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![FIGURE 4: The models of cloud computing and edge computing [71].](publication/341661955/figure/fig3/AS:895666104590337@1590554848773/The-models-of-cloud-computing-and-edge-computing-71_Q320.jpg)
With the booming development of medical informatization and the ubiquitous connections in the fifth generation mobile communication technology (5G) era, the heterogeneity and explosive growth of medical data have brought huge challenges to data access, security and privacy, as well as information processing in Internet of Medical Things (IoMT). Thi...
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... The factor graph characterizes the optimization problems (4), which are constrained by (5) and (6). Then, the message and decision are derived from the interaction characterized by a factor graph. ...
Healthcare IoT (H-IoT) has been rigorously extending its applications to hospitals to deliver important medical data remotely and continuously with various types of smart medical sensors. Medical data of patients should be treated with different urgency levels. Specifically, critical medical data such as data pertaining to heart failure or fall risk must be transmitted first with the highest priority. Thus, priority-aware scheduling for healthcare IoT networks is required to improve the quality of medical services. However, due to the high-dense nature of H-IoT networks, interference and packet collisions can make finding the optimal priority-aware scheduling very challenging. To tackle these practical issues, we develop a priority-aware scheduling algorithm based on a message-passing framework. With a message-passing procedure that relies on a max-product rule, the proposed algorithm simultaneously maximizes the priority metric and throughput of the network. In addition, the proposed algorithm efficiently finds the best scheduling configuration without a central coordinator by enabling low complexity and autonomous decisions of each network node. Our simulation results verify that the proposed algorithm outperforms conventional schemes in terms of priority awareness, throughput performance, and network scalability.
... In modern healthcare settings like operating rooms, emergency rooms, and Intensive Care Units (ICUs), sensors are extensively utilized to monitor and display patients' key health parameters. Additionally, wearable medical devices, driven by sensor technology, are important in providing real-time health monitoring [9]. Machine learning (ML) methods integrated into IoMT systems help eliminate noisy, redundant, and non-informative medical data before transmitting it to the cloud platform. ...
The Internet of Medical Things (IoMT) is a transformative concept in healthcare, leveraging the power of connected devices and technology to improve patient care and health outcomes. These devices are typically connected to the internet or a network and can communicate with each other to exchange data and provide insights to healthcare providers, patients, and other stakeholders. Advanced digital technologies like artificial intelligence, machine learning, and Blockchain integrated into the Internet of Medical Things (IoMT) can better mitigate the impact of pandemics and protect public health. IoMT utilizes medical sensors to capture real-time physiological data of patients and is available to a medical professional to diagnose, recognize, analyze, and make appropriate decisions. Data breaches or cyberattacks could compromise the security and integrity of IoMT devices and data, exposing sensitive information or causing malfunctions or disruptions. Consequently, to make IoMT systems reliable, data protection and secure communication must conform to security standards. Blockchain technology is being used in the healthcare industry to ensure the security of patient records and to streamline the sharing of information among healthcare providers, laboratories, pharmaceutical firms, and other healthcare providers. Overall, digital technologies have been instrumental in managing the COVID-19 pandemic, enabling more efficient surveillance, response, and care delivery. By leveraging these technologies, public health authorities and healthcare providers have been able to better mitigate the impact of the pandemic and protect public health.
... Daily traffic of modeling prediction website[18]. ...
Artificial intelligence (AI) has revolutionized numerous industries and cybersecurity is no exception. AI-powered security platforms are becoming increasingly popular as they provide enhanced protection against cyber threats. These platforms use machine learning algorithms to analyze and learn from data, enabling them to detect and respond to threats more effectively than traditional security systems. In this answer, we will explore the role of AI in security platforms, their benefits, and the future of cybersecurity.
