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

Abstract

Internet of Medical Thing (IoMT) is the most emerging era of the Internet of Thing (IoT), which is exponentially gaining researchers’ attention with every passing day because of its wide applicability in Smart Healthcare systems (SHS). Because of the current pandemic situation, it is highly risky for an individual to visit the doctor for every small problem. Hence, using IoMT devices, we can easily monitor our day-to-day health records, and thereby initial precautions can be taken on our own. IoMT is playing a crucial role within the healthcare industry to increase the accuracy, reliability, and productivity of electronic devices. This research work provides an overview of IoMT with emphasis on various enabling techniques used in smart healthcare systems (SHS), such as radio frequency identification (RFID), artificial intelligence (AI), and blockchain. We are providing a comparative analysis of various IoMT architectures proposed by several researchers. Also, we have defined various health domains of IoMT, including the analysis of different sensors with their application environment, merits, and demerits. In addition, we have figured out key protocol design challenges, which are to be considered during the implementation of an IoMT network-based smart healthcare system. Considering these challenges, we prepared a comparative study for different data collection techniques that can be used to maintain the accuracy of collected data. In addition, this research work also provides a comprehensive study for maintaining the energy efficiency of an AI-based IoMT framework based on various parameters, such as the amount of energy consumed, packet delivery ratio, battery lifetime, quality of service, power drain, network throughput, delay, and transmission rate. Finally, we have provided different correlation equations for finding the accuracy and efficiency within the IoMT-based healthcare system using artificial intelligence. We have compared different data collection algorithms graphically based on their accuracy and error rate. Similarly, different energy efficiency algorithms are also graphically compared based on their energy consumption and packet loss percentage. We have analyzed our references used in this study, which are graphically represented based on their distribution of publication year and publication avenue.

Ensemble Learning-Based Hybrid Segmentation of Mammographic Images for Breast Cancer Risk Prediction Using Fuzzy C-Means and CNN Model

“…Image segmentation refers to the techniques of dividing an image into different regions. The most effective method to analyze anatomical structure in medical is “region growing method” [ 42 , 43 ]. But it does not give proper and more accurate results if it directly applies to the input images that are having noisy and low contrast.…”

Hybrid Security Model for Medical Image Protection in Cloud

“…Medical image encryption has become mandatory with the enhancement of cloud services and the Internet of Medical Things (IoMT) in 2022 [18]. For this reason, the authors suggested a 3D chaotic map system to protect the medical images and accomplish the best results.…”

“…The model makes use of both symmetric and uneven encryption calculation (AES and ECC) to enhance information security against intruders, preventing illegal access to natural resources, improving privacy and time taken to perform cryptographic tasks, and further developing the trust level of the client in the cloud and accelerating the use of more modest keys of ECC in the cryptographic interaction. Table 1 represents the comparative analysis of related work in detail papers [14][15][16][17][18][19][20] Table 1: Comparative analysis of related work Refs.…”

“…[17] A modified AES algorithm has been developed and found to be more secure than the standard AES algorithm for small files. [18] The authors proposed a 3D chaotic map system to protect medical images and achieve optimal results. The proposed method was reliable, offering high robustness and recommended security levels suitable for use in healthcare.…”

Experimental Channel Characterization of Human Body Communication Based on Measured Impulse Response

“…Challenging applications are conceived, such as recovery from paralysis, which would need the communication among implants for the exchange of information inside/outside body. For these scenarios it is necessary to develop body-centric architectures based on both intranet and Internet of Medical Things (IoMT) [2], [3].…”