Preface
Page: ii-iii (2)
Author: L. Ashok Kumar, D. Karthika Renuka, Sonali Agarwal and Sheng-Lung Peng
DOI: 10.2174/9789815196290124010002
The Role of Emerging Technologies in Smart Health Care
Page: 1-26 (26)
Author: Jaskiranjit Kaur* and Parvesh Kumar
DOI: 10.2174/9789815196290124010004
PDF Price: $15
Abstract
Numerous technological advancements like 3-D Printing, Virtual Reality
(VR), Augmented Reality (AR), Artificial Intelligence (AI), Internet of Things (IoT),
Drones, Robots, and Blockchain are now being inscribed for their ability to change the
health care industry and make it a more automated and effective field. Various tools
related to AI, like Google, DeepMind, Atomwise, Chatbot, Enlitic, Freenome, and
Buoy Health, are helpful in makingthe health industry more efficient. There is another
technology which is nanomicelle that can be used for effective drug delivery to treat
various cancers, including breast, colon, and lung cancer. Moreover, self-assembling
peptide nanoparticles that were prepared from SARSCov-1 spike (S) protein,
successfully induced neutralizing antibodies against the coronavirus, subsequently
preventing infection of Vero cells. Furthermore, the application of 3D printing in
medicine can provide many benefits, including the customization and personalization
of medical products, drugs, and equipment; cost-effectiveness; increased productivity;
democratization of design and manufacturing; and enhanced collaboration. IoT enables
real-time alerting, tracking, and monitoring, which permits hands-on treatment, better
accuracy, apt intervention by doctors, and improves patient care delivery results. The
other most promising application isblockchain in the healthcare sector for identity
management, dynamic patient consent, and management of supply chains for medical
supplies and pharmaceuticals. In addition, there are several case studies that describe
the benefits of emerging tools, like recently the use of Emerging Technologies for the
study, diagnosis, and treatment of patients with COVID-19 by using Deep
Convolutional neural networks (CNN), which is a widely used deep learning
architecture, enabled distinguishing between COVID-19 and other causes of
pneumonia through chest X-ray image analysis.
An Overview of Blockchain in the Field of Smart Healthcare System
Page: 27-38 (12)
Author: Ramya Easwaran* and Kumaresan Natesan
DOI: 10.2174/9789815196290124010005
PDF Price: $15
Abstract
Rapid Blockchain is one of the most talked about technologies in the world
at the moment. The origin of blockchain is a cryptocurrency called “bitcoin”. It is a
secure currency that can be used as a medium of exchange worldwide. Blockchain
itself is a decentralised, peer-to-peer distributed ledger capable of storing all
transactions that take place on the network. This property makes blockchain useful for
any type of exchange, such as data, currency and information. Blockchain protects
against potential data theft or corruption in the healthcare network. It is important to
maintain the integrity and validity of patient records to ensure wellness. Artificial
intelligence and blockchain will provide a smart healthcare system for people around
the world by extracting useful information, protecting medical data, simplifying claims
processing, using patient self-generated data and systematising procedures.
Integration of Blockchain and Internet of Things
Page: 39-58 (20)
Author: R. Babu*, Jayashree K., Priya Vijay and Vijay K.
DOI: 10.2174/9789815196290124010006
PDF Price: $15
Abstract
Customers can benefit from the Internet of Things in a number of ways, and
it has the potential to transform the fundamental ways that consumers interact with
technology. The pervasiveness and correspondences maintained for IoT might provide
various conveniences and aids for people, but also open up many security loopholes.
Blockchain, a distributed digital ledger, is finding uses in industries as diverse as
finance, healthcare, utilities, agriculture, real estate, and Supplier Management. The
middleman acting as guardians for specific applications in these enterprises can be
removed in order to provide security and those equivalent applications can be run in a
distributed way with practically no centralized power. Blockchain technology makes
this feasible without sacrificing efficiency or safety, which was previously impossible.
Blockchain and IoT seem to be best on their own in the respective sector in which it is
applied, so businesses can try and exploit this powerful combination known as
Blockchain Internet of Things (BIoT) to bring immense advancements, progressions
and cutting edge innovations in the area of their interest. The term “BIoT” was created
by fusing blockchain with IoT applications.
