Edge Computing for Smart Health: Context-Aware Approaches, Opportunities, and Challenges

• Published in: IEEE network Vol. 33; no. 3; pp. 196 - 203
• Main Authors: Abdellatif, Alaa Awad, Mohamed, Amr, Chiasserini, Carla Fabiana, Tlili, Mounira, Erbad, Aiman
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-05-2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Ambient intelligence; Architecture; Cloud computing; Data compression; Edge computing; Electrocardiography; Emergency response; Feature extraction; Health care; Image edge detection; Medical services; Monitoring; Real-time systems; Remote monitoring; Sensors; Wireless networks
• ISSN: 0890-8044; 1558-156X
• DOI: 10.1109/MNET.2019.1800083

Improving the efficiency of healthcare systems is a top national interest worldwide. However, the need to deliver scalable healthcare services to 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 article, we present our vision of exploiting 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.