Comparative Analysis of Edge Computing Architectures for IoT Systems: Towards Scalable, Secure and Sustainable Deployments

Abstract

The proliferation of Internet of Things (IoT) devices has accelerated the demand for efficient, scalable, and secure data processing solutions. While cloud computing traditionally supported large-scale analytics, its limitations in latency, bandwidth, and privacy prompted the rise of edge computing as a decentralized alternative. This paper provides a comparative survey of edge computing architectures tailored for IoT environments, uniquely introducing a multi-layered taxonomy that encompasses far-edge, mid-edge, and near-edge sub-layers, alongside paradigms such as fog computing, cloudlet, and mobile edge computing (MEC). Unlike prior surveys that focused primarily on latency or deployment topologies, this study conducts a holistic analysis that simultaneously considers scalability, security, and sustainability trade-offs, supported by real-world use cases across healthcare, agriculture, manufacturing, and transportation. Intended for IoT system designers, network architects, and researchers in distributed computing, this survey highlights key architectural trade-offs and outlines future research directions for building optimized, intelligent edge-IoT systems.

Authors

* External Author