ArticleSensors (Basel, Switzerland)2021
A Scheduling Mechanism Based on Optimization Using IoT-Tasks Orchestration for Efficient Patient Health Monitoring.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it, 50 citations in OpenAlex.
- Real-Time Scheduling in IoT Applications: A Systematic Review.Sensors (Basel, Switzerland) · 2022Pooled it
- Energy-Aware Ultra-Reliable Low-Latency Communication for Healthcare IoT in Beyond 5G and 6G Networks.Sensors (Basel, Switzerland) · 2025Article
- Optimizing Spectral Utilization in Healthcare Internet of Things.Sensors (Basel, Switzerland) · 2025Review
- Unraveling the Potential of Attentive Bi-LSTM for Accurate Obesity Prognosis: Advancing Public Health towards Sustainable Cities.Bioengineering (Basel, Switzerland) · 2024Article
- Efficient Classification of ECG Images Using a Lightweight CNN with Attention Module and IoT.Sensors (Basel, Switzerland) · 2023Article
- Machine Learning and Internet of Things Enabled Monitoring of Post-Surgery Patients: A Pilot Study.Sensors (Basel, Switzerland) · 2022Article
- Critical Success Factors and Traceability Technologies for Establishing a Safe Pharmaceutical Supply Chain.Methods and protocols · 2021Review
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Over the past years, numerous Internet of Things (IoT)-based healthcare systems have been developed to monitor patient health conditions, but these traditional systems do not adapt to constraints imposed by revolutionized IoT technology. IoT-based healthcare systems are considered mission-critical applications whose missing deadlines cause critical situations. For example, in patients with chronic diseases or other fatal diseases, a missed task could lead to fatalities. This study presents a smart patient health monitoring system (PHMS) based on an optimized scheduling mechanism using IoT-tasks orchestration architecture to monitor vital signs data of remote patients. The proposed smart PHMS consists of two core modules: a healthcare task scheduling based on optimization and optimization of healthcare services using a real-time IoT-based task orchestration architecture. First, an optimized time-constraint-aware scheduling mechanism using a real-time IoT-based task orchestration architecture is developed to generate autonomous healthcare tasks and effectively handle the deployment of emergent healthcare tasks. Second, an optimization module is developed to optimize the services of the e-Health industry based on objective functions. Furthermore, our study uses Libelium e-Health toolkit to monitors the physiological data of remote patients continuously. The experimental results reveal that an optimized scheduling mechanism reduces the tasks starvation by 14% and tasks failure by 17% compared to a conventional fair emergency first (FEF) scheduling mechanism. The performance analysis results demonstrate the effectiveness of the proposed system, and it suggests that the proposed solution can be an effective and sustainable solution towards monitoring patient's vital signs data in the IoT-based e-Health domain.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.