Security and Privacy Issues in Internet of Medical Things 0323898726, 9780323898720

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Security and Privacy Issues in Internet of Medical Things    First Edition    Rajkumar Buyya  Cloud Computing and Distributed Systems (CLOUDS) Laboratory, University  of Melbourne, Parkville, VIC, Australia    Muhammad Imran Tariq  Department of Computer Science and Information Technology, Superior Univer-  sity, Lahore, Pakistan    Valentina Emilia Balas  Department of Automatics and Applied Software, Faculty of Engineering, “Aurel  Vlaicu” University of Arad, Arad, Romania    Guojun Wang  School of Computer Science, Guangzhou University, Guangzhou, China    Radu Prodan  Institute of Software Technology, University of Klagenfurt, Klagenfurt, Austria   

 

Table of Contents    Cover    Title page    Copyright    Contributors    About the editors    Foreword    Preface    Dedication    Acknowledgment    1: Smart medical sensor    Abstract    Introduction    Sensors    Classification of sensors    Time to change the future of the health-care sector    Physical parameters monitored by medical sensors    Types of sensors    Wireless communication technology 

Conclusions    References    2: Exploration of various Internet of Medical Things techniques for moni-  toring and guiding the Covid-19 infected and uninfected cases    Abstract    Introduction    General IOT health-care architecture and applications    Literature review    Cloud integration    Big data in IoT health care    Security in IOT    IOT in COVID care    Challenges faced by health-care IOT    Conclusions    References    3: Transfiguration of health care from human to machine touchpoint—A  prospect or a pittance?    Abstract   

Introduction    Evolution of IoMT    IoMT devices    Challenges    Case study—FHIR    IoMT security tantrums    Conclusions    References    4: Performance enhancement of IoMT using artificial intelligence algorithms    Abstract    Introduction    What is artificial intelligence?    Health-care domain and artificial intelligence    Artificial intelligence and machine learning    Hardware implementation of artificial intelligence    AI software implementation    Intelligent internet of medical things   

AI and IoMT: Case studies    Challenges and future of intelligent IoMT    Chapter summary    References    5: An overview of the Internet of medical things (IoMT): Applications, bene-  fits, and challenges    Abstract    Introduction    IoMT concepts    Technological challenges of the IoMT    The IoMT and health analytics    Discussion    Trends    Conclusions    References    6: Trust management in the Internet of medical things   Abstract    Introduction 

Literature review    Methodology    Result and discussion    Conclusions    References    7: Future challenges of IOMT applications    Abstract    Introduction    Literature survey    Research methodology    Rejecting standards    Result and discussion    Conclusions    References    Index 

 

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Contributors   Muhammad Junaid Ahsan     Department of Computer Science and Infor-  mation Technology, Superior University, Lahore, Pakistan  Rangel Arthur     Faculty of Technology (FT), University of Campinas  (UNICAMP), Limeira, SP, Brazil  Balamurugan Balusamy     Shiv Nadar University, Chennai, Tamil Nadu,  India  R. Johny Elton     Indsoft Technologies, Tirunelveli, Tamil Nadu, India  Reinaldo Padilha França     School of Electrical and Computer Engi-  neering (FEEC), University of Campinas (UNICAMP), Campinas, SP, Brazil  Genish Thiruppathiraju     School of Computing Science, KPR College of  Arts Science and Research, Coimbatore, Tamil Nadu, India  Syed Aamer Hussain     Razak Faculty of Technology and Informatics,  Universiti Technologi Malaysia, Kuala Lumpur, Malaysia  Yuzo Iano     School of Electrical and Computer Engineering (FEEC),  University of Campinas (UNICAMP), Campinas, SP, Brazil  M. Beena Mol     Department of Civil Engineering, LBS College of Engi-  neering, Kasaragod, Kerala, India  Ana Carolina Borges Monteiro     School of Electrical and Computer Engi-  neering (FEEC), University of Campinas (UNICAMP), Campinas, SP, Brazil  Muhammad Salman Mushtaq     School of Electrical Engineering and  Information Technology, The University of Queensland, Brisbane, QLD, Australia  Yousaf Mushtaq     Department of Computer Science, Superior Univer-  sity, Lahore, Pakistan  Rida Nawaz     Department of Computer Science and Information Tech-  nology, Superior University, Lahore, Pakistan  G. Jerald Prasath     Department of Computer Science and Engineering,  Gandhi Institute of Technology and Management (Deemed to be Univer-  sity), Bengaluru Campus, Bengaluru, Karnataka, India  L. Prinza     Department of Electronics and Communication Engineering,  TKR College of Engineering and Technology, Hyderabad, Telangana, India  J. Mohanalin Rajarathnam     Department of Electrical and Electronics  Engineering, College of Engineering, Trikaripur, Kasaragod, Kerala, India 

G. Ignisha Rajathi     Department of Computer Science and Business Sys-  tems, Sri Krishna College of Engineering and Technology, Coimbatore,  Tamil Nadu, India  Muhammad Qamar Raza     School of Electrical Engineering and Infor-  mation Technology, The University of Queensland, Brisbane, QLD, Australia  Laiba Rehman     Department of Computer Science and Information  Technology, Superior University, Lahore, Pakistan  Savita Dahiya     School of Computing Science and Engineering, Galgo-  tias University, Greater Noida, Uttar Pradesh, India  Muhammad Imran Tariq     Department of Computer Science and Infor-  mation Technology, Superior University, Lahore, Pakistan  Vijayalakshmi Subramanian     Department of Data Science, CHRIST  (Deemed to be University), Lavasa, Maharashtra, India 

 

About the editors  Dr. Rajkumar Buyya is a Redmond Barry Distinguished Professor and Direc-  tor of the Cloud Computing and Distributed Systems (CLOUDS) Laboratory  at the University of Melbourne, Australia. He is also serving as the founding  CEO of Manjrasoft, a spin-off company of the University, commercializing  its innovations in cloud computing. He has authored over 650 publications  and seven text books including “Mastering Cloud Computing” published by  McGraw Hill, China Machine Press, and Morgan Kaufmann for the Indian,  Chinese, and international markets, respectively. Dr. Buyya is one of the  highly cited authors in computer science and software engineering world-  wide (h-index=150, g-index=328, 117,200+ citations). “A Scientometric Anal-  ysis of Cloud Computing Literature” by German scientists ranked Dr. Buyya  as the world's top cited (#1) author and the world's most productive (#1)  author in cloud computing. He is recognized as the Web of Science “Highly  Cited Researcher” for four consecutive years since 2016. Dr. Buyya is recog-  nized as the Scopus Researcher of the year 2017 with Excellence in Inno-  vative Research Award by Elsevier; “Lifetime Achievement Awards” from two  Indian universities, and the “Best of the World,” in the computing systems  field by The Australian 2019 Research Review.  Software technologies for grid, cloud, and fog computing developed  under Dr. Buyya's leadership have gained rapid acceptance and are in use at  several academic institutions and commercial enterprises in 40 countries  around the world. Dr. Buyya has led the establishment and development of  key community activities, including serving as foundation chair of the IEEE  Technical Committee on Scalable Computing and five IEEE/ACM confer-  ences. These contributions and international research leadership of Dr.  Buyya are recognized through the award of the “2009 IEEE Medal for Excel-  lence in Scalable Computing” from the IEEE Computer Society TCSC. Man-  jrasoft's Aneka Cloud technology developed under his leadership has re-  ceived the “Frost & Sullivan New Product Innovation Award.” He served as  founding editor in chief of the IEEE Transactions on Cloud Computing. He  is currently serving as editor in chief of Software: Practice and Experience, a  long-standing journal in the field established ~50 years ago. For further  information on Dr. Buyya, please visit his cyberhome: www.buyya.com.  Dr. Muhammad Imran Tariq is working at the Higher Education 

Department, Lahore, Pakistan since 2006. He received a bachelor’s in com-  puter science from Allama Iqbal University, Islamabad in 2003, master’s in computer science from Preston Institute of Management Science and Tech-  nology in 2008, master’s in computer science from the University of Lahore  in 2013, and finally PhD in computer science from Superior College, Lahore  in 2019. Moreover, he has MCSE, MCP+I, A+, and CCNA certifications. His  research interests include cloud computing, information security standards,  service level agreement, information security metrics, cloud risks and its  mitigation techniques, wireless networks security, image processing, deep  learning, artificial intelligence, sensor networks, multicriteria decision-  making, fuzzy logic, and risk management. He is the author of many impact  factor research papers and conferences. He is also the author of two books  on cloud security. He is the reviewer of internationally renowned impact fac-  tor Journals, associate editor of IEEE Access Journal, and member, editorial  board member of SCIREA Journal of Computer. He is also a member of the  research group of Cloud Security Alliance.  Prof. Dr. Engr. Valentina Emilia Balas is currently full professor in the De-  partment of Automatics and Applied Software at the Faculty of Engineering,  “Aurel Vlaicu” University of Arad, Romania.  She holds a PhD Cum Laude in applied electronics and telecommu-  nications from Polytechnic University of Timisoara. Dr. Balas is the author  of more than 400 research papers in refereed journals and international  conferences. Her research interests are in intelligent systems, fuzzy control,  soft computing, smart sensors, information fusion, modeling and simu-  lation. She is the editor in chief of the International Journal of Advanced Intel-  ligence Paradigms (IJAIP) and of the International Journal of Computational  Systems Engineering (IJCSysE), editorial board member of several national  and international journals, and the evaluator expert for national, interna-  tional projects, and PhD thesis.  Dr. Balas is the head of the Intelligent Systems Research Centre in Aurel  Vlaicu University of Arad and the head of the Department of International Relations in the same university. She served as general chair of the Interna-  tional Workshop Soft Computing and Applications (SOFA) in ten editions  organized in the interval 2005–22 and held in Romania and Hungary.  Dr. Balas participated in many international conferences as organizer, 

honorary chair, session chair, member in steering, advisory or International  Program Committees, and keynote speaker. Recently she was working in a national project with EU funding support: BioCell-NanoART = Novel Bio-  inspired Cellular Nano-Architectures - For Digital Integrated Circuits, 3M  Euro from National Authority for Scientific Research and Innovation.  She is a member of the European Society for Fuzzy Logic and Technology  (EUSFLAT), member of the Society for Industrial and Applied Mathematics  (SIAM), and a senior member of IEEE, member in the Technical Com-  mittee—Fuzzy Systems (IEEE Computational Intelligence Society), chair of  the Task Force 14 in Technical Committee—Emergent Technologies (IEEE  CIS), member in the Technical Committee—Soft Computing (IEEE SMCS).  She is member in the Committee of IEEE Romania Section as Volunteers  Training Coordinator and vice chair of IEEE Computational Intelligence  Society Chapter – CIS 11. From 2021 she is a member of IEEE European  Public Policy Committee Working Group on ICT.  Dr. Balas was past Vice President (awards) of IFSA—International Fuzzy  Systems Association Council (2013–15), is a joint secretary of the Governing  Council of the Forum for Interdisciplinary Mathematics (FIM)−A Multidis-  ciplinary Academic Body, India, and the recipient of the Tudor Tanasescu  Prize from the Romanian Academy for contributions in the field of soft com-  puting methods (2019).  Dr. Guojun Wang received BSc in geophysics, MSc in computer science,  and PhD in computer science from the Central South University, China, in  1992, 1996, and 2002, respectively. He is a Pearl River Scholarship Distin-  guished Professor of Higher Education in Guangdong Province and a Doc-  toral Supervisor at the School of Computer Science, Guangzhou University,  China. He received the 2014 First Prize (in the fifth place) of the National  Natural Science Award, State Council of the People's Republic of China. He  was listed in the “Chinese Most Cited Researchers” (Computer Science) by  Elsevier in the past eight consecutive years (2014–21). He has been a pro-  fessor at Central South University, China; an adjunct professor at Temple  University, USA; a visiting scholar at Florida Atlantic University, USA; a vis-  iting researcher at the University of Aizu, Japan; and a research fellow at the  Hong Kong Polytechnic University, HK. His research interests include arti-  ficial intelligence, big data, cloud computing, mobile computing, trust-  worthy/de-pendable computing, cyberspace security, recommendation 

systems, and mobile health-care systems. He has published more than 400  technical papers and books/chapters in the above areas. His research is  supported by a Key Project of the National Natural Science Foundation of  China, the National High-Tech Research and Development Plan of China  (863 Plan), and the Ministry of Education Fund for Doctoral Disciplines in  Higher Education. He has served as an associate editor or on the editorial  board of several international journals including IEEE Transactions on Par-  allel and Distributed Systems (TPDS), Security and Communication Net-  works (SCN), and International Journal of Parallel, Emergent and Distrib-  uted Systems (IJPEDS). He is the leading steering chair of the IEEE Interna-  tional Conference on Trust, Security and Privacy in Computing and Com-  munications (TrustCom); International Conference on Security, Privacy, and  Anonymity in Computation, Communication, and Storage (SpaCCS); and  the International Conference on Smart City and Informatization (iSCI). He is  a member of IEEE (2008–), a member of ACM (2011–), a member of IEICE  (2011–), and a distinguished member of CCF (2013–).  Dr. Radu Prodan is a professor in distributed systems at the Institute of  Software Technology, University of Klagenfurt, Austria. He received his PhD  in 2004 from the Vienna University of Technology and was associate pro-  fessor until 2018 at the University of Innsbruck, Austria. He has participated  in numerous national and European projects and is currently principal co-  ordinator of the Horizon Europe project Graph-Massivizer (Extreme and  Sustainable Graph Processing for Urgent Societal Challenges in Europe).  He is the author of one book, over 100 journal and conference publications,  and is the recipient of two IEEE best paper awards. 

 

Foreword  The Internet of medical things (IoMT) and its security and privacy are the  most challenging work for scientists that reads very much like cloud com-  puting, artificial intelligence, deep learning, and big data, yet most scientists  will agree it is more than that…much more. Prof. Dr. Raj Kumar Buyya is a  renowned name in the field of cloud computing and the Internet of things,  and whenever any scientist began learning cloud computing or the Internet  of things, he/she always starts reading Prof. Dr. Raj Kumar Buyya's books.  The authors of this book are renowned scientists and they have good contri-  butions to IoT, security, and privacy. The extraordinary work on the Internet  of medical things reveals much more about the core issues of security and  privacy, and honestly speaking, you can rest assured that more of it is true  than you will ever know.  Prof. Dr. Arfan Jaffar, Head of Department (Computer Science), Superior  University Lahore, Lahore, Pakistan 

 

Preface  The application of the Internet of things (IoT) in the health-care envi-  ronment has given birth to a novel moniker the Internet of medical things  (IoMT). IoMT corresponds to a collection of health-care machinery that has  espoused groundbreaking technologies. IoMT consists of interlinked sen-  sors, wearable technology devices, and clinical frameworks. IoMT devices  are capable of performing explicit machine-to-machine and cloud Com-  munication platform through Wi-Fi-enabled devices for the storage and  analysis of information. The significance of IoMT in the field of health care  is undoubted since it is a win-win situation for the current and future pa-  tients through technology enhancements and collection of analytics that  helps in better diagnosis and treatment. Due to its higher accuracy levels,  IoMT devices are more reliable in reporting and data tracking than the sub-  ject patients and help avoid human errors and incorrect reporting. More-  over, they enable targeted simulations with lesser adverse impacts.  IoMT is capable of transforming health-care environments into smart  hospitals. It can assist in tasks like monitoring the patients’ vitals, enhanc-  ing the efficiency of health-care workers, streamlining the health-related pro-  cesses, tracking the medication routines of patients, location of patients’  admittance, and dynamic loading of patients’ records to share with the care-  givers. However, the application of IoMT continues to expand with the im-  provement of technology at a breakneck speed.  With the creation of new technologies, various challenges occur as well.  The topmost challenges faced by IoMT are the security and privacy protec-  tion concerns, and addressing these issues should be the top priority. IoMT  devices are vulnerable to cyberattacks and a security breach can cause a  harm of damage. IoMT devices may act as a pathway for the hackers to enter  the hospital network through unauthorized access which can cause damage  or be fatal to patients. It is vital to keep the patient’s and hospital’s sensitive  information out of the reach of hackers. The incorporation of blockchain, big data analytics, and artificial intelligence (AI) aids in the enhancement of  security and privacy protection.  The reader of this book is supposed to be a computer scientist, a practi-  tioner who is working on IoT and health care, a research community work-  ing on IoMT, security and privacy, risk mitigation techniques. Introducing 

new approaches to deal with threats, risks, and vulnerabilities are also the  key features of this book. IoT developer, health-care engineer, and system  programmer are still looking quality of IoMT research and to cater their  needs, the material in this book is developed in such a way that every type of  the above mentioned audience can take advantage of it. The Book is handy  for working professionals, managers, researchers, academicians, research  scholars, and industry experts to deal with IoT and health-care security chal-  lenges. The rationale of the study is to provide useful information received  from research work to stakeholders. IoT and health care, and their security  are very broad topics and it is very hard to cover each aspect of these topics.  We described the smart medical sensor, IoMT techniques for monitoring  and guiding Covid-19 infected and uninfected, transfiguration of health care  from human to machine touchpoint, IoMT performance enhancement using  artificial intelligence algorithms, modern panorama of the IoMT, data stor-  age and recovery security in IoMT, trust management in Internet of medical  things, ethical issues, medical things for future challenges, applications and  benefits, and future challenges of IoMT.  The key features of the book are IoT and health care, current security and  privacy challenges that arise due to these new innovations, their counter-  measures, which will help the readers to select the best security mitigation  techniques and apply. In this book, the editors and authors have tried their level best to cover many of the topics relating to the security and privacy of  IoMT and they have also provided much additional information in it. How-  ever, this book attempts to provide you with at least the basic information  you need on all relevant topics, as well as references to additional sources  of information. In recent years, many books on the Internet of Things and  health-care security and privacy have been published, and every book has  covered a specific topic, but keeping in view the contents of this book, I am  sure you are reading the right book. 

 

Dedication    To my son Muhammad Ali Haider    My heart, my life, and my soul    Dr. Muhammad Imran Tariq 

Acknowledgment  We acknowledge the support of all our authors to write up the book chap-  ters and the editorial team of Elsevier, especially Fernanda Oliveira, Editorial  Project Manager, Elsevier S&T Books, for giving us the chance to complete  this book and to collaborate with them. We also thank our collaborator Dr.  Asma Amanat, Government Islamia Graduate College for Women, Cantt, La-  hore for her assistance throughout this publication process; and without her  contribution, this book would not have been created. Finally, we, all editors  of this book, thank our families for allowing us the time we spent away from  them writing this book. 

 

1: Smart medical sensor   

a b c Vijayalakshmi Subramanian ; Savita Dahiya ; Genish Thiruppathiraju ;  Balamurugan Balusamyd    a Department of Data Science, CHRIST  (Deemed to be University), Lavasa, Maharashtra, India  b School of Computing Science and Engineering, Galgotias University,  Greater Noida, Uttar Pradesh, India  c School of Computing Science, KPR College of Arts Science and Research,  Coimbatore, Tamil Nadu, India  d Shiv Nadar University, Chennai, Tamil Nadu, India 

Abstract  Medical sensors facilitate health-care applications to save a patient's  life by continuously monitoring the patient's health. The combined fea-  ture of medical sensors and the fastest growing techniques that are  Internet of things improves the accuracy of treatment. Internet of things  techniques serve the smart and very effective medical service. Early  diagnosis of the symptoms helps the health-care provider to get suc-  cess in the treatment to save a patient's life. Many medical sensors are  available in the health-care application that can monitor continuously  patient health. Medical sensors can be wearable and nonwearable.  There are some common parameters such as body temperature and a  heart rate that are used to monitor human activity. These parameters  are measured by using wearable and body-embedded sensors. The data  collected from these parameters are analyzed by the medical devices  for early detection of disease. The advanced internet of things tech-  niques help to connect the sensors, patients, hospitals, and other med-  ical devices. In this chapter, we highlight the use of different types of  sensors with advanced technology (internet of things).   

Keywords  Medical sensor; Health-care sector; Blood pressure; Physical parameters;  Accelerometers; Bluetooth    Medical sensors facilitate health-care applications to save a patient's life by  continuously monitoring the patient's health. The combined feature of med-  ical sensors and the fastest growing techniques that are Internet of things  improves the accuracy of treatment. Internet of things techniques serve the  smart and very effective medical service. Early diagnosis of the symptoms  helps the health-care provider to get success in the treatment to save a pa-  tient's life. Many medical sensors are available in the health-care application  that can monitor continuously patient health. Medical sensors can be wear-  able and nonwearable. There are some common parameters such as body  temperature and a heart rate that are used to monitor human activity. These  parameters are measured by using wearable and body-embedded sensors.  The data collected from these parameters are analyzed by the medical devices for early detection of disease. The advanced internet of things 

techniques help to connect the sensors, patients, hospitals, and other med-  ical devices. In this chapter, we highlight the use of different types of sen-  sors with advanced technology (internet of things). 

Introduction  Identification of early symptoms of disease helps the health-care providers  to adopt an accurate treatment plan to save the patient's life. But, some-  times a periodical health checkup is not possible for all the people due to  reasons such as higher test cost, long travel distance from home to hos-  pital, high transportation cost, and many more. In this situation, remote pa-  tient monitoring is the best option for health care of senior. Remote moni-  toring is a digital technique in which medical data and other types of health  data of a patient such as blood sugar and pressure, weight, and heart rate  are to be collected for the regular monitoring of the patient. This technology  also enhances medical services. Internet of things (IOT) plays a very impor-  tant role in remote monitoring; all devices are connected with each other  making a network of things. In the health-care sector, many different types  of sensors are available. Sensors can be categorized into different forms  such as: active and passive sensors, analog and digital sensors, wearable  and nonwearable sensors, and inductive and ultrasonic medical sensors.  Transformation in the health-care system allows the monitoring of patient  without hospitalization. The application area of a wearable sensor is not lim-  ited. This sensor can be used in the entertainment, health-care, security, and  automation sectors. This provides accurate information of people's activity.  A very light-in-weight wearable sensor is used to monitor psychological  parameters such as blood pressure, heart rate, and brain and muscle activ-  ity. Wearable sensors in the medical area perform different tasks such as fall  detection, protective vest, prosthetics, and sleep apnea treatment [1]. Activity  monitoring medical sensors are the medical devices that are becoming  more popular among health-conscious people. These sensors count every  activity of individual people such as steps, calories, duration and speed of  walking, and many more. For elderly patients, some safety medical moni-  toring devices are also available and these are used for safety purposes such  as fall detection, personal, and tracking. Tracking devices are used for safety  purposes for elderly people and children. These devices can also be in-  stalled at a high security area to keep an eye on every movement of employ-  ees. But for all, the remote medical monitoring devices need to be con-  nected with each other or with a hospital. Every device performs a different  task. The use of IOT makes the monitor device smart, reduces treatment  cost, saves time, and enables early diagnosis of disease. By using IOT in 

emergency cases, there is no need to take the patient to a hospital; the  physician can analyze the parameters online and prescribe the medicine.  Continuous activity monitoring of children and elderly patients is only pos-  sible with IOT. Lots of work has been done by many researchers in this area.  Lots of medical IOT-based monitoring devices have been developed and all  these are very useful for health-care providers as well as patients. In Ref. [2]  researchers designed IOT devices, which are multiple-communication com-  patible. The authors in Ref. [3] defined a resource-based UDA IOT device  and this is very helpful for intensive health-care applications. An IOT-based  medical system designed and implemented by authors [4] supports peer-to-  peer techniques, and to check the performance of the system many exper-  iments are also done with this device. In this medical device, a small smart  box is used to monitor the situation of a patient. To collect information  about patient health and to communicate with all the medical devices, a  wearable device has been designed by the authors [5], which can analyze the  information and process into a meaningful form. This device can also be  connected with the external sources and they can work in emergencies such  as prevent an upcoming heart stroke and fallen patients fall down condition.  Antonovici et al. proposed a biomedical data-acquisition tool, a mobile  application measures two types of parameters such as systolic blood pres-  sure (SBP) and diastolic blood pressure (DBP) with the help of an electronic  sphygmomanometer and communication is done by Bluetooth. This data  can be transferred by using any mobile device. In this proposed method, pa-  tient abnormality is checked by comparing the data with the normal stan-  dard value if the value is less or greater than any fixed threshold [6]. A real-  time application for IOT medical devices is designed using a distributed  environment. This application depends on the internet only. In this appli-  cation, data about the patient is recorded and, in the situation, when the patient is out of the Wi-Fi range or the server is not working, then data can be  record locally and when patient comes into the Wi-Fi range than data can be  sent to the server only [7]. An electronic travel aid is designed for blind per-  sons and this uses the ultrasonic range finder, which is embedded on a belt.  This device helps the user to find the obstacles in a path and to provide a  correct path on the Bluetooth headphone. The capability of this device to  detect the distance of obstacles is very limited but can provide accurate  localization of obstacles [8]. In Refs. [9, 10], the authors designed a 

navigation sensor system for blind people which can detect the things in  front of people with accuracy and inform the blind people via a vibro tactile  mounted on hand gloves.

Sensors  A sensor is a machine that can detect changes in the environment and send  this information to another electronic device such as a computer processor,  the definition of “sensor” given by ANSI (American National Standards  Institute) is any machine or device that can produce output in response to a  specific input. A device that transfers a measured “physical quantity” or  property (input) into an “electrical quantity” is known as a sensor. The block diagram of a sensor is shown in Fig. 1.1.   

Fig. 1.1 Sensor block diagram. This is a block diagram of the sensor.   

Medical sensors  Medical sensors are microcontroller devices that can monitor health data in  real time, process this information into something measurable, and send  this information on the mobile.   

Properties of sensors  The sensor is a very small device that can be placed anywhere like on,  around, and in the body to capture information about the human body. Fig.  1.2 shows some sensor properties. So, to fulfill this requirement, sensors  have major properties as follows [11].   

Fig. 1.2 Properties of sensors. Properties or characteristics of sensors.    Reduce energy consumption  The maximum size of the sensor is approximately 1 cm³ so that energy is  also restricted because of the battery, which is placed inside the sensor.  When a sensor is placed inside the human body, it is very difficult to change  the battery in the case of high energy consumption. To overcome this prob-  lem, always use a technique that can reduce energy consumption.    Reduce energy consumption  Every sensor node is heterogeneous in nature. They require different data  rates and bandwidth from the network and this depends on the type of data.  For example, 12-lead ECG requires a 288-kbps data rate, 6-lead ECG requires  71 kbps, and 12-lead EEG requires 43.2 kbps, while temperature needs  120 bps, audio needs 1 Mbps, and voice needs 50–100 kbps.    No redundancy  No redundancy means that all sensors play an equal role and all sensor  nodes have the same importance in the required application.    Self-maintenance  In the medical field, sensors are not operated by engineers. They have the  self-organization characteristic. When a sensor node is placed inside the  body, then it automatically joins the network without any human involve-  ment.    Limited transmit power  Health concerns are the main aim of medical sensor nodes so as to avoid  interference; it has limited transmitting power. 

