Securing E-health Data across IoMT and Wearable Sensor Networks
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".
Deadline for manuscript submissions: 15 March 2025 | Viewed by 1612
Special Issue Editor
Special Issue Information
Dear Colleagues,
Electronic health systems facilitate the quick access and management of patient health data, and these systems have been widely used thanks to the quick evolution of contemporary technology. Dealing with vast amounts of healthcare data and their ever-changing characteristics has presented significant obstacles for healthcare professionals in terms of data pre-processing, analysis, security, storage, and usability. Several types of sensors, IoMT devices, cloud platforms, and learning models are used to operate these E-health data management systems successfully. Through E-health systems, diagnosis centers have achieved patient reliability, enabling them to rely on the system and ensuring the safety of their confidential data. Particularly, sensor-based health data contains personal information that needs to be secure in the system. This fact explains the necessity of blockchain (BC) and federated machine learning (FML) technology in E-health domains. Training data on local devices or servers, FML technology ensures patient privacy by reducing the risk of data breaches. Even depending on the patient's health condition, federated learning can offer a remote patient monitoring system in which the model performs efficiently by learning from localized data. On the other hand, blockchain facilitates a highly secure and efficient method for healthcare providers to interchange patient data in real time. Moreover, BC allows patients to control E-health systems, deciding who can access and use their health data. By having control over their data management, patients can be more engaged in their care and make well-informed decisions. As a result, the growing pressure on healthcare providers and additional treatment costs can be reduced by a great margin. Further, not all health providers involved in E-health data management will be able to use the blockchain and federated machine learning technology in their system due to insufficient technical expertise and operational costs. So, any proposal should take these points into account and deliver these technologies in a general way, so they can satisfy everyone.
Therefore, the purpose of this Special Issue is to call for creative research works that investigate the potential benefits of eHealth platforms that incorporate blockchain technology and federated machine learning into areas such as data management, remote care, health analytics, and informatics, with a focus on security, privacy, trust, and user adoption and acceptance.
We welcome submissions of any innovative research work covering a broad variety of subjects, including (but not limited to) the following:- The use of blockchain and federated learning in eHealth and mHealth platforms
- Self-health monitoring systems, including deep learning and machine learning models and applications to wearable sensing devices
- Handling big health data using a blockchain system
- Different frameworks and tools to improve privacy and security in the healthcare domainSmart mobile technologies, Internet of Medical Things (IoMT), and sensor networks for physiological signal monitoring.
Dr. Ashraf Uddin
Guest Editor
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Keywords
- blockchain
- federated learning
- eHealth; mHealth
- self-health monitoring systems
- wearable sensing devices
- smart mobile technologies
- Internet of Medical Things (IoMT)
- sensor networks
- health data
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