Intelligent Health Monitoring System for Sensor Networks and Location-Based Services

The main problems of traditional health monitoring systems include data silos, lack of real-time performance, insufficient integration of location-based services, and data security and privacy issues, which affect the accuracy and timeliness of health monitoring and reduce the overall effectiveness of the system and user satisfaction. To solve these problems, this paper proposes an intelligent health monitoring system for sensor networks and location-based services. The system integrates multiple sensors, combines cloud computing and embedded Internet of Things technologies to achieve real-time data collection and analysis, and effectively integrates location-based services to provide users with personalized health management advice. Using machine learning algorithms, the system can identify health risks and generate real-time feedback. In addition, this paper also ensures the security and privacy of data based on data protection mechanisms. The experimental results show that the lowest average response time is 202.06 ms, and the highest data accuracy is 97.76%. The studied system is superior to the traditional system in health monitoring accuracy, response speed, and user satisfaction and has good application prospects.