Securing and Enhancing Efficiency in IoT for Healthcare Through Sensor Networks and Data Management

Abstract

The integration of Internet of Things (IoT) technology in healthcare systems has revolutionized patient care and operational efficiency. This paper presents a comprehensive study focusing on securing IoT networks and optimizing efficiency in healthcare environments through sensor networks and data management. Our research involved a multifaceted analysis of security vulnerabilities within deployed sensor networks. A vulnerability assessment of 150 nodes revealed critical vulnerabilities affecting data transmission and authentication mechanisms. Implementing advanced encryption algorithms significantly mitigated data interception risks, enhancing the confidentiality of transmitted medical records. Efficiency enhancement through data management was evaluated employing edge computing and cloud-based solutions. Edge computing solutions showcased a 40% reduction in data processing latency and a 30% increase in data throughput compared to cloud-based systems. This facilitated real-time analytics for patient monitoring without compromising network stability. Additionally, machine learning algorithms exhibited promising outcomes in diagnostics, improving accuracy by 15% compared to traditional methods. Anomaly detection in vital signs achieved an average precision of 88%, contributing to enhanced diagnostics. Furthermore, predictive maintenance models reduced unplanned downtime of medical equipment by 60%, ensuring continuous functionality. This study's quantitative results highlight the critical role of robust security measures, efficient data management strategies, and the integration of advanced technologies in fortifying IoT-based healthcare systems. These findings provide valuable insights for practitioners and researchers, emphasizing the significance of securing IoT networks while optimizing efficiency to advance healthcare services and improve patient outcomes.