V. C. Patel, Prit Shah, Nitin Rathore, Vinod Patel · 6 authors
The Internet of Things (IoT) generally provides details on every online object. It manages the operations remotely and monitors them without human intervention. It can adjust to the circumstances either instantly or via experience. Users’ security and privacy are now top concerns due to the widespread use of IoT devices in many applications. Cyber-attacks are eroding the current security and privacy rules exponentially. Recently, Blockchain technology emerged as one of the IoT&s;s most sophisticated methods. Blockchain technology was created to enable digital transactions and offer dependable, secure access to distributed ledgers. Blockchain technology was created to enable digital transactions and offer dependable and secure distributed ledger accessibility. Thanks to blockchain technology, trustworthy third parties are not necessary for secure transactions between users. The blockchain will record the rate at which each node transmits data packets. Consequently, Machine Learning (ML) methods are implemented to generate accurate outputs from extensive and intricate databases, thereby facilitating prediction and detecting vulnerabilities in IoT-based systems. It is possible to gather data from heterogeneous sensors by converting the various value types for different sensors. It is not anticipated to attain its peak level in addition to the rapid spread of IoT systems through worldwide protection. Most people lack the knowledge or skills necessary to secure devices on their own due to the pervasiveness of IoT. In the context of the IoT, ML is quite effective in resolving safety issues. This study reviews an IoT secure data transmission model for ML-based data security models in coordination with blockchain. This study demonstrates that the levels of secure data transmission are higher than those of the current models when compared to the traditional ones.
The rapid adoption of blockchain technology in e-procurement systems has revolutionized digital transactions, offering transparency, immutability, and decentralized control. However, despite these advantages, smart contract vulnerabilities pose significant security risks, potentially undermining the integrity of procurement processes. This paper explores the development of a blockchain-based e-procurement system designed to enhance data security while mitigating smart contract vulnerabilities. By integrating advanced cryptographic techniques, formal verification methods, and secure coding practices, the proposed system aims to strengthen transactional security and ensure trust in procurement operations. The study further outlines real scenario test cases designed to evaluate the functionality, security, and reliability of the smart contracts and system components within the Ethereum based procurement model. Our findings suggest that a well-architected blockchain-based e-procurement framework can significantly enhance data security and improve overall trust in digital procurement ecosystems.
This work focuses on multi-dimensional approach to incorporate the Support Vector Machine (SVM) models with Blockchain to secure distributed ledger against APTs.The classifying and high pattern recognition ability of SVM makes the proposed framework easily capture and neutralize malicious activities in the blockchain networks in realtime.The distribution of the blockchain technology and use of machine learning for predictive modeling guarantees a hard-coded countermeasure against new forms of cyber threats.As such, this work is centered on how these technologies can be integrated in harmony: attempting to enhance the accuracy of threat identification without compromising the functionality of the blockchain.This implementation shows the possibility of achieving strong, secure and scalable applications in different applications domains, and so make a way forward for upcoming decentralized cybersecurity solutions.
Smart waste management is vital for reducing environmental impact and improving quality of life in smart cities. This study presents an AI-driven waste classification model that integrates IoT and Blockchain technologies. IoT-connected bins transmit data to a central server, which uses blockchain to ensure secure, transparent data storage. AI algorithms, including machine learning (ML) and deep learning (DL), classify waste in real-time, optimizing waste collection and recycling. Blockchain ensures data integrity, while ML and DL models enhance sorting efficiency. The system aims to improve waste management and sustainability through intelligent decision-making and secure data handling. Performance will be assessed using retrieval metrics and visualization tools to evaluate the impact of hybrid ML and DL models on waste detection and classification.
As a result of IoT-based Wireless Sensor Networks (IoT-WSNs), resource-constrained environments are becoming more efficient and dynamic. Even though IoT-WSNs have many advantages, they also face significant challenges, including their high energy consumption, limited lifespan, and security vulnerabilities. IoT-WSNs for smart cities could be made more energy-efficient and secure by using a blockchain-based approach. Blockchain technology improves energy efficiency and reduces communication costs while ensuring decentralized, secure spectrum management. A smart contract and distributed ledger mechanism reduce redundant data transmissions and facilitate network trust. A blockchain-enabled clustering mechanism allows energy-aware sensing and resource allocation, as well as cognitive radio technology used for efficient spectrum utilization. Compared to existing techniques, the proposed method is more energy efficient, more accurate in sensing spectrums, and more secure. IoT-WSNs provide a solution to energy and security challenges in smart city infrastructure, contributing to sustainable development.
