Tariq Emad Ali, Faten Imad Ali, Mohammed A. Abdala, Ameer Hussein Morad, Győző Gódor, and Alwahab Dhulfiqar Zoltán
Blockchain-Based Deep Reinforcement Learning System for Optimizing Healthcare
The Industrial Internet of Things (IIoT) has become a transformative force in various healthcare applications, provid ing integrated services for daily life. The app healthcare based on the IIoT framework is broadly used to remotely monitor clients health using advanced biomedical sensors with wireless technolo gies, managing activities such as monitoring blood pressure, heart rate, and vital signs. Despite its widespread use, IIoT in health care faces challenges such as security concerns, inefficient work scheduling, and associated costs. To address these issues, this paper proposes and evaluates the Blockchain-Based Deep Rein forcement Learning System for Optimizing Healthcare (BDRL) framework. BDRL aims to enhance security protocols and maxi mize makespan efficiency in scheduling medical applications. It facilitates the sharing of legitimate and secure data among linked network nodes beyond the initial stages of data validation and as signment. This study presents the design, implementation, and statistical evaluation of BDRL using a new dataset and varying platform resources. The evaluation shows that BDRL is versatile and successfully addresses the security, privacy, and makespan needs of healthcare applications on distributed networks, while also delivering excellent performance. However, the framework utilizes high resources as the size of inserted data increases.
DOI: 10.36244/ICJ.2024.3.9
Please cite this paper the following way:
Tariq Emad Ali, Faten Imad Ali, Mohammed A. Abdala, Ameer Hussein Morad, Győző Gódor, and Alwahab Dhulfiqar Zoltán , "Blockchain-Based Deep Reinforcement Learning System for Optimizing Healthcare", Infocommunications Journal, Vol. XVI, No 3, September 2024, pp. 89-100., https://doi.org/10.36244/ICJ.2024.3.9