Classification of sensors  We are living in a world of sensors. We use sensors everywhere such as in  our house, automobiles, vehicles, and in aircrafts. This makes our life easy  and interesting. We can adjust room temperature, make coffee, open the  garage automatically and many more things and all these things are possible  only with sensors [12–18]. So, experts classified sensors into different cate-  gories, which is given in Fig. 1.3.   

Fig. 1.3 Classification of sensors. Sensors are categorized according to  requirement, measurement, signal, and operation.   

According to the power supply requirement  Active sensors  An active sensor is a device that has a requirement of an external source of  power. This type of sensor is mostly used in the manufacturing environment  to observe industrial machines, so that if any anomalies occur, the system  or any component of the system can be changed or replaced before damage  [12–18]. The figure shows some technologies that are based on active sen-  sors. The different types of active sensors are shown in Fig. 1.4.   

Fig. 1.4 Active Sensor technology. Two different kinds of active sensors.    Passive sensor  A passive sensor is a microwave instrument, which is used to receive and  measure natural emissions produced by earth and the atmosphere. This can  measure physical properties such as weather, temperature, surface compo-  sition, atmosphere, and surface roughness of the Earth [19,20]. Passive sen-  sors are shown in Fig. 1.5.   

Fig. 1.5 Passive sensor technology. Two different kinds of passive  sensors.   

According to measurement  Temperature sensor  Temperature is a measured physical parameter, which is mostly used in  industry and in the laboratory. The exact measurement is very necessary in  medical, electronic, and biological research as well as in geological studies.  A resistance temperature detector is one type of temperature sensor that is  used to gather information about temperature from a source and this infor-  mation is converted into an understandable form for an observer [21]. This  is mostly used in medical devices, controlling systems, AC systems, env-  ironmental controls, and in food-processing units. Two different temper-  ature sensors are shown in Fig. 1.6.   

Fig. 1.6 Different temperature sensor. Thermometer and thermocouples  are two types of temperature sensors.    Pressure sensor  Pressure is a type of force that is used to stop a fluid from expanding. This  sensor senses the pressure of some things such as liquid, water level, gas  as well as air and converts this into electrical form. “Pascal” is the standard  SI unit of pressure [22–27]. This can be classified into different modes:  absolute, gauge, and differential. Some pressure sensors are shown in Fig.  1.7:   

Fig. 1.7 Different pressure sensors. Types of pressure sensors.   

According to output signal  Analog sensor  This type of sensor produces a continuous signal, which is proportional to  parameters. This sensor senses parameters such as wind speed, radiation,  and light intensity and gives output in analog voltage.    Digital sensor  This type of sensor produces a digital output, which is directly interfaced. This can detect only two possible values, 0 and 1 [22–27].   

According to principle of operation  Inductive sensor  This sensor uses the electromagnetic induction principle to detect and mea-  sure objects. This sensor can detect metal targets that are approaching the  sensor but without having direct contact with the object and some different  inductive sensors are shown in Fig. 1.8.   

Fig. 1.8 Category of the inductive sensor. Category of inductive sensor.    Ultrasonic sensor  An electronic instrument that uses ultrasonic sound waves (through air) to  measure the distance of the target object and the reflected sound is con-  verted into electrical signal. Fig. 1.9 shows that how the ultrasonic sensor  measures the distance of the target object [28,29] and Fig. 1.10 shows the  Senix ultrasonic sensor:   

Fig. 1.9 Working principle of an ultrasonic sensor. The working  principle of ultrasonic sensor.   

Fig. 1.10 Senix ultrasonic sensor. Key points of the Senix ultrasonic  sensor.

Time to change the future of the health-care sector  Sensors or medical sensors or smart medical monitor devices all are the  same technologies that are used to change the simple health-care system  into smart medical monitoring. The role of the sensing element is not lim-  ited to one or two areas but is spread all over departments such as man-  ufacturing, aerospace, machinery, auto industry, medical sector, robotics,  and many other areas. But, the role played by sensors in the medical sector  changes of life. This helps in providing quality care to the patient by moni-  toring, reading, and counting every minute's details about his/her health  condition. In this busy life, it is very difficult for everyone to take care of  themselves; so, for them, health monitoring technology proves to be useful.  These sensor techniques can monitor every activity of an individual such as  heart rate, calories, blood pressure and temperature, respiration rate which  helps in losing weight and give warning signs for a health-related problem  [30–32]. These sensors can monitor continuously every activity of the pa-  tient. In Fig. 1.11 some features of the medical sensing device are shown:   

Fig. 1.11 Features of smart or medical sensors. Medical sensors’  features and their description. 

Physical parameters monitored by medical sensors  In daily life, people face many health-related problems, which need care and  attention. But, for people, it is very difficult to travel to hospitals for every major and minor health problem. It is important to differentiate the physical  parameters into minor and major so that care can be done at home without  hospitalization in every situation. Fig. 1.12 present some physical param-  eters monitored by medical sensors [33–52].   

Fig. 1.12 Physical parameters monitored by medical sensors. Different  types of sensors used to measure physical parameters and their  description. 

Types of sensors  Different types of medical sensors are used in the health-care sector. The  use of medical sensors in the health-care sector improves the accuracy of  results, controls activity, and improves diagnosis and treatment. Different  types of medical sensors are discussed below in Fig. 1.13.   

Fig. 1.13 Different types of sensors. Various types of medical sensors.   

Pressure sensor  The application area of the pressure sensor is not limited in quantity. This is  used in many applications such as industrial (to monitor liquid flow  process, to measure liquid pressure, and to manage control loop) and life  enhancement (used in the refrigerator to manage the flow of oxygen level to  keep food fresh, used in a vacuum cleaner). The role of pressure in the  health-care sector is also very broad. This works as a life-saving agent for  the patient. This can monitor patients at a high level of accuracy. In health  care, many different types of medical devices are used and most of these de-  pend on accurate pressure measurement [39–61]. Pressure sensors are used  in different types of forms. Fig. 1.14 shows the different uses of pressure  sensors:   

Fig. 1.14 Applications of the pressure sensor. Pressure sensors can be  used in different types of forms.    Pressure sensors are used in ventilator medical devices to help the respi-  ratory function of a patient. The ventilator is used for a patient who is unable  to breathe or is breathing insufficiently so that it can help in the movement  of a mixture of air and oxygen into and out of the lungs.  In Fig. 1.15, the working process of a mixture of air and oxygen of a venti-  lator is explained:   

Fig. 1.15 Working process of a ventilator. The mixing process of water  and air of a ventilator.    These sensors help to control temperature, humidity, pressure, and air-  flow. Fig. 1.16 shows different types of pressure sensors [62,63]:   

Fig. 1.16 Types of medical pressure sensor. Different types of medical  pressure sensor sand their features.   

Temperature sensor  Temperature is the most sensitive parameter for different areas such as  petrochemical, aerospace and defense, health care, automotive, and con-  sumer electronics. The duty of a temperature sensing device is to measure  the variations in the temperature [64]. The use of temperature sensors in dif-  ferent areas is given in Fig. 1.17: 

Fig. 1.17 Applications of temperature sensors. Temperature sensor uses  in different areas.    This temperature sensor can be classified into four different categories.  Fig. 1.18 shows the different kinds of temperature sensors [65,66].   

Fig. 1.18 Different types of temperature sensors. Various types of  temperature sensor.    Similarly, the temperature sensor equally has an important role to play in  the medical area. In the medical field, it is mostly used to measure the body 

surface temperature. The different types of sensing techniques are used to  measure the body surface temperature of a patient who is suffering from dif-  ferent conditions (from infections to hypothermia) [67,68].  TE Connectivity is a Swiss-domiciled leading manufacturer company that  designs sensors for different industries such as aerospace, data communi-  cation systems, oil and gas, aerospace, and medical. TE Connectivity de-  signs different sensors such as digital temperature sensors, negative ther-  mal coefficient, and thermopiles to support the accuracy and performance  of the result in various fields and shown in Fig. 1.19.   

Fig. 1.19 Body surface temperature sensors. Different types of body  temperature sensors and their description.   

Blood pressure sensors  This is a vital sign for human health care. Two types of blood pressures are  low and high blood pressure in which low blood pressure is the sign of  pregnancy, dehydration, heart problems, lack of nutrients, and endocrine  problems and high blood pressure is the common symptom of heart-related  problems and aneurysms. So, accurate blood pressure measurement on a  regular basis is a very important task. The measurement of blood pressure  can be done by an invasive and a noninvasive method.   

Invasive sensors  In the invasive method, a device is inserted into the body to measure the  pressure. The noninvasive method is easy to use for pressure monitoring  and involves the principle of a pressure cuff. Fig. 1.20 shows the invasive  blood pressure method.   

Fig. 1.20 Invasive blood pressure. Description of Invasive blood  pressure.    Noninvasive sensors  BP is always measured in two numbers: 120/80. The top number ‘120’ is  known as systolic pressure and ‘80’ is a bottom number, which is known as  the diastolic [69]. The noninvasive BP measurement method is categorized  into two parts: (1) ntermittent noninvasive blood pressure measurement  and (2) continuous noninvasive blood pressure measurements.  Noninvasive blood pressure measurement: A occluding cuff is used to 

measure the BP, which can be done manually or automatically. Two meth-  ods are manual and automated. The manual method is performed in two  ways: palpation and auscultation.  Palpation method: This method is used to measure the systolic pressure  of the arteries (the heart contracts and pumps blood). The upper portion of  the arm of the patient is used to wrap an inflatable cuff; with this cuff, one  manometer is also attached via a tube, which helps to show the pressure.  The health-care provider looks for the radial pulse, and the cuff is inflated to  stop the blood flow until the artery collapses. In this method, a stethoscope  and any other device is not required. This process can be done without any  skill and experience even in a noisy environment. But, this process gives  only the systolic pressure.  Auscultation: On the other side, Riva Rocci Korotkoff method is used to  count the diastolic pressure of arteries (heart is at rest in between heart-  beats). An expert physician uses the sphygmomanometer and a stethoscope  is used to listen to the Korotkoff sounds. But in this method, various vari-  ables affect the accurate result, and even health-care providers and medical  experts do not properly follow all guidelines for proper measurement of BP  [70].    Continuous  Noninvasive blood pressure measurements: This method allows real-time  BP monitoring as in the operation theater [71,72]. The two techniques are  used to measure the continuous BP measure: (1) radial arterial applanation  tonometry. and (2) the volume clamp method. They are shown in Fig. 1.21.   

Fig. 1.21 The volume clamp method and radial arterial applanation 

tonometry methods of BP measurement. All about the volume clamp  and radial arterial applanation tonometry methods of BP measurement.   

Accelerometers  Generally, two types of accelerometers are used: AC and DC response. AC  response as the name suggests is AC-coupled accelerometer. Static accel-  eration such as gravity and the constant cannot be measured by AC-coupled  accelerometer and this can measure only dynamic events. On the other  hand, DC-coupled accelerometer is used to measure static and dynamic  acceleration. Speed in the fall position is determined by the acceleration  sensor. The four different types of accelerometers are piezoelectric, capac-  itive, servo-, and piezoresistive. The working principle of the acceleration  sensor depends on electricity generation, resistance changes, and capacitive  and heat-induction changes. The acceleration sensor is used to a very large  extent to monitor the in and out movement of smart homes. A home be-  comes a smart home in which continuous monitoring of physical, posture,  and movement are done. This medical sensor is proving very useful for el-  derly patients. This is used everywhere including medical, engineering,  transportation, and industry [73]. These techniques can be embedded in gar-  ments to take more benefits such as continuous monitoring of speed and  cause and time of fall of elderly people in the homes. Fig. 1.22 shows a shoe  sensor connecting via ZigBee for wireless monitoring.   

Fig. 1.22 Shoe accelerometer sensor gait analysis. The figure shows a  shoe sensor connecting via ZigBee for wireless monitoring.   

Light sensors  The other term of the light sensor is the different application areas of the  light sensor, which are consumer electronics, automobiles, detection of  sunlight in agriculture, and security. A photoelectric sensor (passive device)  detects and converts photons (light energy) into electrons (electrical en-  ergy). The four different categories of light sensors are photo-emissive, pho-  toconductive, photovoltaic, and photojunction. The work of photo-emission  is to release free electrons from cesium, which is a light-sensitive material.  The photon energy amount depends on the light frequency. At high frequen-  cies, more energy is used by photons of light to convert them into electrical  energy [74]. Photovoltaic sensors are mostly used in solar cookers. Fig. 1.23  shows a light sensor that is used for security purposes.   

Fig. 1.23 Light sensor. Type of light sensor, which is used for security  purposes.   

ECG  An electrocardiogram (ECG or EKG) is a noninvasive and painless test that  is used to monitor individual health condition. This procedure is done by a  health expert in a hospital, operating room, or medical office. For an ECG, a  sensor is used, i.e., electrodes that monitor the electrical activities of a pa-  tient's heart under different conditions such as breathing problems and  abnormalities in heart function. In the ECG procedure, a total of four elec-  trodes are attached to the chest (right arm, left arm, right leg, and left leg).  In this wet equipment, a conductive gel is used to provide good communi-  cation between the skin and sensor electrodes. Research has also developed  a portable and wearable ECG sensor to continuously monitor the heart  rhythm in-house so that causes of any heart failure and attacks can be easily identified. A lot of research is done on the use of the internet regarding the  ECG sensor for heart monitoring [75–78]. In the market, different types of  heart monitoring devices are available, namely a Holter monitor, event mon-  itor, and implantable loop recorder. Fig. 1.24 shows the ECG procedure  done by medical experts:   

Fig. 1.24 ECG process. The ECG procedure done by medical experts.   

Shoe monitor sensors  The shoe sensor works with the help of a pressure and accelerometer sen-  sor to monitor the locomotion of elderly people in their homes. Wireless  connecting technology with a shoe sensor is used to monitor the motion of  the individual. It is specially designed for elderly people because they face  sudden fall problems. The speed and pattern parameter of a running athlete  is also monitored by a shoe sensor. Gait analysis is another physical param-  eter that can also be measured with this sensing technology. Any changes in  the normal walking pattern due to some physical conditions such as fatigue  experience can also be captured by this shoe sensor. Other medical param-  eters such as a heartbeat, BP (blood pressure) in the running, and walking  condition can be monitored by this technique. Sometimes, problems occur  in the attachment of sensors at perfect places such as the heel, toes, or the  region between them because it is possible that the use of the shoe sensor  does not give enough pressure on all the sensors attached to the shoe. The  placement of different sensors in the shoe is shown in Fig. 1.25. The anal-  ysis of data from different sensors is also a problem because every attached  sensor generates different types of data. In the future, the use of shoe 

sensors can be increased to treat medical conditions in a better way.   

Fig. 1.25 Sports activity monitored by shoe sensor. The sensor used in  the shoe to monitor sports activity.   

Pulse oximetry sensors 

The pulse oximetry sensor is a very small chip-like device (noninvasive sen-  sor) of low cost, which is used to monitor the oxygen saturation level  (SpO2). Very small changes in oxygen level show how it moves extremities  from the heart to other parts of body such as leg, arm, and other parts. This  device can be attached anywhere on the body (toes and earlobe) but is  mostly used in the emergency room and hospitals and is attached to the fin-  ger. Pulmonologists can use this in their private offices. The main purpose  of this monitor is to check the level of oxygen in blood. A pulse oximetry  sensor is used to monitor the health of patients under some conditions that  can affect the oxygen level and these conditions may be asthma, lung can-  cer, heart-related problems, and chronic obstructive pulmonary disease  (COPD). This sensor can be used to evaluate the new lung's work, breathing  problems, and use of ventilator for patient. This sensor contains two light-  emitting diodes (one emitting red light and the other one emitting near in-  frared). This also uses a photodetector, which is used to measure the inten-  sity of the transmitted light [79–82]. Two methods are used by the pulse  oximetry sensor to monitor oxygen saturation in human blood: trans-  mission method and reflection method. In the transmission method, a fin-  ger is put between an LED transmitter and a photodetector (both are in  opposite sides) as shown in Fig. 1.26A. An amount of light is absorbed by  the finger and some by the photodetector. Blood flow will increase with each  heartbeat and a larger amount of light is absorbed by the finger so that less  quantity of light is reached by the photodetector. The method is the reflec-  tion method in which the LED transmitter and photodetector are placed next  to each other, as shown below in Fig. 1.26B. In this method, a fixed amount  of light is reflected due to the finger. With each heartbeat, the blood volume  also increases and the result of this is more reflection of light to the sensor  [83].   

Fig. 1.26 (A) Transmission method and (B) reflection method. The  transmission method and the reflection method are two methods of  pulse oximeter sensors to monitor oxygen. 

Wireless communication technology  Wireless communication technology means devices connect with each  other into a network and data or information between these devices trans-  ferred without wires or cables. This technology allows communication at  very long distances. This technology based on radio frequency and infrared.  Wireless techniques can be further divided into three different categories:  wireless local area network (LAN), wireless wide area network, and wireless  personal area network. The different application areas where wireless tech-  niques have a big role are traveling, transportation, smart homes, health-  care sector, satellite, and broadcast television. But, our focus is on the use  of wireless technology in the medical area [84–86]. Nowadays, the medical  system has improved due to wireless technology. The development of wire-  less technology in the health-care system improves the diagnosing result  and continuous health-care monitoring of patients’ condition, and provides  emergency alert to the medical experts and patients about their health.  Some medical technology that use wireless technology are: smart sensor  techniques, wearable health-care monitoring devices, smart tags, and cards  to access patient records, remote monitoring, internet of things, and track-  ing and security. The sensors used at homes and hospitals are all operated  by wireless techniques. Some of the commonly used wireless technologies  are as follows.   

Bluetooth  This is a low-range communication wireless technique. Bluetooth provides  security to all devices. If two devices want to transfer data, they need to be  connected with each other by using a security key. A scripted authentication  is used so that any type of virus cannot hack this. Different types of data are  transferred between connecting devices. This data can be any form of text  and multimedia messages [87]. Cellphones are used this technique as com-  pared to other technologies to transfer data simultaneously with different  phones to one phone only but not vice versa. This communication tech-  nology uses the 2.4 GHz frequency and the data transfer rate is 1 Mbps. The only disadvantage of this communication technology is the limitation of  range. Devices can be connected via Bluetooth technology within a range of  10 m only. This technique uses the wireless personal network. Fig. 1.27  shows Bluetooth functioning:   

Fig. 1.27 Bluetooth functioning. Medical devices connected via  Bluetooth to maintain health records.   

Wi-Fi  This is nowadays the most commonly used technology in academics, smart  homes, and hospitals. Two devices are connected with each other via radio  waves. Computer systems, mobile phones, tablets, and any other devices  are connected with an internet router. This is a local network, which runs on  the 802.11 standard protocol. The application area of Wi-Fi technology is  growing very fast. This uses broadband access. Wi-Fi has also some advan-  tages such as transmission speed, security, and coverage of data trans-  mission. This is becoming very popular in local and personal area networks.  All the sensor devices in the smart homes are connected with Wi-Fi and can  transfer data with each other [88,89]. Wi-Fi wireless technology is also used  in the health-care sector to monitor the patient, measure heartbeat rate,  blood pressure, and many more things. The other applications area of Wi-Fi  network is sports, media and entertainment, telephones, audios, or videos.  The future of the health-care sector with Wi-Fi technology will be more  bright and also improved. WiFi functioning in a medical room is shown in  Fig. 1.28.   

Fig. 1.28 Wi-Fi functioning in a medical room. In the medical room,  Wi-Fi wireless technology is also used in the health-care sector to  monitor the patient, measure heartbeat rate, blood pressure, and many  more things.   

ZigBee  This is an open global standard network designed for M2M networks. This  is very inexpensive and has low power consumption technology suitable for  industry and medical application. The use of low latency and duty cycle  maximizes the battery life of a product. This can be used in a mesh topology  network where multiple devices are connected with each other in multiple  pathways. This is specially designed for the IEEE 802.15.4 standard protocol  to control and sensor networks. The area covered by this technique is 10–  100 m and is less expensive compared to Wi-Fi and Bluetooth communi-  cation technology. The different parts of the Zigbee n/w structure are: one  coordinator (acts as root and bridge), ZigBee router (intermediary device),  and end devices (communicate with parent node). The responsibility of the  coordinator device is to handle and store information at the transmission  time. The router's responsibility is to pass data. The data in ZigBee is trans-  ferred in two ways: nonbeacon mode and other is the beacon mode [90–95].  The application areas of ZigBee wireless technology are: smart home auto-  mation, smart metering, and remote temperature monitoring. This tech-  nology consists of a stack of layers. These layers are: physical layer, medium 

access control (MAC) layer, application support layer, and application  framework layer. This also supports various types of network topologies:  star, mesh, and cluster tree topology. Fig. 1.29 shows the ZigBee support  topologies and the other one shows the ZigBee communication operations:   

Fig. 1.29 ZigBee wireless technology. The figure shows the ZigBee  support topologies and the other one shows the ZigBee communication operations. 

Conclusions  The medical sensor gains popularity in the health-care sector by providing a  variety of benefits in which one of very great importance is the continuous  monitoring of patients. Continuous monitoring helps in diagnosing and  treatment planning at an early stage of the disease. In the market, a variety of  sensors is available; the purpose of all the sensors is also different. Some  sensors are invasive, noninvasive, wearable, and nonwearable. These sen-  sors can be connected with the body as well as the garments of the indi-  vidual to monitor the activity. Many medical sensors have the capability to  measure more than one psychological parameter. In this chapter, we explain  a variety of medical sensors and also explain their use in different disease  diagnoses. The application area of health-care sensors is not limited to the  medical sector only. The sensor can be used everywhere such as in educa-  tion, transportation, sports and entertainment, military, and in many more  other areas. The connecting wireless techniques are also explained in the  last section, which is used to connect all the devices into forms of a net-  work. Wireless techniques like Bluetooth, Wi-Fi, ZigBee, and any other wire-  less connecting technology are not only used to connect medical devices to  a hospital room but can also be used in home to make it a smart home. 

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  2: Exploration of various Internet of Medical Things techniques for monitoring and guiding the Covid-19 infected and uninfected cases    G. Ignisha Rajathia; R. Johny Eltonb; M. Beena Molc; L. Prinzad; G. Jerald  e f a Prasath ; J. Mohanalin Rajarathnam      Department of Computer Sci-  ence and Business Systems, Sri Krishna College of Engineering and Tech-  nology, Coimbatore, Tamil Nadu, India  b Indsoft Technologies, Tirunelveli, Tamil Nadu, India  c Department of Civil Engineering, LBS College of Engineering, Kasaragod,  Kerala, India  d Department of Electronics and Communication Engineering, TKR College  of Engineering and Technology, Hyderabad, Telangana, India  e Department of Computer Science and Engineering, Gandhi Institute of  Technology and Management (Deemed to be University), Bengaluru Cam-  pus, Bengaluru, Karnataka, India  f Department of Electrical and Electronics Engineering, College of Engi-  neering, Trikaripur, Kasaragod, Kerala, India 

Abstract  The pandemic of COVID-19, which has been the current global chal-  lenge, seemed to have shattered all the trust and hope in the current  technologies. Millions have been affected and yet the technologist  couldn’t defend the onslaught mostly due to the massiveness and  quickness of the spread. Even though the vaccination is in eyesight,  still, survival is a tedious task due to poor clarity about the where-  abouts of the infected and poor monitoring systems. Internet of Things  (IoT) has revolutionized the health care system and can be useful for  proper monitoring of COVID-19 patients, by employing an intercon-  nected network to invigorate the Internet of Medical things. In this  study, we explore the possibilities of the current IoT techniques in  monitoring and guiding the patients. This in turn can help the unin-  fected to be informed and guided to keep tracking their own movement  with respect to the infected. This will help the readers in identifying  varieties of IoMT applications that can be extended in monitoring the  COVID-19 infected patients. Hence, this article will motivate the thriv-  ing researchers to innovate their ideas over it, ultimately helping the  society in fighting the pandemic situation.   

Keywords  Health care; Pandemic; Drone; Smartphone; Cloud storage; Network delay 

Introduction  IOT has revolutionized almost all areas of the health monitoring mechanism  [1,2]. Smart parking [3], smart home [4], smart city [5], smart climate [6],  industrial sites [7], and agricultural fields [8] 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 [9,10].  These linked network devices can be actively used for health monitoring.  They are able to transport the information collected from the patient to dis-  tant 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 inter-  net 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 [11,12] and Cloud Computing [13]. These new fields have supported the  flourish of Internet of things (IoT) [14–16].  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 phys-  ical world. IoT has just started to flourish and in the coming days, its roles  will increase manifold. Greenough [17] has estimated that IoT is expected to  generate a whopping $1.7 trillion in value to the global economy. Another  estimate suggests that 26–30 billion objects shall be invested in IoT in  2020. According to a report submitted by Gartner, billions of connected  things are already in use in 2015. The number will swell further to 25 billion  in the coming years [18]. The year 2020 has been a shocking year due to the  widespread Covid-19 infection and it is still continuing to spread its cluster.  The biggest challenge faced by health experts is monitoring and regulating  the Covid infected patient's data. Since IOT is already being established, it  provides us a platform that allows public health agencies to access the data  for monitoring the COVID-19 pandemic from a distant place, and it has  been observed that a wide number of inventions have been reported in the  last 1 year. Fig. 2.1 shows the current information (October 2020) about  new cases in the six European countiescountries [19] [Source: BBC]. New 

cases in the United States are increasing more rapidly than in other coun-  tries.   

Fig. 2.1 Trends of new cases in the top ten countries during June to  October. Data from 1. Johns Hopkins university's center for systems  science and engineering. https://coronavirus.jhu.edu/data/new-cases.  Published 2019.    So the need of the hour is an indepth idea about the available IOT tech-  nologies and its role in diagnosing and monitoring patients. This article  does exactly that. It reviews the existing works that are being contributed to-  ward the monitoring of Covid-19 infected patients. Hence, through this arti-  cle, the reader can further think about new technologies that can aid in new  contributions that can reduce the number of resolved cases.  Under the next section, general IOT health-care architecture and its appli-  cations have been discussed, followed by a literature survey under the sec-  tion “Literature review”. Cloud integration has been deliberated in the sec-  tion “Cloud integration”, Big Data in IOT Health care and Security in IOT in  the sections “Big data in IOT health-care” and “Security in IOT” respectively.  The next section—“IOT in COVID care”—shows a detailed study of IOT in  Covid care, followed by various challenges faced by health-care IOT in the 

section “Challenges faced by health-care IOT”. Finally, conclusions are pre-  sented in the section “Conclusions”. 

