Nowadays, finding genetic components and determining the likelihood that treatment would be helpful for patients are the key issues in the medical field. Medical data storage in a centralized system is complex. Data storage, on the other hand, has recently been distributed electronically in a cloud-based system, allowing access to the data at any time through a cloud server or blockchain-based ledger system. The blockchain is essential to managing safe and decentralized transactions in cryptography systems such as bitcoin and Ethereum. The blockchain stores information in different blocks, each of which has a set capacity. Data processing and storage are more effective and better for data management when blockchain and machine learning are integrated. Therefore, we have proposed a machine-learning–blockchain-based smart-contract system that improves security, reduces consumption, and can be trusted for real-time medical applications. The accuracy and computation performance of the IoHT system are safely improved by our system.

As Internet of Things (IoT) technology develops, medical equipment, wearables, sensors, and users can be linked together to create an ecosystem known as the Internet of Medical Things (IoMT). IoMT enhances the effectiveness, precision, and affordability of the current healthcare system. It is difficult to guarantee data security and privacy of data in the IoMT ecosystem due to the fact that it incorporates several businesses, heterogeneous networks, and a lot of private information. IoT devices' processing and power requirements make it difficult to ensure their security. In order to secure their data and connections, many IoT systems use asymmetric cryptography and Elliptic Curve Cryptography (ECC). Implementing and deploying these security and privacy solutions for the IoT environment, however, continues to be a substantial problem due to the hardware restrictions of IoT objects. In this paper, we have presented a more effective lightweight cryptographic system based on Elliptic Curve and Dual Cryptography (ECDC) to strengthen ECC and secure communication between IoMT devices. It protects against all kinds of security problems, like authentication, privacy, and integrity, and stops common cyberattacks.

Transportation, temperature control, and the production of pharmaceuticals all rely on 71 percent of the Earth's surface, which is covered by water. Valuable things, including minerals, metals, corals, and coral reefs, can be identified with the help of the IoUT, among other applications. One of the crucial uses is in preventing damage caused by natural disasters. The IoT boosted concepts for a new undersea network (IoUT). Underwater networks have many drawbacks, including a lack of dependability, limited bandwidth, long propagation delays, high processing demands, high energy costs, and node detection with secure communication. Real-time, secure data communication is a significant area of research for us, along with node identification and dynamic network configuration. IoUT faces severe obstacles in the form of these issues. Our strategy involves a dynamic graph for network design, an AFA algorithm based on AI for node recognition, and ECC for a secure communication mechanism. In order to increase discretion and provide the undersea network with adequate robust security.