Consequences and Deliberations in Implementation of Blockchain and Internet of Things Integration
Page: 59-75 (17)
Author: K. Karthigadevi* and G. Srinivasagan
DOI: 10.2174/9789815196290124010007
PDF Price: $15
Abstract
Blockchain technology proposes security facilities to the Internet of Things
(IoT). The things or objects used in daily life are connected to the internet to form IoT.
The blockchain integral safety mechanism can deliver amenities, such as
authentication, accessibility, integrity, secrecy, and authorization to the IoT
applications. The uses of IoT applications are the dream turning into reality. But using
this IoT still faces some challenges, mainly in the areas of security such as data
consistency and reliability. The objects have interacted over the internet; they can also
be supervised and controlled remotely. The use of IoT reduces time, manual work,
tracking and money. With the evolution of IoT, it is essential to deliver more security
for enormous amounts of data. Blockchain is a circulated network with the properties
of integrity and secrecy. The blockchain maintains data security in the network of IoT.
Here to discover the challenges associated with the combination of IoT and blockchain,
a study is taken first, then a blockchain introduction is offered, followed by the
Blockchain-based IoT requirements, demand and Quality of Service (QoS) discussed.
Then, tasks faced while applying this blockchain-based IoT, such as plan, progress and
deployment, are discussed. Then applications of blockchain-based IoT such as
throughput, efficiency, latency, privacy, fork problem, smart contracts, legal issues,
security, storage and proposed solutions are deliberated. Finally, upcoming research
guidelines for the combination of IoT and blockchain are designated.
Blockchain Integrated with Internet of Things-benefits, Challenges
Page: 76-90 (15)
Author: Geeta Amol Patil*, Surekha K.B., Chaithra V. and Anand Kumar S.
DOI: 10.2174/9789815196290124010008
PDF Price: $15
Abstract
All sectors are now using digital ways to facilitate humans. Be it health,
finance, supply chain, communication, transport, IT, or education, all the sectors are
now relying on technologies and the internet for providing facilities and also using
them as sources of information. These sectors, when using traditional ways, faced a lot
of challenges. For example, people earlier going to railway stations to book train tickets
had to wait for long durations in queues, and if all the seats are filled by the time they
reach or their turn comes to reserve seats, it goes all in vain to spend time traveling to
the station and time in queues. Coming to the finance sector, people had to go to banks
to create a bank account and for all the formalities. They had to spend time going to
banks and then wait in queues to get their work done. Also, it took around 1-2 weeks
for every task to complete in banks. So, the process was quite time-consuming,
monotonous and unreliable in practice. Thus many sectors started looking for
alternative methods to perform their daily tasks. Slowly, the sectors started digitizing,
and started using computers to perform tasks, to store and update their data. They also
started using the internet in their daily applications. Each organization of industry is
now available on the internet. All of their information is present and one can apply for
their services using their websites. Thus, IoT comes into the picture here. All the
sectors using the internet to access and provide information, using the cloud to store
their data are using IoT services. Internet of Things (IoT) technology will soon become
an integral part of our daily lives to facilitate the control and monitoring of processes
and objects and to change the way man interacts with the physical world. For all
aspects of IoT to be fully functional, there are a few obstacles to overcome and
important challenges to overcome. These include, but are not limited to, cyber security,
data privacy, power consumption, and metrics. The dedicated Blockchain environment
and its various processes provide a useful way to address these few IoT challenges.
Blockchain Powered Medical Sector – Application, Challenges and Future Research Scope
Page: 91-113 (23)
Author: Divya Palanisamy*, Saranya Rajendran and Praveena Venkatesan
DOI: 10.2174/9789815196290124010009
PDF Price: $15
Abstract
The recent research in the healthcare sector using computer technologies in
the fourth industrial revolution helps to improve the quality of life by accessing the
medical data to monitor, diagnose and treat the patient at the right time from anywhere
in the world. Blockchain is one of the major recent innovations and trending research
topics that plays a vital role in diverse applications like Smart cities, Healthcare
industry, Smart grid, etc. Blockchain, which is fascinated with its features like secure
data sharing, immutability, decentralization, and reliability in data management, has
made it a prominent technology in the healthcare industry. This chapter discusses 1)
The working principle of blockchain technology with its different prospectus in
healthcare. 2) Advantages of blockchain technology over the Internet of Things in
secured patient data management, efficient data sharing with decentralized data
management accessible for authorized users using cryptography techniques. 3) Various
applications of blockchain technology in healthcare, like remote patient monitoring
using Internet of Things (IoT) devices for cardiac and electroencephalogram (EEG)
signal monitoring to diagnose life-threatening diseases. 4) Drug traceability in the
pharmaceutical drug supply chain to ensure product safety with an end-to-end tracking
system and immutable transaction record. Finally, this chapter also presents the
blockchain based challenges and solutions that advocate the future research scope in
healthcare systems.
