Blockchain-Enabled Internet of Things Applications in Healthcare: Current Practices and Future Directions

The fusion of artificial intelligence (AI) with the Internet of Things (IoT) marks a significant advancement in enhancing conventional healthcare systems across various domains, such as monitoring vital signs and patient behaviors. IoT sensors collect extensive information, which is then processed by AI platforms for informed decision-making. However, the pivotal challenges of privacy and security loom large, demanding robust protective measures for patient data against unauthorized access.

While access control has conventionally been employed to address these concerns, a more effective solution lies in leveraging blockchain technology. Consequently, the integration of IoT-based healthcare monitoring with blockchain emerges as a compelling technological innovation, offering a promising avenue to alleviate security and privacy apprehensions associated with data collection. This chapter introduces an architectural framework designed to gather, store, analyze, facilitate intelligent decision-making, and safeguard data using blockchain technology.

The proposed architecture harnesses the computational power derived from the synergy of IoT, blockchain, and artificial intelligence. It represents a versatile solution applicable across a broad spectrum of healthcare optimization initiatives, showcasing the potential to revolutionize and optimize healthcare systems.

The purpose of this study is to harness the power of artificial intelligence, IoT, and blockchain technology in making a system capable of enhancing the healthcare system. Further, the study presents an architecture that, if implemented, can help optimize the healthcare systems.

An architecture-based approach with AI, IoT, and blockchain techniques will be followed in designing architecture that can solve the integrated issues of data privacy and security that occur in healthcare systems.

A systematic architecture will be generated to tackle the healthcare industry problem. Systematic study and architecture will serve as a platform for new research and application development. 

The convergence of blockchain technology and the Internet of Things (IoT) has become a transformative catalyst in healthcare. “Blockchain-Powered IoT Innovations in Healthcare”, a chapter in this book, explores the dynamic interplay between these technologies and their far-reaching impact on healthcare systems. This chapter commences with an introduction to blockchain and IoT, emphasizing their relevance within the healthcare domain. It underscores blockchain's pivotal role in fortifying patient data security, addressing privacy concerns, and rectifying vulnerabilities in healthcare data management and highlights the diverse applications of IoT devices in healthcare, including wearables, remote patient monitoring, and smart hospital equipment. It also addresses how blockchain facilitates secure cross-platform data sharing while preserving data integrity and confidentiality. The implementation of smart contracts in healthcare is explored, showcasing their influence on patient empowerment, data transparency, and the management of clinical trials. The chapter also illustrates how blockchain technology enhances supply chain management and ensures drug traceability, countering the proliferation of counterfeit medications. A comprehensive discussion on patient empowerment and data control reveals how blockchain is revolutionizing patient engagement and informed decision-making. The chapter analyzes the intricate landscape of legal and regulatory aspects, offering guidance on compliance and potential legal implications of blockchain adoption in healthcare. In conclusion, the chapter addresses the challenges, future prospects, and emerging trends in the field of healthcare. It serves as an essential resource for healthcare professionals, researchers, policymakers, and technology enthusiasts navigating the intersection of blockchain and IoT in the ever-evolving realm of healthcare. 

Integrated health profile (IHP) utilizes the power of blockchain technology and smart contracts to construct a decentralized and tamper-proof platform for storing and sharing decentralized health records. Ensuring security and removing all vulnerabilities from accessing doctor-patient data remotely aims to reduce patient wait times and chances of incorrect pre-consultation data. In the IHP system, every patient is linked with a unique identifier, and their health records linked to this unique identifier are stored securely. Everyone gets access to a personal IHP card, which plays a pivotal role in the entire IHP framework. It consists of a database of patient health records, including but not limited to reports, prescriptions, medical bills, and insurance receipts. Each card's unique identifier is printed on the physical card with a QR code linked to it. When scanned by the medical practitioner, the request is validated using an OTP-based two-factor authentication. Upon successful verification, the patient controls what subset of their medical database the practitioner would be able to access. This gives the patient control over the privacy of medical records. Implementation of this framework reduces manual doctor-patient questioning time and waiting time at medical center receptions. Overall, it reduces various administrative tasks and eliminates the need to have, keep, and carry physical records, improving operational productivity. This is done by harnessing the strength of application programming interfaces (APIs) that connect customer-centric applications (CCAs) that are used by customers to discover medical facilities to medical service provider applications (MSPs) that fulfill the medical service. Real-time information on medical facilities is fetched via APIs, giving all CCAs access to real-time information on all MSPs and helping fulfill medical service demands at scale.

