# Challenges and Opportunities in Edge Computing

arXiv: https://arxiv.org/abs/1609.01967 · HTML: https://mlsanity.com/paper/1609.01967/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Sep 2016.

## Built on by (most cited first, 26 of 26)

- [No One Left Behind: Inclusive Federated Learning over Heterogeneous Devices](https://mlsanity.com/paper/2202.08036/) (2022)
- [FedRA: A Random Allocation Strategy for Federated Tuning to Unleash the Power of Heterogeneous Clients](https://mlsanity.com/paper/2311.11227/) (2023)
- [Training Machine Learning models at the Edge: A Survey](https://mlsanity.com/paper/2403.02619/) (2024)
- [Anomaly Detection using Edge Computing in Video Surveillance System: Review](https://mlsanity.com/paper/2107.02778/) (2021)
- [BED: A Real-Time Object Detection System for Edge Devices](https://mlsanity.com/paper/2202.07503/) (2022)
- [AdapterFL: Adaptive Heterogeneous Federated Learning for Resource-constrained Mobile Computing Systems](https://mlsanity.com/paper/2311.14037/) (2023)
- [Adaptive Deep Learning for Efficient Visual Pose Estimation aboard Ultra-low-power Nano-drones](https://mlsanity.com/paper/2401.15236/) (2024)
- [FLEdge: Benchmarking Federated Machine Learning Applications in Edge Computing Systems](https://mlsanity.com/paper/2306.05172/) (2023)
- [Task Offloading for Smart Glasses in Healthcare: Enhancing Detection of Elevated Body Temperature](https://mlsanity.com/paper/2308.07193/) (2023)
- [Real-Time Compressed Sensing for Joint Hyperspectral Image Transmission and Restoration for CubeSat](https://mlsanity.com/paper/2404.15781/) (2024)
- [LLMPi: Optimizing LLMs for High-Throughput on Raspberry Pi](https://mlsanity.com/paper/2504.02118/) (2025)
- [A Constant-per-Iteration Likelihood Ratio Test for Online Changepoint Detection for Exponential Family Models](https://mlsanity.com/paper/2302.04743/) (2023)
- [Enhancing Trustworthiness in ML-Based Network Intrusion Detection with Uncertainty Quantification](https://mlsanity.com/paper/2310.10655/) (2023)
- [From Algorithm to Hardware: A Survey on Efficient and Safe Deployment of Deep Neural Networks](https://mlsanity.com/paper/2405.06038/) (2024)
- [Multi-task learning on the edge: cost-efficiency and theoretical optimality](https://mlsanity.com/paper/2110.04639/) (2021)
- [PFGE: Parsimonious Fast Geometric Ensembling of DNNs](https://mlsanity.com/paper/2202.06658/) (2022)
- [On Achieving Privacy-Preserving State-of-the-Art Edge Intelligence](https://mlsanity.com/paper/2302.05323/) (2023)
- [Rapid Deployment of DNNs for Edge Computing via Structured Pruning at Initialization](https://mlsanity.com/paper/2404.16877/) (2024)
- [An Edge Computing-Based Solution for Real-Time Leaf Disease Classification using Thermal Imaging](https://mlsanity.com/paper/2411.03835/) (2024)
- [TinyML-Enabled IoT for Sustainable Precision Irrigation](https://mlsanity.com/paper/2601.13054/) (2026)
- [Introducing Federated Learning into Internet of Things ecosystems -- preliminary considerations](https://mlsanity.com/paper/2207.07700/) (2022)
- [Automated Speaker Independent Visual Speech Recognition: A Comprehensive Survey](https://mlsanity.com/paper/2306.08314/) (2023)
- [Quantized Non-Volatile Nanomagnetic Synapse based Autoencoder for Efficient Unsupervised Network Anomaly Detection](https://mlsanity.com/paper/2309.06449/) (2023)
- [An End to End Edge to Cloud Data and Analytics Strategy](https://mlsanity.com/paper/2509.12296/) (2025)
- [A Hybrid Proactive And Predictive Framework For Edge Cloud Resource Management](https://mlsanity.com/paper/2511.16075/) (2025)
- [Robust Spiking Neural Networks Against Adversarial Attacks](https://mlsanity.com/paper/2602.20548/) (2026)

## Abstract (arXiv)

Many cloud-based applications employ a data centre as a central server to process data that is generated by edge devices, such as smartphones, tablets and wearables. This model places ever increasing demands on communication and computational infrastructure with inevitable adverse effect on Quality-of-Service and Experience. The concept of Edge Computing is predicated on moving some of this computational load towards the edge of the network to harness computational capabilities that are currently untapped in edge nodes, such as base stations, routers and switches. This position paper considers the challenges and opportunities that arise out of this new direction in the computing landscape.