Blockchain in the Healthcare Domain and Performing Various Security Analysis
Page: 114-136 (23)
Author: Suresh Kumar Nagarajan*, Geetha Narasimhan, Akila Victor, Yash Vaish and Pranshu Tripathi
DOI: 10.2174/9789815196290124010010
PDF Price: $15
Abstract
Blockchain is a promising technology that can be used to improve the
healthcare system. It can be used to store patient data securely and prevent tampering.
It can also be used to improve supply chain management by increasing transparency
and interoperability. This work proposes a web-based application that uses blockchain
to store patient’s data and retailer’s information. The application will also be able to
send encrypted messages securely and anonymously. The application will be deployed
on the Ethereum platform. The benefits of using blockchain in healthcare are Security:
Blockchain is a secure way to store data because it is decentralized and encrypted. This
makes it difficult for unauthorized users to access or tamper with data. Transparency:
Blockchain is transparent, which means that all transactions are recorded on the
blockchain and can be viewed by anyone. This can help to increase trust and
accountability in the healthcare system. Interoperability: Blockchain can be used to
connect different healthcare systems together, which can improve the flow of
information. This can help to improve patient care. Immutability: Blockchain is
immutable, which means that data cannot be changed once it is added to the
blockchain. This can help to ensure the accuracy of data. The challenges of using
blockchain in healthcare are Complexity, Cost, and Regulation. Despite these
challenges, blockchain is a promising technology that has the potential to improve the
healthcare system. This work is a step towards realizing the potential of blockchain in
healthcare.
IOT-Based Smart Healthcare System with Hybrid Key Generation and DNA Cryptography
Page: 137-149 (13)
Author: Vidhya E.*
DOI: 10.2174/9789815196290124010011
PDF Price: $15
Abstract
Many applications, such as smart health care, smart cities, smart homes,
self-driving cars, IoT retail shops, tele-health, traffic management, and so on, will use
IoT devices to generate information. In these tenders, smart health care is single of the
most imperative because it generates sensitive information like disease managing, drug
managing, secluded patient checking, defensive care, and so on. This large amount of
information is acquired and recorded from a variety of sources (mobile phones,
software, sensors, e-mail, applications and so on). These sources contain a basic
encryption process, so hackers can easily hack the information and misuse it. These
issues are taken by researchers, and they find solutions, but they do not fulfill the needs
of encryption. Key generation is critical for encryption and decryption because a strong
key increases the encryption and decryption level. In this chapter, the proposed system
is designed and implemented with a strong key generation (KG) to encrypt (encr) and
decrypt (decp) the information that is compatible with the limited processing
capabilities of IoT devices. In this system, the mathematical key generation algorithm
is created with the hybrid of prime numbers and pseudo random numbers using the
Exclusive OR function. Besides, the DNA Cryptography algorithm is used to encrypt
and decrypt the information. The above system makes it hard for hackers to break into.
When paralleled with illustrious cryptographic schemes, the tentative outcomes of the
proposed system show the best effects for every IoT scheme in terms of encryption
time and key entropy. When equal to other surviving encryption schemes, the proposed
system has a restored avalanche effect and key entropy value for achieving the security
goals. The above security goals illustrate that such a scheme is able to protect IoT
documents from present attacks.
