In Conjunction With
The 11th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT2024)
The 17th IEEE/ACM International Conference on Utility and Cloud Computing (UCC 2024)
Submit Your Paper
The workshop aims to address the transformative integration of the Internet of Things (IoT) into healthcare, signalling a shift towards an interconnected, intelligent, and patient-focused healthcare system. This digital healthcare transformation is characterised by the adoption of IoT and advanced computational technologies like machine learning (ML) and federated learning, which are poised to redefine healthcare monitoring, analysis, and predictive outcomes.
This workshop covers several key areas. These include the development and application of ML algorithms tailored for predictive analytics in health informatics. The implementation of robust security and privacy frameworks to protect patient data. Moreover, discussions will extend to the potential of federated learning for collaborative healthcare data analysis, enhancing predictive model accuracy while safeguarding patient privacy, and the role of wearable IoT devices in continuous health monitoring. Furthermore, the workshop emphasizes green computing and energy-efficient scheduling within IoT-based healthcare systems, aiming to highlight sustainable innovations that minimize environmental impact while ensuring high-quality care.
The workshop will also explore the utilization of synthetic data in the Internet
of Medical Things (IoMT) to overcome challenges associated with data privacy and
availability. By bridging current technological breakthroughs with future research
trajectories, the workshop aspires to establish a foundation for an innovative and
inclusive healthcare ecosystem, leveraging IoT to transform healthcare systems
into entities that are more efficient, effective, and personalized.
This workshop is of particular interest to researchers, healthcare professionals, environmental advocates, and technology developers focused on the intersection of healthcare, technology, and sustainability.
1. Machine Learning Approaches for Predictive Healthcare Analytics 2. Optimize Scheduling Approaches 3. Green IoT Devices and Protocols for Healthcare Applications 4. Quality of Service Optimization in IoT Healthcare Networks 5. Security and Privacy-Preserving Data Sharing Frameworks in IoT Healthcare Systems 6. Federated Learning for Collaborative Healthcare Data Analysis 7. Integration of Wearable IoT Devices with Healthcare Systems 8. Synthetic Data in IoMT (Internet of Medical Things) for Machine Learning Applications
Submitted manuscripts must represent original unpublished research that is not currently under review for any other conference or journal.
Manuscripts are submitted in PDF format and may not exceed six (6) IEEE-formatted *double-column* pages, including figures, tables, and references.
All manuscripts undergo a double-blind peer-review process and will be reviewed and judged on correctness, originality, technical strength, rigor in analysis, quality of results, quality of presentation, and interest and relevance to the conference attendees.
Your submission is subject to a determination that you are not under any sanctions by IEEE.
The official IEEE author guidelines and templates can be accessed on the following webpages: Latex, Microsoft Word and Overleaf
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