Название: Security and Privacy Issues in Internet of Medical Things
Автор: Rajkumar Buyya, Muhammad Imran Tariq, Valentina Emilia Balas
Издательство: Academic Press/Elsevier
Год: 2023
Страниц: 237
Язык: английский
Формат: pdf
Размер: 10.2 MB

Security and Privacy Issues in Internet of Medical Things addresses the security challenges faced by healthcare providers and patients. As IoMT devices are vulnerable to cyberattacks, and a security breach through IoMT devices may act as a pathway for hackers to enter hospital networks, the book covers a very timely topic. The incorporation of blockchain in the healthcare environment has given birth to the Internet of Medical Things (IoMT), which consists of a collection of healthcare systems that espouse groundbreaking technologies. Systems consist of inter-linked sensors, wearable technology devices and clinical frameworks that perform explicit, secure machine-to-machine and cloud platform communications.

IOT has revolutionized almost all areas of the health monitoring mechanism. Smart parking, smart home, smart city, smart climate, industrial sites, and agricultural fields are a few examples of it. Of all the fields, health-care management got immense benefits as the monitoring and tracking facilities are significantly improved. IoT is the art of linking computers to the internet with the help of sensors and networks. These linked network devices can be actively used for health monitoring.

They are able to transport the information collected from the patient to distant locations for further actions. To aid the spread of usage of IOT, the cost of the supporting technology has sharply decreased. This makes it possible for everybody to engage in sensing data. Also, the wide availability of internet across the globe and the transmission speed has helped immensely to achieve the real-time communication. Furthermore, the usage of Cloud, where all the information is being stored, has led to the inventions of Big Data and Cloud Computing. These new fields have supported the flourish of Internet of things (IoT).

With the recent advances in radio-frequency identification (RFID), low-cost wireless sensor devices, and Web technologies, the IoT approach has significantly added the fuel for the spread of usage of IOT and facilitated machine-to-human and machine-to-machine communication with the physical world. IoT has just started to flourish and in the coming days, its roles will increase manifold.

Perhaps the top concern in having a cloud-based storage is the security. The major problem in the IoT is the attack from a hacker. The sensor data should not be easily accessible to the hacker. It is very important to review the recent security methods in IoT. Generally, a Cloud Service Provider (CSP) consists of three servers, namely: The Authentication Server (AS), the Key Generation Center (KGC), and the Database Server (DS). Often, a Lattice-based Secure Cryptosystem is used in the smart health-care field. It consists of four phases: the setup phase, the key generation phase, the data encryption phase, and the data decryption phase. The three phases, the lattice polynomial vectors are used as input in the first phase, and the KGC is generated, i.e., the private and public key, in the second phase and shared with the Database Server (DS). Finally, the message is used as an input parameter.

Artificial Intelligence (AI) technology has allowed the machine to discover, fit, and enhance based on the various datasets used to train AI. Artificial Intelligence (AI) has grown considerably in almost all fields of life in the recent past. To better the health-care sector and achieve a smart health ecosystem, the potential of existing technologies such as AI needs to be incorporated in giving better services. AI can serve as the major enabler for the IoMT domain assisting medical experts in all forms of health-care services ranging from clinical decisions to automated diagnosis and much more. Incorporating Machine Learning and widely researched Deep Machine Learning methods can be highly proficient in decision-making based on the existing medical data analysis. Combining IoMT with AI, patient monitoring can be carried out using AI-assisted interfaces, allowing continuous monitoring with less professional intervention with higher scalability. Using AI-assisted smart homes, robots, and virtual assistants can help provide care to elderly and disabled patients with minimal human interaction. Additionally, the use of analysis tools on the data gathered from the linked IoMT devices/sensors can help predict health-care situations such as pandemic and epidemic diseases. Similarly, during emergency cases, the AI IoMT systems can help the professionals take the best measures for saving lives .

The significance of IoMT in the field of healthcare is undoubtedly a win-win situation for patients through technology enhancements and a collection of analytics that helps in better diagnosis and treatment. Due to higher accuracy levels, IoMT devices are more reliable in reporting and data tracking and help avoid human errors and incorrect reporting.