IoT-Based Secure Healthcare Framework Using Blockchain Technology with A Novel Simplified Swarm-Optimized Bayesian Normalized Neural Networks

Abstract

The Internet of Things (IoT) is growing in popularity nowadays and has many potential uses, particularly in healthcare. A large amount of sensing data is generated from a variety of sensing devices as a result of the growing needs of the IoT. The use of artificial intelligence (AI) methods is crucial for real-time, scalable, and accurate data processing. However, there are certain obstacles in the way of the layout and implementation of a successful analysis of the big data approach. These include a lack of suitable training data, resource limits, and a centralized architecture for the data. However, emerging blockchain technology provides a distributed system. It is advocated for getting rid of centralized control and solving AI issues, and it allows for safe data and resource exchange across the many nodes of the IoT network. Thus, this research develops a novel simplified swarm-optimized Bayesian normalized neural network (SSO-BNNN) for the secret transmission of medical images to address the aforementioned challenge. The neighborhood indexing sequence (NIS) approach is also used to encrypt the hash value. Several experiments were conducted to verify the outcomes of the suggested approach, and several facets of those results are discussed. Experimental results show that the proposed excellent findings with the best accuracy, sensitivity, and specificity were produced by the model.