (IoT) ecosystems, and facilitating edge-to-cloud integration. Through this abstract, we aim to provide insights into the current state of edge computing, highlight ongoing research efforts, and outline future directions for leveraging edge computing to enable next-generation applications and services in diverse domains.