General IOT health-care architecture and applications  The overview of the IoT architecture is shown in Fig. 2.2[20]. The data are  stored in the cloud; therefore, it needs to be secured. Security is a very  important aspect in the IoT, as when we deal with data transmission from  the sensor to the cloud center, there is a possibility of data theft. Also, the  data encryption is fairly complex. As Cloud has a distributed environment,  we can store the medical data with more flexibility for remote patients. This  can be accessed by doctors and vice versa. The handshake process between  the IoT and cloud during the start gives additional complexity for real-time  processing. Dehury et al. [21] proposed a novel framework to reduce the  complexity in IOT and cloud. This research proposed a Service Management  Framework for IoT devices in Cloud (SMFIC). It contains three types of lay-  ers and five important components. They are consumer layer, which is used  to collect data from smart home, patient, social network and smart health-  care service, service provider layer, virtualization, and security and privacy;  the final layer is the middle layer; it is managing the services between the  provider and the consumer based on the available resources.   

Fig. 2.2 IOT architecture. 

Literature review  Some of the interesting works and the contributions on health care are as  follows: Misra et al. [22] and Gil et al. reviewed IoT and pointed out critical  challenges in IOT. Gómeza et al. [23] developed an architecture using the  features of ontology and developed an efficient model for monitoring the  health. An IOT-based COVID diagnosing system was introduced by Li et al.  [24] His model collects the data from the patients and it could check the re-  sults; the diagnosis is automatically generated as confirmed, suspected, or  suspicious [25]. Peeri et al. used the data collected from the Centers for Dis-  ease Control and Prevention (CDC, USA) website, and a comprehensive re-  view was provided from the obtained information regarding clinical signs  and symptoms, treatment and diagnosis, transmission methods, protection  methods, and risk factors for COVID-19. Mohammed et al. [26] in his work  examined the applications of IoT technologies in the medical and health-  care field and he verified its potential. Mohammed et al. [27] in his work  developed a smart helmet with mounted thermal imaging. This can be use-  ful for identifying the infected among the crowd.  A machine learning algorithm using IOT was proposed by Kumar and  Gandhi [28] which can be used for early detection of heart diseases. It con-  tains a three-tier architecture for collecting sensor data from wearable de-  vices. It stores the data into the cloud. It has a regression-based prediction  model and is applicable for heart diseases. Finally, the proposed framework  was implemented by using Apache HBase and Apache Mahout for cloud  storage and data prediction analytics.  Parthsarathy and Vivekandan [29] proposed a new patient monitoring sys-  tem which can detect arthritis at an early stage. The proposed framework  had three levels, the first level being collecting the data from sensors. The  second level collects the data and stores in the cloud. In the third level, they  were using an optimization technique using swelling and uric acid (UA) and  the framework was Implemented using Apache red-shift and Openstack.  Kim and Chung [30] in their proposal introduced few sensors in the normal  household rooms where chronic disease patients lead daily lives. However,  they didn’t do any real-time data processing and this method involves high  cost. The IoT's role is to collect real-time data and process it to find the  health problems by analyzing physical and behavioral trends in homes. Sim-  ilar works can be found out in [31–33]. The elderly and helpless people can 

be effectively monitored using socializing platforms by using IoT devices.  Quite often, elderly people tend to fall and are a major cause of concern. To  avoid this, they have used a fall detection algorithm. It uses RFID and col-  lects the information related to the fall and the location. When the circuit de-  tects any fall, the information along with the location details will be immedi-  ately shared with health-care people by providing an alert. 

Cloud integration  Cloud platform and cloud computing offers a lot of flexibility and scalability.  It has a lot of resources to process the collected data. Cloud-based com-  puting uses a cloud storage called cloud storage repository, where they  store the sensor data. The usage of cloud technology in the medical field  provides lot of options in enhancing health care. For example, the physio-  logical attributes are measured from the patients and stored in the cloud  storage in a different type of format. Once the user subsystem completes  the data collection from IoT medical devices, they are sent to the cloud sub-  system for diagnosis. The cloud system accesses sophisticated algorithms  to diagnose any disorders. Based on the detected results, an alert message  can be sent to the doctor, hospital, and caretakers. This process is as shown  in Fig. 2.3. This research [34] involves a hierarchical computing architecture  (HiCH) that monitors the patient involving autonomous data management  and processing at the edge of the layer.   

Fig. 2.3 IOT in health-care systems.    In the remote health-care system, network delay causes a major problem  in providing a solution. To avoid the delay, a new framework was proposed  and is called UbeHealth. UbeHealth can analyze the challenges in network  delay and QoS (Quality of Service) parameters such that it provides perfor-  mance enhancement in health care in smart cities [35]. A new fuzzy 

rule-based neural classifier has been proposed for diagnosing the disease in  [36]. It reduces the seriousness of the health disease. This method analyzes  the data processing from the cloud by a secured storage mechanism. This  comprises different phases such as data retrieval, data aggregation, data  partition, and data merging. 

Big data in IoT health care  The health-care industry is often involved in a huge volume of clinical data  that cannot be stored in a normal storing mechanism. In such cases, Big  data storage technology is essential. Recent studies indicate that the combi-  nation of Big data and cloud storage can have a huge impact on the flourish  of IOT-based remote health care. For example, Amazon Elastic MapReduce  (EMR) provides a way to handle big data by getting onto the cluster. Apace  pig is being used to Load the sensor data from Amazon S3 to Hbase. Apace  pig can also be used for analyzing the data in the distributed database. This  enhances the health-care application dramatically [37]. Ullah et al. [38] have  developed a lightweight model for semantic annotation of Big data in the  IoT heterogeneous data. This model was proposed for predicting the air  quality in urban areas and to provide a healthier life for the urban resident.  The proposed UHBigDataSys was implemented using the Spring Frame-  work. It was used to analyze the Air Quality Indicators (AQIs) parameters for  Urban Health care [39]. 

Security in IOT  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. Zhou et al. [40] presented an IoT-  oriented data placement method with privacy preservation. In this proposal,  they optimized the data access time, increased resource utilization, and re-  duced energy consumption by satisfying the constraints of data privacy. A  Nondominated Sorting Genetic Algorithm II (NSGA-II) was used to attain  the privacy-preserving and energy saving. Meanwhile, Pirbhulal et al. [41]  used a radio-frequency identification encryption technique to provide secu-  rity to the medical data in IoT.  The primary objective of this research is to develop a framework for data  privacy based on a biometric-based security system with a resource-  constrained wearable health monitoring system [42]. the Internet of medical  things (IoMTs) information is analyzed for optimizing security in medical  applications. 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 Cryp-  tosystem 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 pri-  vate and public key, in the second phase and shared with the Database Serv-  er (DS). Finally, the message is used as an input parameter. It will be com-  bined with a random polynomial. Based on the request from the user to ac-  cess the medical data, the KGC transfers the secret key pair to the DS using  a secure channel, followed by the processing of the plaintext message using  the input parameters by the DS and the secret key pair. 

IOT in COVID care  Since IoT devices are able to track heart rate, blood pressure, and blood glu-  cose remotely, they allow the Covid patients to stay at home while their doc-  tor remains informed. If the data shows a patient is approaching a crisis,  they can be quickly transported to a hospital, but otherwise they can remain  under IoT device monitoring in the safety of their homes, thereby main-  taining the distance and the safety. Also, the senior care homes became vec-  tors for the spread of COVID-19; doctors struggled to find ways to care for  the chronically ill and aged patients without putting other patients at risk.  IOT allows the vulnerable seniors to be monitored without risking their  lives. Smart thermometers were able to track the spread of the virus through  fever spikes mapped from data from their devices. This centralized data  helps people in their own communities keep track of where an outbreak may  be potentially happening. The ability to create a different profile for each  user in a household made these thermometers even more accurate in col-  lecting and sharing cleaned, anonymized data. Wearable technologies have  flourished in the society and they are the combination of electronics with  anything that is able to be worn [43]. The Juniper Research team [44] defines  them as app-enabled computing technologies that collect the input and  process it while they are worn in the form of bands, glasses, and watches.  Health-care providers are set to spend a whopping $20 billion annually until  2023 on wearable IoT devices. This will help them to monitor more patients  aggressively. Some of the IoT wearable devices are Smart Themormeters  [45,46], Smart Helmets [27], Smart Glasses [47], IoT-Q-Band [48], EasyBand  [49], and Proximity Trace [50].  Disinfecting the patient rooms and the medical kits can be very demand-  ing in the midst of a pandemic. Nonsurgical robots connected to the IoT  have been put into service in hospitals around the globe, cleaning patient  rooms, disinfecting and sterilizing, and fighting COVID-19 contamination.  They use a special UV light that effectively kills the virus. Since the light can  be harmful to humans, the robot enters the room without the presence of a  human. The door is closed when the process is being carried out. Once fin-  ished, the robot alerts workers outside the room that it is safe to open the  door again. This reduces the risk of the frontline workers significantly and  speeds the rate at which patient rooms can be turned over and made ready  for the next occupant. In an emergency, IoT has the ability to track tagged 

equipment and alert to problems. If a wheelchair overturns, a nebulizer mal-  functions, or an oxygen tank starts to run empty, help can be dispatched im-  mediately. Medical IoT continues to protect patients and health-care work-  ers alike in the face of COVID-19 and will grow even stronger in a postpan-  demic world. Drone is an unmanned aerial vehicle (UAV) that works with  the help of sensors, GPS, and communication services. The implementation  of IoT within drones allows us to do a variety of tasks such as searching,  monitoring, and delivering [51,52]. The invention of smart drones allows us  to operate by a smartphone and a controller with a minimum of time and  energy. This technology allows us to control the outbreak of Covid to a cer-  tain extent. Disinfectant drone [53], medical drone [54], surveillance drone  [55], announcement drone [56], and multipurpose drone [57,58] are a few  examples that are being used in the health-care domain. Further, advance-  ments in developing intelligent robots have contributed significantly in  fighting Covid. During the panic situation like these, networked robots with-  in the cloud, the Internet of Robot Things have been successfully imple-  mented in a few countries that can make life easier [59]. Autonomous robots  [60], Telerobots [61], Collaborative robots [62], and Social robots are under  use to fight the pandemic and the introduction of new robots is increasing.  Telerobots can offer a variety of services that include remote diagnosis, re-  mote surgeries, and remote treatments for the patients with zero human  interaction [63]. The health worker can measure patients’ temperatures with-  out having to reach them. The DaVinci surgical robot is a popular example,  which can be operated by a surgeon himself by isolating the patients. There-  by, the health worker can protect himself [64]. IoT Buttons are a small de-  vice that can be programmed and connected to the cloud through wireless  communication. The significance of it is that these devices can perform dif-  ferent repetitive tasks by pressing only one button. For example, one type of  IoT button is assigned to a patient who can raise an issue if he finds the re-  strooms unclean [65]. IoT-based smartphones have played a dominant role  in health care. Many smartphone applications have been developed over the  past 5 years targeting the health-care domain. Some applications have been  developed to fight Covid too. nCapp [66], DetectaChem, Stop Corona [67],  Social Monitoring [68], Selfie app [69], Civitas [70], AarogyaSetu [71] are  some examples. Of the several apps, the MobileDetect app has been ap-  proved by the Food and Drug Administration (FDA). This allows the user to 

test the nasal swab and the results of the test can be obtained within 10–  30 min. nCapp [72], a mobile app, was developed in China using the Inter-  net of Medical Things on a cloud platform. This application can automat-  ically create a diagnosis report based on the data collected from patients. It  basically categorizes three cases: confirmed, suspected, or suspicious. For  the confirmed cases, there are four conditions to be evaluated, including  “mild, moderate, severe, and critical.” They also have a database that sup-  ports in the diagnosis, and hence it can be useful in faster diagnosis [73].  IoT-based smart thermometers have been developed to record constant  measurements of body temperatures; they can be produced at low cost and  can be worn on the skin under clothing [74]. Hence, the usage of these de-  vices can be useful in the early detection of Covid cases [46]. Kinsa's ther-  mometers, Tempdrop, Ran's Night, iFever, and iSense are the other smart  thermometers in use. All these devices help people to improve the chance  of diagnosing covid at an early stage. Smart glasses are another example of  an IOT-based device that is becoming extremely popular now. The biggest  advantage of using smart glasses is that they can work with no human inter-  actions. Even crowd monitoring can be done using the optical and thermal  camera-based smart glasses. This technology aids us to control the spread  of covid in crowded places like airports, railway stations, etc. Rokid [75],  smart glasses with infrared sensors are some examples that can monitor up  to 200 people [63,72]. 

Challenges faced by health-care IOT  The IoT has taken various shapes in numerous applications. It provides a  vital support in the health-care system such as patient monitoring. How-  ever, implementing a real-time health-care unit has its own set of problems  and they are listed as follows.    •Wearable devices can make the patient uncomfortable.  •The data is often transmitted to the monitoring center and it may  contaminate the quality of the data due to noise. Hence, an accu-  rate, denoising technique is necessary.  •Monitoring involves an expert and is a supervised process which  increases the cost.  •Live monitoring requires an immense number of sensors and  hence requires a lot of energy to process. This increases the power  leakage and energy consumption and can be a headache.  •Monitoring a multiple number of users in the IoT increases the  complexity.  •Finally, the privacy of the patient is at stake as the data is stored in  cloud environment. 

Conclusions  In the 21st century, the IoT is a boon to monitor patients from remote areas.  It can be helpful in providing in emergency situations like a pandemic. In  this work, we reviewed all the significant works that are found in general  health care and Covid simultaneously. This can help the users to find the  shortcomings of IoT in Covid care. Also, we have discussed its short-  comings, so that the user can experiment alternative solutions in Covid  care. 

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3: Transfiguration of health care from human to machine  touchpoint—A prospect or a pittance?    G. Ignisha Rajathia; R. Johny Eltonb; M. Beena Molc; L. Prinzad; G. Jerald  e f a Prasath ; J. Mohanalin Rajarathnam      Department of Computer Sci-  ence and Business Systems, Sri Krishna College of Engineering and Tech-  nology, Coimbatore, Tamil Nadu, India  b Indsoft Technologies, Tirunelveli, Tamil Nadu, India  c Department of Civil Engineering, LBS College of Engineering, Kasaragod,  Kerala, India  d Department of Electronics and Communication Engineering, TKR College  of Engineering and Technology, Hyderabad, Telangana, India  e Department Of Computer Science And Engineering, Gandhi Institute Of  Technology And Management (Deemed To Be University), Bengaluru Cam-  pus, Bengaluru, Karnataka, India  f Department of Electrical and Electronics Engineering, College of Engi-  neering, Trikaripur, Kasaragod, Kerala, India 

Abstract  The combination of human things and Internet of Things (IoT) given as  the digital information via., Internet had revolutionized global integrity.  With the interconnectedness around the globe exceeding enormous  IoT devices with a huge value in future is the real target. Although data  collection and sharing the procured vitals across horizons for bene-  ficial notions are counted to be strongly agile and efficient, it has a  tossed downside in its privacy and security tones. The medical space is  no exception that there are tons of devices that produce data in health-  care systems in medical environments such as the Internet of Medical  Things (IoMT). IoMT has been built upon an enormous combination  of technologies, including advanced sensors, IoT connectivity, Artificial  Intelligence (AI), etc. The machine care rendered to humans never  misses an essential vital out of its trained Norma, which acts as a prospective view for the future of human lives. The decreasing cost of  sensory devices has greatly empowered IoT device manufacturers to  build economically affordable and completely connected health-care  products. The challenge clings on which all the data consumed by var-  ious systems need interoperability with rationalized vector features of  the data for preserving the Patient Health Information (PHI). Further a  clear mention on a case study FHIR attributes to the chapter insight  has been discussed.   

Keywords  Telemedicine; Chronic illness; Heart rate; Health data; Data sharing; Con-  nectivity 

Introduction  Telemedicine, medical connectivity technology, extends health-care services  beyond the walls of the hospital. Remote patient monitoring (RPM) enables  many patients struggling with chronic disease to mitigate frequent visits to  medicos. Heart patients and diabetics can specially obtain great benefit  from RTM technology. The movable or portable RTM equipment can mon-  itor a patient's heart activity and glucose levels and send alert signals to  medicos automatically. Virtual home assistants are also another utility ser-  vice to home for many elderly patients. Such devices interact with the ailing  persons, remind them to take medicines at the appropriate timing, and can  be accessed remotely by the physicians and the family members too.  The establishment of interconnection of connectivity around the globe ex-  ceeds 18.5 billion of IoT (Internet of Things) devices with a value of $254.2  billion in 2026 [1].  With this simple introduction, let us move on to the chapter contents.  This chapter has been organized as follows: Section “Evolution of IoMT”  delineates the evolution of Internet of medical things, followed by the var-  ious IoMT devices in existence in the section “IoMT devices”. The varied  challenges faced while incorporating the fruitful inherence of IoMT in real-  time implication have been elaborated in the section “Challenges”. Section  “Case study—FHIR” clearly enlightens about a case study FHIR, thereafter  IoMT Security Tantrums have been mentioned in the section “IoMT security  tantrums”. The entire summary of the discussions of this chapter has been  stated shortly in section “Conclusions” as the conclusions. 

Evolution of IoMT  Today, as technology is the name of the game, people can incorporate ad-  vancements in their day-to-day life and use it to make life more comfortable  and sophisticated. One such example is the smartwatch which evolved as a  smart wearable which will be able to collect the precise health details like  health condition, heartbeat rate, distance, stress level during different activ-  ities, and stores in the cloud. In the early 2000s, the era saw the onset of a  wireless technology, Internet of things (IoT). This is now an emerging ad-  vancement which has occupied every nook and corner of the world. It includes sensors, robotics, actuators, monitors, thermostats, seamless com-  munication, etc. Data sharing and wireless connections are done through  this, making it one of the emerging future technologies. This has paved the  way to the Internet of wearable things (IoWT) [2].   

Internet of wearable things  IoWT is the combination of IoT and wearable devices. Samsung gear and  Apple watches are the well-known smartwatches in the market. This IoWT is  now being used to achieve an extra mile in health care. The same way as  IoWT, when these wearables are used in the health-care industry, they give  rise to another term and industry called the Internet of medical things  (IoMT).   

Internet of medical things  This is used in the medical domain to help in detecting and monitoring the  patients’ health and complete body condition through remote sensing. Mo-  bile applications and portable devices are widely used for managing and  maintaining health-care data. The minor accuracy and precision problems of  the present scenario can be improved by the extensive use of this tech-  nology and can get accessible health care available to all.  Telecommunication is the communication where a patient can consult a  doctor. Telehealth or Telemedicine is the distribution of health-related ser-  vices through technology establishing distance patient and clinician contact.  This tends to save lives during critical situations through IoMT by accessing  the health-care officials and hospitals remotely without any complications.  IoMT tools are rapidly revolutionizing health-care delivery and related ser-  vices. They play a central part in tracking and monitoring the patient's health by helping them to survive from numerous chronic illnesses. All these 

became a possibility due to their seamless potential to collect, analyze, and  transmit the health data appropriately. This connectivity between these om-  nipresent medical devices and sensors is accelerating the health-care man-  agement system leading to an overall success rate in patient care both in-  side the care facility walls and in remote locations. These IoMT technologies  paired with methodical devices diagnose the diseases more accurately with  insignificant errors and affordable costs. Paired with smartphone appli-  cations, they allow sharing of the patient's health data with the doctors with  ease helping them with better survival even from chronic illness.  With the continuous biomaterials, the level of transistors has gone much  higher and is still much more capable of taking the new biomedical signals  from the tissues which are alive and active, which have been the real source  for the emergence of the IoMT. Amidst its phenomenal trend of acquisition  in various fields of interest, the health-care sector has incorporated IoT, as a  prodigious boon for its bizarre intervention in nimble attention as well as  chronic treatment, in the formulation of Internet of Medical Things.  IoMT is the collection of various medical devices and applications which interfaces the health-care system with the IT phase, via online computer net-  works. The machine-to-machine communication is enabled by using WiFi or  any methods of transfer of vital information. Such interconnection of con-  nectivity, around the globe deals with IoT. IoT is a big deal as it tends to  establish global connectivity irrespective of geographical boundaries making  asset tracking more cost-effective, health care more personalized, and en-  ergy consumption more efficient. IoT is the polestar digitalizing the globe,  giving a whole new perspective of things around and changing the shape of  ever-growing industries. IoT connects the devices through the internet and  facilitates data sharing even from remote locations which is now being wide-  ly utilized in the health-care industry.  IoMT enables virtually any medical devices to connect, analyze, and send  data across the web; not only can digital devices such as heart rate monitors  connect to the internet, but nondigital items like hospital beds, pills, and  other essentials of health-care products too are enabled to share data in real  time. The same can be shared with any authorized person who is legiti-  mately in need of the information regarding the vitals of the dear ones.  The elaborate discussion of the contents in the chapter focuses on the  following key points. 

IoMT devices  The greatest prospective boon with the advancements in biosensor tech-  nology makes possible wearable smart devices that monitor the user's  health. GE, Siemens, Philips, Microsoft, etc., utilize IoMT devices [3] like  Abiliofy MyCite, eVisit, Amiko.IO, InfoBionic's MoMe Kardia, and PillCam  TM do toward remote diagnostics, Predictive maintenance with virtual home  assistants, and clinical observations using ubiquitous devices or robust  tools, underpinning both patients and clinicians, unveiling everything in the  digital touchpoints. Sensors and AI are a boon in movement monitor, fit-  ness tracker, lifestyle monitor, etc. IoMT devices are greatly used in hospi-  tals and clinics by improving health-care quality and also simultaneously  reducing cost. It effectively works with CT scanners, MRI scanners, etc., at-  tached to remote monitors, which can be replaced with high-performance  IoMT devices.  IoMT devices, which are embedded in any apparel, attached to the skin,  implanted in the body, and sensors provide complete freedom to the pa-  tients to move on to their desired places, yet being closely monitored with  their risky vitals [4] as shown in Fig. 3.1. This advanced equipment relies on  a biological matter and some sensory devices to detect the characteristics of  blood flow, blood glucose, blood pressure, respiration, and other compli-  cated parts of the human body. Also, nonbiological medical sensory devices  measure body movements, stress, temperature, electrical activity of the  heart and muscles, and any other characteristics based on the anomaly of  the patients.   

Fig. 3.1 IoMT devices.    Health-care devices represent one of the rapidly growing domains among  IoT. According to a recent study, the value of this sector is predicted to  reach $176 billion by 2026.  The available IoMT devices used for monitoring which have experienced  the windfall of IoT with medical applications [5] include remote patient  monitoring, virtual home assistant, glucose monitoring, stress monitoring,  heart rate monitoring, hand hygiene monitoring, connected inhalers, and  Parkinson's disease monitoring [6] as shown in Fig. 3.2.   

Fig. 3.2 Diversification of monitoring.    Remote patient monitoring (RPM) is one of the most familiar and fre-  quently used applications of IoMT [7]. This can be used for innumerable dis-  eases as they reduce regular clinical visits of the patient. These devices func-  tion in such a way that they monitor the patient's health condition closely  and fetch these details to the preselected contacts. Any abnormality or any  deterioration in the patient's health can be easily detected based on these  readings. They tend to analyze these readings with appropriate algorithms.  These alerts and health details are shared not only with their preselected  contacts but also with the health officials and they can easily monitor the patient's health. Today, with the help of IoT, we not only get all the infor-  mation in our hands but also get quality health care at our doorstep with the  aid of these specialized devices. These are widely used for people of all age  groups with no restrictions. But the major challenge lies in managing and  maintaining the confidential health data of the patient with privacy and secu-  rity.  With almost half of the world population reeling with diabetes, the 

glucose monitoring sensors help them cope with the disease and combat it  successfully. Stress monitoring sensors [8] help people with chronic illness  associated with it. Heart rate monitors go with cardiovascular disease moni-  toring and alerting in case of extremity.  By the substantial use of IoMT devices, we can reduce the mortality rate  of several chronic diseases like Cardiovascular diseases (CVDs) [9] accom-  panied by high blood pressure. CVDs [10] are the number one cause of  death globally: more people die annually from CVDs than from any other  cause. An estimated 17.9 million people died from CVDs in 2016, repre-  senting 31% of all global deaths. Of these deaths, 85% are due to heart at-  tack and stroke. To predict the risk of these types of diseases, various fac-  tors such as age, sex, pain, cholesterol, blood sugar, etc. are considered.  These health data can be easily monitored by means of smart wearables and  can be analyzed for better predictions. IoMT connects the medical devices  and obtains the essential data connected to the computer system provider  and stores the data in the cloud. These devices are capable of producing,  storing, analyzing, and disseminating the health data. Wearables, remote pa-  tient monitors, sensor-enabled beds, infusion pumps, and health moni-  toring devices are among the IoMT products. The main purpose of these de-  vices blended with IoMT technology is to enhance the patient satisfaction  and intensify the quality of care given by the health-care management sys-  tems.  Stress and trauma-related disorders are also one of the major reasons af-  fecting people of almost all age groups across the globe. In that case, the  stress level can be detected by specialized IoMT devices and wearables [11].  The human stress level can be classified into eustress, neustress, and dis-  tress. When the devices are connected to the internet, they become smart,  which can detect the level and intensity of stress. Wearable sensors con-  nected through mobile phones to share data via calls, texts, etc. are such  types to name a few. Based on the respiration rate, stress monitoring can be  done where computer vision methodologies are utilized. 

