TY - JOUR
T1 - Edge computing for smart health
T2 - Context-aware approaches, opportunities, and challenges
AU - Abdellatif, Alaa Awad
AU - Mohamed, Amr
AU - Chiasserini, Carla Fabiana
AU - Tlili, Mounira
AU - Erbad, Aiman
N1 - Publisher Copyright:
© 1986-2012 IEEE.
PY - 2019/5/1
Y1 - 2019/5/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85063696484&partnerID=8YFLogxK
U2 - 10.1109/MNET.2019.1800083
DO - 10.1109/MNET.2019.1800083
M3 - Article
AN - SCOPUS:85063696484
SN - 0890-8044
VL - 33
SP - 196
EP - 203
JO - IEEE Network
JF - IEEE Network
IS - 3
M1 - 8674240
ER -