## 1 Introduction
Adaptive communication models that combine system performance, scalability, and real-time medical insights by boosting data transmission efficiency depending on particular healthcare solution demands need to be developed to tackle this issue. Achieving flattened multi-sensor integration and interoperability is one of the key hurdles in IoT-based healthcare ecosystems. It remains difficult to manage data fusion, generate effective sensor communication, and maintain shared interoperability since plenty of the current health monitoring systems are based on fragmented and heterogeneous sensor architectures. Scalability and simple integration into current health information systems and urban IoT infrastructures are hampered by large variations in sensor approaches, operating frequencies, and proprietary communication protocols, which add complexity to comprehensive data standards. MQTT-based IoT messaging and HL7 Fast Healthcare Interoperability Resources (FHIRs) are two recent improvements in standardized frameworks that attempt to address these shortcomings in interoperability [9; 10], but acceptance is still lagging because of ongoing standardization shortages, existing system limitations, and regulatory complexities. As IoT-enabled healthcare solutions keep developing, data security and patient privacy become more important challenges transcending interoperability. End-to-end encryption, reliable authentication procedures, and strict compliance with healthcare data protection security like the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA) [11] are prerequisites for widespread IoT health solutions. In order to prevent data breaches, unauthorized access, and cyberattacks on critical healthcare infrastructure, robust cybersecurity measures must be implemented given the increasing number of connected medical devices distributing highly sensitive patient data. Scalable and secure data-sharing frameworks are becoming incrementally more significant as IoT health monitoring devices [12] connect more and more with smart city infrastructures. Decentralized health data management systems, encrypted data-sharing protocols, and secure cloud infrastructures must be implemented to tackle these challenges and protect patient data while facilitating real-time, data-driven healthcare delivery. Recent research efforts have been focused on bolstering real-time processing frameworks, improving data transmission protocols, and expanding sensor-to-network communication in IoT healthcare settings [13; 14; 15] in order to mitigate such barriers. A key driver in increasing data processing performance, reducing network bottlenecks, and maintaining continuous health monitoring in insightful, application scenarios [16] is the widespread use of versatile IoT communication models that incorporate low-latency transmission protocols and edge computing architectures. The emergence of edge computing architectures in IoT-based healthcare solutions is among the most significant improvements in this domain. Edge computing renders it possible to preprocess data in real time at the sensor or gateway level, as opposed to conventional cloud-centric approaches that are frequently limited by latency and bandwidth issues. It lessens the computational load on centralized healthcare servers while improving transmission efficiency. These solutions might instantly recognize abnormalities, filter redundant data, and trigger alerts by integrating AI-driven signal processing capabilities. This boosts system responsiveness and supports medical decision making. New opportunities for extensive, real-time health monitoring are being brought about by the convergence of IoT health monitoring with smart city infrastructures. It opens up new opportunities for public health monitoring, emergency response synchronization, and AI-enhanced demographic surveillance when IoT-enabled healthcare solutions are plugged into smart city ecosystems.