Challenges  Long before the hospital staff notices a problem, the service provider or the  monitor of IoMT attached to the vital recorders will be alerted with the is-  sues to be corrected and rectified for flawless run. The challenges [4] were  not the investment of technology, rather a scary worry-free gain. Device di-  versification management, security vulnerabilities from data breach, flexi-  bility of its usage, interoperability, availability of resources, real-time pro-  cessing, accuracy in defining the establishment between measured features  with disease diagnosis, critical analytics of the personnel vulnerability re-  port, exponential growth of data with avalanche effect every day, mainte-  nance of patient's vitality reports, watermarking digital images of the patient,  heavy budgetary of infrastructure, lack of standardized protocol are the  major challenges [12,13] to name a few.  This digital medical technology aims at improving the life span of patients  by reducing the in-person frequent visits, especially for chronic illnesses in-  cluding heart ailments, diabetes, etc., but it also holds some perceptible  challenges, which, when it can be improved and modified, will prove to be  more systematic and well structured to meet the market needs. As medical  devices are more prevalent in recent years offering umpteen benefits, they  also lead to serious security and privacy [14,15] issues. As the health-care  system gathers, stores, and makes informed decisions and analysis based  on sensitive and life-critical medical information, the cybercriminals who ex-  ploit the vulnerabilities in these IoMT devices may be able to gain unautho-  rized access to confidential personal and health-care information as well as  to the hospital network. Attacks on these linked devices can have a wide  range of consequences. Security attacks [16] on these devices also lead to  significant and dangerous physical harm, sometimes even life-threatening  damage to the patients.  Security threats [17] and vulnerability when using IoT devices are common  as they tend to deal with huge data. This happens generally due to unse-  cured API (Application Programming Interface) connections. IoT facilitates  connection between two or more devices; in this case, the communication  between these two devices rarely takes place with robust security.  As health data comprises the personally identifiable data, these data secu-  rity issues can be solved by securing it based on industry standard regula-  tions. The integrity and confidentiality of IoT solutions and data, as well as 

the mitigation of cybersecurity risks, are critical to success. The IoT devices  must be designed with digital security from scratch to prevent the ecosys-  tem from data vulnerabilities. The modern IoT devices and blended tech-  nologies are complex. When devices are virtually connected, when data  sharing takes place, security is not addressed appropriately. Added to this,  there is no common solution that fits into solving these security issues. So  the best way to prevent these is the risk mitigation procedures before com-  plete deployment.  According to Safeatlast, there are currently over 26.66 billion active IoT  devices, with 127 new devices connecting to the internet every second. Glob-  al spending on IoT technologies is projected to hit USD 1.3 trillion by next  year, and the number of IoT devices will exceed 75 billion by 2025. By 2021,  there will be 25 billion IoT-connected devices, up from around 14.2 billion  today. By 2023, the health-care security market is expected to grow signif-  icantly up to $8.7 billion, according to a Frost & Sullivan [18] study released  in April 2019. He has stated the varied range of segments such as on-body  segment, In-Home segment, etc.  The manufacturers of these medical devices along with R&D phases also  have various vital tasks to be done to ensure essential factors are embedded  like the user interfaces, verification and validation efforts along with full life  cycle services. Problems to these production services include reduced time  to market, reduced production cost, compliance with medical-related regula-  tions, and end-to-end product services. Another triggering concern is the  security issues which have to be designed from the base of these devices to  reap the expected market benefits.  Though the devices are strictly monitored for their effect on patients, the  industry has lagged behind in coping with the risks of the possible cyberse-  curity threats in recent years. Just like any other IoT device and connected  computer systems, they are vulnerable to security breaches [19] affecting  and creating a threat to the safety and effectiveness of the device. This link  shows many frequently asked interesting questions that arise in the human  mind. Hospitals are susceptible to cyberattacks because of connected de-  vices and legacy systems, according to new research. The proliferation of medical IoT devices, combined with unpartitioned networks, inadequate ac-  cess controls, and legacy systems, has created a large and vulnerable attack  surface that cybercriminals may exploit. The critical factors which pave the 

way for attacks include unsecured legacy systems, health-care networks hav-  ing a 3:1 device-to-people ratio and devices with unsecured networks are  more susceptible to attacks. Further, more challenges are shown in Fig. 3.3.   

Fig. 3.3 Challenges—A glimpse. 

Case study—FHIR  A specific case study as a fortune-teller is the interoperability base using  Fast Health-care Interoperability Resources (FHIR) on the Azure system [20]  toward remote monitoring or otherwise towards home health care or clinical  trials of the patients-subjects using IoMT.  In this era where all the data and statistics play a vital role in the research  and development process and almost essential for analytical techniques,  even books and libraries are becoming digitalized to make them easily  accessible and retrievable when necessary. In just the same way, health and  clinical records need more digitization for precise analysis and better devel-  opment in that associated domain [21]. Semantic interoperability and com-  putable phenotyping of the health data are the need of the hour. The main  challenge involved in this lack of development is that they do not facilitate  the use of secondary data which supports large-scale research collaboration.  The collection and storage of the patient's health data in a systematized  format to authorize access to health officials in a digital standardized format  to retrieve it when needed is termed as Electronic Health Records (EHR)  [22]. Clinics and research centers utilize this EHR for several research and  analysis purposes. The available health data of the patient may be classified  into structured, unstructured, semistructured, and clinical notes. Structured  data involves the patient's statistics including analytical graphs and lab test  reports. Unstructured data includes the present disease state of the person  and the genetic diseases associated with it. Similarly, clinical notes contain  the prescriptions and medical reports, and at times the unstructured data  classification comes under a semistructured form and deals with the confi-  dential patient health data. The consignment of data available will provide  great support to patients as well as health officials [22]. As the data is het-  erogenetic data coming from different EHR systems, the data also varies in  its nature and interoperability. The significance of this kind of data to the future generation can be determined using phenotypic vision. The immediate  interpretability and portability of the phenotypes is essential to be main-  tained through a standardized framework. Some common data models used to represent these EHR data include Clinical Data Standard (CDS) and Com-  mon Data Models (CDM). FHIR is used to integrate the structured and un-  structured data and model it accordingly for clinical research.  As the health care deals with web service connections for efficient 

resource handling which can be better operated by Fast Health-care Interop-  erability Resources (FHIR) [23] by preserving the stipulated interoperability,  this can be done by the standardized connections to HL7, one of the top  applications of FHIR in the context of an integrated health monitoring sys-  tem, which is worth exploring. This FHIR shows extended support to the  health monitoring system. This will prove to be of the utmost benefit to peo-  ple who suffer from chronic diseases like blood pressure, heart frequency,  blood sugar levels, and obesity. This provides an integrated environment for health monitoring solutions done with the aid of existing health standards  to provide interoperability. This will help health-care officials and even pa-  tients to exchange and share health data across institutional borders. This  helps to watch closely the health conditions of patients suffering from car-  diovascular disease, elderly patients, and other chronic diseases. So, this  needs the aid of Telemedicine or Telehealth with the collaboration of IoMT  devices and systems. 

IoMT security tantrums  The Internet of medical things (IoMT) the spectacular innovation has  opened eyes for many health care approaches and for maintaining signif-  icant relationships with the patients. In this technological era, Access to  health care should be of good quality, affordable, appropriate, and health  products should be indispensable to advance universal health coverage, ad-  dress health emergencies, and promote healthier and happier populations.  Medical devices play a gigantic role in diagnostics and therapeutics in the  treatment of diseases because doctors and physicians may be unable to per-  form treatment in health-care facilities [24].  The growth in the Internet of medical things (IoMT) will reach $72.08 bil-  lion by 2022 with more than 30 billion connected medical devices in the  health-care ecosystem. Thus, according to the survey conducted by the  health-care department, four in six health-care companies are now having  IoMT medical devices installed, with seven out of eight strategizing its use.  Smart devices need better security to protect patients and their records;  hence, we have to enable cybersecurity to ensure safety and security issues.  GE Healthcare, Medtech, Medtronic, and Philips, and even technology giants such as Apple, IBM, Cisco, and Qualcomm are developing capabilities  in IoMT applications with a variety of integrated medical devices from preg-  nancy test kits to Sugar IQ diabetes assistant. This technology allows de-  vices to record, generate, collect, analyze, and transmit data, making IoMT  devices a connected infrastructure of health systems and services. 70% of  the health-care organizations utilize this technology for maintenance and  monitoring practices and 64% of the technology's use is for patient moni-  toring and record analyzing. The health-care industry is rapidly moving to an  absolutely digitized environment. As a result, devices have been introduced  to the hospital ecosystem and bedside workflows to help extend and  streamline care throughout the hospital, and many devices are incorporated  to monitor remotely patients at home or work while using vigorous medical  devices.  Unfortunately, this new technology has also opened the door to increased  risk and crime and security problems because of new potential points of the  exposure for health-care IT infrastructures. Without enforcing robust stan-  dards for safely using these medical devices within the new IoMT platform,  each network-connected medical device within a health provider's 

ecosystem will open up the possibility for patient health information expo-  sure, as well as the potential for other unauthorized use of critical systems  and applications. IoMT devices form a connected system of sensors and de-  vices tagged around the patient to capture, measure, and identify key data;  stratify risks; make decisions; and initiate necessary action plans. The  health-care industry is on the verge of becoming more proactive patient care  by supporting and embracing the IoMT enterprise. It became very much  challenging to provide full protection with the expansion in using medical  devices in the health-care ecosystem; the security part in IoMT poses a per-  ilous problem that keeps on growing, because of the data sensitivity and  critical information. These kinds of attacks may lead to patients’ privacy is-  sues, not only in terms of data privacy, but also because there is a risk [25]  that their lives can be in danger.  Cybersecurity is the most needed one of all as health care is promoting  and storing repositories in computer systems as well as in mobile devices;  every medical device then becomes like a “wormhole” into the hospital's IT  network, and the hackers or attackers are now traversing through a new  strategy called “Destruction of service,” or “DeOS,” which has the capacity  to mutilate the whole network. This cyberattack needs further considerations  because it targets an organization's entire online presence as well as their  level ability to recover from the attack later. We have some security strate-  gies to break free from cyber issues. These strategies need to include not  only a screening and threat mitigation standard for current devices but also  a plan for maintaining security on a continuing basis. These strategies must  not disrupt the clinical workflow, but they should provide clinicians with the  proper knowledge on what to do if a data compromise occurs. The outage  attack stops an IoMT device from working and may result in death or phys-  ical injury to the patient. With physical attacks, attackers need to physically  install a pseudo sensor in the IoMT architecture in order to receive unautho-  rized health information. The hacker may physically alter the device to state  false readings, resulting in the patient receiving heavier medication, for  example. The message corruption attack is the result of inserting a virus  with IoMT data when sent to the physician causing corruption of the orig-  inal data. In the false node attack, one patient data may be replaced with an-  other patient data and would unknowingly result in an inaccurate diagnosis  to both patients by which there will be threat to the lives of both the 

patients.  In the present days, with the rapid development of wearable sensors and  wireless communication, researchers are subsequently interested in improv-  ing the health-care sectors in response to human needs by digitizing and  spreading out health-care institutions and providing perpetual and remote  medical monitoring [26]. Generated medical data are very essential and  must be cautious about any kind of data theft. Blockchain has evolved as  the most assured and decentralized platform. It provides many powerful fea-  tures without the handling of third parties including tamper-proof im-  mutability, traceability, data integrity, confidentiality, and privacy. With the  evolution of blockchain technology (BCT), analysts are focusing on how to  use blockchain strategies to bring robust security to health-care applications  and devices. However, such integration is very difficult and challenging due  to the different requirements in these two technologies. There are lots of  existing solutions to applying blockchain technology on IoMT, but still there  are some inaccuracies that need BCT in a stronger and more efficient man-  ner [27]. Other malicious activities like health data, hacking of medical de-  vices, gaining access to hospitals networks, and exploitation of exchanged  and stored information which threaten the lives of the patients can also be-  come trouble-free by blockchain technology. This necessitates exploring  optimized solutions to deal with the threats and on IoMT.  Blockchain has the potential to conquer the barriers of conventional ways  to deal with the security issues and patient privacy and is considered to be  the “spine of future IoT” (i.e., IoMT, ML, etc.) with various benefits like en-  hanced security, reduced cost, true traceability, increased speed and effi-  cient mechanism, etc. Therefore, integrating Blockchain with IoMT can pro-  vide resilience to several attacks related to the “user authentication problem  and key management” problem such as “replay,” “Third part accessibility,”  “stealing of records,” “password guessing,” “illegal session key compu-  tation,” “health data disclosure,” “destruction of service (DoS),” etc., and in  turn it provides better health-care services in a safe and secured both in real  time and in a virtual environment. 

Conclusions  A paradigm shift from traditional health-care systems to digital advance-  ment, along with privacy and governance security notch is being explored  with different case studies of technical tools. IoMT affirms an unshakeable  position in the technological era, with its unleashed full potential and feder-  ating system devices, toward the progress of Industry 4.0. It always has its  own set of boon and the benefits are tremendous, whereas the pitfalls and  challenges must be handled with care to outwit the sinking of benefits of  using IoMT. A proper balance is mandated in the technological boom in the  future decades. 

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  4: Performance enhancement of IoMT using artificial intelligence algorithms   

a b Muhammad Salman Mushtaq ; Yousaf Mushtaq ; Muhammad Qamar  Razaa; Syed Aamer Hussainc    a School of Electrical Engineering and  Information Technology, The University of Queensland, Brisbane, QLD, Aus-  tralia  b Department of Computer Science, Superior University, Lahore, Pakistan  c

Razak Faculty of Technology and Informatics, Universiti Technologi 

Malaysia, Kuala Lumpur, Malaysia 

Abstract  This chapter presents a comprehensive review of the application of  IoMT in the health-care domain with a focus on artificial intelligence. It  discusses the various aspects of AI techniques and their implemen-  tations. The discussion also encapsulates the use of AI in different  health-care problems, its use in the analysis of various critical condi-  tions, and even the prediction of outcomes helping make decisions.   

Keywords  Intelligent health care; Efficient diagnosis; Machine learning; Human ma-  chine interface; Wearable devices; Artificial intelligence (AI) 

Introduction  Internet of things (IoT) is an expansion of internet services for machines,  enabling machine-to-machine communication. It is defined as the domain  of cyber-physical systems to communicate and record events. The domain  has significant applications in agriculture, environmental monitoring,  health, and transportation. As already discussed in the preceding chapters,  the most active research domain and the most critical application of the  Internet of things is its health-care application named the Internet of med-  ical things (IoMT) [1,2].  IoMT is an assemblage of things (devices) connected over the Internet,  providing health care or health-care-related services to the user. It is an  infrastructure that links medical devices, applications, and services, per-  forming intricate tasks systematically. The link permits health-care per-  sonnel to monitor the patient's condition remotely, efficiently performing  clinical operations and procedures. The facility profoundly affects the med-  ical services’ effectiveness for remote users with limited medical facilities or  having difficulty accessing medical facilities frequently. Additionally, the  wearable IoMT devices make monitoring very easy through machine-to-  ma-chine communication, linking the tiny gadget to the health-care moni-  toring unit, or even a doctor's smartphone that can track the activities of  his/her patients seamlessly [3–5].  The digital disruptions in the modern era have introduced artificial intel-  ligence in the health-care sector. Artificial intelligence (AI) technology has  allowed the machine to discover, fit, and enhance based on the various  datasets used to train AI [6–8]. This has allowed the private and public sec-  tors to work on developing systems by improving technology, thereby han-  dling health and services. The prominent factors behind this progress are  undoubtedly the smartphones and IoMT, which facilitate innovation for im-  proving lives. The remote monitoring and assistance without the physical  health-care professional's presence have allowed many health-care assis-  tance applications to be possible. Digitization has allowed the term data to  be more significant than ever in the health-care sector, where information is  empowering the tools based on analysis and intelligent programming. To  handle the analysis of such datasets and extract useful information and  decision factors from them, the use of intelligent systems and their appli-  cations has greatly increased [5,9,10]. 

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 moni-  toring

with

less

professional

intervention

with

higher

scalability 

[5,9–11]. Using AI-assisted smart homes, robots, and virtual assis-  tants can help provide care to elderly and disabled patients with minimal  human interaction [12–15]. 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 [10,11,16–18]. 

What is artificial intelligence?  Considering the technology hype cycle published by the Gartner Research  Institute from the health-care side, it can be observed that the leading tech-  nologies that are at the top of the Gartner hype cycle are all in some way  linked with artificial intelligence. In the study, AI is presented as the intel-  ligence associated with machines or devices different from human or living  beings' intelligence [19–21]. It can be perceived as an intelligent instrument  that can complete tasks or objectives by understanding the linked param-  eters, maximizing the chances of completion. AI can also be defined in  terms of machine learning (ML), which presents the machines as learners  who can mimic human attitudes in understanding and assessment, thereby  improving the decision process [22–24]. AI systems are composed of hard-  ware and software modules where the software domain is mainly linked to  AI algorithms. In contrast, the hardware domain is concerned with the cen-  tral processing units (CPUs) and graphical processing units (GPUs)  [25–28].  In daily life, AI can be observed in many places, prominently in Apple Siri  and Amazon Alexa. Google also uses AI in its translating software for lan-  guage processing. Similarly, high-end technology in the health-care domain  has attracted great interest in the recent past. As already discussed [29–31],  AI applications in health care are immense due to their capability to manage  complex systems where massive datasets exist. Cumulatively, health-care AI is needed in places where both machines and humans are required as part  of the process, which is more stochastic and less deterministic than the  procedure in other sectors [32–34]. AI provides the predictive capabilities  and a central control mechanism to handle complex datasets and learn from  the same algorithms it uses to improve decision-making and provide  recommendations. In humans, handling complex datasets is a challenging  task and thereby results in wrong diagnosis, for example, wrong medication  [35–38]. 

Health-care domain and artificial intelligence  Artificial intelligence instruments, theories, and methods have been used in  the health-care domain for five decades to benefit the health-care sector by  improving professional assistance [39–42]. This leads to the accuracy and  efficiency of gradually improving AI methods, inspiring an increase in the  adoption of technology in medical applications. This is added with the read-  ily available AI software thanks to the researchers and developers, making  its adaptability even easier [43–46].  AI application in the medical domain is vast, and many AI systems are  being integrated into health care. The existing medical applications of AI in  the diagnosis, automatic cataloging, therapy, the recent addition of genetic  disease verdict, wearable devices-based monitoring, schedule management,  data visualization, assistance in robotics, surgical assistance [45–50], and  many more have revolutionized medical practices. In the context of the ap-  plicability of AI in the medical domain, there has been considerable work.  The main subclass of AI is neural networks (NN), which are the most com-  monly used analytical tools in the medical domain [51–56]. Similarly, fuzzy  NN has also been extensively used in the heredities, cardiology, and ra-  dioscopy. Other applications of AI range from nutrition suggestions to med-  ical data management and medicine [57–65].  The initial use of technology in health care was based on the potential  advantages it can bring to the field. In 1960, early researchers such as Ax,  Holtzman, Ledley, and Lusted proposed the basics of computer appli-  cations in health-care assistance and their potential in testing and diagnosis.  That paved the way for AI application in the 1960s when AI tackled empirical  and inductive problem handling [64–70], and the decision-making abilities  of AI in psychology were presented. Further development was proposed by  Slack and Van Cura in 1968 when they conducted interviews of patients  using computer simulations. Going further, the use of AI-assisted auto-  mated diagnosis of thyroid dysfunction was carried out by Nordyke et al. in  1971, while an artificial intelligence program to recommend antimicrobial  treatment to medical doctors was suggested in 1973 by Shortliffe et al.  [71–75].  In the next decade, the focus was also shifted toward the efficiency of AI  techniques. The work of Chandrasekaran in 1983 evaluated the AI systems  and suggested improvements in measuring diagnosis accuracy and 

estimating how much will system scale up. These include optimizing the  system's intermediate evaluation stages, resulting in authentication against  standards, and working on the systems’ scalability [75–82]. WB Schwartz et  al. presented AI assistance in the diagnosis of diseases, Mulsant and Servan  in 1985 proposed the use of AI for medical datasets’ databasing, while Ren-  nels and Miller presented AI research on anesthesia and intensive care  [83–86].  At the start of the next decade, the progress made by AI in medicine was presented by Shortliffe in 1993. The development of AI for computer vision  and mammography was presented by Vyborny and Giger in 1994. In the  same year, Kahn proposed using AI in decision-support systems for the  radiology domain.  According to experts, the next AI age in medicine (AIM) was started in  2009 with several viewpoints. There has been significant work done in the  next decade with the big data domain's onset and focus on data analytics  and decision support systems. In this context, Dilsizian and Siegel [40] pro-  posed using AI for cardiac imaging employing big data for personalized  medical diagnosis and treatment. Similarly, Tenório used AI methods to develop a decision-support system for diagnosing celiac disease. Shortliffe in  2012 presented the MYCIN software, which provides computer-based med-  ical consultations. Kantarjian, in 2015, introduced the use of AI in cancer  care and research under the big data domain [87,88].  The current and future AI perspectives in health care are represented by  Briganti, which postulates that deep AI algorithms can handle the ever-  increasing data arising from wearables, smartphones, and IoMT. However,  the use of AI in clinical practices is minimal and is mainly linked to the  detection of atrial fibrillation, epilepsy, hypoglycemia, or diagnosis. The  diagnosis is primarily based on histopathological examination or medical  imaging. Advancements in the form of enhanced medical techniques are yet  to be implemented in the health-care services providing autonomous and  personalized treatments. Still, the AI systems in health care are semiau-  tonomous, requiring limited but essential human supervision to guarantee  proper diagnosis and therapy. Health-care specialists want to have assisted  systems but not the systems replacing them. In comparison, the new AI sys-  tems can examine patients and make recommendations based on the data.  The challenge is to create crossbreed systems that can efficiently merge 

health professionals’ knowledge with AI's software traits [88–90]. 

Artificial intelligence and machine learning  The term machine learning (ML) is closely linked with AI, and was first used  in 1959 by Arthur Lee Samuel. ML can be defined as the techniques that use  knowledge-improving operations or to predict accurately on a set of data  gaining valuable comprehension. So, instead of following the set of rules,  the machine learning algorithms work on learning from experience. There  are three main types of ML operations, supervised, unsupervised, and rein-  forcement. The supervised ML performs based on the existing examples,  having historical data points to predict future situations. While unsuper-  vised ML does not have defined categories, it is expected to use ML algo-  rithms to organize the data. In comparison to both, reinforcement-based  machine learning has a designated task to optimize or complete. The learn-  ing is performed based on the system's feedback, and the desired behavior  is attained [91–94].  Machine-learning methods can be divided into four categories based on  the mode of operation. These include categorization, grouping, learning,  and prediction. The categorization or classification algorithms are trained in  advance on the existing sorted data such as the problem dataset before  being applied to the actual data. For clustering or grouping, the task can be achieved at two levels; first, the datasets’ collection is analyzed to identify  similar nature clusters and a real-time dataset similarity check. Association  learning is a generalized classification case which uncovers the relation-  ships between attributes in a dataset [92–96].  This chapter presents a comprehensive review of the application of IoMT  in the health-care domain with a focus on artificial intelligence. It discusses  the various aspects of AI techniques and their implementations. The discus-  sion also encapsulates the use of AI in different health-care problems, its  use in the analysis of various critical conditions, and even the prediction of  outcomes helping make decisions.  In contrast, classification acquires a relationship between features based on all-to-one approaches identifying the class. Numeric prediction or regres-  sion is another generalization of the type where the attributes are contin-  uous. Although splitting the expected numeric feature into a limited number  of periods, every regression algorithm can be used for sorting.  Based on the understanding of machine learning, there is an expected  similarity between ML and data mining. However, the ML is based on 

algorithms focused on gaining valuable information and insights from the  datasets. The constant use of these algorithms is concentrated on vigor-  ously changing the conditions and stresses on modifications, reorientation,  and revising of algorithms based on previous events. So, ML continuously  adapts to the new data and uncovers new trends [97–100]. 

Hardware implementation of artificial intelligence  The term AI was coined by John McCarthy in 1956, which led scientists to a  race for conscious machines, which is still at its peak. Looking at the tech-  nology in the mid-20th century, the AI concept in all its form would seem to  be a dream. However, since technology, specifically semiconductor tech-  nology, has started following Moore's law presented in 1965, Intel co-  founder Moore projected that the transistors on an integrated chip (IC)  would double every 2 years; the dream has started to take shape. Until now,  the law has held its prediction and has thereby generated the computational  power that has allowed the human race to achieve in decades what it could  not in centuries. The transistor count has increased so much in 6 decades  that in the 1970s, Intel's chip had 2300 transistors while in 1989, the Intel IC  had a million transistors, and that is still a small number in comparison to  the Intel processor in 2010 with a million and in 2016 with 10 billion tran-  sistors [101–105].  This massive growth in semiconductor technology has allowed bringing  down the production cost where a smartphone costing a few hundred dol-  lars has more processing capability than the billion-dollar computers of the  1970s. Considering the processing units, the most straightforward approach  in implementing AI algorithms is through general-purpose CPUs in a multi-  thread or multicore configuration. Additionally, GPUs which can perform  convolutional computations can be advantageous compared to CPUs for  large algorithms with massive calculations. The combined solutions involv-  ing both CPU and GPU called coprocessing have also been used and ana-  lyzed to be more efficient than individual benchmarks. Apart from them,  even higher efficiency can be achieved through programmable processing  units such as field-programmable gate arrays (FPGAs) and application-  specific integrated circuits (ASICs). According to the AI algorithms imple-  mented, these programmable units can customize capability, power han-  dling, and form factor. This is because programmable architectures can be  customized for a specific application, and performance parameters can be  designed according to the situation. Apart from these high-end devices,  edge computing devices such as IoTs can provide the highest power effi-  ciency and form factor performance. The main areas of concern in the IoT  or, in this case, the IoMT domain, are the processing and memory limita-  tions. The power-efficient architecture requires low-to-medium power, 

requiring processors, thereby limiting the processing range [106–108].  Further, the form factor and the hardware architecture limit the onboard  memory of the system. To handle such issues, researchers have imple-  mented AI algorithms with the help of analogy ICs, spintronics, and mem-  ristors. These implementations can merge the computations with memory,  improving memory access. Additionally, the AI algorithm's efficiency is fur-  ther improved by optimizing the number of bits for data representation, fur-  ther improving memory management and allowing power efficiency. So, cu-  mulatively with the appropriate AI algorithm like using deep learning  method in place of shallow training for complex datasets or using pre-  training techniques along with balanced dataset having a continual avail-  ability are some of the optimizing factors which can improve the system  performance [109–112]. 

AI software implementation  The main implementation concerns of AI are linked with algorithms. The  commonly used algorithms in the AI or ML field are regression, K-nearest  neighbor, decision trees, random forest, K-means, neural networks (NN),  and deep NN.   

Linear regression  Linear models typically use a simple approach to best fit the linear attributes  within the data points using different linear model algorithms. Linear regres-  sion or least-square regression is a standard form of the linear model mainly  used in statistical analysis and ML by modeling the relationship between the  data points. It can also quantify the amount of correlation among the vari-  ables in the dataset. In addition to that, based on the prior values, it can also  predict future values. However, since the regression assumes the corre-  lation is linked with the connection, without comprehending the data's con-  text, the regression analysis results will be disproportionate. Since the linear  models are pretty simple, their application is quite limited and they cannot  be used for complex scenarios [113–116].   

