Real-Time Data Analytics and Decision-Making in Modern Sensor Networks

This study introduces a novel technique to analyze data and make real-time judgments in sensor networks. This system handles several sensor data issues using innovative machine learning, secure computing, flexible filtering, anomaly detection, and blockchain technology. We evaluated the framework's efficacy by comparing it comprehensively with existing methodologies and finding that it enhanced performance across several performance indicators. The approach is accurate, quick, resource-efficient, error-tolerant, and data-safe. Complex machine learning algorithms improve data analytics accuracy and prediction. This enables rapid, precise judgments in shifting scenarios. For data-sensitive applications that must fulfill tight regulatory criteria, the technique uses safe multi-party computer technology to safeguard and secure data. Due to noise reduction and signal clarity, adaptive filtering improves data quality. However, anomaly detection systems find and resolve unusual data patterns, making them more dependable. Blockchain-based data security ensures unambiguous, unchangeable records. This prevents unauthorized data access and changes. For critical applications to perform properly, sensor data must be accurate and trustworthy. An ablation study details how each item fits within the framework and how vital it is to improve the total system. The recommended technique, with its holistic approach, represents a significant technological breakthrough in sensor networks, enabling healthcare monitoring, environmental sensing, and manufacturing automation.