A hybrid model for telemedicine data transmission based on blockchain and adaptive compression

Abstract

Telemedicine systems are revolutionizing healthcare by enabling remote diagnostics, consultations, and monitoring, particularly in regions with limited access to medical services. In low-resource rural areas, where infrastructure is weak and network bandwidth often does not exceed 20 kbps, telemedicine becomes a key tool to address healthcare inequalities. However, challenges such as data vulnerability to cyberattacks, low bandwidth, and high power consumption of wearable devices hinder its progress. This article proposes a hybrid model that integrates permissioned blockchain (Hyperledger Fabric) for secure medical data management, adaptive compression based on a convolutional neural network (CNN) to optimize bandwidth usage, and the LoRa protocol for energy-efficient long-range communication. Simulations conducted in MATLAB and NS-3 demonstrate a 25% reduction in data transmission latency, 30% lower energy consumption, and 100% resilience against cyberattacks compared to traditional methods. The model was tested on synthetic datasets (ECG, video streams, text reports) and demonstrated scalability for up to 500 devices within the network. The results are particularly relevant for low-resource regions where access to healthcare is limited due to poor infrastructure. The proposed solution offers a cost-effective and scalable platform for global telemedicine systems, contributing to the digitalization of healthcare.