Blockchain Powered IoT Solutions for Privacy Preserving Healthcare Data Management

This paper proposes improving blockchain-powered IoT systems for healthcare data management and real-time face recognition by focusing on accuracy, speed, and security. Advanced convolution methods, like depthwise separable and pointwise convolution, enhance feature extraction. This simplifies calculations without impacting facial feature recording. Batch normalization for stable training, ReLU activation for nonlinearity, and global average pooling to minimize dimensionality. These pieces optimize sorting for quick processing and real-time responsiveness. The recommended blockchain-powered IoT solution protects healthcare patient data well in terms of privacy, security, scale, and accuracy. It enables working with large files and accessing healthcare information in real time simply with little system latency and effective access control. Energy-efficient design reduces its environmental impact and operating expenses. After extensive testing, the recommended technology outperforms earlier ones for facial recognition and healthcare data management. It always scores better on data safety, system scalability, and energy economy. The recommended solution is adaptable and effective for large, real-time security and healthcare operations. It will be safe, scalable, and energy-efficient throughout time.