... Our main area of interest lies in the benefits brought about by cloud computing, edge computing, and artificial intelligence technologies for the IoMT. Sun et al. [13] also investigate how to make sure research use medical resources properly and keep medical data safe and private, so that patients can get good medical care. Lastly, research deals about the current difficulties and potential future research paths in the intersection of edge-cloud computing, artificial intelligence, and Internet of Medical Things (IoMT).A significant limitation research face is the pressing need to address the security of medical data, patient privacy, and the reduction of energy consumption, especially in light of the ongoing advancements in the medical field. ...
... The performance metrics like accuracy and reliability are crucial in assessing AI algorithms; they are not typically expressed using specific Eqn (10) to (13) as they depend on the specific algorithms and tasks. ...
Artificial Intelligence and their applications” is a book about the science of Artificial Intelligence (AI). As we stand on the cusp of a new era, where machines are not just tools but companions in our daily lives, it becomes imperative to understand the profound impact of AI on our society, economy, and, most importantly, our way of thinking. Artificial Intelligence is a field that constantly evolves, and the possibilities it presents are as limitless as our imagination. This book aims to be a valuable resource for your exploration of the ever expanding universe of Artificial Intelligence. Whether you seek to deepen your technical knowledge, engage in ethical discourse, or simply marvel at the wonders of AI, this book aims to provide a thoughtful and comprehensive guide. So, let us embark on this intellectual adventure together, as we navigate the ever-expanding frontier of Artificial Intelligence. Artificial Intelligence (AI) and intelligent systems are changing the way humans interact with each other and the world around us. AI impacts every aspect of our lives, ranging from customer services, retail, education, healthcare, autonomous cars, robotics, industrial automation, computer vision, natural language and more. This book is organized into distinct chapters that provide comprehensive coverage of important topics. This book is an exploration into the depths of Artificial Intelligence, a journey that traverses the origins, evolution, and the potential future of this transformative field. From intelligent algorithms and machine learning to robotics and natural language processing, AI is not merely a tool; it is a catalyst for innovation that has the power to redefine how we live, work, and interact with the world. We will demystify complex concepts, explore real-world applications, and ponder the ethical considerations that come with the power to create intelligent systems. We edited this book because we are excited about the emergence of Artificial Intelligence as an integrated science. We develop the science of AI together with its engineering applications. This book presents both theoretical foundations of AI and an indication of the ways that current techniques can be used in application programs. It details the wide range of possible application areas where artificial intelligence can be used. The chapters within this publication are sure to provide readers with the tools necessary for further research and discovery in their respective industries and/or fields. Additionally, this publication could be extremely beneficial for use in coursework by instructors of various computer science and engineering programs. The book can be used as an introductory text on artificial intelligence for advanced undergraduate or graduate students in computer science or related disciplines such as computer engineering, philosophy, cognitive science, or psychology. This book is not just about the technology; it is about the people behind it, the thinkers, researchers, and pioneers who have dedicated their lives to pushing the boundaries of human understanding. This book will take you to the cutting edge and beyond with innovations that show how to improve existing solutions to make you a key asset as a consultant, developer, professor or any person involved in artificial intelligence
... However, the authors failed to provide a comprehensive overview of the actual impact of blockchain technology in the medical and healthcare fields. Sun et al. (2020) presents a comprehensive overview of edge-cloud computing and artificial intelligence in internet of medical things. Although the authors provide an in-depth analysis of the technology and application, the discussion of the potential security risks and privacy issues of these systems is limited. ...
The healthcare industry is undergoing a profound transformation driven by the integration of medical robotics and AI leading to remarkable advancements in diagnosis and treatment. However, as the industry embraces these high-tech innovations, it faces a burgeoning challenge in ensuring the security of medical information throughout its lifecycle. This chapter provides an in-depth analysis of the security challenges in storing and exchanging medical data within this rapidly evolving healthcare landscape, with a specific focus on the emerging role of AI-assisted diagnostics and medical robotics. The utilization of AI in diagnostic procedures offers unprecedented precision and speed, but it also raises significant security concerns. Threats to patient data, confidential records, and the functionality of AI systems have intensified, demanding a robust security framework. This chapter explores the vulnerabilities associated with AI-assisted diagnostics and discusses the potential consequences of security breaches, which may extend to misdiagnoses and harmful interference in robotic surgeries.
