Affiliations: [a] Department of Information Technology, Dr. NGP Institute of Technology, Coimbatore, Tamilnadu, India
| [b] Department of Computer Science and Engineering, Hindusthan Institute of Technology, Coimbatore, Tamilnadu, India
Correspondence:
[*]
Corresponding author. P. Rajasekaran, Department of Information Technology, Dr. NGP Institute of Technology, Coimbatore, Tamilnadu, India. E-mail: rajasekaranp9060@gmail.com.
Abstract: Internet of Things (IoT), a distributed healthcare system has integrated different medical resources with sensors and actuators. In this research paper proposes a secure healthcare monitoring system for IoT based distributed healthcare systems in the cloud using blockchain and deep learning (DL) mechanisms. The proposed system involved three phases: secure data transmission, data storage, and disease classification system. Initially, the patients are authenticated via blockchain mechanism and their data is encrypted via Effective Key-based Rivest Shamir Adelman (EKRSA), in which the keys are generated using Circle chaotic map and Linear inertia weight-based Honey Badger Optimization (CLHBO) algorithm. Next, in the data storage phase, these encrypted IoT data are securely stored in the cloud using blockchain technology in a distributed manner. Finally, in the disease classification, the data are gathered from the publicly available dataset, and these collected datasets are preprocessed to handle missing values and data normalization. After that, the proposed system applies a radial basis kernel-based linear discriminant analysis (RBKLDA) model to reduce the dimensionality of the dataset. At last, the disease classification is done by optimal parameter-centered bidirectional long short-term memory (OPCBLSTM). The proposed EKRSA system archives maximum throughput of 99.05% and reliability of 99.66, which is superior to the existing approaches. The OPCBLSTM is investigated for its disease classification process, the proposed one achieves 99.64% accuracy with less processing time of 6 ms, which is superior to the existing classifiers. The experimental analysis proves that the system attained better security and classification metrics results than the existing methods.
Keywords: Internet of Things (IoT), healthcare monitoring, secure data transmission, blockchain, disease prediction, machine learning (ML), deep learning (DL)
