## 1 Introduction
A more thorough patient monitoring and improved clinical decision support originated from the development of multi-sensor systems that integrate technologies like electrocardiography (ECG), oxygen saturation (SpO\({}_{2}\)), electromyography (EMG), galvanic skin response (GSR), and motion-tracking technologies. Such systems have also significantly boosted the accuracy and extent of digital health assessments. Incorporating edge computing architectures with low-latency communication protocols maximizes data processing efficiency, reduces dependency on centralized cloud computing, and enables the prompt identification of abnormalities and system resilience. In line with rising demands for integrated healthcare ecosystems and efficient smart city integration, these improvements in technology have widened up the possibility for scalable, secure, and energy-efficient health monitoring solutions. Multi-sensor IoT health monitoring solutions have lately progressed amid notable developments in wireless communication, sensor miniaturization, and real-time analytics. The timely detection of abnormalities has been enhanced by the merging of AI-driven health analytics with real-time physiological data collection, facilitating built-in alerts, predictive analytics, and the recognition of health fluctuations at the outset. By simply integrating IoT-enabled health solutions into telemedicine infrastructures, remote patient health monitoring systems most especially have radically shifted the way healthcare is delivered [5]. They make certain that real-time physiological data are adequately shared and processed in order to facilitate remote clinical evaluations. Emerging IoT health ecosystems improve personalized care by fostering efficient interaction between patients, healthcare providers, and smart urban health infrastructure. As IoT-enabled technologies develop further, their impact transcends well beyond monitoring a single patient; they could ultimately be applied to guide health policy decisions, streamline hospital resource management, and even deliver population-level smart health oversight on an extensive basis. IoT-enabled health monitoring is evolving quickly, but there are still a number of important challenges to overcome, especially when it comes to real-time data transmission, sensor communication, system scalability, and strict security standard compliance. For healthcare applications like movement analysis, neuromuscular evaluations, and cardiac analysis that need ultra-low latency and high-performance monitoring in order to provide reliable and helpful data to healthcare professionals, they have to be operated practically in real time. There are compromises among latency, bandwidth capacity, and energy efficiency brought about by frequently implemented wireless communication protocols such as Wi-Fi (Wireless Fidelity) and Bluetooth Low Energy (BLE) [6]. Although Wi-Fi allows for fast data transfer, in high-density healthcare environments, it requires significant amounts of power and can be vulnerable to network bottlenecks. Large-scale healthcare implementations could face limitations due to BLE's limited bandwidth and inadequate data throughput, despite its low-power operation optimization. The implementation of low-power, long-range IoT communication technologies, including NB-IoT and Long-Range Wide-Area Network (LoRaWAN), which are increasingly endorsed for having the capacity for establishing scalable remote health monitoring infrastructure [7; 8], is an alternative option. These protocols are especially appropriate for the continuous monitoring of patients with increased risk in residence-based care settings because they convey wide-area data transmission with low power consumption. Their slower transfer speeds restrict their ability to gather high-frequency biomedical parameters, exposing a basic compromise between real-time responsiveness and energy efficiency.