## 1 Introduction  
By enabling highly connected, real-time health monitoring ecosystems featuring the use of biomedical sensors, cutting-edge communication technologies, and complex data processing frameworks, the Internet of Things (IoT) has totally reshaped the world of healthcare. The growing demand for ongoing, remote patient monitoring, notably for the management of chronic diseases, rehabilitation from surgeries, and eldercare, is a major driver of this approach toward societal transformation [1]. Traditional healthcaremodels are changing dramatically in their pursuit of data-centric, preventative, and highly customized medicine. Such models have previously hinged on established in-person consultations and reactive forms of therapy. Real-time patient monitoring is distinguished as an integral part of clinical decision making in this constantly evolving setting, facilitating preventative care and greatly boosting patient outcomes. Patient care, disease prevention, and comprehensive health management are all influenced by a complex system of healthcare professionals, medical establishments, innovative technologies, and regulatory frameworks that jointly make up a health ecosystem. These ecosystems are typically confined to big healthcare institutions, notably clinics, hospitals, and specialized care centers, where physicians establish diagnoses and manage patients mostly through recurrent relationships with patients and subsequent data analysis [2]. The easy share of clinical information, the merging of interdisciplinary healthcare services, and the accessibility of medical infrastructure that enables both therapeutic and preventive procedures are all key to the effectiveness of such ecosystems. Concerns ranging from fragmented data, inadequate interoperability, and limited access to real-time patient information are prevalent in traditional health ecosystems and can compromise clinical decision making and interfere with the continuity of qualitative care. By taking advantage of digital transformation, IoT-driven sensor networks, cloud-powered analytics, and artificial intelligence (AI) for providing real-time, data-driven healthcare services, a connected health ecosystem optimizes traditional healthcare setups. This approach allows for the uninterrupted gathering of data, secure transmission, and smart processing throughout different medical contexts by smoothly integrating medical devices, wearable sensors, remote monitoring systems, and electronic health records. In addition to strengthening patient monitoring, this integrated model supports more proactive, tailored, and reliable healthcare delivery. A connected health ecosystem facilitates the systematic detection of abnormalities, the predictive modeling of disease evolution, and continuous remote health monitoring and management, as opposed to traditional health systems where the observation of patients is intermittent and exclusive to hospitals and medical centers. Through the application of biomedical sensors, edge computing, and wireless communication protocols to provide real-time, automated, and continuous health evaluations, the integration of IoT-enabled monitoring technologies has radically changed the medical field [3]. Such advances enhance patient monitoring capabilities while rendering it more feasible to identify health abnormalities at an early stage as it allows for the straightforward gathering, exchange, and analysis of physiological and physical data [4]. IoT-enabled systems feature the real-time tracking of key health parameters within medical centers, remote healthcare environments, and smart urban infrastructures, in contrast to traditional health monitoring solutions that primarily depend on random assessments conducted at clinical appointments.