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The integration of the Internet of Medical Things (IoMT) and the Internet of Things (IoT), which has revolutionized patient care through features like remote critical care and real-time therapy, is examined in this study in response to the changing healthcare landscape. Even with these improvements, security threats are associated with the increased connectivity of medical equipment, which calls for a thorough assessment. With a primary focus on addressing security and performance enhancement challenges, the research classifies current IoT communication devices, examines their applications in IoMT, and investigates important aspects of IoMT devices in healthcare. The evaluation extends so far as to examine an IoMT-based system in the healthcare space, highlighting limitations in Industry 5.0 and healthcare institutions. IoMT devices are systematically categorized according to how well they can strengthen infrastructures; this process exposes operational flaws, gaps in the literature, and real-world risks. The importance of this research is in its thorough analysis of the security and operational facets of IoMT devices, with the explicit objective of promoting secure and efficient medical practices. [ABSTRACT FROM AUTHOR]

Patient privacy and data protection have been crucial concerns in Ehealthcare systems for many years. In modern-day applications, patient data usually holds clinical imagery, records, and other medical details. Lately, the Internet of Medical Things (IoMT), equipped with cloud computing, has come out to be a beneficial paradigm in the healthcare field. However, the openness of networks and systems leads to security threats and illegal access. Therefore, reliable, fast, and robust security methods need to be developed to ensure the safe exchange of healthcare data generated from various image sensing and other IoMT-driven devices in the IoMT network. This paper presents an image protection scheme for healthcare applications to protect patients' medical image data exchanged in IoMT networks. The proposed security scheme depends on an enhanced 2D discrete chaotic map and allows dynamic substitution based on an optimized highly-nonlinear S-box and diffusion to gain an excellent security performance. The optimized S-box has an excellent nonlinearity score of 112. The new image protection scheme is efficient enough to exhibit correlation values less than 0.0022, entropy values higher than 7.999, and NPCR values around 99.6%. To reveal the efficacy of the scheme, several comparison studies are presented. These comparison studies reveal that the novel protection scheme is robust, efficient, and capable of securing healthcare imagery in IoMT systems. [ABSTRACT FROM AUTHOR]

Introducing IoT devices to healthcare fields has made it possible to remotely monitor patients' information and provide a proper diagnosis as needed, resulting in the Internet of Medical Things (IoMT). However, obtaining good security features that ensure the integrity and confidentiality of patient's information is a significant challenge. However, due to the computational resources being limited, an edge device may struggle to handle heavy detection tasks such as complex machine learning algorithms. Therefore, designing and developing a lightweight detection mechanism is crucial. To address the aforementioned challenges, a new lightweight IDS approach is developed to effectively combat a diverse range of cyberattacks in IoMT networks. The proposed anomaly-based IDS is divided into three steps: pre-processing, feature selection, and decision. In the pre-processing phase, data cleaning and normalization are performed. In the feature selection step, the proposed approach uses two data-driven kernel techniques: kernel principal component analysis and kernel partial least square techniques to reduce the dimension of extracted features and to ameliorate the detection results. Therefore, in decision step, in order to classify whether the traffic flow is normal or malicious the kernel extreme learning machine is used. To check the efficiency of the developed detection scheme, a modern IoMT dataset named WUSTL-EHMS-2020 is considered to evaluate and discuss the achieved results. The proposed method achieved 99.9% accuracy, 99.8% specificity, 100% Sensitivity, 99.9 F-score. [ABSTRACT FROM AUTHOR]

In this paper, the Internet ofMedical Things (IoMT) is identified as a promising solution, which integrates with the cloud computing environment to provide remote health monitoring solutions and improve the quality of service (QoS) in the healthcare sector. However, problems with the present architectural models such as those related to energy consumption, service latency, execution cost, and resource usage, remain a major concern for adopting IoMT applications. To address these problems, this work presents a four-tier IoMT-edge-fog-cloud architecture along with an optimization model formulated using Mixed Integer Linear Programming (MILP), with the objective of efficiently processing and placing IoMT applications in the edge-fog-cloud computing environment, while maintaining certain quality standards (e.g., energy consumption, service latency, network utilization). A modeling environment is used to assess and validate the proposed model by considering different traffic loads and processing requirements. In comparison to the other existing models, the performance analysis of the proposed approach shows a maximum saving of 38% in energy consumption and a 73% reduction in service latency. The results also highlight that offloading the IoMT application to the edge and fog nodes compared to the cloud is highly dependent on the tradeoff between the network journey time saved vs. the extra power consumed by edge or fog resources. [ABSTRACT FROM AUTHOR]