Efficiency Validation of Secure Remote Location-Based WBAN Communication for Human Health Monitoring System

Currently, researchers are increasingly focusing on medical sciences due to the growing importance of security and health monitoring. The primary objective of researchers is to develop a communication mechanism that is energy efficient in the medical field. However, only a small number of researchers considered the security of patient data. One category of E-health service is remote human health monitoring, which involves capturing and transmitting the patient's biological sensed statistics to a medical representative or practitioner for evaluation. The Wireless Body Area Network (WBAN) idea is crucial in recording and delivering patient physiological data, and it is extensively employed in remote health-monitoring applications. This article presents a model called Secure Remote Location-Based Healthcare Monitoring (SRL-HM) System, which combines the Secure Distributed and Energy Efficient Routing (SDEER) protocol with Elliptical Curve Cryptography (ECC). The model aims to enhance security and energy efficiency in healthcare monitoring. In this context, the protocol is combined with the concept of Artificial Intelligence (AI) and the idea of a Feed Forward Back Propagation Neural Network (F2-BPNN). Particle Swarm Optimization (PSO), a swarm-based metaheuristic algorithm, is used in conjunction with AI techniques to address the security and energy limitations of a WBAN-based health monitoring paradigm. To validate the model, we executed a comparison of the simulation results with seven other techniques. Our observations indicate that the throughput and energy consumption of ECC-based SDEER surpasses those of previous works. In comparison to current approaches, the proposed strategy provides a maximum throughput of 3.85 packets per slot and reduces energy consumption in sensor nodes.