# Optimized Scheduling of IoT Devices in Healthcare Facilities: Balancing Cost and Quality of Care
## 1 Introduction  
The rapid advancement of Information Technology (IT) has fundamentally transformed healthcare, particularly in patient monitoring within hospital settings. As hospitals and medical facilities increasingly adopt digital solutions, the ability to continuously track patients' vital signs, predict potential complications, and optimize healthcare operationshas improved significantly. Advanced IT solutions, including cloud computing, IoT, AI, and edge computing, enable real-time data collection, remote monitoring, and predictive analytics, enhancing both patient outcomes and hospital efficiency [1]. The incorporation of AI-driven decision support systems to enhance patient care is studied in [2; 3]. IT-based monitoring solutions bring clear benefits to healthcare. They boost workflow efficiency, improve early disease detection, and reduce human error. One key use is indoor positioning systems, which track patients and equipment in real time. These rely on wireless networks and cloud computing to support hospital navigation and emergency response [4; 5]. Wearable IoT devices and sensor-based systems also allow continuous monitoring of vital signs. This supports personalized care and cuts down on manual checks [6; 7]. A review in [8] explores Industry 4.0 in healthcare, while [9] focuses on IoT-based medical devices. As noted in [10], the Healthcare IoT (H-IoT) enables real-time monitoring and quicker interventions, improving patient outcomes. It also simplifies data collection and analysis, making healthcare more efficient. According to [11], IoT helps reduce hospital readmissions and supports remote care. Finally, AI-driven predictive analytics is transforming decision-making. It detects early warning signs and suggests timely actions [12; 13]. These advancements have not only improved patient care but also reduced operational costs for healthcare providers. Despite growing adoption of IoT and AI in healthcare, most existing scheduling solutions focus on general aspects like task timing, system responsiveness, or network performance. For instance, optimized scheduling models have been proposed to reduce delays and task failure rates [14], while others introduced decision support systems to prioritize urgent care when resources are limited [15]. Some approaches integrate deep learning or predictive analytics to improve sensing accuracy and tailor device usage based on patient needs [16; 17]. However, these models often overlook the specific challenge of assigning individual IoT devices to patients in a way that balances cost with quality of care. Studies from different regions highlight critical contextual considerations. For example, research in Japan shows that elderly users benefit from voice-based IoT interfaces due to ease of use [18], while work in India emphasizes the need for scalable, cost-effective solutions in resource-limited healthcare environments [19]. European reviews also stress the importance of adapting device allocation strategies to diverse care models and patient needs [20]. Although these contributions have advanced the field, none offer a comprehensive framework that integrates operational cost, device characteristics, and patient well-being. This underscores the need for a new approach--one that formulates device-patient allocation as a multi-objective scheduling problem, explicitly balancing economic efficiency with high-quality, personalized care. How can IoT medical devices be optimally allocated to patients in healthcare settings while simultaneously minimizing operational costs and maximizing quality of care? Specifically, we investigated whether a bi-objective scheduling approach can effectively balance these competing objectives while accounting for real-world constraints such as device compatibility, setup times, availability windows, and battery limitations. This study aims to develop a comprehensive optimization framework for the allocation of IoT medical devices to patients that explicitly addresses the trade-off between cost efficiency and care quality.