Guest Editors
Dr. Sathishkumar V E, Jeonbuk National University, Korea
Dr. Venkatachalam K, University of Hradec Kralove , Czech
Dr. Mohamed Abouhawwash, Michigan State University, USA
Summary
Currently, the Internet of Things (IoT) is gaining more attention in various sectors like smart cities, healthcare, agriculture, Industry 4.0, and smart grid environments. Nowadays healthcare industries got more popular due to IoT-based technological trends such as remote healthcare monitoring, elderly care rehabilitation monitoring, ambient assisted living, automated disease prediction, and diagnosis. The primary goal of the IoT platform is to provide ubiquitous network connectivity and sensing ability to improve patient satisfaction during a pandemic situation like covid-19. Moreover, it will minimize readmission in hospitals by providing cost-effective service with highly sophisticated treatment and diagnosis. Also, cognitive radio-based IoT was used with various interconnected wearable devices, drones, and robots to keep track of medical records and integrate medical devices for improving healthcare applications. It can provide rapid medical healthcare services during pandemic situations by dynamically monitoring, tracking, controlling, and diagnosing patients using the novel Cognitive Internet of Medical Things (CIoMT) technology. The rapid growth of the Internet of Medical Things (IoMT) provides a cost-effective and safe solution to many real-time healthcare applications through the integration of IoT and medical devices established in smart cities. There is more possibility for getting different real-time attack patterns within the botnet due to some vulnerable IoT devices deployed in large-scale botnets. In the IoMT context, all the devices can be capable of joining multiple botnets simultaneously which may lead to more challenging issues like an attacker and legitimate traffic behaviours. So, there is a need to identify the time-related attack patterns using the low, medium, and high interaction honeypots deployment and data collection process.
The challenges faced by the IoMT can be categorized in the context of networking, communication, intelligence, and computing technology. However, proving security and privacy in the IoMT becomes more challenging issues due to the limited memory, computing, and energy capabilities of various medical sensors, computing, and clinical systems deployed in healthcare services. To overcome these issues, friendly jamming schemes are introduced to ensure security and privacy without much computation cost. Moreover, it can significantly minimize the risk of eavesdropping attacks without affecting legitimate transmission. More recently, a novel artificial intelligence-based secure communication scheme is introduced to prevent the security and privacy-related attacks and other vulnerabilities involved in the IoMT devices. There is the possibility of getting more latency in healthcare services due to direct communication of data between the sensor and cloud layers. So, novel edge and fog-based computing techniques are introduced in the healthcare system that can easily overcome the overall processing latency. Further, the blockchain-enabled IoMT framework has been deployed in the hospitals and healthcare areas to improve the efficiency of monitoring and diagnosis done through medical resources. This innovative framework can easily overcome the challenges like privacy, security, and interoperability issues faced in the existing healthcare systems. Similarly, a secure and energy-efficient IoMT framework is introduced to minimize the communication overhead and energy consumption involved during the transmission of data between the sensor devices. Therefore, the emergence of IoMT is accepted as a promising solution to provide effective elderly healthcare in hospitals and remote real-time healthcare monitoring at home. Thereby, it provides reliable healthcare services to remote patients even in the absence of medical resources and also avoids unnecessary hospitalizations during pandemic situations.
IoMT devices are developed as the point of care analytical platform for the patients, doctors, practitioners, and caretakers that can evaluate the various physiological parameters of patients. It is the combination of both wearable medical electronic devices and portable sensing platforms connected with internet communication to periodically monitor the patient’s molecular levels that are very much required in the digitalization of eHealth data. According to a recent literature study, the major application areas of cognitive IoMT such as real-time tracking, remote monitoring, rapid diagnosis, reduction of medical industry workloads, prevention, and control are recognized as the proper solution for solving the issues faced during the diagnosis of coronavirus disease.
Keywords
• IoT-based ambient assisted living and rehabilitation monitoring frameworks
• IoMT Cyber- Physical Systems
• Smart and secure healthcare systems
• IoT sensors and microsystems for healthcare applications
• Point of care diagnostic devices for healthcare applications
• Machine learning techniques for healthcare prediction and diagnosis
• Bioinformatics for healthcare applications
• Rehabilitation monitoring and assessment systems
• AI-based biomedical applications
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