Logistic regression  Logistic regression is a modification of the linear model for classification  problems. The main difference between the linear and logistic models is the  nature of the dataset. The logistic regression is applied in binary datasets,  while for continuous variables and the linear nature of datasets, linear re-  gression is used. The logistic regression is efficient in classification due to  its shape. However, like the linear model, the logistic models also have the  drawback of overfitting in nature [117–125].   

Nearest neighbor  The algorithm classifies the dataset based on the classifying object's prox-  imity to the sample space's training sample. The idea is to link the object  near the training sample based on the assumption that it will have similar  prediction values. Since the algorithm is modest in the ML category, it is re-  garded as a lazy learner because its prediction depends only on particular  instances. However, the nearest neighbor's performance is quite prominent  in the case of a noisy training dataset. Also, it performs well in automation  since it gives decent predictions when there is a dataset with missing data  [126–128]. 

Decision tree  As the name shows, the algorithm works like a tress from the top node re-  ferred to as the root node down to the leaves where branches represent the  results or outcomes while leaf nodes depict the classes. Their drawback is  because of the inability toward the noise. One way to improve this is  through pruning, which removes every situation in its originator that does  not enhance the rule's estimated correctness. To train the decision tree  algorithm, the training dataset is used to find the attributes that best split  the training set concerning the target. The drawbacks of decision trees in-  clude scalability and greedy nature. The scalability hampers the algorithm  from being used for large datasets and in data-mining applications. The  algorithm's greediness arises because of its relatively fast decision nature,  thereby preventing it from getting the best overall solution. It is like being  stuck in the local maxima instead of finding the global maxima. However,  despite these drawbacks, the method has fast and high classification perfor-  mance in datasets where class mapping is composed of a thin region of  concept space. So, the algorithm can be beneficial in handling the scenario  of continuous-valued attributes. It is also recognized as the best option as a  preprocessing method for other algorithms because of its robustness to-  ward the predictor types [6,124,125,128,129].   

Random forest  The random forest algorithm is the modification of the decision tree and  can be considered the average of multiple decision trees where each of them  is trained with a random sample of the dataset. It assumes that every single tree in the forest is weaker than the complete decision tree. So, combining  them gives better performance because of the diversity. The algorithm is  relatively easy to train; however, it is very slow in generating the output  predictions compared to the other algorithms [4,19,33].   

Gradient boosting  Like the random forest, gradient boosting is also derived from the decision  trees. The difference being that in gradient boosting, the decision trees are  trained sequentially. Each tree is trained using the data incorrectly identified  by the preceding tree. This allows the gradient boost algorithm to focus  more on the complex scenarios and less on the easily predictable cases  [16,19,27].   

K-means algorithm  This is a type of unsupervised algorithm for clustering. The classification  difference is that in clustering, the grouping of items in clusters is done  without predefined classes. So, the grouping rules are defined by the algo-  rithm itself without any human supervision.   

Dimensionality reduction  Dimensionality reductions contain a group of AI algorithms useful in data  analysis. They remove the redundant data from the dataset by eliminating  outliers and unrelated data. This operation is critical in sensor-based de-  vices such as IoMT, where the sensor repeatedly transmits the status mes-  sages without any helpful information. Storing and analyzing such data  wastes resources; so, removing such redundant data helps improve the sys-  tem's performance.   

Reinforcement learning  Another learning domain technique is reinforcement, which directs the sys-  tem toward achieving a goal by knowledge, decision-making, and auto-  mation. The goal is to maximize the outcome, which is in the form of a  numerical reward. The way toward achieving this reward is not fixed, and the  system can select the best methodology which maximizes the scenario. That is why literature defines the reinforcement-learning method by the problem  it intends s to solve, not by the algorithm used in completing the task. This  type of learning has a convenient approach compared to other learning tech-  niques such as supervised learning. In practical scenarios, there is not  enough data available that is relevant and representative of the problem,  which can be used for learning. Therefore, it is expected that the system  should learn autonomously from experience and then use this experience in  decision-making. Another advantage of using reinforcement learning is its  rule of using the scenario and focusing on the end goal compared to the  other learning methods, which focuses on the subproblems and not con-  sider how they will merge into the big problem [32,40,41,45,46].  Additionally, it is usually considered that the learning mechanism in rein-  forced learning works even if there are considerable ambiguities in the envi-  ronment it must confront. However, there is a planning phase before the  system is subjected to the actual scenario. The planning must consider the  matters as to how the real-time environment models are developed and 

enhanced. In such cases, the reinforced learning might involve supervised  learning but in a minimal scope determining which skills are essential.   

Neural networks  Neural networks (NN) consist of biological neurons, which work by sharing  messages. The AI version of these neurons is the artificial neural network  (ANN). The NN consists of a set of connected input–output units with an  assigned weight for each connection. The whole architecture is based on  how many neurons are connected and the way they are connected. Mainly,  the neurons are divided into input, output, and intermediate neurons, con-  sidering their mode of operation. As the name suggests, the input and out-  put neurons serve as the channel neurons to receive and transmit infor-  mation. However, the intermediate neurons have a critical role to play where  they channel the information between input and output. The channel's channeling is through the network paths and the neurons’ state in these paths  defines the state and associated weights of the neural network [44–47].  NN's working mechanism is based on the network's development con-  sidering the changing interconnections of neurons and adaptation of  weights. The direction of the adaptation in network is set based on the initial  conditions, and mathematical calculations define the expansion of specific  network attributes. The advantages of using NN include its application for  classification on the dataset for which it has not been trained. In this case, it  has much better performance than other techniques, especially in noisy  data. Also, NN can handle the correlated data much better than the other  learning methods because of its higher order variable connections, thereby  improving the connectivity. However, compared to other algorithms, NN  has the limitation of deciphering outputs in the form of symbolic patterns  [17,25,126].   

Deep learning  Deep learning is an ML class that processes several nonlinear information  layers using supervised or unsupervised learning for extracting valid conclu-  sions, analysis, and classification. The architecture may consist of many  ranked layers for information processing in a nonlinear fashion. They are  developed because the low-penetrating ANNs are incapable of handling the  big complex datasets that are now appearing to quite a large extent in audio  processing, image processing, data mining, and other data-intensive 

applications.  The basic principle of deep learning is hidden in the hierarchical pro-  cessing of data using multiple architecture layers. This distribution allows  the pretraining of the layer's input before it is provided to the next layer.  Based on this pretraining, deep learning is divided into three architectural  types: generative, discriminative, and hybrid. In the case of generative archi-  tecture, the pretraining of layers is performed in an unsupervised manner.  This approach solves the difficulty of training the NN architecture with mul-  tiple layers, thereby enabling deep learning. NN can have deterministic pro-  cessing ability where each layer's output is tacked with the original data or  with different information combinations creating a deep learning scheme.  The descriptive architectural model considers the NN outputs as limited  distribution for all the possible label structures in the given input order. It,  therefore, uses an objective function for the optimization of the input series  following the layered path. The hybrid model creates a combined effect by  linking the properties of generative and discriminative models for its pro-  cessing. 

Intelligent internet of medical things  IoMT has the primary goal of enhancing medical services by acquiring data  from health-care devices and systems. Doing so uses several instruments  for its operations, such as optical devices, movement sensors, sensors for  concentration measurements, heart-rate systems, temperature sensors,  humidity sensors, and pressure sensors to perform real-time monitoring of  patients. The collected data is then analyzed and transmitted to profes-  sionals for consultation and decision-making. The main challenges existing  in such an architecture are related to the management and processing of  massive data. AI comes as the leading agent in such a scenario where ML  algorithms and big data techniques can handle real-time data, generating  critical decision-making outcomes. Due to these advantages, AI and IoMT  find their applications in many places in the health-care sector, as evident  from the discussion in this section.   

Health-care assistance  In assisting the patients and specifically the elderly patients, AI has proved  to be a valuable addition combining with the IoMT hardware power. The pa-  tients in need of constant attention and monitoring are challenging cases in  the current highly populous world. Considering the developments in this  field, the use of smart-home features and intelligent tools linked with IoMT  devices for patients with loss of autonomy was presented by Dahmani et al.  Similarly, IoMT-based facial recognition and gesture tools can also be con-  figured using AI algorithms for passing commands in disabled people. This  will also allow them to communicate with machines without sensors at-  tached to their bodies. Hudec also used an AI-based system for blind peo-  ple to carry out their activities. The system as presented can improve using  IoT tools for warning, sensory mechanisms, and object recognition. AI can  also be beneficial for pregnant ladies by providing them with assistance in  dietary routines, keeping a check on body parameters using sensors, and  keeping a backup with the cloud services so that information can be shared  with the physician in case of an anomaly.  Assistance is also needed for older adults in fall risks, which can create  complications for them. IoMT-based intelligent systems can detect falls  using Doppler radars and a sparse classifier and can reduce the risks in  using IoT technologies by communicating the situation to emergency per-  sonnel. Older people can also be provided with an ambient environment at 

their homes according to their aging needs through smart-home infra-  structure based on AI and IoT systems. This can provide them with activity  monitoring, safety checks, and medication cycles.   

Prediction techniques  AI also has vital applications in the prediction systems, which are the most  crucial medical domain tools as knowing the future state is critical in pro-  viding timely care. NN algorithms are primarily used to evaluate prediction  scenarios. One such common scenario is the respiratory outpatients, where  NN models have proved to be quite efficient. These models work in the  form of deep-learning methods in combination with IoMT devices. One  such case exists in applying CNN predictions for predicting gallstones’  chemical composition using IoMT tools. Another case is kidney diseases,  where the beginning stages’ predictions are significant for the patient's  treatment. These days, IoT-based intelligent systems incorporating ML  methods such as NN and regression are used to predict kidney diseases  with 97% accuracy.  Diabetes is also a prevalent disease whose treatment is very much depen-  dent on the accuracy of its diagnosis in the early stages. Prediction of dia-  betes is performed using neural classifiers like decision trees or NN used  along with IoMT sensors to estimate patients’ conditions and compli-  cations. The algorithms have shown to be 94% accurate in their prediction  of diabetes. Predictions for cancer diagnosis can also be performed using  deep learning methods like deep convolutional neural network (DCNN) or  deep fully convolutional network (DFCNet) using the computational ma-  chines giving high accuracy performance.   

Robotics technology  The combination of robotics and IoMT can enhance the protection mecha-  nisms, responsiveness in actions, and precision in health-care processes.  The main area where substantial development has taken place in the health-  care sector is the social robots field. These are widely developed and re-  searched for monitoring and assistance in caring for children and the elderly. A few examples of such robots are RI-MAN and CareBot, which are  developed to manage humans in their critical ages (kids and older people).  In addition to that, robotics, along with AI and IoMT, has also been used for  clinical assistance, nursing tasks, and rehabilitation centers. Apart from 

physical health-care advantages, robotics has also played its role in the  therapeutic domain to reduce stress, develop cognitive abilities, and partic-  ular teaching purposes. A significant case study of this nature can be PARO  (a baby seal) or ARASH (a humanoid).  Educational health robots have also been used for disabled people to pro-  vide education. One such example is RASA, an assistive robot used for teaching Persian sign language to deaf children. Such educational robots  have also been used for assistance in the treatment and education of special  children such as autistic and Down syndrome kids.   

Personalized treatments  In the modern world, where the services are getting customized, and the  focus is improving the individuality of the users, the health-care services  also need to move toward personalized treatments. These are driven by indi-  vidual data analysis acquired from various sources. Prominent among these sources are sensors and wearable devices from the IoMT domain, and anal-  ysis is mainly carried out with artificial intelligence's learning power. In big  data, the medical field also uses ML and big data techniques for clinical  practices. Utilizing AI algorithms for individualized treatment involves com-  plex predictions based on predictive models carried out on the individual's  data validated by clinical tests. The ensemble of IoMT and AI has enabled  health-care professionals to acquire, gather, and enhance the statistical anal-  ysis of a patient's condition. The DeepSurv system was developed using  deep-learning techniques for personalized treatment recommendations and  for creating a statistical model for patients’ treatments. Apart from that, even  highly sophisticated and accurate systems are also developed for patients  requiring precision in diagnosis and therapy. One such system is BioSen-  Health, which is based on an IoMT observation system intended to transmit  real-time patient data to health-care professionals. The data include body vi-  tals such as heart rate, temperature, and oxygen level, which are then visu-  alized using graphs.  Critical illnesses such as Alzheimer's disease and diabetes, which require  frequent examination, are also handled using personalized treatment tech-  niques. IoMT has allowed the monitoring of Alzheimer's patients using hy-  brid feature vector techniques to analyze data with very high accuracy. Simi-  larly, diabetes treatments are also created in a 5G-Smart Diabetes person-  alized treatment system that uses IoMT wearable devices and ML 

algorithms for diabetes monitoring with analysis based on sensor data. The  system predicts the patient's condition based on the data, which effectively  deals with the disease, focusing on an individual. 

AI and IoMT: Case studies  Considering the importance of IoMT and AI in dealing with medical condi-  tions, this section presents some case studies to show how these tech-  nologies are reaching the core of the problem and making a difference in  improving the situation.   

Dentistry  AI algorithms, particularly vector machines, ANN, random forest, and near-  est neighbors, are incorporated in systems to identify dentistry issues such  as cysts, tumors, oral cancers, and metastatic conditions with high accu-  racy. Even IoMT tools can also be used for biopsy and cytology by incorpo-  rating ML algorithms such as CNN for image analysis with considerable  accuracy. Diagnosis of temporomandibular joint disorders (TMDs) diag-  nosis can also be made using the IoMT-aided natural language processing  technique. The scheme tries to identify TMD type mimicking using natural  language processing tools with sufficiently high accuracy. In addition to  treatments, the use of AI and IoMT can improve work procedures for den-  tists. Using telemedicine IoMT technologies, FaceTime for dentists with pa-  tients can be improved by incorporating voice, text, and even translations in  the stream to reduce the amount of record keeping. IoMT enabled-implants  and wearable devices can also be helpful in actively providing data for self-  monitoring and self-management. Constant noninvasive observing of well-  being and conduct will allow a more profound knowledge of the processes  causing disease.   

Stress management  IoMT-based sensors integrated with games built on intelligent algorithms  for stress detection are very accurate in diagnosis. Sensor inputs are converted and measured to identify the level of stress. Similarly, wearable IoMT  devices can also be linked with smartphones to detect stress conditions.  Such data can also be integrated with ML techniques to estimate situations  and conditions that elevate stress. Voice-based stress monitoring can also  be done employing a fuzzy logic technique using the linguistic model's out-  come. MRI is also used for the identification of personality disorders using  machine-learning techniques. Similarly, the machine-learning approach is  also used with IoMT devices for temperature, humidity, and accelerometer  data for stress detection.   

Brain-tumor diagnosis  A most critical detection and treatment exists in the case of a brain tumor.  Because of the critical nature of detection, ML methods are used in clinical  procedures with high accuracies. Existing work includes using the naive  Bayes classifier on MR imaging to diagnose a brain tumor that is then pro-  cessed through K-mean clustering to detect the area with high accuracy.  Segmentation techniques based on edge detection can also be used for de-  tecting brain tumor regions by implementing the K-mean clustering method.  Metabolic values can also detect brain tumors by creating a vector pattern  from the values and automaticallyy detecting the anomaly. The automated  detection trained using the random forest method has substantial accuracy  in detection. Segmentation using support vector machines (SVM) can also  detect tumors. The extraction of features is done based on the form, inten-  sity, and texture of the segmented tumor area.  Additionally, deep NN can also perform segmentation on greyscale im-  agery for feature extraction. All these methods present high accuracies in the  detection of tumor points. The automated systems based on these tech-  niques make the process easy and reliable for the professionals’ decision-  making.   

Heart-disease diagnosis  IoMT and AI can also be combined in heart diseases to provide remote pa-  tient monitoring. Using the heart patient's existing dataset, ML algorithms  can predict a heart disease occurrence. With deeper analysis, the occurrence  of a heart attack in the future can also be predicted. Using the ejection frac-  tion ratio, the AI systems can classify heart-failure genotypes and pheno-  types in different analytic echocardiographic structures. Therefore, auto-  mated AI systems can perform monitoring and automated analysis to detect  and predict heart conditions. 

Challenges and future of intelligent IoMT  Intelligent IoMT has the potential to provide the best health-care services  efficiently and accurately. However, challenges in this ensemble also need  to be addressed to make the technology robust and applicable in every sce-  nario. Prominent among these challenges are data security, hardware com-  patibility issues, data congestion, and AI accuracy.  To handle these issues, the future of IoMT is mainly based on the com-  munication technologies used by the domain. The use of the modern 5G  and 6G technologies that mainly target the IoT domain will boost its ap-  plicability in many applications. In addition to that, the blockchain tech-  nologies which have been newly introduced for secure e-health datasets dur-  ing transmissions will ensure data integrity and security. The breach in  blockchain technology is relatively low since there is a considerable cost in  terms of resources and time linked with it, making it practically impossible.  Edge computing can also improve the latency and user-centric service for  IoT devices. With the inclusion of such technology, localized and rapid deci-  sion-mak-ing can be achieved without requiring on-site medical profes-  sionals. Scalability is also a prominent issue in futuristic systems. The cur-  rent hardware and software architectures of intelligent IoMT systems must  have the capability of scaling according to the distributed requirements and  challenges that can arise in the future.  Focusing on the hardware aspects of IoMT, the primary research chal-  lenge in the IoT domain is the power-handling capability of the devices and  power source for the sensors. With the development of technology in the  medical field, the need for portable sensors is increasing. These sensors,  therefore, require continuous power, which battery sources cannot handle.  So, other efficient means of power need to be explored and researched to  optimize the systems.  Considering the sensors involved in IoMT-based systems, the cost of  sensors also needs to be optimized. This will increase their adaptability in  low-income communities and serve as the source of improving health-care  conditions in middle- and low-income societies. Therefore, cost mini-  mization and design optimization can enhance IoMT technology's inclusion  in the new localities. In addition to that, health monitoring based on sensor  technology or wearables requires continuous access. Therefore, the sensors  and hardware design need to be optimized for power efficiency. 

Chapter summary  Summarizing the details, it can be concluded that IoMT and artificial intel-  ligence are associated study areas that have a deep impact on the design  and development of health-care systems that will shape the medical do-  main. Wearable IoMT sensors linked with AI-enabled systems can provide  real-time observation of vital patient parameters. The collection and storage  of this data allow its temporal analysis to predict patient's conditions and  critical decisions on the part of professionals. This also allows the devel-  opment of modern medical techniques such as personalized treatments,  robot assistants, and decision support systems to assist professionals in  their workload and giving patients a comfortable experience. 

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  5: An overview of the Internet of medical things (IoMT): Applications, bene-  fits, and challenges    Ana Carolina Borges Monteiroa; Reinaldo Padilha Françaa; Rangel Arthurb;  a a Yuzo Iano      School of Electrical and Computer Engineering (FEEC),  University of Campinas (UNICAMP), Campinas, SP, Brazil  b Faculty of Technology (FT), University of Campinas (UNICAMP), Limeira,  SP, Brazil 

Abstract  The Internet of Things (IoT) represents the connection between the  physical world and the constant transmission of virtual data, and the  IoMT is exclusively dedicated to the health sector, relating to medical  devices. IoMT consisting of the main characteristics is the exchange of  information, aiming to improve the management of a given healthcare  system that the device does part, used to achieve these goals going far  beyond a data connection, such as mobile devices equipped with smart  applications, medical equipment for home use, care applications,  emergency care kits, i.e., is the perfect integration between indis-  pensable information from the patient's reality and the availability of  data for consultation with doctors. The IoMT also provides person-  alized service, with data-driven treatment through the use of devices  adapted to patients, since it is possible to point out consistent data  about the patient, impairing a more assertive service, based on past  data. Through IoMT technology, especially the way data is collected,  stored, and processed, medicine can benefit from greater agility in the  verification of patients’ hospital clinical conditions and even early  preparation for possible health crises of patients and potential failures  of medical equipment. In summary, the IoMT accelerates advances in  data science, enabling health institutions to benefit from faster treat-  ment, reduction of medical errors, and new therapies. This chapter  aims to provide a panorama of the application and potential of the  Internet of Medical Things, showing the importance and relevance of  this disruptive technology, demonstrating a landscape view as key con-  cerns, advantages, and challenges, with a concise bibliography.   

Keywords  Analytics; Big data; Machine learning; IoMT; IoT; Artificial intelligence;  Smart medical sensors; Privacy; Security; IoMT devices; Medical data 

Introduction  Technology has become indispensable for society in many areas, partic-  ularly when relating to quality of life, as shown by the great contribution of  technology in health. Numerous advances have been made with techno-  logical support such as efficient and safe operating systems, modern equip-  ment allowing greater efficacy in treatments, adoption of electronic medical  records, advances in engineering, and biomedical technology aiming at  faster and more effective diagnoses. In addition to advances, discoveries,  cures, new drugs, and research in different fields of medicine, there is diag-  nostic equipment and management and communication software that is  accessible through mobile devices. Technology provides greater agility in  care, practicality, reliability, and credibility [1].  Health services are a controversial and expensive issue with regard to  public health care in several countries, with rare exceptions of excellence. In  this sense, the Internet of Medical Things (IoMT) can help transform the  service of medicine, being key to reducing medical costs, improving the  quality of services, and making medical care accessible and personalized for  most people, including low-income patients and those distant from the  main centers of medical excellence [2,3].  The IoMT is intended to lead to a future where everything in medicine will  be connected, reducing bureaucracy in hundreds of procedures, making var-  ious medical activities much more agile, and making life easier for all med-  ical staff and patients. The IoMT includes all devices used in the field of  medicine that are connected to the Internet and that can communicate by  sending or receiving messages [2].  Innovation is a vital factor in the healthcare industry; the challenge is to detect diseases as soon as possible and start treatment for cheaper cures, in  contrast to expensive treatments when a disease has reached an advanced  stage. Hospitals are increasingly adopting sensor-based technologies and  smart chips, RFID tags, and real-time location systems (RTLS) to orches-  trate better the flow of patients, doctors, nurses, medical equipment, and  supplies, all of which are aggregated within the broad scope of the IoMT [4].  This IoMT technology used in the daily life of medicine offers numerous  benefits, making doctors’ routines faster and more accurate, providing the  care patients need and full access to their health data. This is achieved via  automatic cloud storage, autonomous recording of information, ease of 

data sharing, more complete medical histories, strengthening of preventive  and self-care actions, patient empowerment, continuous monitoring, and  greater access to health information [5].  Through the IoMT, personalized medicine will be allowed with a new  health model that will benefit drug manufacturers, hospitals, doctors, and  especially patients. It will usher in a new era of medicine, where patients will  have more control over their health to improve their quality of life. The tech-  nological evolution of fast and affordable care services, or trends such as  medical services with the support of nursing professionals and doctors on-  line using telehealth technologies in the cloud, or exams and diagnoses  made in a few minutes, will also be possible, enabling medical services for  real-time monitoring of the health of patients with chronic diseases [6].  Like any new technology, the IoMT also needs to overcome challenges re-  lated to ensuring digital security and data confidentiality between doctor and  patient. Another problem regarding the democratization of the IoMT is the  high costs involved in the integration of all the sensors, devices, equipment,  and digital systems of health institutions, in both public and private net-  works. There are also classic problems of technological innovation such as  resistance to change by old professionals, corporatism of the medical class,  and delay in the regulation of new services [7].  The main technologies associated with the IoMT include making intense  use of artificial intelligence (AI) to analyze health data received from con-  nected devices that integrate medical equipment for the extraction and anal-  ysis of data and information collected through medical records, image  exams, and wearables, among others considering the main vitamin of the  IoMT and its main application in medicine, big data. Consisting of the  important actors of the new IoMT revolution in precision medicine, en-  abling more assertive and preventive medicine, supported by the associ-  ation of AI, big data, and cloud computing [8].  Health AI basically consists of automated statistics from a base and vari-  ables, consisting of computer systems capable of making decisions based  on standards analyzed in a large amount of data, instead of a predefined  fixed logic, such as those used in conventional systems. This is attainable  by collecting IoMT data from common medical devices, and hospital equip-  ment that is connected to the Internet, to feed the database for AI and to  learn intelligent technology, expanding the capacity and insight into 

symptoms and trends of medical clinics. It is thus possible to understand a  scenario, model and predict demand, through an ideal cut, from the regis-  tration and tracking of data to monitoring the patient's health status regu-  larly [9].  An AI-oriented system is able to “learn” and increase its accuracy over  time, contributing to more assertive decision-making, diagnostic processes,  identification of predispositions to diseases, treatment prescriptions, and  systems operation. This optimizes the service for the most vulnerable pa-  tients, starting with the capture of data from multiparameter IoMT monitors,  devices, and sensors, such as heart rate, respiratory rate, blood pressure,  and patient temperature. Using this information, AI tools can classify risk to  patients [10].  Therefore, this chapter aims to provide a panorama of the application and  potential of the IoMT, showing the importance and relevance of this disrup-  tive technology, demonstrating a landscape view as key concerns, advan-  tages, and challenges, with a concise bibliography. 

