# Edge Computing For Smart Health: Context-aware Approaches, Opportunities, and Challenges

arXiv: https://arxiv.org/abs/2004.07311 · HTML: https://mlsanity.com/paper/2004.07311/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Apr 2020.

## Built on by (most cited first, 10 of 10)

- [Pervasive AI for IoT applications: A Survey on Resource-efficient Distributed Artificial Intelligence](https://mlsanity.com/paper/2105.01798/) (2021)
- [Data and Model Poisoning Backdoor Attacks on Wireless Federated Learning, and the Defense Mechanisms: A Comprehensive Survey](https://mlsanity.com/paper/2312.08667/) (2023)
- [Distributed Reinforcement Learning for Privacy-Preserving Dynamic Edge Caching](https://mlsanity.com/paper/2110.10349/) (2021)
- [Edge Intelligence for Empowering IoT-based Healthcare Systems](https://mlsanity.com/paper/2103.12144/) (2021)
- [Emotion Recognition for Healthcare Surveillance Systems Using Neural Networks: A Survey](https://mlsanity.com/paper/2107.05989/) (2021)
- [Reinforcement Learning for Intelligent Healthcare Systems: A Comprehensive Survey](https://mlsanity.com/paper/2108.04087/) (2021)
- [COMPASS: Unsupervised and Online Clustering of Complex Human Activities from Smartphone Sensors](https://mlsanity.com/paper/2306.15437/) (2023)
- [CSM-H-R: A Context Modeling Framework in Supporting Reasoning Automation for Interoperable Intelligent Systems and Privacy Protection](https://mlsanity.com/paper/2308.11066/) (2023)
- [Design of an Edge-based Portable EHR System for Anemia Screening in Remote Health Applications](https://mlsanity.com/paper/2507.15146/) (2025)
- [Real-Time Human Activity Recognition on Edge Microcontrollers: Dynamic Hierarchical Inference with Multi-Spectral Sensor Fusion](https://mlsanity.com/paper/2602.00152/) (2026)

## Abstract (arXiv)

Improving efficiency of healthcare systems is a top national interest worldwide. However, the need of delivering scalable healthcare services to the patients while reducing costs is a challenging issue. Among the most promising approaches for enabling smart healthcare (s-health) are edge-computing capabilities and next-generation wireless networking technologies that can provide real-time and cost-effective patient remote monitoring. In this paper, we present our vision of exploiting multi-access edge computing (MEC) for s-health applications. We envision a MEC-based architecture and discuss the benefits that it can bring to realize in-network and context-aware processing so that the s-health requirements are met. We then present two main functionalities that can be implemented leveraging such an architecture to provide efficient data delivery, namely, multimodal data compression and edge-based feature extraction for event detection. The former allows efficient and low distortion compression, while the latter ensures high-reliability and fast response in case of emergency applications. Finally, we discuss the main challenges and opportunities that edge computing could provide and possible directions for future research.