Fog Driven IoT Framework for Critical Patient Health Care Monitoring System

Recent advancements in wireless communication technology have profoundly influenced various aspects of daily life, particularly in healthcare, where continuous monitoring systems have become increasingly prevalent. A key innovation in this field is the Body Area Network (BAN), which connects multiple medical sensors and applications through specialized sensor networks designed to function independently across various platforms. BANs are widely utilized in medical and healthcare applications, ranging from basic patient monitoring systems to sophisticated tools for diagnosing life-threatening conditions. One of the most significant challenges for medical professionals, such as doctors and technicians, is making an early diagnosis, especially when dealing with critical illnesses that require prompt intervention. Patients’ health conditions can fluctuate over time, and not all healthcare facilities have the capacity to provide specialized care at all times. Wireless Body Area Networks (WBANs) offer a partial solution to this issue by enabling continuous patient monitoring. However, the time-sensitive nature of physiological data means that any delays, packet loss, or network overhead could negatively impact a patient’s health. This research proposes a fog-centric IoT framework designed to enhance data processing and diagnosis speed, particularly for critically ill patients requiring immediate care. The simulation results demonstrates that the proposed model can minimize response time up to $80 \%$ compared to conventional models. The findings confirm that the proposed framework effectively meets its objectives while maintaining high application performance.