Edge Computing Landscapes: A Comprehensive Taxonomy and State-of-the-Art Analysis

Abstract

The needs of scenarios such as the rapidly expanding Internet of Things (IoT) ecosystem or real-time services are examples of situations where a centralized cloud model may not suffice‚ as all data resides on the cloud and traditional cloud computing practices have limitations? Low latency‚ high bandwidth and privacy issues arise due to the ever-growing amount of data being generated by connected devices? One way to address these issues is to move computation‚ storage and networking resources closer to the edge where data is generated? Edge computing can minimize latency in latency-sensitive applications and can also help reduce network congestion by offloading processing from the core network? This paper surveys the recent works in edge computing‚ classifying the existing body of work in edge computing into four categories based on four dimensions: physical architecture‚ algorithmic optimization techniques‚ performance objectives‚ and security mechanisms? The survey provides examples of representative works for each dimension‚ analyses the corresponding performance-energy trade-offs based on processing performance‚ energy consumption‚ and security objectives‚ and presents future research challenges such as hardware heterogeneity‚ network dynamics‚ and the development of energy-efficient edge systems? The paper also outlines research directions to enable the development and deployment of cloud-to-edge and cloud-to-thing computing environments in the future?

Introduction

The text is a survey of edge computing, focusing on its architecture, optimization, objectives, and security. The rapid growth of IoT, smart infrastructure, and high-quality streaming has generated huge amounts of data, making traditional centralized cloud computing less effective because of high latency, unpredictable network delays, and limited bandwidth. Edge computing addresses these problems by moving computing, storage, and networking resources closer to where data is generated.

Key Points

• Edge computing enables faster, low-latency processing near IoT devices instead of sending all data to distant cloud data centers.
• Major challenges include heterogeneous hardware, limited resources, changing network conditions, resource management, and security.
• The survey proposes a four-dimensional taxonomy:
  1. Architectural Design – Far Edge, Near Edge, Cloudlet, and Multi-Tier architectures.
  2. Algorithmic Optimization – mathematical/heuristic methods, machine learning, deep reinforcement learning, and game theory.
  3. System Objectives – reducing latency, improving energy efficiency, preserving bandwidth, and minimizing cost.
  4. Security and Privacy – encryption, lightweight cryptography, authentication, trust management, decentralized identity, and hardware security.

Computing Continuum

The text explains the relationship between IoT, Mist, Edge, Fog, and Cloud computing:

• IoT: Collects data through sensors, cameras, and actuators.
• Mist Computing: Processes data directly on IoT devices.
• Edge Computing: Processes data on nearby gateways or local computing devices.
• Fog Computing: Provides an intermediate regional layer between edge devices and the cloud.
• Cloud Computing: Uses centralized data centers for large-scale storage, complex processing, machine learning, and long-term analysis.

Architectural Layers

Edge resources are organized according to their distance from data sources:

Far Edge → Near Edge → Cloudlet → Multi-Tier/Core Cloud

Processing closer to the user generally provides lower latency and faster response, while the cloud offers much greater computing and storage capacity.

Conclusion

This survey paper gives a clear, well-organized picture of edge computing by sorting today’s research into a simple set of categories. The paper traces the technology’s evolution, showing how it shifted from network layers and algorithms to system goals and security safeguards. The final part of the paper says edge computing has moved beyond old, fixed algorithmic models and now relies on smarter, more flexible AI solutions. Engineers are stuck balancing two demands: quicker networks drain the battery, while security work needs lots of computing, so the system has to manage speed, power use, and safety together. To make edge computing run smoothly in real life, it needs to handle hardware standards, keep moving devices working, cover security costs, and still power everything with greener energy.

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Copyright

Copyright © 2026 Dr. Meena Kumari, Dr. Rajesh Kumar, Mr. Naveen Monga. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.