MintMart is a decentralized Web3 platform designed for simplifying the process of buying, selling and creating NFTs (Non Fungible Tokens). Currently there are various existing platforms in the market which have features like transparent transactions, auctions in marketplace etc. However they lack some focus in areas of secure transactions, irregular platform fee and royalty compensation for owners which plays an important role in such platforms. The proposed system is designed with an objective to encounter such problems and make the use of NFT marketplace more seamless for the users. The proposed system uses blockchain technology to confront the existing issues. Royalty compensation distribution becomes easier using smart contracts and libraries like OpenZeppelin which helps in better integration of royalty features using its ERC-721 standard. Also MintMart offers fixed minim MintMart is a decentralized Web3 platform designed for simplifying the process of buying, selling and creating NFTs (Non Fungible Tokens). Currently there are various existing platforms in the market which have features like transparent transactions, auctions in marketplace etc. However they lack some focus in areas of secure transactions, irregular platform fee and royalty compensation for owners which plays an important role in such platforms. The proposed system is designed with an objective to encounter such problems and make the use of NFT marketplace more seamless for the users. The proposed system uses blockchain technology to confront the existing issues. Royalty compensation distribution becomes easier using smart contracts and libraries like OpenZeppelin which helps in better integration of royalty features using its ERC-721 standard. Also MintMart offers fixed minimal platform fee making it more accessible for users The proposed system can successfully support multiple digital formats like images, videos etc. and has also achieved cross chain compatibility allowing users to interact with various blockchain networks. MintMart plans to expand its features in the near future. These include personalized recommendations, live bidding for NFTs on the marketplace, etc., thus aiming to increase user engagement. The proposed system can successfully support multiple digital formats like images, videos, etc., and has also achieved cross-chain compatibility, allowing users to interact with various blockchain networks. MintMart plans to expand its features in the near future. These include personalized recommendations, live bidding for NFTs on the marketplace, etc., thus aiming to increase user engagement.
In a business network, blockchain is an agreed on, unchangeable ledger that makes it easier to monitor resources and keep track of events. From the trading of digital currencies like Bitcoin and Ethereum to a variety of uses in the private as well as public sectors, Blockchain technology has advanced. In a distributed setting, it facilitates confidence between untrusted organizations without requiring regulatory intervention. Blockchain provides a number of benefits, such as a shared register of records, which enhance company values. One of the most crucial components of blockchain application that enable safe operations without the involvement of outside parties is the smart contract. Over the past few decades, the usage of smart contracts has increased dramatically. The emergence of additional technologies, such as the distributed ledger, decentralized autonomous organizations, distributed finance, Internet of Things and AI is linked to this technology. The use of sophisticated smart contracts is applicable in every aspect of our everyday lives, from the logistics sector, the arts, economics and the Internet of Things. Smart contract is enforceable and self-executing computer program designed to carry out provisions of an agreement independently without involvement of third party. Since blockchain technology has developed rapidly, smart contracts have also exposed to number of security vulnerabilities, and some attacks resulting from these defects have caused significant losses. Any mistake in development of smart contracts might result in significant financial loss. The smart contracts are also susceptible to theft from even a little code mistake. Because of this, businesses recognize the need of smart contract security audits to reduce the risk of frauds and to boost transparency and security. Smart contracts audit is essential to build trust between parties involved in blockchain enabled business processes. This article present an in depth overview of smart contract, blockchain systems, smart contract vulnerabilities and smart contract audit process
In recent years, the term Metaverse emerged as one of the most compelling concepts, captivating the interest of international companies such as Tencent, ByteDance, Microsoft, and Facebook. These company recognized the Metaverse as a pivotal element for future success and have since made significant investments in this area. The Metaverse is still in its developmental stages, requiring the integration and advancement of various technologies to bring its vision to life. One of the key technologies associated with the Metaverse is blockchain, known for its decentralization, security, trustworthiness, and ability to manage time-series data. These characteristics align perfectly with the ecosystem of the Metaverse, making blockchain foundational for its security and infrastructure. This paper introduces both blockchain and the Metaverse ecosystem while exploring the application of the blockchain within the Metaverse, including decentralization, consensus mechanisms, hash algorithms, timestamping, smart contracts, distributed storage, distributed ledgers, and non-fungible tokens (NFTs) to provide insights for researchers investigating these topics.
