Monitoring the Transmission of Data From Wearable Sensors Using Probabilistic Transfer Learning

New applications like activity tracking and healthcare monitoring depend on secure and timely data transfer using wearable sensors. This work aims to reduce data loss and latency when transmitting wearable sensor information to analysis terminals by introducing an innovative probabilistic transfer learning approach. Using dynamic transmission slot allocation based on risk thresholds and time sensitivity, the suggested method intelligently arranges and prioritizes the transfer of aggregate sensor data across various sources. High-risk data is given preference when allocating slots in a two-step algorithm that divides the data into emergency and normal classifications to guarantee timely delivery without undue delays. Over time, for better activity identification, the transfer model of learning steadily learns and improves the slot assignment accuracy based on feedback. Comprehensive analysis of various queuing scenarios and transmission disruptions shows notable improvements over existing approaches, with waiting times, data loss, and transmission delays reduced by up to 10.49%, 2.42%, and 13.86%, respectively. Most importantly, 3.28% more accuracy is achieved in identifying distinct activities from the supplied wearable sensor data. This can be achieved by the dependable data supply by the probabilistic modelling approach. With its comprehensive architecture for efficiently managing limited communication resources, the suggested approach can provide real-time health surveillance, smart environment services, and other digital-physical systems requiring trustworthy data streaming. More interaction with statistical engines, improvements to security and privacy, and scalability validation on larger distributed platforms are possible areas for future work.