Blockchain innovation offers an information structure with built-in safeguards, including agreement, decentralization, and encryption, which ensure the accuracy of operations. It has broad use in a variety of fields, including smart factories, the Internet of Things (IoT), and healthcare. It is particularly relevant in the areas of healthcare information safety and privacy preservation. Digital healthcare records have been deployed faster because of communication technology, however, this has also increased risks to patient confidentiality, safety, and medical information. Another strategy to deal with the issue of medical data confidentiality and safety is the application of blockchain. Medical and health information includes treatment data gathered during patient care alongside private prevention of illness. Blockchain systems can be used in numerous capacities in healthcare organizations, including confidentiality and safety, to shield information about patients from unwanted access. Nevertheless, healthcare networks confront numerous security concerns, including connectivity, reliability, exchange of clinical information delivery, and patient deliberation, as a result of the inexperienced design of safety measures. Furthermore, deployment and information administration are the main issues for blockchain in healthcare due to the enormous amount of manufactured hardware gadgets. The platform is a communication mechanism that combines the use of computers with health records. It has been suggested that Hyperledger is the most developed collaborative chain technology. In contrast with various blockchain platforms, Hyperledger concentrates on developing enterprise-level standardized implementations. The Hyperledger framework is used for sensitive data in digital welfare, but neither restricted access nor thorough permission was taken into account. It is simple to determine fraudulent data collection by a malevolent person via confirmation of data by additional subjects, and the original, unmodified information can be recovered. Data can only be influenced if a bad person has access to over 50% of the blockchain network's nodes through security breaches, which is nearly unattainable. As a result, blockchain technology can stop information from being faked or falsified, improving the data's durability and dependability.

Within the framework of Industry 5.0, the incorporation of blockchainenabled IoT applications into smart agriculture and healthcare has significant consequences for long-term economic growth and the creation of ecosystems for smart health management. The environmental, economic, industrial, and stakeholder implications of smart agriculture's transparent supply chains, which are made possible by blockchain technology and precision farming techniques, lessen the environmental impact of conventional farming by cutting down on resource use. Blockchain technology and Internet of Things (IoT) devices safeguard patient records, allowing for more eco-friendly procedures with less paper waste. These innovations maximize the use of agricultural resources, which boosts output while decreasing expenditures from an economic perspective. Better patient outcomes and lower healthcare costs are possible because of blockchain technology's assurance of data integrity and interoperability. This promotes a fairer and more inclusive economic climate, giving smaller players a voice. Innovation and integration are propelled by the industrial sector. By fostering an environment of open communication and cooperation, the tenets of Industry 5.0 aim to bring together many sectors of the economy to improve technological standards, strengthen cybersecurity, and standardize procedures. Awareness, training, and community participation are all made easier by stakeholders, which include NGOs. For these technologies to be adopted in an ethical and responsible manner, their advocacy for legislation that supports them and the protection of data ownership rights are vital. In the future, there will be more international cooperation, new technological solutions to problems with energy efficiency, and the creation of universal benchmarks.

The increasing use of blockchain technology in supply chain management has made it imperative to understand the possible security risks associated with its implementation. This research aims to identify important security issues related to supply chain management's use of blockchain technology by doing a thorough analysis of the body of existing literature and looking at actual cases of blockchain deployments. These dangers include the possibility of data privacy breaches, smart contract weaknesses, and 51% attack vulnerability. The report also offers suggestions for reducing these risks, including using multi-factor authentication, regularly carrying out security audits, and enforcing strict access rules. The study's conclusions broaden our knowledge of the security risks associated with blockchain-based supply chain management (BC-SCM) and offer useful guidance to companies thinking about implementing this technology. 

This survey provides a state-of-the-art survey of the applications of blockchain technology in the healthcare industry with a few case studies. It aims to explore the significance of blockchain technology in healthcare and its potential to enhance healthcare services in the context of federated blockchain. The paper covers background information on healthcare technology and the benefits, challenges, and limitations of using blockchain technology in healthcare. In this regard, we proposed provenance-based architecture, Hyperledger Fabric-based architecture, and integrated architecture, which contains Aadhar verification and patient history features in the context of India. Overall, the paper demonstrates that blockchain technology has the potential to improve the healthcare industry significantly, but more research and development are required to overcome the challenges and limitations. 

