Edge Computing For Smart Health: Context-aware Approaches, Opportunities, and Challenges
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Data CompressionEdge-computingEvent DetectionSimilar Papers 제목 키워드 기반
SmartEdge: Smart Healthcare End-to-End Integrated Edge and Cloud Computing System for Diabetes Prediction Enabled by Ensemble Machine Learning
The Internet of Things (IoT) revolutionizes smart city domains such as healthcare, transportation, industry, and education. The Internet of Medical Things (IoMT) is gaining prominence, particularly in smart hospitals and…
Cloud ComputingDiabetes PredictionEdge-computingPrediction+1HealthEdge: A Machine Learning-Based Smart Healthcare Framework for Prediction of Type 2 Diabetes in an Integrated IoT, Edge, and Cloud Computing System
Diabetes Mellitus has no permanent cure to date and is one of the leading causes of death globally. The alarming increase in diabetes calls for the need to take precautionary measures to avoid/predict the occurrence of d…
Cloud ComputingDiabetes PredictionregressionMyDigitalFootprint: an extensive context dataset for pervasive computing applications at the edge
The widespread diffusion of connected smart devices has contributed to the rapid expansion and evolution of the Internet at its edge. Personal mobile devices interact with other smart objects in their surroundings, adapt…
Activity RecognitionEdge-computingLink PredictionRecommendation SystemsEdge Intelligence for Empowering IoT-based Healthcare Systems
The demand for real-time, affordable, and efficient smart healthcare services is increasing exponentially due to the technological revolution and burst of population. To meet the increasing demands on this critical infra…
Edge-computingSmart Home Environment Modelled with a Multi-Agent System
A smart home can be considered a place of residence that enables the management of appliances and systems to help with day-to-day life by automated technology. In the current paper is described a prototype that simulates…
Management