Preethi Preethi, Mohammed Mujeer Ulla, R. Sapna, Raghavendra M Devadas
Over the last few years, the conceptualization of Smart Home has received acceptance. The extensive issues regarding a smart home include offloading computational tasks, data security aspects, privacy issues, authentication of Internet of Things (IoT) devices, and so on. Presently, existing smart home automation addresses either of these issues, nevertheless, Smart Home automation that also necessitates decision-making for offloading computational tasks with improved QoS (i.e., latency and throughput) and systematic features apart from being reliable and safe is a definite necessity. To address these gaps in this, work a QoS-improved method called, Blockchain-modeled Swarm Optimized Lyapunov Smart Contract Deep Reinforced Tasks Offloading (BSOLSC-DRTO) in smart home is proposed. The BSOLSC-DRTO method is split into two sections, namely, Offloading Computational Tasks based on the Particle Swarm Optimized Lyapunov model and Temporal Difference Deep Reinforced Secured Offloading. First to solve the offloading issue and therefore improve the QoS, we developed a Particle Swarm Optimized Lyapunov model using a Lyapunov optimization function. This optimization problem aims to minimize latency and improve throughput considerably. Second, to boost the offloading security, we propose a trustworthy access control using the Temporal Difference Deep Reinforced Secured Offloading model that can safeguard devices against illegal offloading. Then to handle the computation management for addressing the offloading decisions in the queue temporal difference function is applied, therefore improving the smart contract accuracy and precision involved in offloading computational tasks. Evaluation results from experiments and numerical simulations exhibit the notable advantages of the proposed BSOLSC-DRTO method over existing methods.•Develop a Particle Swarm Optimized Lyapunov model to minimize latency and significantly improve throughput.•Proposed a Temporal Difference Deep Reinforced Secured Offloading model for trustworthy access control, protecting devices against illegal offloading
Anwar Ali Sathio, Muhammad Malook Rind, Shafique Ahmed Awan
The integration of Internet of Things (IoT), Fog Computing, and Green Blockchain technologies is transforming digital systems by making them more efficient, scalable, and sustainable. IoT generates vast data that requires real-time processing, but this creates challenges in latency, bandwidth, and resource management. Fog Computing addresses these by decentralizing data processing to the network's edge, reducing latency and improving performance for time-sensitive applications in smart cities, healthcare, and automation. Despite advancements, traditional systems' energy consumption is a concern as IoT devices grow. Green Blockchain optimizes energy usage by employing energy-efficient consensus algorithms like Proof of Stake (PoS), reducing the computational load of Proof of Work (PoW). This chapter explores how IoT, Fog Computing, and Green Blockchain together enhance scalability, security, and energy efficiency, offering sustainable solutions for smart environments and their convergence enables decentralized, low-latency, and energy-efficient systems for future industries.
The integration of Artificial Intelligence (AI) and Blockchain technology has opened new possibilities in secure distributed systems, addressing several inherent challenges in cybersecurity, trust management, and data privacy. This article explores the synergies between AI and Blockchain, focusing on how they can complement each other in creating secure, transparent, and efficient distributed systems. AI's capabilities in predictive analytics, machine learning, and decision-making combined with Blockchain's decentralized, immutable ledger offer enhanced security and operational efficiency for applications ranging from financial transactions to healthcare and supply chain management. Through a comprehensive analysis of recent advancements and case studies, we highlight the practical implications of these technologies in fostering secure distributed systems and provide a roadmap for their future integration.
ABSTRACT IoT is a rapidly developing technology with a wealth of creative application possibilities. However, IoT wireless sensor networks are vulnerable to Denial of Service (DoS) attacks due to their insecure nature. Although network integrity and security have been ensured through the use of distributed ledger and blockchain technologies, privacy preservation concerns frequently arise with traditional approaches. So, deep learning‐based Physics‐informed neural networks (PINN) and Honesty‐based Distributed Proof‐of‐Authority (HDPoA) are developed to enhance transaction security and detect intrusions. Initially, the mobile nodes were deployed in different regions to gather transactions and an intrusion detection system to analyze attacks. First, the Intrusion Detection System (IDS) uses a deep learning approach for detecting the intrusion in the network. For that, the collected data from the deployed nodes are pre‐processed using Variational auto‐encoder and min‐max normalization to standardize input dataset values. Then the features are selected using wild horse optimization and classified using PINN to predict data attack or non‐attack. After that, Homomorphic variable tag generation is used for normal transactions with multiple copies in the same document, which are then converted into hash values using the Keccak hashing function. The miner validates transactions based on rank‐based priority. Honesty‐based Distributed Proof‐of‐Authority (HDPoA) was used for network security, making it suitable for deployment in blockchain‐based IoT applications. The proposed deep learning‐based PINN classifier reached 97.2% accuracy and 96.52% specificity. Homomorphic variable (HV) tag generation takes 0.4 s, while the Keccak algorithm takes 0.3 s for hash generation, and the HDPoA protocol has 420 s for block generation time.