The healthcare industry is using the Internet of Things (IoT) extensively. The healthcare sector manages sensitive information, including an individual’s medical history, blood pressure, and other relevant data. Consequently, the technologies used in this domain are becoming more susceptible to attacks because of their heightened sensitivity. Therefore, it is essential to protect the data. Blockchain technology has been shown to provide substantial benefits in achieving this goal. Incorporating blockchain technology into IoT devices has yielded substantial benefits, thanks to notable technical progress. This chapter thoroughly examines the characteristics of blockchain technology that enhance its efficiency in managing sensitive data and ensuring data security and privacy. Furthermore, this chapter systematically explains the region’s many security obstacles and how blockchain technology might successfully overcome them. The study’s results indicate that future research should explore how gender, age, and knowledge of blockchains affect the adoption of blockchain technology in innovative healthcare systems. An alternate field of research involves evaluating the many elements that influence the adoption of the technology known as blockchain.

Automation is taking over the entire globe. To boost efficiency, businesses, governments, and nonprofits are all using automation in their own fields. The demand for and necessity of automation is high in the agricultural industry. The majority of nations have begun to supply and use smart farming solutions. This proposed study focuses on smart poultry farming, an essential aspect of farming, and designs a new prototype. A number of Indian farmers keep chickens as pets, yet their farms' output, longevity, efficiency, and treatment of animals are all lacking.

Therefore, sustainable and lucrative farming is the outcome of integrating cutting-edge technology such as robotics, Internet of Things (IoT) sensors, and artificial intelligence (AI). This prototype incorporates a number of Internet of Things (IoT) sensors for several features, such as lighting (LDR), air quality (MG135), water quality (pH), temperature (DTH11), and lighting (LDR). Minimizing human intervention, keeping tabs on the bird's well-being, making the most of available resources, and increasing output are all outcomes of the automated system's development. With the help of an AI-IoT smart system, poultry producers will be able to manage their farms more efficiently and triumph over a number of obstacles.

The interdisciplinary convergence of computer vision and object detection is pivotal for advancing intelligent image analysis. This research surpasses conventional object recognition methodologies by delving into a more nuanced understanding of images, akin to human visual comprehension. It explores deep learning and established object detection systems such as convolutional neural networks (CNN), Region-based CNN (R-CNN), and you only look once (YOLO). The proposed model excels in realtime object recognition, outperforming its predecessors, as previous systems typically detect only a limited number of objects in an image and are most effective at a distance of 5-6 meters. Uniquely, it employs Google Translate for the verbal identification of detected objects, offering a crucial accessibility feature for individuals with visual impairments. This study integrates computer vision, deep learning, and real-time object recognition to enhance visual perception, providing valuable assistance to those facing visual challenges. The proposed method utilizes the Common Objects in Context (COCO) dataset for image comprehension, employing object detection and object tracking with a deep neural network (DNN). The system's output is converted into spoken words through a text-to-speech feature, empowering visually impaired individuals to comprehend their surroundings effectively. The implementation involves key technologies such as NumPy, OpenCV, pyttsx3, PyWin32, OpenCV-contribpython, and winsound, contributing to a comprehensive system for computer vision and audio processing. Results demonstrate successful execution, with the camera consistently detecting and labeling 5-6 objects in real time.

The use of information and communication technologies in agriculture to increase productivity, efficiency, and sustainability is known as smart farming. The implementation of predictive analytics and data-driven insights in smart farming enhances the effectiveness of agricultural systems as a whole, decision-making processes, and resource allocation. This study compares the use of the algorithms long short-term memory (LSTM), support vector machine (SVM), deep belief network, Naive Bayes, artificial neural network (ANN), and gated recurrent unit (GRU) in optimizing agricultural operations. The study highlights how using LSTM in smart farming has the ability to transform traditional agricultural methods, resulting in sustainable, higher-yield output while minimizing resource loss and environmental impact. Smart farming has emerged as a new approach for modernizing and optimizing agricultural practices through the integration of cutting-edge technology, with a focus on machine learning in particular. Various algorithms were applied to this dataset, producing measurable results like accuracy, loss, correct detection rate (CDR), and false discovery rate (FDR). Through an extensive comparative study, it was identified that the long short-term memory (LSTM) algorithm was the most promising choice for the dataset. Following the application of machine learning algorithms on different training to test splits like 80-20, 70-30, 65-35, 60-40, and 55-45, it was found that LSTM has the best accuracy on average, ranging from 95% to 98%. The exploration of LSTM demonstrated its potential to significantly enhance decision-making processes for farmers and researchers, ultimately improving agricultural efficiency and outcomes.

