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

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.

Introduction

The evolution of computational intelligence and Internet of Medical Things (IoMT), along with the advances of next-generation wireless technologies, has boosted the development of traditional healthcare processes into smart-healthcare services. Smart-health (s-health) can be considered as the context-aware evolution of mobile-health, leveraging wireless communication technologies to provide healthcare stakeholders with innovative tools and solutions that can revolutionize service provisioning. In particular, s-health enables remote monitoring, where patients and caregivers leverage mobile technologies to provide information about their health remotely, a service that is expected to reduce hospitalization considerably and allow for timely delivery of healthcare services to remote communities at low costs.