... In the context of Artificial Intelligence (AI), the evolution of computing paradigms from centralized data centers to the edge of the network heralds a transformative shift in how AI applications are developed, deployed, and operated. This transition is critical to realizing the full potential of AI across a wide range of industries, from healthcare to agriculture and industrial maintenance, by leveraging the immediacy and context-aware capabilities of edge computing (Sun et al., 2020;Singh and Gill, 2023). Specifically, the edge computing paradigm is characterized by processing data directly in the devices where it is collected, such as smartphones, wearables, and IoT. ...
The AI-SPRINT project, launched in 2021 and funded by the European Commission, focuses on the development and implementation of AI applications across the computing continuum. This continuum ensures the coherent integration of computational resources and services from centralized data centers to edge devices, facilitating efficient and adaptive computation and application delivery. AI-SPRINT has achieved significant scientific advances, including streamlined processes, improved efficiency, and the ability to operate in real time, as evidenced by three practical use cases. This paper provides an in-depth examination of these applications - Personalized Healthcare, Maintenance and Inspection, and Farming 4.0 - highlighting their practical implementation and the objectives achieved with the integration of AI-SPRINT technologies. We analyze how the proposed toolchain effectively addresses a range of challenges and refines processes, discussing its relevance and impact in multiple domains. After a comprehensive overview of the main AI-SPRINT tools used in these scenarios, the paper summarizes of the findings and key lessons learned.
... Its capabilities in scalability, data sharing, and distributed computing are essential for managing the burgeoning volume of clinical and omics data. Additionally, Sun et al. [21] and Gifari et al. [22] illuminate the role of cloud computing, along with edge computing and artificial intelligence, in the Internet of Medical Things (IoMT). This combination of technologies addresses challenges in data access, security, privacy, and information processing in IoMT, facilitating the efficient processing of medical big data and ensuring the availability of high-quality medical services. ...
... As discussed by Joseph & Brown [4], cloud computing provides essential ondemand utility computing, pivotal for handling large-scale genetic information and individualized patient data. This technology's role in processing and analyzing vast data sets, as highlighted by Agapito & Cannataro [12] and Sun et al. [21], marks the advent of the Big Data era in life sciences, enhancing the scope and accuracy of personalized medicine. ...
This review critically examines the role of cloud computing in personalized medicine, employing a systematic literature analysis methodology. The study involved a comprehensive search and evaluation of scholarly articles from academic databases and journals, focusing on publications within the last decade. Key terms such as "cloud computing", "personalized medicine", "genomic data management", and "patient-centric healthcare technology" guided the literature search. The study illuminates the significant role of cloud computing in revolutionizing personalized medicine. It highlights the importance of cloud computing for managing large-scale genetic data and individualized patient care, as well as its role in enhancing patient-centric care through innovations like cloud-fog diagnostics. Challenges in data security, privacy, and ethical considerations are acknowledged, emphasizing the need for robust governance and compliance. The future of cloud computing in personalized medicine is poised for growth, with immense opportunities for innovation, yet accompanied by challenges in data management and healthcare equity. The ongoing evolution of cloud computing in healthcare promises substantial advancements, albeit with a need for careful consideration of its complexities to fully realize its potential.
... The Internet of Medical Things (IoMT) is an application of the IoT for medical and healthcare systems. Some of the main benefits of IoMT, along with other new technologies, are [6], [7]: ...
... I r Dp 1 " H pIDp 1 ||Ns 2 ||Np 1 q, I r Dp 2 " H pNp 1 ||Ns 2 ||IDp 2 q, (6) Compute: Ready to communicate securely with W using Kpw and with S (via W ) using Kps over non-secure channel. ...
... Computing the hash value p X 5 and comparing it with X 5 , the origin, freshness, and integrity of the received message are also verified. In step (6), W generates the random nonce N w1 and uses it to generate the shared key with the server as K ws " H pN s1 ||N w1 ||R w q. In steps (7)- (8), W hides the nonce N w1 in X 6 ; then, generates new values for its CRP and temporary IDs, i. e., p r C w , r R w q, I r D w1 and I r D w2 for use in the next session. ...