Sunil Prajapat, Pankaj Kumar, Ashok Kumar Das, Ghulam Muhammad
The integration of artificial intelligence (AI) with the Internet of Things (IoT) has transformed numerous domains through the AI of Things (AIoT). Nonetheless, AIoT encounters issues related to energy usage and carbon emissions as mobile technology continues to progress. Generative AI (GAI) possesses significant potential to mitigate carbon emissions associated with AIoT, owing to its higher reasoning and generative powers. Conventional security protocols frequently encounter issues with computational efficiency, latency, and overall security comprehensiveness. Blockchain technology, characterized by its decentralized and immutable properties, is a viable approach for improving electronic healthcare data transmission and node authentication in IoT networks. This research examines secure data transmission and node encryption in IoT systems, with a particular emphasis on data management. Conventional approaches encounter constraints in computational efficiency, latency, and comprehensive security. This study presents a novel protocol that combines GAI and blockchain technology with quantum encryption to enhance authentication and ensure secure data transmission. The algorithm comprises multiple consecutive processes, including the encoding and transmission of node requests, followed by the authentication process utilizing hash functions and digital signatures. The authentication approach utilizes a challenge-response technique, guaranteeing that only nodes with authentic credentials can advance. Thereafter, a dynamic key exchange protocol and quantum encryption method provide secure data delivery. The results indicate the procedure’s effectiveness in ensuring secure and regulated access to patient data, underscoring its significance in medical facilities. The system’s functionalities are augmented by a thorough evaluation employing machine learning. The findings indicate that the system exhibits an accuracy of 99.4%, precision of 99.10%, recall of 98.66%, F1-score of 98.50%, and security of 99.2%. An extensive analysis and comparison with the state-of-the-art methods demonstrate the significant advancements of the suggested method in tackling cryptographic security challenges. The algorithm offers a thorough approach to protecting IoT applications, especially in managing healthcare data.
B S Anupama, N. R. Sunitha, R Ankitha, C Rachitha · 6 authors
Continuous expansion of Blockchain Technology (BCT) has resulted in significant advancements across different domains, such as finance, healthcare and supply chain management. With Bitcoin as an example, blockchain technology has completely changed the nature of digital currencies and decentralized financial systems. Scalability and transaction throughput of BC networks is one of their main concerns. This problem has been continuously addressed by Bitcoin, whose decentralized architecture frequently leads to delayed transaction processing times. To improve the Students Digital Fee Payment System (SDFPS) in higher education, we suggest a technique that uses weak blocks in the subchain to speed up transaction processing and boost Bitcoin transaction rates. We offer a comprehensive evaluation of Bitcoin’s performance in relation to the subchain concept, a novel approach to boosting scalability without sacrificing security. Main goal of this research is to apply layered subchain technique to improve speed and efficiency of Bitcoin transactions. Technology behind Bitcoin highlights how important scalability is for SDFPS in blockchain networks. Subchains are introduced, and their potential for accelerating transaction processing is discussed. An in-depth description of the subchain implementation’s architecture and design is made, highlighting the main elements and how they work together. Subchain solution’s development process includes the creation of weak blocks, validation of strong blocks, and the integration with the main blockchain is implemented.