IoMT concepts  The Internet of Things (IoT) allows all objects to be connected to each other  via the Internet, representing the connection between the physical world and  the constant transmission of virtual data. When it comes to medical devices  (smartphones, tablets, computers, IoMT sensors, and connected moni-  toring equipment), this is called the Internet of Medical Things (IoMT). The  IoMT is exclusively dedicated to the health sector, seeking to develop de-  vices connected to the Internet. The main characteristic is the exchange of  information, and with communication power, aiming to improve the man-  agement of a given healthcare system that the device does part. In this way a  personalized service can be provided, with data-driven treatment through  the use of devices adapted to patients, for sending and receiving messages,  also increasing their performance through external data [11].  IoMT technologies that are used to achieve these goals can take many  forms, going far beyond a data connection, such as mobile devices  equipped with smart applications, medical equipment for home use, care  applications, and emergency care kits. The IoMT is the perfect combination  of indispensable information from the patient's experience and the availability of data for consultation with doctors. Through IoMT technology,  medicine can benefit from greater agility in the verification of patients’ hos-  pital clinical conditions and even early preparation for possible health crises  of patients and potential failures of medical equipment [12].  Through the IoMT, it is possible to monitor a patient in real-time by ob-  taining and collecting vital patient data from the bedside continuously in a  centralized and updated way. The patient's use of wearable and implantable  medical devices (e.g., bracelets, glasses, watches, and other devices that  connect to the Internet) allows a doctor to observe the patient's health sta-  tus, which monitors a series of items, such as heartbeat (having conditions  and properties to detect irregularities in the heartbeat), movement, and  number of steps, since through monitoring intervention at home since it  has already been properly guided by your doctor, a patient can anticipate the  elective appointment schedule or instead of going to the emergency [13].  In addition, through the IoMT it is possible to monitor medication, as the  technology allows the possibility of controlling temperature and humidity  among other variables (related to the wrong dispensing of medicines or  their poor packaging) of these drugs in hospital pharmacies, as well as the 

bedside check before a drug is administered to the patient. The IoMT can  enable forward planning in the maintenance, operation, and monitoring of  medical equipment based on the information collected through the IoMT  sensors, making it possible to anticipate equipment breakdowns, conse-  quently increasing the availability of the installed medical equipment. These  devices can also be operated remotely by IoMT technology (remote con-  trol), reducing the need for on-site personnel [14,15].  One of the main application examples of the IoMT, used by doctors’ of-  fices and clinics, is RFID tags, which allow monitoring and management of  medical assets and equipment through radio frequency within a healthcare  environment; hospitals also use smart beds, which provide readings of sev-  eral vital indicators of the patient, informing a central digital system about  their medical condition, and thus providing better care for patients.  The benefits include consistent data, as a common problem in health is  lack of information about a patient on the part of health professionals, lead-  ing to little assistance, and thus not allowing a lot of data to be collected,  preventing the service from being as helpful as it could be. With regard to  remote access, another possible benefit is the release of hospitals and  healthcare clinics allowing patients to be monitored, which in addition to  ensuring greater comfort and tranquility for the patient, also saves all these  patients from having to be cared for in a clinic or hospital environment.  Thus, the union of consistent and high-volume data through the use of  IoMT devices implanted in the patient and even the different types of IoMT  sensors installed and spread around the healthcare environment guarantees  an analysis and personalized service for each case, and may help in later  treatment or even to avoid any complications for the patient altogether. It al-  lows for constant feedback about the patient's reactions to the treatment ap-  plied and enables the health professional to check in real-time the patient's  behavior and clinical status according to the treatment given, deciding be-  tween continuing or changing a medical approach [16].  Uniting virtual reality with the IoMT, it is possible to obtain devices that  can be used in the simulation of paths to identify a patient's spatial disori-  entation, which may indicate mild cognitive impairment, an indication of  Alzheimer's disease. A swifter diagnosis and confirmation of this type of  disease increases the chances of delaying its evolution and carrying out an  appropriate treatment [17]. 

It is also possible to monitor glucose levels in patients using an IoMT de-  vice that helps to control blood glucose levels continuously, taking readings  at regular intervals. In addition, it is possible to use IoMT devices as smart  cardiac pacemakers, monitoring the patient's health conditions from a dis-  tance. Some IoMT devices are capable of assessing whether professionals’  hands are dirty or contaminated with any disease, through sensors that de-  tect the lack of proper hand hygiene in public hospitals; this prevents dis-  ease from being transmitted to vulnerable people [18,19].  Through resources such as cloud computing and artificial intelligence,  the IoMT in medicine can provide technologies such as telemedicine, since  while in a country it contains certain more developed regions are able to  make the most of telemedicine, other more distant places, and low pur-  chasing power do not have connectivity or any other technical feasibility to  implement remote service [20,21].  However, with the help of IoMT technology, a doctor can speak to a pa-  tient more frequently, since teleconsultation is made very convenient  through smartphones, aiming at remote assistance, for example, helping to  circumvent many situations in remote assistance that only would be pos-  sible in a physical examination in person. IoMT devices can measure, for  example, blood pressure, pulse, and heart rate, and even calculate the  body's oxygenation curve (via an oximeter, which measures the volume of  oxygen in the blood), and smart pacemakers that can be evaluated remotely  and routinely. In the follow-up of patients with intermittent cardiac arrhyth-  mias, patients can pass information in a teleconsultation to the doctor, seen  telemedicine to the cardiology specialist [22]. 

Technological challenges of the IoMT  Technological challenges of the IoMT in health can be related to the ideal  digital infrastructure to collect and interpret large volumes of data, as it is  necessary for health institutions to have an IT infrastructure capable of pro-  cessing the data. This includes data centers that follow a cloud trend, high-  performance databases, and big data, which requires huge storage. The  trend of migration from processing to solutions and services for cloud com-  puting capable of processing big data in health care offers more ease in  computing demand and less investment of time, money, and physical infra-  structure compared to traditional storage, and is mostly digitally safer [23].  Digital security and privacy of information remain a challenge since the  sensitive medical digital health data that are generated are still unprotected,  which is a concern among health institutions, which must guarantee data  protection through firewalls, access controls, and encouraging a culture of  digital security. The exchange of online information between health systems  and solutions also needs end-to-end encryption, in addition to anonymizing  data before making it public—that is, open for filtering professionals and  specialists—as well as removing information that can identify patients, but  leaving clinical information and clinical data [11,24–27].  Health institutions have a complete database of hundreds or thousands  of patients, which can be used to commit fraud or other cybercrimes. The  available information includes document numbers, income data, hospital  debts, bank and card data, and disease reports, among other types. Since  the contributions of the IoMT in the health field are obvious, it is necessary  to understand the complexities of this intersection from two main perspec-  tives related to the general panorama of the health system in each country  and the possible contributions of the IoMT, considering the regulatory bar-  riers and challenges for the development of this technology in this sector  [28].  This digital concern was more evident in relation to hospital management  due to the pandemic of the new coronavirus (COVID-19), protecting sensi-  tive information from digital threats and preventing cyberattacks. As well as  considering networks and systems that offer a low degree of difficulty to  hack, it has a large amount of patient information, both personal and relat-  ing to health and treatments. Possible scenarios involving cyberattacks on  the IoMT include hackers controlling machines that keep people alive; this 

is extremely unlikely, but not impossible. However, among the possibilities  of incidence of threats, the incidence of basic threats is higher, mainly  through ransomware and phishing [1,20,29,30]. Another problem that IoMT technology in health faces is that most hos-  pital equipment is old and was not designed to be used online; it is thus  more vulnerable and is replaced or repaired only with difficulty. Thus, when  it is added into the network, it becomes an access point through which  hackers can find important data about patients and treatments. Also, hos-  pital networks are not segmented, which may result in hackers being able to  infect one machine and thus obtain easy access to all the others that are on  the same network [31].  The IoMT can help improve the health service by helping to overcome  challenges at a lower cost, with a significant increase in efficiency, which  can have a greater impact on the treatment of chronic diseases, the treat-  ment of infectious diseases, and health promotion and prevention, and can  improve management efficiency. In view of the positive impact in relation to  the existing barriers related to human capital deficiencies, regulatory and  security, and privacy barriers; as well as the connectivity and interoperability  infrastructure [32].  With regard to regulation, since in most countries, it is a bureaucratic bar-  rier, services for outpatient care, whether routine or emergency, are carried  out on an inpatient basis, as well as diagnostic and therapeutic support ser-  vices, which involve the incorporation of new technologies such as the  IoMT. Bearing in mind that IoMT technology is developed by means of de-  vices, however much it may impact on health services, there should be no  incidence of rules that regulate health services on the IoMT, with the excep-  tion of IoMT devices that control functions of a medical device or that trans-  fer information, meeting appropriate regulations for applications in the  health area, considering the dynamics of technological development and the  risks brought by it with respect to digital privacy and digital information  security [33].  Another point of attention is to understand how systems based on arti-  ficial intelligence are being fed, i.e., machine learning, which performs anal-  ysis of X-ray exams, considering images with pneumothorax (leakage of air  from the lung to the rib cage), as this technology can be applied via  telemedicine and through probes implanted to remove air, and identify the 

degree of the disease that has already been diagnosed and is being treated.  If this understanding and knowledge in dataset data analysis is correct, and  consequently fed the machine learning solution correctly, the machine will  most likely learn to identify probes and understand that images like this will  always be of the pneumothorax [34].  In general, the IoMT aims to enable advanced services through the inter-  connection between things (physical and virtual), contributing both to im-  proving people's quality of life and to increasing the efficiency of health  units, based on information and communication technologies (ICT), facing  challenges related to increases in health spending, mainly due to aging pop-  ulations and the consequent decreases in economically active people. Thus,  the use of this technology can contribute to the advancement of several  health sectors, expanding access to quality health care through the creation  of an integrated view of patients, ensuring greater access and efficiency in  the provision of public services to the population, performing decentral-  ization health, and even improving the efficiency of health institutions [35].  In summary, technological development, especially the way that data is  collected, stored, and processed, is accelerating advances in data science in  health institutions through the IoMT and its potential to include other dis-  ruptive technologies in health can enable better administrative and clinical  decision-making as well as allowing patients to benefit from faster treat-  ment, reduction of medical errors, and new therapies. All this makes it pos-  sible to offer more appropriate and personalized care, in addition to identi-  fying potential health problems before they become critical [2]. 

The IoMT and health analytics  The use of new technologies and the development of intelligent health management software are extremely important for health institutions, as they  promote data management with quality and efficiency. Computerization and  the arrival of the IoMT provide the health scenario with transformations with  a real impact on patients and doctors [36].  Initially, the idea of the IoT was the connection of the Internet with phys-  ical objects, especially sensors. In health, its applicability is noted from the  integration of health management systems with diagnostic imaging devices,  for example. If a certain patient undergoes a scan, a doctor in their office will  have an instant view of the result.  Health analytics is the set of methodologies used in order to analyze large  volumes of data (big data) related to the health sector. This data analysis  contributes to the clinical care of the patient and the management of health  resources, with a contribution to reducing waste, improving the quality of  services offered, and enhancing patient health care, in a sustainable manner  [37,38].  In this panorama, artificial intelligence is also present, consisting of digital intelligence through IoMT sensors in different ways of collecting patient  data, either through smartwatches and applications that monitor blood  pressure and heart rate, for example, or through data collected from labo-  ratory results, medical diagnoses, and hospitalization records, in addition to  other categories, being equivalent to complete health history, including pa-  tient names among other specific data [39,40].  The use of the collected data enables new smart technology solutions  supported by advanced artificial intelligence and machine learning, which  focuses on individual patients to suggest changes in their health care and  treatments. The main function of health analytics is the transformation of  data that is apparently disconnected into essential information that guides  decision-making with a consolidated basis.  In the medical area, the implementation of computational intelligence,  whether through AI and its techniques, with health analytics obtains the  benefits of using business intelligence and analytics tools for hospital man-  agement, generating better treatment of patients and an accurate diagnosis  of hospital management, in healthcare institutions of any size enables the  search and interpretation of information stored in the digital system, aimed 

at supporting decisions within the individual's life cycle. Among the data  that are collected and managed are control and evaluation data used for the  control of patients, aiming at greater safety in care, with regard to the pre-  sentation of allergies, intolerances of medications, symptoms, procedures  performed, and medications, among others [41].  Health analytics is basically an innovation in the way of creating value  from the amount of available data, considering the constant evolution of  technology in the contemporary world and expanding the use of information  technology tools, in a new approach in the optimization of clinical big data and hospitals. The health sector works with massive volumes of data and, to  improve the quality of services and people's lives, this information will need  to be collected, either with the computerization of medical records or with  the use of wearable devices, for measurement of vital indicators in real-time.  Thus, the intensive use of health analytics, in addition to improving health  care results, brings more quality of life to the population, and offers insights  to support medical decision-making and use improved resources, such as simulation, optimization, and predictive analysis, creating more compre-  hensive medical records to deliver personalized treatments to patients.  Through a preanalysis of artificial intelligence and health analytics tools, it  is possible to generate trends on patient diagnoses in an automated way, by  the code of the International Classification of Diseases (ICD). When this  technology is applied in public health systems, it is possible to identify dis-  eases that have the highest incidence rate at a regional level and even by  neighborhood, enabling greater control and prevention of epidemics [42].  The personalized care of the IoMT is linked with health analytics in rela-  tion to wearable devices, as it allows monitoring of heartbeat and blood oxy-  genation, adding to the electronic medical record of the patient in the cloud  (cloud computing), and can be accessed by a specialist cardiologist in real-  time. Ingestible IoMT sensors (in pill form) produce a small digital signal in  the stomach, which is transmitted to an app warning the patient and even  the doctor if the treatment and medication are being followed correctly or  not [26,27].  Considering the performance of IoMT technology, going from the emer-  gency room of a hospital to the comfort of home, without having to make an  appointment, the patient will be able to monitor their condition contin-  uously, undergo physical examinations, and reduce risks of more serious 

occurrences by taking preventive measures as soon as the patient receives a  diagnosis. This will reduce health expenses and unnecessary visits to the  doctor, and a health system, whether public or private, will also benefit from  being able to identify and prioritize the most serious cases, thus reducing  the cost of treating chronic diseases [43].  Over the years that the IoMT and health analytics are employed on a large  scale in health institutions, millions of patients monitored by connected  IoMT equipment, sensors, and devices will generate and continuously sup-  ply databases with data on the health of the population. This will allow, for  example, a vaccination campaign to be anticipated by predicting the immi-  nence of an epidemic or assessing which medical therapies are bringing bet-  ter results for a particular type of disease.  This will be possible through data mining from different sources (IoMT)  and supported by intelligent algorithms (AI), representing a robust health-  care system (health analytics) to identify pathologies, receive recommen-  dations to define the most appropriate treatments, and accelerate the devel-  opment of new drugs from tests on monitored patient groups.  Thus, the IoMT enables a more assertive, preventive, and therefore less  expensive medicine, which is fundamental considering the perspective of  the aging world population since this technology will make it possible to  monitor patients, particularly elderly ones, at home, remotely. If the IoMT is  associated with medical guidance services (health analytics), doctors will be  connected by making consultations via video conference, meaning that  there will be less impact on the flow of people in hospital outpatient clinics.  Due to the exponential growth of the IoMT in health care, doctors are un-  likely to prescribe any treatment or medication without first consulting the  electronic medical record (cloud computing), where they are able to com-  pare it with thousands of similar cases. 

Discussion  It is important to highlight the relationship between the IoMT and data sci-  ence, considering that together they assist in the discovery of useful infor-  mation from large or complex databases, as well as data-driven decision-  making. This can be defined as a set of strategies and tools (data science)  as well as techniques for data collection (IoMT), enabling the transfor-  mation and analysis of this data into substantive knowledge of the problem  under analysis, in the focus of this study, health through statistical, mathe-  matical analysis and computational processing (data-driven analysis).  Modern technology allows, through advanced methods of analysis with  sophisticated intelligent algorithms (AI), the processing of large volumes of  data (big data) in different formats (structured, semistructured, and un-  structured data), involving collection, extraction, transformation, prepro-  cessing, selection of records, reduction of dimensionality, normalization,  creation of subsets of data, exploratory analysis, and data mining. Mainly fo-  cused on analyzes aimed at classification, association, grouping, anomaly  detection, and prediction, and postprocessing oriented toward pattern inter-  pretation, filtering, visualization, and use of these insights in medical deci-  sion support systems.  In addition to large volumes, there are other important characteristics in  the composition of the concept. One of its main characteristics is the variety  of data to be processed; this data can be structured, semistructured, or un-  structured. Other factors that characterize big data are innovation in the  adoption of distributed processing, the speed necessary for the processing  of the large and diverse databases stored (through the collection by IoMT  devices), and the possibility of processing in real-time.  Within the scope of the health sector, the possibilities of adopting tools  and strategies oriented to both AI and big data are numerous for analysis,  monitoring, prediction of events (cases), and health and disease situations  in the population, as well as their association with their social determinants.  The health sector produces a huge amount of data about people who access  health systems in a country, and this is only possible considering that the  IoMT favors, through sensors and devices, the digital and technological  means of collection, storage, and management, and through integration  with other technologies and external databases and real-time processing,  provides analysis and visualization of health data. 

It is important to discuss data security in health, especially in view of the  many new technologies that are emerging. Data is extremely important in  any sector of society, and even more in the health sector, which is one of the  most vulnerable to risks and threats. However, the vulnerability is often not  in the digital system but in the data security protocols. In this context, it is  worth highlighting the disadvantages of the IoMT, given the collection and  management of a large amount of important information regarding the care  provided to each patient.  Medical data, such as records of consultations, procedures, and exams,  are confidential. In addition, the data also need to be completely reliable to  ensure quality and accuracy of care, considering its use in surgical planning  or treatments more assertive. Medical data are essential to contextualize the  problem, investigate causes and take action in the case of each patient who  is attended by some specialty or even in an eventuality, such as in emer-  gency rooms, stating how essential are for even life of the patient is not put  at risk.  Information collected can be clinical, relating to history of health and con-  sultations, which is all strictly confidential, or personal, consisting of data  relating to patient identification, location, copies of personal identification  documents, and so on. Both clinical and personal information can comprise  a medical record that, after being united, becomes protected by consensus.  It is also worth mentioning the value of a patient's clinical record into  which, through descriptive, predictive, and prescriptive analyses, health ana-  lytics models are increasingly incorporating machine learning and artificial  intelligence platforms. Among other features, these intelligent approaches  satisfy a legitimate desire in the healthcare chain for a range of tools that au-  tomatically extract structured information from large quantities of unstruc-  tured data. This set of technological solutions and procedures (which can  be used and employed as a “backbone” of big data operations) can process,  extract, integrate, store, track, index, and report insights from unstructured  raw data, reinventing and revitalizing the use, for example, of the patient's  medical record (paper or electronic), which can be exploited endlessly by  data analytics environments.  Or even considering that a health institution like a hospital, implementing  IoMT technology in beds, and even smart sensors integrating digitalized  medical records in a semistructured way, was a mass of data, which can 

now be a valuable asset, especially with the arrival of machine learning en-  gines to revitalize unstructured data. Health analytics is not technology but  science, and artificial intelligence (AI) is currently the most powerful instru-  ment to “liquidate data” and transform it into medical clinical insight and  cognition.  Analytics technology is the first step toward health institutions achieving  excellence in medical services, through health analytics and AI mechanisms  (big data, data analytics, deep learning) allowing services to be far more effi-  cient than those that conventionally for the liturgy of the medical record,  representing real opportunities to track, illustrate, inform, and produce  health value with health analytics.  Clinical data collected by IoMT technology is usable analytically, allowing  through artificial intelligence algorithms blocks of unstructured data to be  identified, immersed in millions of clinical records, text, and images, with-  out any hermeneutic rule, since this is beyond the capacity of a human  brain. With machine learning algorithms, it is possible to recognize, identify,  and provide the corresponding correspondence between the patterns found  in data volume processing. Natural language processing (NLP) technology  is used to support health organizations in the mining of medical records,  automating the process of reviewing unstructured data, separating and ex-  tracting what is relevant, while still coding the result obtained with stan-  dardized and interoperable terminologies. It is possible to employ intelligent  solutions oriented with recognition standards, which use machine learning,  cognitive technology, and optical character recognition (OCR) to analyze,  enable, and decode these unstructured data, allowing manuscripts and texts  to be mapped and separated into semistructured data.  Unlike physical assets, data is not tangible; it can be shared without de-  priving its holders of the original benefits. Above all, it can be reused thou-  sands of times for different uses. The same artificial intelligence that bene-  fits from structured data supports data structuring processes, allowing the  creation of machine learning models, providing intelligent automation, and  through all medical data collected by IoMT technology in order “to reveal  hidden information” in clinical data, accelerating analytics by quickly identi-  fying patients with COVID-19, and exploring numerous features in predictive  research. Health systems currently generate huge silos of pandemic data  that can be used in clinical, pharmaceutical, or epidemiological research. 

Trends  Artificial intelligence (AI) through billions of equipment, IoMT devices, and  connected sensor data, produces an ever-increasing amount of information  that will strengthen the IoMT and the ability to create value from this infor-  mation base. Through machine intelligence techniques (machine learning),  AI will change interactions between doctors and patients, developing intel-  ligence in mechanisms such as software and robots similar to human intel-  ligence, bringing numerous benefits in higher productivity formats, improv-  ing the performance of professionals, and analyzing thousands of items in just a few seconds, helping to solve clinical cases [44].  The move from centralized and cloud computing architectures to edge  computing structures for the IoMT, as the set of layers associated with edge  computing architecture, will evolve into an even more unstructured format,  consisting of more flexible, intelligent, and responsive IoMT systems, al-  though often at the expense of additional complexities, consisting of a huge  variety of devices and services connected in a dynamic mesh [4,18].  AI is applied to combat growing digital attacks in the digital age, espe-  cially with the increase of the IoMT, through automation via AI to under-  stand and correct the digital security gaps of what types of processes are  needed to stay ahead of cybercriminals in the threat landscape. The identification of cyberattacks on IoMT devices as a gateway is an important gap  that is currently not being addressed in the general digital security strategy  of health institutions.  Machine learning and other technologies based on artificial intelligence  are essential to detect and stop digital attacks targeting patient IoMT devices  and to protect data and other high-value assets. Digital security with func-  tionality is aimed at reducing false alerts, increasing cybersecurity effec-  tiveness, providing more efficient digital investigation, and increasing the  capabilities to discover and respond more quickly to cyberattacks that have  not been identified by the defense systems of the perimeter of the IoMT net-  work. These tools are able to monitor network traffic continuously. AI-  oriented systems are used for detection and response of closed-loop and  the detection of anomalies in behavior between IoMT devices, as the most  effective approaches for better protection of healthcare environments.  5G technology (fifth generation of mobile connection) will be funda-  mental for the revolution of remote medical assistance by telemedicine with 

the IoMT, as a lack of latency will allow a remote service with all aspects of  face-to-face service, and will enable support of applications of the IoMT.  Greater benefits associated with speed, latency, and capacity will be applied  to IoT devices that will be operating on 5G technology. This technology will  allow increasing connection speeds, enabling the necessary structure for the  IoMT to gain strength and become more present in everyday healthcare.  With 5G it will be possible to obtain a greater performance gain from the  IoMT, enabling more efficient data management. Greater data storage and  download speeds will allow diagnostics to be much faster and more effi-  cient. Faster and wireless processing will make it possible to create more  patterns via AI, facilitating and accelerating the process until a diagnosis is  made. The use of medical IoMT devices will be even more functional with  5G, extending their use to an industrial scale, allowing much smarter de-  vices that will be able to monitor more precise and specific indicators, being  able to make the application or extraction immediately. This will make  medicine much more preventive in nature, no longer focusing on the dis-  ease and having as the main objective the maintenance of the patient's well-  being. 

Conclusions  The IoMT applied in medicine is the integration of medical devices into a  digital communication network that makes it possible to obtain data on the  patient's health, and the exchange and collection of information regarding  patients and diseases, allowing connected devices to interact with each  other, including mobile apps that can be connected to other electronic de-  vices (e.g., wearables such as smartwatches). The IoMT aims to promote  more efficient service and consultations, without the need for human inter-  vention and assisting in such procedures, in order to avoid waste due to waiting and excessive manual processing of information.  The IoMT assists in the prevention of health problems, as some devices  collect data about the human body in real-time, identifying critical and alert  states for the patient. As well as faster diagnostics taking into account that  the data collected about the patient are stored in the cloud, it becomes pos-  sible to establish more efficient treatment by detecting more quickly the  types of diseases that are affecting patients. It also allows for better interac-  tivity with technologies that enable the transmission of videos and audio in  real-time, eliminating the problem of distance and allowing a doctor to par-  ticipate in a meeting or consultation from any location (telemedicine)  [31,45].  The IoMT has great potential to contribute to a more accessible and effi-  cient health system, helping to improve these services to the population.  However, despite innovations in the health sector, the challenges of privacy  of patient clinical data, connectivity in remote areas, and availability of re-  sources for assessing the cost-effectiveness of technologies, among others,  must be overcome. In addition, in view of the impact of the IoMT for health,  the potential for remote monitoring of patients’ conditions is clear: contin-  uous monitoring of the patient, facilitating care and assistance; location of  assets in health units; support for the diagnosis of sepsis; the possibility of  decentralized diagnosis; and the identification and control of epidemics  [15,32,38,41].  The general lack of data protection impacts the IoMT, as privacy risks  arise from the low security of IoMT devices. It is necessary to build an envi-  ronment of digital trust and an appropriate institutional environment for  information security. Especially in the health sector, where the collected data  is sensitive information for the patient, the protection of this data is critical 

for the development of the IoMT in relation to the collection, processing,  storage, sharing, and access to personal data in the health field [13,46].  It is normal for environments in the health sector to be computerized, a  trend that facilitates the routine of the area, considering hospitals, private  offices, clinics, or diagnostic centers. All processes involving patients gen-  erate information that is recorded, with storage and management of the col-  lected data being aided by technology. However, to prevent security breach-  es of digital hospital systems, it is necessary to implement health manage-  ment solutions, whether through basic measures such as restricting access  to confidential data with authorized logins and passwords, or through solu-  tions with high-impact cryptographic tools that protect data during traffic  and storage [47]. 

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  6: Trust management in the Internet of medical things    Rida Nawaz; Laiba Rehman; Muhammad Imran Tariq    Department of  Computer Science and Information Technology, Superior University, Lahore,  Pakistan 

Abstract  In recent years, as a new technology based on the extensive connection  of objects in heterogeneous networks, the Internet of Things (IoT)  paradigm has been introduced and has attracted people's attention. We  propose a robust management model of the IoMT network based on  social media concepts and priority standards. The rapid development  of Internet of things (IoT) technology in the field of health care has  brought a variety of security threats and risks. With the increasing use  of sensor objects in the medical field, providing comprehensive protec-  tion has become a major challenge, which has led to the emergence of  the Internet of things. The definition of Internet of Medical Things is  that medical devices are connected with each other and IT infra-  structure of the organization through Internet. It is a serious problem  and said the problem is increasing day by day due to more security, pri-  vacy, and confidentiality issues. In addition, it uses the capabilities of  available equipment to improve the health of patients. IoMT also offers  many opportunities to innovate health care and provide multiple op-  tions for precision surgery. OSN can benefit many sensitive appli-  cations, such as e-health and medical services. The emerging field of  the Internet of medical things (IoMT) is promoting trust between var-  ious IoMT devices to ensure accurate and reliable communication, which is very important for major diseases such as COVID-19. To cope  with this situation, a trust management mechanism called a fuzzy trust  management mechanism is proposed to prevent Sybil attacks on the  Internet of medical things (FTM-IoMT). The IoMT security can be enhanced through multifactor verification based on fuzzy logic and fuzzy  filtering processing. Compared with more advanced methods, the pro-  posed scheme shows better results. The main goal is to study the dy-  namic environment in the medical field and to realize adaptive access  control. The security of the IoMT system is gradually improving. It re-  duces communication delays, proactively manages security risks, and  ensure data protection for patients and doctors in health care.   