Security Enhancement in Cloud and Edge Computing Through Blockchain Technology
Page: 150-173 (24)
Author: Santanu Koley* and Pinaki Pratim Acharjya
DOI: 10.2174/9789815196290124010012
PDF Price: $15
Abstract
The cloud computing (CC) network is designed to tackle the security and
privacy challenges of centralized cloud services by distributing computing and storage
resources among networked nodes. Cloud computing, on the other hand, is restricted by
the performance of linked devices, posing problems in state authorization, stats
encryption, consumer privacy and more. Blockchain technology (BT) is the most
popular circulated network technology right now. It is utilized in numerous fields like
bitcoin, IoT, etc., to tackle the consistent issue of distributed data. The difficulties that
CC networks present for security and privacy are covered in this chapter. Analysis and
solutions brought to edge computing networks by BT in terms of data encryption,
authentication and user privacy. In this chapter, the advantages of combining the cloud
computing network with blockchain technology will be discussed. Finally, memory,
workload, and latency problems for related future studies have been discussed.
Effective Automated Medical Image Segmentation Using Hybrid Computational Intelligence Technique
Page: 174-182 (9)
Author: Manoranjan Dash*, Raghu Indrakanti and M. Narayana
DOI: 10.2174/9789815196290124010013
PDF Price: $15
Abstract
In biomedical domain, magnetic resonance imaging (MRI) segmentation is
highly essential for the treatment or prevention of disease. The demand for fast
processing and high accurate results is necessary for medical diagnosis. This can be
solved by using computational intelligence (CoIn) for data processing. The CoIn can be
achieved by using well-known techniques such as fuzzy logic, genetic algorithm,
evolutionary algorithms and neural networks. The computational complexity of a
medical image segmentation depends on the characteristics of data as well as suitable
algorithms. The selection of CoIn methods is very important for better segmentation of
a medical image because each algorithm outperforms a different medical image data
set. The hybrid CoIn (H-CoIn) is one of the solutions to overcome the problem of
individual algorithms in medical image segmentation. The H-CoIn is a combination of
two or more intelligence algorithms (like fuzzy logic, evolutionary algorithms and
neural networks). The drawbacks of individual intelligence algorithms can be
overcome by using H-CoIn. In a medical image segmentation process, two or more
variables or objectives need to be optimized for H-CoIn. This problem can be solved by
using multi-objective optimization techniques, where simultaneously minimization or
maximization can be performed. In this chapter, the various CoIn algorithms'
performance has been discussed in detail for medical image segmentation and
compared with state-of-the-art techniques. The H-Coin algorithm has been
implemented in a large medical dataset and attained an accuracy of 98.89%. Further,
the H-Coin algorithm is reliable and suitable to overcome the inter-observer and intraobserver variability.
IoT-Botnet Detection and Mitigation for Smart Healthcare Systems using Advanced Machine Learning Techniques
Page: 183-200 (18)
Author: S. Jayanthi* and A. Valarmathi
DOI: 10.2174/9789815196290124010014
PDF Price: $15
Abstract
The Internet of Things (IoT) age is quickly evolving, with millions of
devices and many more intelligent systems, like healthcare. Attackers mostly aim for
these IoT devices. These devices are infected with malware, which turns them into bots
that are used by attackers to disrupt networks as well as steal important data. To
address this issue, efficient machine learning combined with appropriate feature
engineering is proposed to detect and protect the network against vulnerabilities. The
proposed model will detect Distributed Denial of Service (DDOS)-based botnet attacks
in the smart healthcare system. Hacktivists frequently use DDoS assaults to overwhelm
networks and make them unusable. For healthcare providers who depend on network
connections to enable efficient patient data access, this can be a serious problem. DDoS
attacks are motivated by a social, political, ideological, or economic motive tied to a
scenario that enrages cyber threat actors. Two modern Machine Learning (ML)
methods, including (i) Support Vector Machine (SVM) and (ii) Light Gradient
Boosting Machine (Light GBM), are used to validate the data set. From the extensive
experimental analysis, feature-based algorithms are superior to other competing models
in that they (i) have the highest detection rate with high accuracy, and (ii) have less
computational complexity with minimal training and test time.
Smart Healthcare Classifier - Skin Lesion Detection using a Revolutionary Light Weight Deep Learning Framework
Page: 201-216 (16)
Author: Sanjay Vasudevan*, Suresh Kumar Nagarajan and Sarvana Kumar Selvaraj
DOI: 10.2174/9789815196290124010015
PDF Price: $15
Abstract
Skin lesion diagnosis has recently gotten a lot of attention. Physicians spend
a lot of time analyzing these skin lesions because of their striking similarities.