The whole industry has changed as it progresses from 1.0 to 4.0 in accordance with regulations. Horticulture has seen tremendous advances in automated technology production management, which has revolutionized crop cultivation and management techniques. These technological developments have improved product quality, increased crop productivity, decreased labor costs, and reduced negative environmental consequences. The purpose of this book chapter is to investigate the current state of automated production management in horticulture, including its applications, techniques, and potential future directions. This chapter presents an overview of how technology is being used in automated production management, such as robotic harvesting, sensor-based monitoring, and precision irrigation. In addition, the chapter investigates the possible benefits and future possibilities of automated production management in horticulture. It focuses on developing trends, such as the incorporation of artificial intelligence and machine learning algorithms, which can improve decision-making processes and resource allocation. Data analytics, remote sensing, and Internet of Things (IoT) technologies are also covered for real-time monitoring and system optimization. Some aspects of automated horticulture are also investigated, such as crop diversity, development phases, and environmental variables.

Finally, this book chapter presents a complete review of automated production management in horticulture, emphasizing the industry's disruptive significance. It also tackles farmers' and researchers' existing issues with automated horticulture production systems. Horticulture stakeholders may improve productivity, sustainability, and profitability by embracing automated production management in an increasingly competitive and resource-constrained environment.

The emergence of interconnected systems, blockchain approaches, and the Internet of Things (IoT) is creating novel possibilities for information-driven choices in agricultural production. The knowledge provided in this chapter points out the innovative possibilities of integrated technologies with respect to small-to-large scale, wholesale, vendor, end user, and sustainable farmland. The aim of this study is to incorporate hyperledger formulation in a decentralized blockchain building with the Internet of Things gadgets in automated agriculture. This involves placing actuators and sensing devices on the ground to develop an infrastructure of coupled gadgets that continuously acquire and convey data from the realm of agriculture. This section outlines data collection methods practiced in agriculture using IoT devices as well as blockchain. It emphasizes wired and wireless connectivity and describes the types of sensors used to monitor soil conditions, weather patterns, crop health, and other relevant parameters. An example is that farmers may receive weather- and climatepredicted data promptly. The heart of the research is to understand the study on data analysis in the agricultural sector using blockchain technology. From descriptive analysis for historical data review to predictive analysis for forecasting crop yields and disease attacks, this section provides an overview of the techniques in practice. It examines the increasing trend towards the adoption of edge computing in agriculture, enabling real-time data analysis directly from the ground, which may reduce latency, improve decision-making speed, and reduce the need for centralized cloud processing. The concluding portion addresses pragmatic applications for unified systems in IoTdriven statistical analysis and agribusiness. Supply chain (SC) optimization, surveillance of crops, smart watering systems, and agricultural precision farming are several examples. Identifying a handful of the complications that arise with this scientific convergence, the study examines pitfalls that parties in the agricultural sector have to navigate. It involves prerequisites for suitable physical labor, cooperation hardships, and data safety worries. New technologies designed to address the foregoing problems and efforts to fix those conflicts with farm-integrated IoT-driven data visualization are looked at along with their demonstrated intelligent farming approaches. Prospective paths in IoT-driven information mining and a system with embedded components for agriculture-related access without agent marketing are laid out in the investigation's summary. The work discusses the potential effects of artificial intelligence (AI) and machine learning (ML) promotions, strengthened sensory technological advances, and the possible incorporation of blockchain for safeguarding data and the ability to track individuals for sustainable farming and planetary food sustainability. The investigation of embedded equipment and IoT-driven analytical methods in the agricultural industry is addressed in this scientific statement. It offers an explanation of the manner in which agribusiness is adapting to herald in an additional phase of ecological responsibility, preciseness, and operational effectiveness.