# A Review on Edge Analytics: Issues, Challenges, Opportunities, Promises, Future Directions, and Applications

arXiv: https://arxiv.org/abs/2107.06835 · HTML: https://mlsanity.com/paper/2107.06835/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 115 references that are in the map.
- First posted on arXiv 1 Jul 2021.

## Builds on (most cited first, 17 of 17)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference](https://mlsanity.com/paper/1712.05877/) (2017)
- [Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1611.08050/) (2016)
- [Federated Learning in Mobile Edge Networks: A Comprehensive Survey](https://mlsanity.com/paper/1909.11875/) (2019)
- [Understanding data augmentation for classification: when to warp?](https://mlsanity.com/paper/1609.08764/) (2016)
- [REVAMP2T: Real-time Edge Video Analytics for Multi-camera Privacy-aware Pedestrian Tracking](https://mlsanity.com/paper/1911.09217/) (2019)
- [Survey on Multi-Access Edge Computing for Internet of Things Realization](https://mlsanity.com/paper/1805.06695/) (2018)
- [Blockchain for the Internet of Vehicles towards Intelligent Transportation Systems: A Survey](https://mlsanity.com/paper/2007.06022/) (2020)
- [Role of Bloom Filter in Big Data Research: A Survey](https://mlsanity.com/paper/1903.06565/) (2019)
- [Fully Convolutional Crowd Counting On Highly Congested Scenes](https://mlsanity.com/paper/1612.00220/) (2016)
- [6G Communication: Envisioning the Key Issues and Challenges](https://mlsanity.com/paper/2004.04024/) (2020)
- [Developing an edge computing platform for real-time descriptive analytics](https://mlsanity.com/paper/1705.08449/) (2017)
- [Approximate Edge Analytics for the IoT Ecosystem](https://mlsanity.com/paper/1805.05674/) (2018)
- [Preventing DDoS using Bloom Filter: A Survey](https://mlsanity.com/paper/1810.06689/) (2018)
- [Big Computing: Where are we heading?](https://mlsanity.com/paper/2005.06964/) (2020)
- [6G Communications: A Vision on the Potential Applications](https://mlsanity.com/paper/2005.07531/) (2020)

## Built on by (most cited first, 5 of 5)

- [Edge Deep Learning in Computer Vision and Medical Diagnostics: A Comprehensive Survey](https://mlsanity.com/paper/2605.06714/) (2026)
- [Achieving Trustworthy Real-Time Decision Support Systems with Low-Latency Interpretable AI Models](https://mlsanity.com/paper/2506.20018/) (2025)
- [Energy-Efficient Fast Object Detection on Edge Devices for IoT Systems](https://mlsanity.com/paper/2602.09515/) (2026)
- [Boosting Resource-Constrained Federated Learning Systems with Guessed Updates](https://mlsanity.com/paper/2110.11486/) (2021)
- [Internet of Everything in the 6G Era: Paradigms, Enablers, Potentials and Future Directions](https://mlsanity.com/paper/2604.25018/) (2026)

## Abstract (arXiv)

Edge technology aims to bring Cloud resources (specifically, the compute, storage, and network) to the closed proximity of the Edge devices, i.e., smart devices where the data are produced and consumed. Embedding computing and application in Edge devices lead to emerging of two new concepts in Edge technology, namely, Edge computing and Edge analytics. Edge analytics uses some techniques or algorithms to analyze the data generated by the Edge devices. With the emerging of Edge analytics, the Edge devices have become a complete set. Currently, Edge analytics is unable to provide full support for the execution of the analytic techniques. The Edge devices cannot execute advanced and sophisticated analytic algorithms following various constraints such as limited power supply, small memory size, limited resources, etc. This article aims to provide a detailed discussion on Edge analytics. A clear explanation to distinguish between the three concepts of Edge technology, namely, Edge devices, Edge computing, and Edge analytics, along with their issues. Furthermore, the article discusses the implementation of Edge analytics to solve many problems in various areas such as retail, agriculture, industry, and healthcare. In addition, the research papers of the state-of-the-art edge analytics are rigorously reviewed in this article to explore the existing issues, emerging challenges, research opportunities and their directions, and applications.