Secure Internet of Things (IoT) using a novel Brooks Iyengar quantum Byzantine Agreement-centered blockchain Networking (BIQBA-BCN) model in smart healthcare

Abstract

Smart Health Care offers efficient, sustainable, along with real-time human services, and the concept of enhanced Internet of Things (IoT) lies behind the emergence of this smart Health Care (HC). Nevertheless, the association of these IoT-centric sensors with other organizations generates security threats that an illegal user might utilize it owing to the data’s openness. In the HC sector, data security and privacy are the prime concern in which the alteration in data values as sensors could modify the diagnosis process, which may cause serious health problems. The immutability and transparency of the blockchain make it a viable option for the safe storage and management of healthcare data. However, the Cloud Storage Server (CSS), consensus latency, majority attacks, Byzantine problem, uncomfortable data Throughput (TP), et cetera, make the blockchain vulnerable to healthcare data transmission. To solve the current disadvantages, a secure IoT was introduced in healthcare using Brooks Iyengar Quantum Byzantine Agreement-centered Blockchain Networking (BIQBA-BCN). This study ensures the sincerity and equity of health data exchange. Based on Blum Blum Shub and Okamoto Uchiyana Cryptosystem (OUCS) mutual authentica1tion, the proposed work provides an OUCS-based mutual authentication system (BBS-OUC). Attackers are prevented from entering the BCN, ensuring the storage remains trustworthy. In addition, the Key Weight Block Function-Quasi-Cyclic Moderate Density Parity Check (KWBF-QCMDPC) algorithm safeguards the confidentiality and dependability of IoT user data. The cryptosystem technique protects sensitive data when the blockchain platform is distributed. The proposed methodology, known as BIQBA-BCN, results in a security level that is %94 effective. Finally, the BIQBA-BC consensus mechanism is used to distribute data to the corresponding Hospital Server (HS). By achieving a higher data TP, lower delay, higher Success Rate (SR), shorter consensus latency, and node communication time, the experimental results show that the proposed methodology is exceptionally safe against assaults and highly scalable.