Efficient Energy Consumption Optimization for Wireless Sensor Health Monitoring System in Mobile Edge Computing

Wireless sensor health monitoring system integrates biomedical engineering technology and wireless sensor network, which can monitor human physiological information in real time. The monitoring system takes the network technology as the platform, and sends the status information of the monitoring object to the coordinator in real time through mobile edge computing server to realize data collection. A mathematical model of wireless sensor health monitoring network energy consumption optimization is established in this paper, which takes into account the constraints of the actual network system, such as computing task offloading, power, bit error rate and actual delay. To improve the performance of the method, ensure the complete decline of the function value and the convergence of the iteration matrix, an energy optimal resource allocation algorithm for health monitoring system based on improved quasi Newton method BFGS is proposed. It reduces the computational complexity during the iteration process, maintains superliner convergence, and has strong numerical stability in calculations. Simulation results show that the proposed resource allocation algorithm effectively reduces the time delay to process computing tasks and energy consumption of health monitoring sensor nodes, which enables the health monitoring system to process large-scale medical data and information more effectively and timely.