Keywords  Health care; Internet of things (IoT); Framework; Bayesian network; Neu-  roTrust; Sybil attacks 

Introduction  The IoT concept was introduced by Ashton in 1999. IoT is defined as a dy-  namic global network infrastructure with capabilities of self-configuring, at-  tributes, and personalities, integrated as part of the same network. IoT tech-  nology enhances patient freedom and resilience, improves the quality of life,  prevents the occurrence of domestic accidents, monitors daily patients, and  helps the environment with the aim of promoting traditional health to enter  the field of health and wellness. It is a system for use by public or private  healthcare providers. Supporting environmental life and consideration for  human health is a particular priority area for relationships with the elderly  and the frail, and for physical development in the real world. Use resources  such as technology and IoT to identify patient health, patient behavior, and  environmental knowledge set problem rules, and study the relationship be-  tween patient health and behavior. For certain biological conditions, the  Internet of Medical Things (IoMT) is used to provide a structured genetic  characterization and trust management protocol. The Department of  Physics supports the calculation of reliability metrics based on the capabil-  ities of objects in the IoMT network [1]. By continuing to use IoT technology  in the healthcare sector, IoMT has been defined as IoT integration in the  healthcare industry, meaning a group of medical devices and applications.  IoT offers different solutions in real time by integrating high signal measure-  ment devices into medical devices. The Internet of Medical Things (IoMT)  is a component of medical devices and accessories, and this architecture  provides a medical computer system over an integrated network [2]. By con-  necting patients with physicians who do not have access to medical infor-  mation over a secure network, clinical commitment and unnecessary burden  on the medical system can be reduced. According to Frost & Sullivan's re-  search, the global IoMT market in 2016 reached $ 20 billion as shown in  Fig. 6.1. The IoMT market consists of smart devices such as mobile phones  and health/medical monitors, which can be used in indoor, hospital, and  hospital environments. Nearly 60% of the world's healthcare companies  have designed and implemented IoT technology; another 28% is expected to  fully implement their own IoT solutions in 2020.  

Fig. 6.1 The ventures increase rate for IoT in USB billion for clinical  frameworks from 2015 till 2020.    The technology in the IoMT system is divided into two categories:    1.Local Patient Systems  2.The best health devices (iCloud user solutions, data systems, net-  work devices, and data management).    Fig. 6.2 shows that companies such as (software developers and semi-  conductors) are generally considered to be collaborators of IoMT landscape  systems technology. IoMT devices are integrated into the healthcare system.  IoMT technology offers many services such as improved health services,  disease management, data analysis, patient satisfaction, and low paying ser-  vices based on economic research. However, the diversity of IoT networks  has been demonstrated in a wide range of health and risk areas. Medical  system security has been accepted for various reasons:    1.Medical technology often exchanges important information about  a patient's illness. Incompatibility and complex issues arise due to  the large number of smartphones connected to the Internet. 2.Privacy and security risks for people facing the system in a med-  ical environment.   

Fig. 6.2 Measurement of medical care licenses in clinical consideration  innovation from 1989 to 2014.    IoMT is another inflationary environment that allows you to be in and out  of the network at any time. Therefore, maintaining a multinode performance  that helps mitigate storage challenges requires a secure, integrated ap-  proach to cloud/fog management. Delivery in IoMT between source and  destination consists of very important data that need to be delivered on  time, and the telephone network is a good solution to this problem. With  the NeuroTrust system introduced, data is transmitted only if the receiver is  secure and the source node can be detected using reliable parameters. The  form of trust work performed on the part of the patient, that is, the calcu-  lation, depends on the time, meaning the approval calculation is carried out  after the allotted time. The most reliable features used by NeuroTrust are the  reliable, convenient, and cost-effective delivery of the package. Safety param-  eters are used to evaluate reliability and accuracy [3]. The Internet of Medical  Things (IOMT) is a set of networks and health devices that contain the orig-  inal connection to the Internet between doctors and patients. The medical  service manager is used to improve the performance of this device and  store and distribute the amount of information that the e-Health system can  improve. Many weak domains can launch IoMT. For example, numerous  IoTs utilize this sort of correspondence, bringing about assaults focused on  the remote networks; yet, these perspectives are fundamental necessities in  any e-Health framework [4]. The fundamental driver of this issue is the way  that IoMT comprises a blend of little gadgets that bring new sorts of as-  saults. Existing trust systems have a subset of these characteristics. How-  ever, it is the combination of all these features and the customizable expres-  siveness of Cassandra that makes it unique and powerful enough to express  the guidelines of this case study, patient health information. This is not only 

the confidentiality of sensitive personal information but also the success of  the entire project [5]. Layers are the health system and cloud level. Network  color serves to connect to IoMT devices and is the first prelude to Sybil's at-  tacks on electronic health systems. Many threats can result in the following:  Sybil attacks such as forged data sent to electronic health systems using  stolen IoMT. In IoT and IoMT systems, a secure management system is di-  vided into two parts: a distributed and a coordinated security system. At the  user/client level, a distributed security management system is available for  all devices [6]. The proposed system overcomes the limitations of a security  management system providing a central security center that reduces the  computing power of each node compared to its distribution. The main  objectives of FTM-IoMT are described as follows:    (1)A well-designed system to manage networks between Internet  service providers and strengthen IoMT networks.  (2)Predict the enemy with the Sybil node using the right algorithm  and the right decision.  (3)Use with caution to maximize the computational power and relia-  bility of impact management.  (4)Provide evaluation tools to make the server smarter.  (5)Reduce the above calculations to the user level.

Literature review  Social networks and Internet of things  The IoMT network proposes ways to consider the suitability of the biolog-  ical function of a material in the priority of the IoMT as Trust Act to build  integrations/relationships [1]. The Internet of Things (IoMT) is a multilink  system, where each node consists of a set of connected IoT devices, clinical  systems, and signal measurement devices that are kept clean in the field.  Digital transformation in the healthcare industry has enabled continuous  communication of health information and improved the efficiency of delivering unique messages. Pharmacies typically collect, apply, and donate  important data about a patient's condition.  About the decision making process based on this information. With IoMT  devices to access medical networks and get in full contact without health  data, this is happening in countries in Central and North Africa. In this re-  gard, it is important to highlight the current state of education and the latest  research on IoT systems in the medical environment. The medical depart-  ment welcomes the general public. These documents introduce valuable  security measures to cover existing problems and provide safe access to  medical devices. The security and confidentiality of the IoMT are important  because the data transmission between the patient and the hospital con-  tains important data. Today's systems require specialized tools not only to  meet data integrity challenges but also to maintain reliability. A secure do-  main management strategy using a live application system is being developed.  Introducing the party system. To provide security for the IoT cloud, a  cloud-based control system that uses computers is being developed. It col-  lects data using a variety of tools to study and understand function for the  most accurate diagnosis and effective treatment of disease. Because it col-  lects data from multiple sources, it requires security between end-to-end  communication devices. Frameworks use unwavering quality to guarantee  security. In a protected framework, clients can collaborate with IoT gadgets  by means of nearby/worldwide organizations. They will be evaluated. The  control system is designed to use Bayesian network to solve the problem of  attack on the communication network (software-defined network, SDN).  This study revealed that safety and health issues are very important and  need to be addressed. 

To eliminate most of the crazy attacks machines rely on Bayesian inter-  ference to detect microbes. The SDN building consists of SDN controllers,  switches, openers, and various medical devices and customers. Two types  of identities are transferred: individual identities and multiple identities. The  developed system uses Baye's rules to complete security between IoT  nodes. It is a serious problem and said the problem is increasing day by day  due to more security, privacy, and confidentiality issues. To prevent Sybil at-  tacks, an enhanced IoT device system was developed using the strategy [7].  One of the main limitations of all these recommendations is that they treat  security and trust in a systematic way, and do not support modeling and analysis of trust and trust relationships at the organizational level.   

TRM-IoT  TRM-IoT guarantees cooperation among hubs and furthermore recognize  the bargained or interloper hubs in IoT/CPS. TRM-IoT gives the treatment of  trust establishment and quantitative assessment among contraptions in the  IoT/CPS network by separating the acts of devices. To endorse the reason-  ability and generosity of contraptions, the TRM-IoT model is evaluated  using trust estimations, as overall trust. Trust management (TM) plays an  important role in the Internet of things to improve the security and privacy  of user information, data mining, and data fusion processes, and data ser-  vices that meet smart requirements, and reputation is a measure to evaluate  the security level. Trust is placed in the derived entity. Experience or knowl-  edge (directly or indirectly obtained) from previous interactions on the sub-  ject [8]. Trust management is a relatively new field of research, but several  systems with different design characteristics have been proposed. Here are  some terms used to describe the field of trust management design [9]. The  trust registry stores information about trust relationships and their asso-  ciated trust values. For certain actions, a trust relationship is always estab-  lished between the two parties. That is, one party trusts the other party to  perform the action. A symbol is used here to indicate a trust relationship.  For each trust relationship, the value T (subject: subject, operation) called  the trusted value describes the trust level [10].   

Fuzzy cloud  A fuzzy-based system has been created that will maintain trust among users  in the cloud system. The introduced system allows users to select a trusted 

cloud specialist provider (CSP) from other accessible CSPs [11–28]. Each  CSP is evaluated utilizing four endorsement standards to acquire a solid last  score. These believed designs incorporate CSP accessibility, security, ex-  penses, and usage. The proposed system provides cloud users with a level  of trust to get the best service from the CSP [11–28].   

IoT in health care  The researchers compared previous studies on the use of IoT in healthcare  systems and concluded that there are many benefits to having a service-  oriented architecture, such as wearable devices for intelligent health care,  efficient use of limited resources; cloud-based storage and transmission of  medical data and images; wireless health monitoring; ubiquitous electronic  health care; and systems that could accommodate the infirm and the elderly.  Since then, more emphasis has been placed on the use of a service-oriented  architecture in the IoT [29]. New technologies such as big data, cloud com-  puting, blockchain, and health perception are revolutionizing the operation  and delivery of the healthcare industry, and many big data analysis functions  in the healthcare industry are listed, especially when used for decision sup-  port. Maintain the analytical ability, predictive ability, ability to analyze un-  structured data, and traceability of the maintenance model are the key fea-  tures of the big data that can be used in IoMT. After all, the IoMT system is  a complex ecosystem composed of different components and systems  (such as medical devices, smart devices, hubs/gateways, cloud services, databases, big data, and clinical information systems), which influence each  other to improve health maintenance [30]. There is a great need to integrate  medical equipment with protection mechanisms. Motivated with the aid of  using this observation, this paper is about the fundaments of the Internet of  the Medical Things and especially the security measures to protect the med-  ical devices from attacks [31]. In order to reduce costs and improve manage-  ment, medical equipment is increasingly connected to the Internet. The  medical network must ensure the uninterrupted operation of all network de-  vices so that the security solutions used must be able to dynamically detect  malicious devices and reduce the incidence of false alarms [32]. However,  analysis of the healthcare operating system shows that most medical data  (good or bad) are still only available on paper. As a result, cryptographic  mechanisms are largely irrelevant, and physical locks are very useful in pre-  venting unreliable access to confidential information medical data [33]. 

Sources of health system  A recent report by Pew found that when looking for health information or  seeking medical or health solutions, 86% of adults go to see a health professional, 68% go to see friends or family, 57% use the Internet, and 54% of  people use books to solve problems. When the National Cancer Institute  asked respondents where they go for cancer treatment first, the Internet was  the best option. Although trust in doctors has remained stable, trust in the  Internet has declined, over time. Internet use has become the first port of  call for respondents seeking information. When looking for medical infor-  mation, it is important to trust the source of the information. A study com-  pared trust in individual doctors, health insurance companies, and the med-  ical industry and examined whether the relationship between trust and satis-  faction differs depending on the type of trust [34]. Some HSNs can easily  establish a large number of connections, while other HSNs require a large  amount of personal information to make any of them a member. Many pa-  tients store personal information in their health files. Share your opinions  and experiences [35]. Internet health care is the application of information and communication technology in various fields of health care. Internet  benefit management promises to simplify and reduce the cost of employers  and provide more choices and control. In an online study of people who fre-  quently visit health care websites, 90% of respondents believe that they can  manage their health, while 82% of respondents stated that they get better  information about the medicine by using Internet. The doctor or pharmacist  can monitor the health activities of the patients while sitting in their offices  [36]. One of the dangers of centralized trust is that the failure of centralized  control will lead to the failure of the entire system. The distributed trust  scheme avoids treating centralized authorization as a single point of failure.  Generally, distributed trust security schemes are more robust than central-  ized schemes. The work of authentication and authorization can be shared  among participants, which leads to better scalability [37]. However, outside  the health sector and interdisciplinary fields, interest in attitudes, behaviors,  and factors influencing attitudes, conceptual debates, and political debates  have proliferated. Health information brokers enable Internet users to ob-  tain objective information about various medical, health, and healthcare is-  sues to help them make health-related decisions, as well as information  about various healthcare providers [38]. Therefore, the important role of 

trust in the use of information agents makes them suitable entities to study  previous online trust-building training. Belief in integrity is defined as the  belief that the health information center adheres to a series of ethical prin-  ciples and values by providing inappropriate and complete information and  links. According to the Health Research Center, although supervision can  effectively deal with environmental health risks, the fatal risks of medical  services often require immediate intervention. An independent disaster in-  vestigation revealed that with the development of a “club culture” among se-  nior professionals, there is a serious lack of self-regulation, which means  that evidence of criticism and wrong decisions is ignored and damage can  be prevented [39]. In the healthcare sector, there is evidence that the concept  seems to include trust in capabilities (skills and knowledge) and whether  the trustee is working in the best interests of the founder. The latter usually  requires honesty, confidentiality, and caution, and shows respect, [40] while  the former can include technical and social/communication skills. Disease-  related susceptibility may lead to dependence on medical conditions with  stronger emotional and instinctive components [41]. 

Methodology  Health equipment that can replace data and services can create an IoMT  network and the number, in general, depends on sensors. In the context of  collaboration, the sensors work in collaboration to get various types of the information. For example, many sensors can simultaneously send the treat-  ment information of the disease to the patient. In order to make this activity  correct, things must be placed with the Exchange service. The approved fac-  tor is required to ensure that the safety of the IoMT security is mutually sup-  ported to communicate to coordinate links (relationship) and exchange  information. This connection is similar to human communication connec-  tions. If you manage the type of relationship (strength or injury), some of  the past and public institutions can recommend each other for new friends.  We expect IoMT networks to follow some friendly practices and they can be  applied to the biomedical setting. Fig. 6.3 is about the research method-  ology adopted in this chapter.   

Fig. 6.3 Methodology of research.    At IoMT, healthcare devices are private and communicate with healthcare  providers responsible for environmental vulnerability. The biggest challenge  in transmitting sensitive information is maintaining integrity.   

How NeuroTrust works  The NeuroTrust approach was developed to address the underlying causes  of this vulnerable environment and to create a more robust environment.  NeuroTrust's architecture includes patients, smart homes, servers with  servers, secure management systems built into the server to calculate  billing, dedicated monitoring devices, and tuning doctors with packages.  The average level of assessment delivery with confidence intervals can be  any number between 0 and 1. At the point when the shrewd homeworker  gets the data, it initially assesses the source hub level of trust and afterward  advances the scrambled data to the objective worker for checking time-  driven trust assessments. In the event that the trust calculation meets the  limit, it sends a written statement to the monitoring device.   

Forms of trust and order  Accounts are evaluated in two different ways, for example, direct and indi-  rect assessments. Reliability is automatically assessed using the obser-  vations previously observed on a node, and reliability is assessed directly by  collecting suggestions or experiences from neighboring nodes. Direct trust  is, at first, determined dependent on the accessible perceptions and the  hubs performing calculations can figure the boundaries in a succession.   

Indirect trust evaluation  The backhanded trust in NeuroTrust is utilized when there are no previous  discernments/data about a particular center. To get the ideas, the appro-  priate center delivers a request to the connecting hubs.   

The proposed FTM-IoMT mechanism  FTM-IoMT is partitioned into two sections. The former segment depicts  how the machine functions and the later part clarifies how the introduced  framework functions.    Scheduling of mechanism  Fig. 6.4 shows the FTM-IoMT function. Here, we will evaluate the request 

swelling that makes up the server. If a large number of nodes are required,  the FTM-IoMT system can use the primary notification function (FIFO) to  prioritize requests. When a node is selected and the server works with that  node, the server updates its local database based on the experience gained  in transactions. With regard to human safety, FTM-IoMT is used to assess  environmental safety.   

Fig. 6.4 Process of the model.    IoMT network information. Its purpose is to evaluate rather than prioritize  the systems required for tumor selection.   Processing of mechanism  Another important reason for FTM-IoMT is to build and manage trust be-  tween IoMT departments in mental health centers. FTM-IoMT is crazy about  assessing the integrity of the IoMT node. Reliability is evaluated by logic, in-  tegrity, accuracy, and each node relative to other nodes in the network. The  reliable value is calculated using logical precipitation. The terms of the rule  apply to each node. By default, the server decides whether to operate or ig-  nore inflation. The TH port is also configured to remove viruses. The air fil-  ter is installed in the FTM-IoMT. It consists of two algorithms with specific  gateways. Improve the reliability of the IoMT system, which is a time 

management system. When the server evaluates a lookup query, the query  trust value is stored in the database and the query location is assigned a  specific amount of time. 

Result and discussion  The features implemented are designed to manage secure and scalable con-  tent across the IoMT network.  The recommended object performance depends on some parameters of  behavior in the IoMT network. We propose to study three properties: sta-  bility, integrity, and consistency. Joining is related to the ability to exchange  services/messages over a temporary connection.  It is assumed that the analysis of the results can identify suspicious  behaviors in terms of interactions between objects to exchange messages  and services and work together to reduce losses in the IoMT network. A dif-  ferent approach is used in trust management recommendations based on  social interactions.  Table 6.1 shows the distribution of articles classified by journals. As can  be seen, the articles are mainly published in Computer Society, Medical  Informatics, Engineering Management, Network and Service Management,  and Multimedia Transaction. Most of the published articles on overqual-  ification can be found in the Medical Journal Newsletter.    Table 6.1    List of journals 

Frequency 

Percentage 

Computer Society 

2 

8.00 

International Journal of 

1 

4.00 

1 

4.00 

International Journal of Medical 1 

4.00 

Advance Computer Science  Journal Pre-proof  Informatic  Transactions on Engineering  Management 

1 

4.00 

Access 

1 

4.00 

Transactions on Multimedia 

1 

4.00 

Transactions on Network and 

1 

4.00 

Requirement Engineering Meets 1  TM 

4.00 

Service Management 

Computer Security 

1 

4.00 

Global Conference on WCN 

1 

4.00 

Transactions on Information  Technology 

1 

4.00 

Social Science and Medicine 

1 

4.00 

Decision Support System 

1 

4.00 

Health, Risk and Society 

1 

4.00 

Trust and Health care 

1 

4.00 

Journal of Communication in  Health care 

1 

4.00 

Others 

7 

28.00 

Total 

25 

100 

Table 6.2 shows the distribution of published articles related to overqual-  ification by years. In terms of the number of articles published by years,  there has been no significant fluctuation until 2020. In 2020, after Odai  Enazian, Nimra Dilawar, Weizhi Meng, and Leonard Barolli's studies of  overqualification, it can be said that researchers’ attention to the overqual-  ification literature has increased. As a demonstration of this thought, in re-  cent years there has been an increase in the studies regarding overqual-  ification. As seen in Fig. 6.5, the majority of the studies on overqualification  were conducted in the 2006–2020 period.    Table 6.2    Year of publication 

Frequency 

Percentage 

2003 

2

8.00 

2004 

2

8.00 

2005 

1 

4.00 

2006 

1 

4.00 

2007 

2

8.00 

2009 

1 

4.00 

2012 

2

8.00 

2016 

1 

4.00 

2017 

5 

20.00 

2018 

3 

12.00 

2019 

1 

4.00 

2020 

4 

16.00 

Total 

25 

100 

 

Fig. 6.5 Distribution chart according to journals.    Table 6.3 shows the distribution of published articles related to overqual-  ification according to the countries where the studies have been conducted.  As seen in Fig. 6.6, among the countries, most studies about overqual-  ification have been conducted in the USA (two articles: 16.67%). After USA,  most studies have been conducted in China and India.    Table 6.3    Countries 

Frequency 

Percentage 

USA 

4 

16.00 

United Kingdom 

3

12.00 

Jordan 

1 

4.00 

Pakistan 

1 

4.00 

India 

2 

8.00 

Malaysia 

1 

4.00 

China 

3

12.00 

Egypt 

1 

4.00 

South Africa 

1 

4.00 

Parana 

1 

4.00 

Denmark 

1 

4.00 

TamilNadu 

1 

4.00 

Canada 

2 

8.00 

Berkeley 

1 

4.00 

Japan 

1 

4.00 

Poland 

1 

4.00 

Total 

25 

100 

 

Fig. 6.6 Distribution chart according to publication year.    Table 6.4 shows the distribution of articles about overqualification ac-  cording to the sectors in which the study was conducted. As seen in Fig.  6.7, in the vast majority of the articles, studies were carried out without tar-  geting any specific sectors; the most preferred sectors are the Internet of  medical things and health care.    Table 6.4    Sector 

Frequency 

Percentage 

Internet of medical things  5 

20.00

Trust management 

7 

28.00 

Internet of health care 

6 

24.00 

Information and technology 4 

16.00 

Internet of physician 

3 

12.00 

Total 

25 

100 

 

Fig. 6.7 Distribution chart according to sector.    Table 6.5 shows the distribution of articles about overqualification ac-  cording to research types. As seen in Fig. 6.8, the most used research type  is qualitative (41.67%). In the studies related to overqualification, quanti-  tative and theoretical researches are not preferred as much as qualitative re-  searches.    Table 6.5   

 

Type of research 

Frequency 

Percentage 

Quantitative 

5 

20.00 

Qualitative 

8 

32.00 

Mix method (quantitative  7  and qualitative) 

28.00 

Theoretical 

5 

20.00 

Total 

25 

100 

Fig. 6.8 Distribution chart according to type of research.    Table 6.6 shows the distribution of articles according to authorship. In  Fig. 6.9, we distributed the authors from each paper we have studied; most  papers have three authors.    Table 6.6    Number of contributors 

Frequency 

Percentage 

Single author 

3 

12.00 

Two authors 

9 

36.00 

Three authors 

6 

24.00 

Four authors 

4 

16.00 

Five authors 

3 

12.00 

Total 

25 

100 

 

Fig. 6.9 Distribution chart according to authorship. 

Conclusions  The main contribution of this work is the TMP protocol used to increase  trust in IoMT. We believe that the results obtained from this recommen-  dation are of great significance for the development of IoMT applications in  the hospital environment, as well as for life support and technical moni-  toring.  In this article, we proposed an intelligent trust management engine that  uses strict rules to measure trust by combining multiple parameters with  trust aggregation to ensure that there is no rejection of trust management.  For this reason, a trust mechanism based on fuzzy logic was proposed,  called FTM-IoMT. FTM-IoMT provides reliable and intelligent medical facil-  ity management. FTM-IoMT works in a centralized infrastructure in which  local servers provide various services to trusted requesting nodes.  In addition, NeuroTrust uses a simple encryption mechanism to ensure  the integrity of the transmitted data before transmitting patient readings. We  also conducted extensive simulations to evaluate the effectiveness of the  proposed mechanism against various potential attacks. The results show  the effectiveness of NeuroTrust compared with other existing methods.  Future work will focus on reducing server overheads and delays in data  packet delivery, as well as improving the efficiency and performance of the  fuzzy trust mechanism of the IoMT network. 

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  7: Future challenges of IOMT applications    Muhammad Junaid Ahsan    Department of Computer Science and Infor-  mation Technology, Superior University, Lahore, Pakistan 

Abstract  The Internet of medical things (IoMT) is a new technology that aims to  improve patient quality of life by enabling personalized e-health ser-  vices that are not constrained by place or time. We are interested in the  contribution of IoT in the health-care domain since IoT spans a wide  variety of domains. This chapter focuses on people's interest in IoT in  the domain of health care, as well as its implementation and potential  problems in the area of medical services in the health-care system. This  thesis will aid researchers in comprehending previous contributions re-  garding IoT in the health-care industry.   

Keywords  Quality of life; Health care; Sensors; Bluetooth; Network; Data cloud    The Internet of medical things (IoMT) is a new technology that aims to im-  prove patient quality of life by enabling personalized e-health services that  are not constrained by place or time. We are interested in the contribution of  IoT in the health-care domain since IoT spans a wide variety of domains.  This chapter focuses on people's interest in IoT in the domain of health  care, as well as its implementation and potential problems in the area of  medical services in the health-care system. This thesis will aid researchers  in comprehending previous contributions regarding IoT in the health-care  industry. 

Introduction  The Internet of medical things (IOMT) has completely transformed the way  electronic devices function. It has increased the efficiency, accuracy, and  reliability of systems and applications used in health services. As IOT deals  with various domains, however, my focus is to present cognate research  contributions about applications used in the health-care domain as well as  future challenges of applications used in IOMT. As the title of the page  clearly indicates, I hope it will be very useful for scholars and researchers in  this field, as it will inform them of the possible challenges and research  work priorities. The concept of remote health-care facilities by IOMT has  revolutionized the medical field. The IOMT infrastructure is a series of med-  ical instruments that are linked together through communication tech-  nology. These devices use a range of sensors, such as visual sensors for  temperature measurement, EEG/EMG/ECG sensors, BP sensors, blood  sugar sensors, blood oxygen sensors, and respiration sensors, all of which  are used to track a patient's health, and such other devices are intercon-  nected through communication channels and provide the status of patient  health by sharing data with a doctor with remote data cloud centers; this  procedure generates a large amount of data for analysis which is called big  data. From the speed of development of digital world, we can access that more mobile applications will be developed for the purpose of health care  as the traditional healthcare methods are not able to cater the requirements  of the patients. Now every person also needs remote surveillance of the pa-  tient which is possible with the help of mobile health-care facilities. People  are using smartphone devices for medical health care purpose which can  capture, transmit, analyze, and store health statistics.  Fig. 7.1 explains how other devices communicate with IoT and all actors  are connected with one another by using a centralized database that is  accessible by all staff members [1]. By using this procedure, we can enhance  the quality of services in m-health as a desire by patients. The three signif-  icant components of IOMT are (a) body sensor network, (b) data cloud cen-  ter, and (c) gateways. IOMT acts like a backbone of working numerous  applications for providing better health-care services to faraway stake-  holders, medical information collected by sensor nodes connected with the  patient's body, shared data of the patient's condition with his doctor as well  as with relatives of patients for inspecting the patient's condition at anytime 

from anywhere. A gateway works like a central hub in between the data cloud  center and medical devices. It also works as a surveillance component of  the smart system of the health-care domain lined with hospital sites through  nodes. By keeping in mind the services provided by m-health and the  growth rate of technology in the field of the health sector, modern health  facilities enable health telesurgery and telerehabilitation that sanction re-  mote intensive care and tracking of patients at home/hospitals. A strong  network channel and communication system is basically required for this  purpose of health and the most important thing in it to control latency,  which means how much time it will take to respond to a request [2–5].   

