Energy-Efficient IoT e-Health Using Artificial Intelligence Model with Homomorphic Secret Sharing

Abstract

Internet of Things (IoT) is a developing technology for supporting heterogeneous physical objects into smart things and improving the individuals living using wireless communication systems. Recently, many smart healthcare systems are based on the Internet of Medical Things (IoMT) to collect and analyze the data for infectious diseases, i.e., body fever, flu, COVID-19, shortness of breath, etc. with the least operation cost. However, the most important research challenges in such applications are storing the medical data on a secured cloud and make the disease diagnosis system more energy efficient. Additionally, the rapid explosion of IoMT technology has involved many cyber-criminals and continuous attempts to compromise medical devices with information loss and generating bogus certificates. Thus, the increase in modern technologies for healthcare applications based on IoMT, securing health data, and offering trusted communication against intruders is gaining much research attention. Therefore, this study aims to propose an energy-efficient IoT e-health model using artificial intelligence with homomorphic secret sharing, which aims to increase the maintainability of disease diagnosis systems and support trustworthy communication with the integration of the medical cloud. The proposed model is analyzed and proved its significance against relevant systems.

Internet of Medical Things (IoMT)-Based Smart Healthcare System: Trends and Progress

“…al. [ 7 ] have compared their energy-efficient IoT e-health model with the attribute-based encryption (ABE) and privacy-enhanced data fusion system (PDFS) model using parameters EC (energy consumption), PDR (packet drop ratio), DT (delivery time), and DL (data leakage). For reducing latency by minimizing the number of hops, they use the heuristic formula, h ( n )= d + t d , for each node, where t d is the delivery time and distance d is comprised of distance from source node i to corresponding neighbor n i , which is denoted by d ′.…”

“…Then, this data will be transferred over the internet to the next component dealing with the prediction and analysis phase. After receiving the medical data, analysis can be done using a proper AI-based data transformation and interpretation technique [ 7 ]. In case of serious problems, doctors or other medical requirements can be approached with the help of smart AI-based applications in smartphones [ 8 ].…”

Integrated Estimation of a Cyber-Physical System’s Sustainability

“…In addition, the scientific provisions of this work may be used in development of industry solutions in the field of CPS application; for example, in IoT e-health when modeling energy-efficiency using artificial intelligence as shown in [36][37][38][39][40]. Moreover, as noted in [33], according to experts, systems built on the basis of CPS allow provision of technological leadership and, hence, competitive advantages in many sectors of the digital economy including healthcare, transport, energy, construction and others.…”

“…Analysis of special literature [31][32][33][34][35][36][37] has allowed us to identify the following main elements of the CPS:…”

“…Analysis of [23,[33][34][35][36][37][38][39][40] on the topic under consideration allowed us to summarize the fundamental properties of cyber-physical systems that should be taken into account while designing the functioning of cyber-physical systems. There main ones are sustainability, self-programming, adaptability, autonomy, efficiency, functionality, seamlessness, reliability, ability to self-study, usability, transparency, friendliness to human environment, fast speed of functioning and adaptation to environmental changes, complexity (number of incoming components), high power consumption, creation product value, and economic value-added.…”