The Internet of Medical Things (IoMT) is a promising framework for expanding and improving telemedicine services. A common cloud-based IoMT architecture consists of three layers of entities, the first layer (such as smart sensors and devices), the second layer (such as gateways), and the third layer (such as cloud servers). Obviously, in these networks, the protection of sensitive information against security threats as well as authentication between the entities is a key issue. On the other hand, the devices involved in the first and second layers usually suffer from poor computational capabilities as well as a lack of physical protection, which should be considered in the design of security protocols. Recently, Alladi et al. have proposed a lightweight authentication protocol for the cloud-based IoMT that addresses these challenges, using Physically Unclonable Function (PUF). In this paper, we first provide thorough cryptanalysis of their scheme and clarify its important vulnerabilities that lead to protocol collapse. Then, we propose a new lightweight protocol based on PUF to perform strong mutual authentication and key agreement between parties in the IoMT networks. The formal (using BAN logic) and informal security analysis demonstrate that our scheme is resistant to several well-known attacks, including physical attacks. Also, our evaluation of computational cost and security features clearly shows that the proposed scheme outperforms similar schemes in security and efficiency. Another important advantage of our protocol is that it performs the authentication and key agreement process separately for each pair of layers in the three-layer cloud-based IoMT architecture.
... continuously improved, and the attention to historical relics has gradually formed a joint force. At the same time, it also shows the long history and culture of a province [17]. As shown in Figure 2. ...
In order to study the environmental protection and planning of traditional rural settlements based on big data and Internet of things technology, it is proposed to select and obtain big data types (text data and POI data) suitable for Traditional Village Research Based on the basic types and application analysis of big data. Taking a village in a city as a research case, this paper discusses the application, advantages and limitations of the above two kinds of data in the protection and development of traditional villages, puts forward the development path and development strategy of Chaoxi village based on big data analysis, further summarizes and summarizes general strategy suggestions, and puts forward new ideas for the protection and development of traditional villages based on the combination of traditional data and big data. Experiments show that the application of big data helps to provide a more accurate regional dimension analysis and positioning for the protection and development of traditional villages. The amount of data is large, the types are rich, and it is easy to obtain and analyze. As a supplement to traditional data, the integration and integration of big data and native data can provide new ideas and approaches for the prevention and control of traditional villages. New system planning has been established, such as public service facilities planning, including sanitation facilities, medical care, elderly care, children's play venues, protection and renewal of ancient buildings, public places, water supply facilities, educational land, tourism services, etc.
... The data created by the Internet of Medical Things (IoMT) have been managed and processed using AIoT [25]. An intelligent architecture was presented by [26], to handle visual data obtained from health systems assisted by IoT, a processing method is required.. Three modules make up their architecture: a cloud administration module, an edge control module, and an end processing module. ...
Technology developments and convergence in the areas of communication, sensor, and information processing have shaped the Internet of Things' (IoT) present status. However, as data and service requirements have risen quickly, IoT is facing new difficulties. Emerging technologies and intelligent methodologies can be applied to create intelligent architectures and services for AIoT in order to address these difficulties. An introduction and evaluation of current advancements in AIoT are provided in this study. It examines several AIoT computational frameworks and shows the difficulties and possibilities for the successful implementation of AIoT technology to solve complex issues across a range of applications.The convergence of artificial intelligence and IoT is discussed from four aspects: (1) History of AI (2) Confluence of methods, platforms and architectures for AIoT (3) confluence of, devices, energy, sensors methods for AIoT (4) New ML and training methods for AIoT, and (5) Cybersecurity in AIoT. Furthermore, the article explores the enabling technologies for AIoT, such as smart sensors, edge computing. In summary, this article surveys recent developments and discusses the convergence of artificial intelligence and IoT, providing insights into the potential of AIoT in various fields. 2