Fig. 7.1 Communication of IOT.   

Applications of IoT in health care  5G technology is recently introduced by providing high-speed communi-  cation characteristics and differentiate it from others. It provides high-  speed, fast communication services, intelligent connectivity, and very low  delay rate. It also overcomes latency issue in communication systems and  due to which it is highly used for connectivity in between health care devices  [6]. IOMT plays a key role in the digitization of health. William D. de Mattos  et al. describe the linkage of devices used in m-health with a machine to ma-  chine and 5G technology. He represents that it will provide solutions form-  health [7]. As a medical nursing system explained by Chao-His Huang et al.,  this system depends on IoT architecture; it uses WSN, RFID, 2G–3G, sen-  sors, Wi-Fi, ZigBee, and Bluetooth for the transmission of data; it also has 

the ability to supply drugs automatically [8]. Liliana Chiuchisan et al. present  an intelligent system for the test of Parkinson's infection; they discussed a home-based monitoring and decision support system; it is also helpful for a  physician in treatment, diagnosis, rehabilitation, prescriptions, and progress  of the patient [9]. Lei Yu et al. presented a smart hospital model that is to-  tally integrated with IoT, it is a better hospitalization system as compared  with other traditional hospital system and the presented hospital system is  maintained a record of previous medicines, all blood tests, and doctor ad-  vices [10]. Michael Fischer et al. provide a simple idea; they advise training a  bot by using book information. This bot will be helpful for nonprofessionals  in diagnosing diseases and multiple sensors could be integrated with it by  using IoT [11]. Avik Ghose et al. present a system for aged persons; this sys-  tem is a end-to-end health-care system medical system to monitor patients,  in this system back end IoT technique is used [12].   

Monitoring of patients  A system for remote monitoring of health consists of a portable patient  monitoring unit (PPMU) at the home of patients or in service vehicles used  for the medical emergency and decision support system, which is a real-  time working unit in hospitals [13–16]. PPMU is composed of sensors and  some electric circuits which are used to measure vital parameters like heart  rate, variability in heart rate, blood pressure systolic, oxygen saturation, tem-  perature of the body, pulse rate, index of body mass, muscular activation,  respiration rate, blood glucose, urine report, and a unit for processing of  gathered or acquired data via network devices. These network devices up-  load acquired data on servers for analysis purposes [17]. Now in days, re-  mote monitoring of patients becomes very crucial, patients are facing prob-  lems like visiting their physician, monitoring of test and reports. In this situ-  ation of lockdown, all around the world only effective approach to overcome  these challenges is to work via IoMT approach. We can sort out our health-  related issues more conveniently through IoMT approach [11,18–21].   

Monitoring of heart disease  It is a very core system in the health-care domain because the monitoring of  vital organs can disclose a lot of illnesses like arrhythmia. As with ECG elec-  trodes attached to the patient's chest, which are used to measure physio-  logical signs, the collected signs will be transmitted to a central server after 

performing the required signal specification, packetizing, and digitalizing. It  may be a cloud-based server or a remote server. The doctor would be able  to see the patient's condition in real time if a GUI is placed between the  server and the doctors. It will also allow the creation of the required signal  sensor nodes through internet connectivity, which will be used to produce  real-time status [22]. Sensors like the Pedometer and Pulsometer have be-  come common in Android-based monitoring systems. Patients were shown  a wireless sensor and an off-the-shelf sensor-based ECG device [23]. The  AMPED sensor used in the detection system of the heart rate is discussed  [24].   

Brain and neurological condition monitoring system  Patients with Dementia, Parkinson’s, Epilepsy, Stroke, and Alzheimer's dis-  ease benefit from the IOMT program, which tracks neurological signs in the  brain. IoT systems were developed to track psychiatric patients in order to  enhance research and develop new therapies for these patients [25–29].  Contactless or contact-based attainment sensors are possible. In signal ac-  quired nodes, wristwatches with embedded multisensors, EEG sensors, sali-  vary alpha-amylase biosensors, and plugwise sensors are all common [17].  Some camera-based monitoring systems for patients with neurode-  generative diseases discussed in which the camera used is HC-V720; this  camera is a high-definition camera and measure every movement of patient  and having embedded sensors based on the autonomic nervous system  developed for neuro patients [30,31].   

Diabetic patient management system Diabetic patients should monitor their glucose levels on a regular basis to  ensure that their diet is sufficient and that their glucose level is stable.  IOMT, on the other hand, is a Bluetooth-enabled platform designed for real-  time diabetic patient monitoring that doctors can access through a smart-  phone or web-based application [32].   

Monitoring of elderly patients  Elderly patients are being monitored at home in real-time scenarios. Mul-  tiple sensors, such as single and multichannel cameras, vibration sensors,  gyroscope sensors are used for data acquisition. These sensors are linked  with network processors and microcontroller is used for communication  with central servers to establish alarm systems that also have the authority 

to take decisions. A mote-based monitoring system is developed [18,33]. A  processing instrument having an accelerometer has been developed to ob-  serve the movement of patient in-home, it is called a wheelchair monitoring  system [34]. By using the mentioned monitoring system, elderly patients  could be monitored easily and their falling off the chair and chances of cer-  tain unexpected problems could be reduced.   

Health and health care  The evolution of 5G in IOMT facilitates the health-care system and pro-  motes health; the doctor and other hospital staff could monitor patients  from their homes. In this consideration, wireless body sensors are required  for monitoring the health of patients. Several body sensors have been pro-  duced for health-care facilities. The basic requirement of sensor node archi-  tecture is its size and power consumption. It must contain a storage device,  processor, and transceiver. Here the size of the sensor is directly propor-  tional to the size of the antenna used in the transceiver. IOMT in health care  has abilities to provide health-related facilities to patients at their home such  as a visiting doctor's clinic and tests for the diagnosis of diseases like in the  current pandemic of COVID-19 it was difficult to visit the doctor's clinic.  IOMT provides a solution for treatment from home. The voice recording program keeps administrative activities under control, but few studies show  that it aids health professionals in providing proper treatment without the  need for data or inquired tasks [35].  Telemedicine solutions are available. Legal concerns around patient pri-  vacy and confidentiality, conflicting health system priorities, and a perceived  lack of demand are more likely to be considered obstacles to telemedicine  adoption in developing countries. Telemedicine offers a facility that takes  care of patients who are located in remote areas, far from local health cen-  ters where physicians are accessible. As a result, patients who use these  programs can stop sitting in a doctor's office and obtain immediate treat-  ment. Telemedicine programs are typically more advanced in technology  and much more comprehensive than conventional physical health services. 

Literature survey  IoMT devices have the ability to keep track of every type of patient record as  well as to maintain the current status in order to ensure patient health. Ac-  cording to Paul Buss Communications, money saved by e-health can range  between 10% and 20% of total health-care costs. Since gaining a foothold in  other industries, IOT now has a foothold in the medical domain in the form  of IOMT, and no other department has reaped the benefits as well. Accord-  ing to a recent case study in the form of statistics from Becker's hospital,  73% of patients are tracked by IoT devices. IoT is thought to have saved up  to 57% of a company's facility costs. By 2025, the market size of IoT health  care is expected to reach USD 337.41 billion, up from USD 28.42 billion in  2015, it is growing at a CAGR of 28% over the prediction period [8]. The re-  search revealed that IoMT values is increasing periodically, as well as its  value will be further increase as it offers great help to the health-care domain  [36–39].   

Challenges of IOMT applications  Although IOMT applications provide great benefits in health care, they also  face some challenges that require to be resolved. The IOMT applications  could not be implemented as a health-care solution without addressing  these challenges.   

Inputs of gathered data on a large scale  Thousands of devices are interconnected in the health-care system, with  many of them transmitting data from different locations and operating in  real time, producing large amounts of data that require a large amount of  storage space. The said storage space is also required the use of proper Arti-  ficial Intelligence algorithms and cloud computing to organize gathered  data. The said technique will take some time to mature.   

Security risks of collected data in IOMT  While IoT health care is advantageous to the industry, it also poses a num-  ber of security risks. Hackers may gain access to medical devices linked to  the network, allowing someone without permission to steal or change data.  Hackers can also compromise an entire hospital system's network and in-  fect IoT devices with an unknown ransomware virus. Patients, as well as  their heart rate monitors, blood pressure readers, and brain scanners, will be kept hostage by the hackers. 

Existing IT infrastructure has reached the end of its useful life  IT infrastructures are superannuated in many hospitals. This infrastructure  will not support proper integration of IoT devices. As a result, health-care  facilities must rebuild IT processes and implement new, up-to-date soft-  ware. They must also take advantage of virtualization (technologies such as  SDN and NFV), ultrafast networks with low latency and delay rates, as well  as broadband and mobile networks such as Advanced LTE or 5G. 

Research methodology  This research is based upon an exhaustive systematic literature review. It  has different phases as shown in Fig. 7.2.    1.Planning  2.Conducting  3.Reporting results   

Fig. 7.2 Methodology work flow.   

Search terms  International journals and conference papers have been mostly used to gain  the knowledge for conducting surveys.    •The top five off-loading models were chosen from books and pub-  lished papers.  •Boolean operators such as AND, OR have been used to limit the  searched line or phrase.  •Different terms are used to obtain data from the internet. They are:    Literature review of IOMT OR Internet of Medical Things.    •Literature review OR survey of IOMT.  •Applications of IOMT.  •Risks of using IOMT.  •IOMT Applications AND their issues. 

•IOMT applications AND challenges.  •IOMT applications AND future goals.  •IOMT applications AND future challenges.    Think air OR MAUI Resources of search:    •Google scholar  •Research gate  •International journals  •Conference papers  •IEEE Explorer  •ACM Digital library  •Science Direct  •Different websites   

Accepting standards  Those Published papers were accepted which had the most discussion  about IOMT and the challenges of IOMT applications.    •The role of IOT in the health-care domain.  •Importance of IOT in the health-care and medical field  •Involvement of IOT in the medical domain as IOMT  •Applications of IOMT  •The role of IOMT applications and their growth rate 

Rejecting standards    •Rejection of those articles that involve only IOT, our concern area  of interest was IOMT and those papers which discuss on IOT did  not answer my research questions.  •Rejected articles that did not discuss tools for IOMT and its chal-  lenges.  •Rejecting local and unauthorized published papers 

Result and discussion  Table 7.1 describes the distribution of research articles according to their  journal names. It is clearly visible in Table 7.1 that most of the articles pub-  lished in the journals of medical, computer science, and the internet of  things. Among these journals, the most publications are on the internet of  things and the journal of computer applications as shown in Fig. 7.3.    Table 7.1    List of journals 

Frequency 

Percentage 

Cumulative  frequency 

Computers in Human 

3 

4.35 

3 

2 

2.90 

5 

2 

2.90 

7 

2 

2.90 

9

International Journal of  2 

2.90 

Behavior  IEEE Journal of  Biomedical and Health  Informatics  Journal of internet of  Medical Internet  Research  JMIR Medical  Informatics 

Medical Sciences  JMIR Public Health and  1 

1.45 

1 

1 

1.45 

2 

International Journal of  3  Computer Applications 

4.35 

5 

American Journal of the  3 

4.35 

8 

Chinese Medical Journal  3 

4.35 

11 

JMIR Medical Education  2 

2.90 

13 

Journal of  Communications 

2.90 

15 

Surveillance  JMIR mHealth and  uHealth 

Medical Sciences 

2 

IEEE/CAA Journal of 

1 

1.45 

16 

2 

2.90 

18 

2 

2.90 

20 

IEEE Internet of Things  2  Journal 

2.90 

22 

Journal of Computer and  3 

4.35 

25 

Automatica Sinica  Big Data Mining and  Analytics  Journal of  Communications 

System Sciences  Internet of Things 

4 

5.80 

29 

Others 

40 

57.97 

69 

Total 

80 

116 

 

Fig. 7.3 Distribution of articles according to journal names.    Table 7.2 indicates the distribution of research articles published related  to the internet of medical things and future challenges according to years. In  terms of the number of research articles published by year, no significant  fluctuation has been seen until 2010. In 2010, researchers started study on  the internet of medical things and we can say that IOMT literature gained  the attention of researchers. In recent past years’ ratio of research regarding  IOMT has been increased rapidly as seen in Fig. 7.4.    Table 7.2   

Year of 

Frequency 

Percentage 

publication

Cumulative  frequency 

2008 

1 

1.45 

1 

2009 

2 

2.90 

3 

2010 

3 

4.35 

6 

2011 

4 

5.80 

10 

2012 

5 

7.25 

15 

2013 

5 

7.25 

20 

2014 

6 

8.70 

26 

2015 

6 

8.70 

32 

2016 

7 

10.14 

39 

2017 

7 

10.14 

46 

2018 

8 

11.59 

54 

2019 

8 

11.59 

62 

2020 

9 

13.04 

71 

2021 

9 

13.04 

80 

Total 

80 

116 

 

Fig. 7.4 Distribution of articles according to publication.    Table 7.3 presents the country-wise distribution of published research  articles related to IOMT and its future challenges according to the countries  where the studies have been conducted. As seen in Table 7.3, among the  countries, the most articles about IOMT and future challenges have been 

conducted in the United States (13 articles; 18.84%). After the United States,  the majority of studies have been conducted in China, Germany, Spain, and  the United Kingdom. Although a lot of studies regarding IOMT and future  challenges have been carried out in Europe and America, it is assumed that  the studies about IOMT and future challenges in Asia and Africa are not  sufficient as shown in Fig. 7.5.    Table 7.3    Countries 

Frequency 

Percentage 

Cumulative  frequency 

United States 

13 

18.84 

13 

Germany 

5 

7.25 

18 

Spain 

5 

7.25 

23 

United 

5 

7.25 

28 

Canada 

3 

4.35 

31 

China 

6 

8.70 

37 

Italy 

4 

5.80 

41 

France 

3 

4.35 

44 

Taiwan 

2 

2.90 

46 

Scotland 

2 

2.90 

48 

Japan 

3 

4.35 

51 

Jordan 

2 

2.90 

53 

France 

3 

4.35 

56 

Sweden 

3 

4.35 

59 

Pakistan 

3 

4.35 

62 

Hungary 

3 

4.35 

65 

Portugal 

3 

4.35 

68 

Brazil 

3 

4.35 

71 

Romania 

3 

4.35 

74 

Turkey 

3 

4.35 

77 

India 

3 

4.35 

80 

Kingdom 

Total 

80 

116 

 

Fig. 7.5 Distribution of articles according to the countries.    Table 7.4 indicates the distribution of research articles regarding IOMT  and its future challenges according to the sector in which the research was  conducted. As seen in Table 7.4, in majority of research articles, studies  were carried in the mixed sector based on the research studies that have  been carried out by targeting specific sectors; the sector most preferred is  IoT, and then application used in the IOMT sector. The number of studies  carried out in a specific education sector is very few. Thus, future re-  searchers may contribute to the IOMT and its future challenges in the liter-  ature conducted by certain sector-based studies as shown in Fig. 7.6.    Table 7.4    Sector 

Frequency 

Percentage 

Cumulative  frequency 

Medical Technologies 

8 

11.59 

8 

Internet of Medical 

15 

21.74 

23 

15 

21.74 

38 

Information and  technology 

12 

17.39 

50 

Education 

4 

5.80 

54 

Mix sectors 

26 

37.68 

80 

Things  Applications used in  IOMT 

(applications,  communications, IOT  and IOMT)  Total 

80 

116 

 

Fig. 7.6 Distribution of articles according to the sector.    Table 7.5 represents the distribution of articles about IOMT and its future  challenges according to their authorship pattern. The statistics shows that  research papers that have two authors and four authors are relates to IOMT  and thereafter, three authors have the highest number. The statistics also  shows that the articles that have six authors have less interests towards  IOMT. Both groups have a low percentage, so it has been concluded that  the most preferable pattern of authors grouping is two, three, or four au-  thors per research article as shown in Fig. 7.7.    Table 7.5    Number of  contributors 

Frequency 

Percentage 

Cumulative  frequency 

Single author 

9 

13.04 

9 

Two authors 

18 

26.09

18 

Three authors 

17 

24.64 

17

Four authors 

18 

26.09

18 

Five authors 

10 

14.49 

10 

Six authors 

8 

11.59 

8 

Total 

80 

116 

 

Fig. 7.7 Distribution of articles according to authorship pattern.    In a safe, scaleless, and secure (SSS) health-care system, data is funda-  mental for diagnosis as well as decision-making for connected clinics. Of  the data in the health-care system transported by two methods, one is a  store and forward method and the other is the real-time data transportation  method. By using these tools, patients could be treated remotely or in a real-  time environment. In these systems, security, privacy, sensitivity of data,  and reliable and sustainable transportation of data are significant features.  Blockchain technology is designed to establish accountable, trusted trace-  ability, integrity, and reliability of data sharing which has core value for the  health-care system. The health-care system is composed of multiple entities  with several actors like patients, doctors, paramedical staff, different ana-  lysts, medical personnel, researchers, educational institutions, and equip-  ment providers performing actions according to their role. The health care  system also maintains the confidentiality of the patients. From a security  standpoint, blockchains can only be accessed by approved parties involved  in a specific network, and their access may be clocked or controlled by  appropriate security policy (role-based or attribute-based). By protecting the  Privacy of sensitive data, only authorized actors should be allowed to access  sensitive data from the blockchain storage. The use of blockchain should be  governed by a sound security policy (role-based or attribute-based). Only  approved actors should be able to access sensitive data stored on the  blockchain, in order to protect its secrecy. Defense against unauthorized  data alteration or deletion is provided by using blockchain and maintaining  data integrity in health care. be governed by a sound security policy

(role-based or attribute-based). Defense against unauthorized data alter-  ation or deletion is provided by using blockchain and maintaining data in-  tegrity in health care. Data protection and integrity are crucial, but so is data  access time in health care. This system is time-sensitive and bandwidth-  hungry, and IoMT services only need a small amount of data and a long bat-  tery life. They also need a fast and stable link. 5G allows for long-term com-  munication between devices, as well as reliable and accurate patient diag-  nosis. 

Conclusions  Data security, information confidentiality, safety reputation, availability, and  consistency are all critical aspects of these applications. Blockchain tech-  nology was investigated in this regard to ensure the protection of patient  data and information transmission. Flexibility in different market sectors  through network stability improvements and supply protection, time-saving  authentication, up-to-date information, transparency, patient-doctor privacy,  secure monitoring of medications, maintenance of patients history, are all  advantages of this technology and bandwidth issue. 5G is considered to be  a solution to these issues, as it has a wide bandwidth and speed 40 times  faster than 4G, and so on. 

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Index    Note: Page numbers followed by f indicate figures.    A    Accelerometers 19–20  Active sensors 5, 6f  Analog sensor 7  Analytics technology 93  Artificial intelligence (AI)     •

hardware implementation 65–66 

•

health-care domain and 63–64 

•

internet of medical things (IoMT) 70, 84–85, 89, 91–94 

  •

cases 72–74 

•

challenges 74 

•

future 74 

•

health-care assistance 70–71 

•

personalized treatments 72 

•

prediction techniques 71 

•

robotics technology 71 

•

wearable devices 72 

  •

and machine learning 64–65 

•

overview 61–63 

•

software implementation 66–70 

  •

decision tree 67 

•

deep learning 69–70 

•

dimensionality reduction 68 

•

gradient boosting 68 

•

K-means algorithm 68 

•

linear regression 66 

•

logistic regression 66–67 

•

nearest neighbor 67 

•

neural networks (NN) 69

•

random forest 67 

•

reinforcement learning 68–69 

  Authentication server (AS) 38    B    Bayesian network 102  Big data 37–38  Blockchain 57  Blood pressure sensors 16–19    •

invasive sensors 18 

•

noninvasive sensors 18–19 

  Bluetooth 24–25, 120  Body surface temperature sensors 18f    C    Cardiovascular diseases (CVDs) 51–52  Chronic illness 51–53  Cloud-based control system 102  Cloud integration 36–37  Cloud service provider (CSP) 38  Cloud specialist provider (CSP) 103 Cloud storage 36–38  Connectivity 47–49  COVID     •

internet of things (IoT) health care in 38–40 

  Cybersecurity 56–57 

D    Database server (DS) 38  Data cloud center 117–118  Data sharing 48  DaVinci surgical robot 39–40  Decision tree 67  Deep learning 69–70  Digital sensor 8  Dimensionality reduction 68  Drone 39–40    E    Electrocardiogram (ECG) 21  Electronic health records (EHR) 54–55    F    Fast health-care interoperability resources (FHIR) 54–55  5G technology 94, 119, 121  Fuzzy cloud 103  Fuzzy trust management (FTM) 101–102, 107–108    G    Gradient boosting 68    H    Health analytics 89–91  Health data 48–49, 51–55  Heart rate monitors 49, 51 

Hierarchical computing architecture (HiCH) 36–37    I    Indirect trust evaluation 107  Inductive sensor 8, 9f  Internet of Medical Things (IoMT) 48–49, 61, 83    •

applications 35, 119 

•

architecture 35, 35f 

•

artificial intelligence (AI) 70See alsoArtificial intelligence (AI) 

  •

cases 72–74 

•

challenges 74 

•

future 74 

•

health-care assistance 70–71 

•

personalized treatments 72 

•

prediction techniques 71 

•

robotics technology 71 

•

wearable devices 72 

  •

benefits 94–95 

•

big data 37–38, 91–92 

•

cardiovascular diseases (CVDs) 51–52 

•

challenges 40–41, 52–54, 122 

•

cloud integration 36–37 

•

communication 118, 118f 

•

components 118 

•

concepts 85–87 

•

in COVID 38–40 

•

data  

 

 

•

inputs 122 

•

security 92 

•

security risks 122 

•

devices 49–52 

•

evolution 48–49 

•

fast health-care interoperability resources (FHIR) 54–55 

•

5G technology 94, 119, 121 

•

fuzzy cloud 103 

•

fuzzy trust management (FTM) 101–102, 107–108 

•

and health analytics 89–91 

•

health care 103–104 

  •

devices 105 

•

digitization 119 

  •

health system, sources of 104–105 

•

internet of wearable things (IoWT) 48 

•

machine learning 36, 94 

•

monitoring 33 

  •

brain and neurological condition 120 

•

diabetic patient management system 120 

•

diversification of 51f 

•

of elderly patients 120–121 

•

of heart disease 120 

•

of patients 119 

 

 

•

overview 99–102, 117–121 

•

patient monitoring system 36 

•

research methodology 106f, 122–129 

•

security 38 

•

security tantrums 55–57 

•

social networks and 102–103 

•

stress 52 

•

technological challenges 87–89 

•

technology 100 

•

telemedicine 121 

•

trauma-related disorders 52 

•

trust management (TM) 103 

•

indirect trust evaluation 107 

•

NeuroTrust 106 

•

result 108–112 

•

trust, forms of 107 

  •

virtual reality 86 

  Internet of wearable things (IoWT) 48  IoMT SeeInternet of Medical Things (IoMT)    K    Key generation center (KGC) 38  K-means algorithm 68    L    Light sensors 21  Linear regression 66  Logistic regression 66–67    M    Machine learning 36, 64–65, 94  Medical sensors 3    •

physical parameters monitoring 11, 12–14f 

•

pressure sensor 16f 

  Medical service manager 101–102  Medical system security 100–101  MobileDetect app 39–40  Mote-based monitoring system 120–121   

N    Natural language processing (NLP) technology 93  Nearest neighbor 67  Network delay 37  Neural networks (NN) 69  NeuroTrust 106  Nondominated Sorting Genetic Algorithm II (NSGA-II) 38    P    Passive sensor 5, 6f  Patient monitoring system 36  Portable patient monitoring unit (PPMU) 119  Pressure sensor 7, 8f, 11  Pulse oximetry sensors 22–24    R    Random forest 67  Reinforcement learning 68–69  Remote patient monitoring (RPM) 47, 50  Robots 39–40    S    Safe, scaleless, and secure (SSS) health-care system 128–129  Sensors 3   

 

•

accelerometers 19–20 

•

active sensors 5, 6f 

•

analog sensor 7 

•

blood pressure sensors 16–19 

•

invasive sensors 18 

•

noninvasive sensors 18–19 

  •

classification 4–8, 5f 

•

digital sensor 8 

•

electrocardiogram (ECG) 21 

•

health-care system 8–10 

•

inductive sensor 8, 9f 

•

light sensors 21 

•

medical sensors 3 

  •

physical parameters monitoring 11, 12–14f 

  •

passive sensor 5, 6f 

•

pressure sensor 7, 8f, 11 

  •

applications 15f 

•

medical sensor 16f 

•

ventilator 15f 

  •

properties 3–4 

•

pulse oximetry sensors 22–24 

•

shoe monitor sensors 21–22 

•

temperature sensor 5–7, 7f, 11–16 

  •

applications 17f 

•

body surface temperature sensors 18f 

•

types 17f 

  •

types 11–24 

•

ultrasonic sensor 8, 9f 

•

wireless communication technology 24–26 

 

 

•

bluetooth 24–25 

•

Wi-Fi 25 

•

ZigBee 25–26 

Service Management Framework for IoT devices in Cloud (SMFIC) 35  Shoe monitor sensors 21–22  Smartphone 39–40  Smart thermometers 39–40  Social networks     •

and internet of medical things (IoMT) 102–103 

  Software-defined network (SDN. 102–103  Stress 52  Sybil attacks 101–103    T    Telemedicine 47–49, 55, 121  Telerobots 39–40  Temperature sensor 5–7, 7f    •

applications 17f 

•

body surface temperature sensors 18f 

•

types 17f 

  Trauma-related disorders 52  Trust management (TM) 103    •

indirect trust evaluation 107

•

NeuroTrust 106 

•

result 108–112 

•

trust, forms of 107 

  U    Ultrasonic sensor 8, 9f   

V    Ventilator 15f  Virtual reality 86    W    Wearable devices 72  Wheelchair monitoring system 120–121  Wi-Fi 25  Wireless communication technology     •

sensors 24–26 

  •

bluetooth 24–25 

•

Wi-Fi 25 

•

ZigBee 25–26 

  Z    ZigBee 25–26