# Optimized Scheduling of IoT Devices in Healthcare Facilities: Balancing Cost and Quality of Care
## 2 Background  
Healthcare IoT software management is vital for hiding complexity and enabling easy deployment [21]. Smart resource allocation is equally crucial for cost control and performance amid fluctuating device demand. Poor allocation can harm care during peaks and waste resources during lulls. Effective resource management ensures efficient and cost-effective device use. Proper system design is key for successful IoT adoption and better healthcare service [22]. Strong resource allocation is also essential for reliable remote monitoring [23]. Management must balance scalability and cost within financial and performance limits. IoT's healthcare benefits come with rising operating costs [24], making efficient resource use critical. Device variety in cost and comfort impacts patient acceptance, requiring scheduling to balance cost, comfort, and clinical needs. Recent studies address IoT scheduling in healthcare, especially for home visits and monitoring. The authors of [14] presented a smart PHMS with optimized scheduling, reducing task starvation (14%) and failure (17%). A review in [25] analyzed real-time IoT healthcare scheduling, comparing approaches and outlining future directions. The authors of [26] proposed Prioritized Scheduling (PS) for delay-sensitive tasks, outperforming EDF variations. The study in [15] presented a decision support system to allocate limited devices, prioritizing critical cases. The architecture described in [16] combines IoT with deep learning to improve sensing accuracy and personalize resource allocation based on patient needs. Finally, the authors of [17] integrated machine learning with IoT to predict demand, enhancing care and operations. To present a well-rounded view of the global research landscape, it is essential to highlight key contributions from international research centers, especially those in Europe and Asia. For instance, a study by [18] in Japan evaluated various user interfaces for IoT devices among senior citizens, finding that voice interfaces were more suitable for elderly users due to shorter setting times and higher usability scores. In India, [19] discussed the impact of IoT in healthcare, emphasizing the challenges and opportunities in adopting digital healthcare solutions in resource-constrained environments. Additionally, the authors of [20] reviewed IoT-based ambient assisted living solutions for the growing elderly population and their care needs, categorizing them by health focus, IoT technologies, aims, and experimental evaluation. The work in [27] performed heart disease prediction using an IoT-based framework and an improved deep learning approach, applied to the Hungarian dataset. While this body of work demonstrates significant advancements in IoT-based healthcare scheduling, most studies focus on general resource use, network performance, or task timing. Crucially, none directly address the problem of assigning specific IoT devices to patients in a way that balances operational cost with quality of care. This gap in the literature highlights the need for novel approaches that integrate clinical priorities, device characteristics, and patient comfort. Even recent international studies, while innovative in their respective domains, do not provide comprehensive frameworks for patient-specific device allocation that optimizes both economic and clinical outcomes. The systematic analysis of this expanded literature reveals several methodological patterns crucial to our research design. First, successful implementations consistently incorporate multi-objective optimization techniques that balance technical efficiency with patient-centered outcomes. Second, regional variations in healthcare delivery models significantly impact device allocation strategies, necessitating adaptable frameworks. This methodological synthesis directly informs our research approach by establishing the assessment metrics, constraint parameters, and evaluation frameworks employed in our study.