Dinesh Kumar K, J. Gobinath, Jayavadivel Ravi, S. Saravanakumar · 6 authors
This research proposes a new approach for developing and implementing a smart communication system of IoT through developmental blockchain technology. Regarding the proposed solution, it is suggested that a distributed ledger can improve security, reliability, and efficiency of communication of IoT devices. They tackle issues such as data integrity, privacy and security in IoT networks as well as the problem of trust in such networks. The solution provides a decentralised and secure IoT ecosystem communication hence allowing monitoring, controlling and administration of the IoT build ecosystems in real time. Blockchain technology, however, when combined with nextgeneration communication interfaces and modern data analysis techniques, can offer various superior characteristics such as device identification, origin of information, etc. to IoT applications. It also enhances the dependability and capacity of IoT systems regarding capability and efficiency. Analysis and performance comparisons substantiate the performance benefits of the proposed intelligent communication system based on blockchain technology In addition these theoretical and performance analyses and experimental evaluation confirm the applicability and potential of the blockchain-based intelligent communication system to transform IoT communication models and facilitate the new generation of eIOT applications in a number of fields. The key to the solution to incorporate blockchain fundamental infrastructure to augment the communication channel of IoT devices. Using cryptography and consensus methodologies with the decentralized framework, integrity and confidentiality of the data is properly maintained because of the barriers of malicious interception or manipulation of the data. The communication system also boosts trust within the network participants because it offers a decentralized and immutable ledger of data transactions on the blockchain. Instead, one of the compelling benefits of this solution is that real-time monitoring, controlling, and management of the IoT systems can be made. This helps the stakeholders to closely monitor and manage the IoT deployments in a very efficient and enhanced manner the performance and reliability of the overall systems.
The arrival of Machine Learning (ML) completely changed how we can unlock valuable information from data. Traditional methods, where everything was stored in one place, had big problems with keeping information private, handling large amounts of data, and avoiding unfair advantages. Machine Learning has become a powerful tool that uses Artificial Intelligence (AI) to overcome these challenges. We started by learning the basics of Machine Learning, including the different types like supervised, unsupervised, and reinforcement learning. We also explored the important steps involved, such as preparing the data, choosing the right model, training it, and then checking its performance. Next, we examined some key challenges in Machine Learning, such as models learning too much from specific examples (overfitting), not learning enough (underfitting), and reflecting biases in the data used. Moving beyond centralized systems, we looked at decentralized Machine Learning and its benefits, like keeping data private, getting answers faster, and using a wider variety of data sources. We then focused on a specific type called federated learning, where models are trained without directly sharing sensitive information. Real-world examples from healthcare and finance were used to show how collaborative Machine Learning can solve important problems while still protecting information security. Finally, we discussed challenges like communication efficiency, dealing with different types of data, and security. We also explored using a Zero Trust framework, which provides an extra layer of protection for collaborative Machine Learning systems. This approach is paving the way for a bright future for this groundbreaking technology.
In the era of big data, information security and privacy protection have become important issues facing today's society. This study proposes a distributed network security architecture based on blockchain to enhance the security of information privacy protection. The proposed architecture consists of three primary levels: equipment layer, network service layer, and application layer. It also integrates smart contracts. In addition, this study also proposes a vulnerability detection method based on improved tree convolutional neural networks. The incorporation of a "continuous binary tree" approach effectively addresses the limitation inherent to conventional tree convolution, wherein the number of nodes is fixed. This refinement enables a more effective capture of the hierarchical structure and semantic nuances inherent to smart contract code. The experiment used multiple datasets, each containing multiple IoT attack types and smart contract vulnerability code snippets. These datasets were evaluated based on a set of criteria, including but not limited to accuracy, recall, F1 scores, gas costs, and execution delays. Experiments have shown that the proposed method performs well in accuracy, precision, recall, and F1 scores compared to existing state-of-the-art methods, with an accuracy range of 89.62% to 98.36%, significantly better than Oyente (about 75%) and Securify (about 85%). Specifically, the proposed method achieved 96.14% accuracy in detecting reentrant attacks, compared to 78% for Oyente and 82% for Securify. The findings indicate that the architectural design exerts a substantial influence on enhancing network security performance, thereby ensuring the stability of the system by effectively mitigating the variability in response time.
Over the past few years, the financial industry has undergone significant transformation as a result of a flurry of technological advancements.This examination of the most recent innovations that have altered the financial landscape focuses on fintech, blockchain technology, artificial intelligence (AI), and digital currencies.Startups in the fintech sector have created brand-new financial services with the goal of improving customer service, efficiency, and accessibility.Block chain technology has revolutionized data management and secure transactions, paving the way for decentralized finance (DeFi) and smart contracts.AI has improved risk assessment, fraud detection, and personalized financial services through advanced data analytics.Digital currencies, particularly crypto currencies and central bank digital currencies (CBDCs), have also challenged conventional monetary systems and introduced new paradigms for global financial transactions.Consumers, regulators, and financial institutions face both challenges and opportunities as a result of these innovations, according to this research.To fully utilize these technologies' potential and address associated risks, the findings emphasize the need for industry-wide adaptation and ongoing collaboration.
