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.

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.

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.

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.

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.

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 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.

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.

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. 

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. 

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.

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 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.

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.

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.

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.