Clinicians can use a deep learning-based automated classification system to identify the
type of skin lesion and enhance the quality of medical services. As deep learning
architecture progresses, skin lesion categorization has become a popular study topic. In
this work, a modern skin lesion detection system is provided using a new segmentation
approach known as wide-ShuffleNet. The entropy-based weighting technique is first
computed, and a first-order cumulative moment algorithm is implemented for the skin
picture. These illustrations are used to differentiate the lesion from the surrounding
area. The type of melanoma is then established by sending the segmentation result into
the wide-ShuffleNet, a new deep-learning structure. The proposed technique was
evaluated using multiple huge datasets, including ISIC2019 and HAM10000.
According to the statistics, EWA and CAFO wide-ShuffleNet are more accurate than
the state-of-the-art approaches. The suggested technology is incredibly light, making it
ideal for flexible healthcare management.
Recent Trends in Telemedicine, Challenges and Opportunities
Page: 217-228 (12)
Author: S. Kannadhasan*, R. Nagarajan and M. Shanmuganantham
DOI: 10.2174/9789815196290124010016
PDF Price: $15
Abstract
Recent networking advancements in a variety of areas have encouraged the
introduction of applications for the Internet of Things (IoT) and Artificial Intelligence
(AI). This article analyses the implications of technologies like IoT and AI in
Healthcare via a careful analysis of 85 peer-reviewed scientific journal publications.
The study shows a previously unheard-of rise in the number of publications written in
the last ten years, a wide range of publishing sources, a wide range of authors, and
several technical papers in philosophy and architecture, all of which point to an
evolving field with plenty of room for publication in the years to come. Medical
research is currently combining the administration and analysis of telemedicine data as
well as the development and use of artificial intelligence in numerous fields and
enterprises (AI). Due to the difficulty of implementing telemedicine, it has been
required to develop cutting-edge methods and expand its capabilities.
Sustainable Development for Smart Healthcare using Privacy-preserving Blockchain-based FL Framework
Page: 229-243 (15)
Author: D. Karthika Renuka*, R. Anusuya and L. Ashok Kumar
DOI: 10.2174/9789815196290124010017
PDF Price: $15
Abstract
Artificial Intelligence (AI) methods need to learn from an adequately large
dataset to achieve clinical-grade accuracy and validation, which is vital in the
healthcare field. However, sensitive medical data is usually fragmented, and not shared
due to security and patient privacy policies. In this context, our work aims at
classifying abdominal and chest radiographs by applying Federated Learning (FL)
without exchanging patient data. FL framework has been implemented on distributed
data across multiple clients. In the framework, a multilayer perceptron is used as a deep
learning model for the classification task. FL is a novel approach in which machine
learning models are built with the collaboration of multiple clients controlled by a
central server or service provider. FL model ensures data privacy and security by
retaining the training data decentralized. FL model provides security and privacy for
patients by training individual models in distributed clients and sharing merely the
model weights.
Smart Ambulance for Emergency Cases to be Reported to Hospitals at the Earliest using Deep Learning Algorithms and Blockchain-based Distributed Health Record Transactions for smart Cities
Page: 244-259 (16)
Author: V. Kavitha* and Partheeban Pon
DOI: 10.2174/9789815196290124010018
PDF Price: $15
Abstract
The everyday eating habits and lifestyle choices that people make have a
significant impact on how long they live on the planet. Ancient people ate food that had
an acceptable ratio of fat, vitamins, minerals, and carbohydrates, which helped them
live a long life. Nowadays, individuals live shorter lives and experience many crises
like heart attacks and mental despair that cause them to drive carelessly and cause
accidents. This is due to our current diets of junk food and style of life. For the people
and the individuals, this results in a tremendous loss. Here, saving people's lives
depends largely on the passage of time. The extent of the injury or the patient's
emergency situation, the amount of traffic that makes it difficult for the ambulance to
reach its destination, and the hospital's capacity to accept patients and save lives are
just a few of the many factors that affect the time limitations. In the current situation,
hospitals are using the available services to meet time restrictions, which correctly
route the ambulance. The main disadvantage of this system is that hospitals handle all
the data, making it easy to tamper with medical records and risk losing the integrity of
the data. The goal of intelligent ambulances is to forecast the shortest amount of time
needed to admit the patient to the local hospitals that have the resources to care for
them, preventing the need to transfer patients to other hospitals, as well as to determine
the most efficient route to the destination. The patient's life can be saved as a result.
