Secure Smart Healthcare Monitoring in Industrial Internet of Things (IIoT) Ecosystem with Cosine Function Hybrid Chaotic Map Encryption

Abstract

The technological progression is raised as a hybrid ecosystem with the industrial Internet of Things (IIoT). Among them, healthcare is also broadly unified with the Internet of Things to develop an industrial forthcoming system. Utilizing this type of system can be facilitating optimum patient monitoring, competent diagnosis, intensive care, and including the appropriate operation against the existing critical diseases. Due to enormous data theft or privacy leakage, security, and privacy towards patient-based informative data, the preservation of personal patients’ informative data has now become a necessity in the digitized community. The produced article is underlined on handsomely monitoring, perceptively extracted keyframe, and further processed lightweight cosine functions using hybrid way chaotic map keyframe image encryption. Initially, a regular concept of extracted keyframe is deployed to salvage meaningful detected frames by transmitting an alert autonomously to the administration. Then, lightweight cosine function for encryption is employed. This encryption incorporates keyframe exceedingly secure and safe from the outside world or any adversary. Our proposed methodology validates effectiveness throughout the IIoT ecosystem. The produced outcome is highly acceptable and has minimum execution time, robustness, and reasonably adopted cost-effective, secure parameter than any other (keyframes) image encryption methods. Furthermore, this methodology has optimally reduced bandwidth, essential communicating price, transmission cost, storage, and immediately judicious analysis of each occurred activity from the outside world or any adversary to remain secure and confident about the real patient-based data in the smartly developed environment.

A comprehensive systematic and bibliometric review of the IoT-based healthcare systems

“…Khan, Khan [ 95 ] focused on attentively observing, deftly retrieved the keyframe, and then processed the light cosine functions utilizing a hybrid method of chaotic map keyframe picture encryption. At first, a standard idea of extracted keyframe was used to recover significantly identified frames by sending an alert to the administration on its own.…”

“… • High throughput • Improving sensitivity • High accuracy [ 92 ] 2019 24 • Circuit World • Proposing novel implementation of IoT-based non-invasive sensor system for real-time monitoring of intravenous fluid level for assistive e-healthcare • High power-efficient • Low cost [ 93 ] 2019 60 • Journal of Artificial Intelligence and Systems • Introducing intelligent baby behavior monitoring using embedded vision in IoT for smart healthcare centers. • High accuracy • High efficiency [ 94 ] 2022 7 • IEEE Systems Journal • Proposing an anonymous IoT-based e-health monitoring system using blockchain technology • Maximizing the payoffs • Less computational cost • Supporting blockchain technology • High robust • High efficiency • High adaptive [ 95 ] 2022 8 • Scientific Programming • Examining secure smart healthcare monitoring in IIoT ecosystem with cosine function hybrid chaotic map encryption • Low execution time • High robustness • Low cost • High security • Low bandwidth …”

A Smart Card-Based Two-Factor Mutual Authentication Scheme for Efficient Deployment of an IoT-Based Telecare Medical Information System

“…The TMIS generates the finest patient monitoring, a well-organized diagnostic process, and intensive support and care compared to conventional healthcare operations. To facilitate these highly technological adaptations, however, data leakage and privacy thievery threaten the confidentiality of patients’ personal information in the current revolution [ 12 , 13 ]. In recent years, numerous ECC-based authentication and key agreement techniques [ 14 ] have been presented to address these security and privacy issues.…”

Blockchain Technologies in Healthcare System for Real Time Applications Using IoT and Deep Learning Techniques

“…Recently, several metaalgorithms have been developed with the express purpose of enhancing the e ciency of ML programmes. There are a number of access control problems inherent to IoT-Healthcare approaches [15] and a number of algorithms have been developed to solve them.…”