# Edge AI: A Taxonomy, Systematic Review and Future Directions

arXiv: https://arxiv.org/abs/2407.04053 · HTML: https://mlsanity.com/paper/2407.04053/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jul 2024.

## Built on by (most cited first, 21 of 21)

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- [Edge Artificial Intelligence: A Systematic Review of Evolution, Taxonomic Frameworks, and Future Horizons](https://mlsanity.com/paper/2510.01439/) (2025)
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- [Enhancing Time-Series Anomaly Detection by Integrating Spectral-Residual Bottom-Up Attention with Reservoir Computing](https://mlsanity.com/paper/2510.14287/) (2025)
- [An Agentic Framework for Rapid Deployment of Edge AI Solutions in Industry 5.0](https://mlsanity.com/paper/2510.25813/) (2025)
- [Toward Agentic Environments: GenAI and the Convergence of AI, Sustainability, and Human-Centric Spaces](https://mlsanity.com/paper/2512.15787/) (2025)
- [Enabling Physical AI at the Edge: Hardware-Accelerated Recovery of System Dynamics](https://mlsanity.com/paper/2512.23767/) (2025)
- [UNIFERENCE: A Discrete Event Simulation Framework for Developing Distributed AI Models](https://mlsanity.com/paper/2603.26469/) (2026)
- [PCA-Driven Adaptive Sensor Triage for Edge AI Inference](https://mlsanity.com/paper/2604.05045/) (2026)
- [Clustered Edge Intelligence: Beyond Just Convergence of Edge Computing and AI](https://mlsanity.com/paper/2607.20937/) (2026)
- [Carry-Through Checksum: A Lightweight Fault-Detection for CNN Inference at the Edge](https://mlsanity.com/paper/2609.16742/) (2026)

## Abstract (arXiv)

Edge Artificial Intelligence (AI) incorporates a network of interconnected systems and devices that receive, cache, process, and analyze data in close communication with the location where the data is captured with AI technology. Recent advancements in AI efficiency, the widespread use of Internet of Things (IoT) devices, and the emergence of edge computing have unlocked the enormous scope of Edge AI. Edge AI aims to optimize data processing efficiency and velocity while ensuring data confidentiality and integrity. Despite being a relatively new field of research from 2014 to the present, it has shown significant and rapid development over the last five years. This article presents a systematic literature review for Edge AI to discuss the existing research, recent advancements, and future research directions. We created a collaborative edge AI learning system for cloud and edge computing analysis, including an in-depth study of the architectures that facilitate this mechanism. The taxonomy for Edge AI facilitates the classification and configuration of Edge AI systems while examining its potential influence across many fields through compassing infrastructure, cloud computing, fog computing, services, use cases, ML and deep learning, and resource management. This study highlights the significance of Edge AI in processing real-time data at the edge of the network. Additionally, it emphasizes the research challenges encountered by Edge AI systems, including constraints on resources, vulnerabilities to security threats, and problems with scalability. Finally, this study highlights the potential future research directions that aim to address the current limitations of Edge AI by providing innovative solutions.