Phishing is a serious threat to cryptocurrency networks; Bitcoin and Ethereum are prime targets for these attacks. This paper discusses some aspects of phishing attacks on these platforms. While the simpler architecture of Bitcoin leads to more direct phishing attempts, the more complex ecosystem in Ethereum introduces a wide range of attack vectors through dApps and smart contracts. A comparative analysis of phishing attacks in both blockchains shows that while both have their fair share of attacks, Bitcoin seems to bear the brunt of phishing attacks. Current defense strategies, like 2FA and anti-phishing tools, as well as recommendations for increasing network security against phishing are discussed in this paper. Understanding these phishing mechanisms is crucial in strengthening the security of blockchain platforms and mitigating future attacks.
As Industry 5.0 emerges, the convergence of advanced technologies like the Internet of Things (IoT) and blockchain is vital in shaping the future of industrial automation. Industry 5.0 emphasizes the collaborative relationship between humans and machines, requiring robust, decentralized systems to ensure security, accountability, and trust in interconnected ecosystems. Currently, IoT data processing is cloud-centric, which introduces challenges like fragmented data silos, limiting the potential for seamless and secure real-time analytics. Blockchain technology offers a solution by providing a decentralized and transparent ledger that can enhance data integrity and security across IoT applications. This study investigates the integration of blockchain with the IoT in the context of Industry 5.0, highlighting the potential for improved data management, security, and human-machine collaboration. By conducting a comprehensive analysis of IoT application designs and blockchain platforms, we evaluate existing literature to uncover the challenges, benefits, and limitations of this integration. Our research contributes by proposing a framework for selecting optimal blockchain platforms for IoT applications in Industry 5.0, providing actionable recommendations for enhanced data trust and resilience. Future research directions are also outlined to address the evolving demands of this technological convergence, ensuring that IoT ecosystems are secure, scalable, and human-centered in the era of Industry 5.0.
The integration of Internet of Things (IoT) devices in healthcare has revolutionized patient care by enabling real-time monitoring, personalized treatments, and efficient data management. However, this technological advancement introduces significant security risks, particularly concerning the confidentiality, integrity, and availability of sensitive medical data. Traditional security measures are often insufficient to address the unique challenges posed by IoT environments, such as heterogeneity, resource constraints, and the need for real-time processing. To tackle these challenges, we propose a comprehensive three-phase security framework designed to enhance the security and reliability of IoT-enabled healthcare systems. In the first phase, the framework assesses the reliability of IoT devices using a reputation-based trust estimation mechanism, which combines device behavior analytics with off-chain data storage to ensure scalability. The second phase integrates blockchain technology with a lightweight proof-of-work mechanism, ensuring data immutability, secure communication, and resistance to unauthorized access. The third phase employs a lightweight Long Short-Term Memory (LSTM) model for anomaly detection and classification, enabling real-time identification of cyber threats. Simulation results demonstrate that the proposed framework outperforms existing methods, achieving a 2% increase in precision, accuracy, and recall, a 5% higher attack detection rate, and a 3% reduction in false alarm rate. These improvements highlight the framework's ability to address critical security concerns while maintaining scalability and real-time performance.
N. A. Natraj, Krishnapillai Vijayakumar, Sundaravadivazhagan Balasubaramanian, V. Kiruthika
Healthcare uses many facts to organize scientific investigations, collect scientific data, handle health insurance claims, and monitor patients daily. In practice, blockchain initiatives have focused on developing distributed ledgers for virtual currency, but this generation's enthusiasm has spread to the scientific industry. Programmable blockchains may solve some of the healthcare industry's biggest problems, such as a loss of powerful clinical report documentation, fragmented fitness statistics, and communication gaps. Blockchain is ready to transform healthcare, despite the lack of study. Blockchain's decentralised principles can improve patient data privacy and accessibility, which might weaken the present healthcare hierarchy and build a new one where patients pay for their own treatment. This study analyzes existing literature to identify healthcare stakeholders' main challenges and examine blockchain generation's potential solutions. Many issues and limits with this era should also be considered.