Intelligent and Energy-Efficient Approach for Detecting Patient Locations

In the Internet of Things (IoT), Wireless Sensor Networks (WSNs) play a pivotal role in landscape, connecting devices for smart applications. The IoT is a groundbreaking innovation of the modern era, particularly vital for advancing healthcare through smart applications. Leveraging sensor data from consumer electronics mobile devices such as smart mobile phones, physicians can remotely monitor patients’ health within a smart healthcare system. Efficiency, rapid response, and data security are vital as the system aids patients with limited sensor battery life. Yet, mobile patient monitoring faces challenges due to unknown network interference. The work utilizes Radial Basis Function Neural Network on edge servers as an innovative method for patient localization. The approach aims to ensure continuous health data provision through mobile phone networks and anticipate vulnerable patient locations during emergencies when cellular coverage is lacking. Mathematical analysis and simulations underscore the efficacy of the proposed strategy in terms of energy efficiency in consumer electronics networks, emphasizing its importance for predictive patient positioning in contemporary healthcare systems.