The aforementioned can be accomplished by using a deep learning algorithm to predict
the injury and the time limit to admit the patient to the hospital, matching the injury
with the treatment options available in the hospital and mapping the appropriate
hospital, as well as by finding the quickest route with the least amount of traffic to get
to the destination within the allotted time limit, giving first aid in the ambulance, and
handling the data transfer of health records in a secure manner. Therefore, in a smart
city, the smart ambulance can quickly save lives.
Authentication Techniques for Human Monitoring in Closed Environment
Page: 260-279 (20)
Author: V. Vishu* and R. Manimegalai
DOI: 10.2174/9789815196290124010019
PDF Price: $15
Abstract
Human monitoring and trailing in a blocked or closed environment such as a
jail or psychological shelter is an important research concern. Industry 4.0 has enabled
the monitoring of physically or mentally challenged people in asylums and criminals
who are sentenced to serve their terms in jails with various tools such as sensors,
wireless systems and sophisticated cameras. The hidden nature of monitoring and
reporting in closed environments without any new technologies such as IoT, RFID,
etc., may lead to ill-treatment of the inmates in the above-mentioned places. The
traditional physical monitoring system can end up with wrong reports about the
inmates and can hide the real scenarios. Personal opinions and characteristics of
officials as well as the prisoners may vary based on their health and behavioral
patterns. The automation of human monitoring involves monitoring of security,
activity, fitness, and health factors of the inmates in the closed environment. The
human-activity monitoring is carried out by acquiring and analyzing the body signals
of the inmates. Passive tags are attached to the wristband of each person in the RFID
human monitoring systems. Minimal human intervention and effort is one of the
biggest advantages of the human monitoring system. Authentication, intelligent
decision making and minimum use of resources are the main challenges in designing a
human monitoring system. Intelligent decision making algorithms are applied to predict
human behavioral patterns. This work gives a summary of different authentication
protocols and methodologies used with the Internet of Things (IoT) and RFID devices
in human monitoring systems. It presents the components and infrastructure of a typical
human monitoring system and summarizes the sensors and IoT devices used for the
same. A wide investigation is conducted on security and privacy issues while storing
the private and confidential details of the inmates. A comprehensive survey on different
authentication techniques and data security issues in closed human monitoring is
presented in this work.
Abbreviation
Page: 280-282 (3)
Author: L. Ashok Kumar, D. Karthika Renuka, Sonali Agarwal and Sheng-Lung Peng
DOI: 10.2174/9789815196290124010020
Subject Index
Page: 283-288 (6)
Author: L. Ashok Kumar, D. Karthika Renuka, Sonali Agarwal and Sheng-Lung Peng
DOI: 10.2174/9789815196290124010021
Introduction
New technologies like blockchain and Internet of Things (IoT) are constantly improving the state-of-the-art in healthcare services. The trend of keeping medical records in digital formats is also increasing the reliance of modern healthcare service providers on these new technologies. This edited book brings a collection of reviews on blockchain and IoT technologies that are driving innovation in digital and smart healthcare systems. The editors bring an academic and practical approach to assist professionals and readers in understanding computerized healthcare solutions. 16 referenced chapters provide knowledge about fundamental framework, research insights, and empirical evidence for effective smart healthcare applications. The chapters also cover benefits and challenges of specific smart health frameworks, giving an informative overview of the subject. Key themes of the book include: 1. Technological Foundations for Smart Healthcare 2. Blockchain Applications in Healthcare 3. Internet of Things (IoT) in Healthcare 4. Artificial Intelligence (AI) Integration 5. Security, Privacy, and Authentication 6. Medical Imaging and Deep Learning 7. Telemedicine The content in the book is designed to help administrators and healthcare professionals to understand the basics of blockchain tech and IoT in smart healthcare systems and strengthen the competitive advantage of their clinics.