The increasing ability of internet-connected daily life electronic gadgets has propelled smart homes into a global trend. The Internet of Things (IoT) enables ambient devices to communicate and interact seamlessly through various sensors. Emerging technical concepts like Web3 and Industry 5.0 require decentralised and intelligent systems near the network's edge. Petabytes of IoT sensor-generated data cause a shortage of storage on the Cloud servers, adding a delay factor to the IoT system. Standard cloud-based IoT systems can't fully function in areas with unstable internet. This paper addresses these challenges and proposes a solution to integrate edge computing concepts. The proposed system is developed using a Raspberry Pi 3 Home Server (RHS) driven by the Support Vector Machine (SVM) algorithm. The designed prototype includes a fire and smoke detection system with MQ2 gas, dust, temperature, and flame sensors. The SVM and these sensors form a data fusion module integrating with Network Mapper (NMAP), Message Queuing Telemetry Transport (MQTT) broker, MariaDB SQL server, and InfluxDB time series database. The experiments demonstrate a fundamental edge operation with a latency of 2.45 ms (milliseconds), while NMAP integration ensures data security and device verification for sensor data storage. The synthetic simulations show positive outcomes for the data fusion-based monitoring system, where alerts are promptly triggered as sensor values change, with an overall system latency of approximately 24 ms. The developed system manages home automation, real-time monitoring for fire, smoke, gas leaks, network scans, anomaly detection, appliance usage tracking, and cloud data backup. A multi-level alert system ensures early threat mitigation, with alarms, SMS, notifications, and email alerts to maximize awareness.
Abstract: This research investigates the transformative potential of incorporating blockchain technology into computer science education. In light of the rapid evolution of the digital landscape, traditional educational frameworks often fail to meet the demands of the industry, resulting in a significant skills gap among graduates. This paper analyzes how the integration of blockchain can revolutionize computer science curricula by enhancing learning experiences and equipping students to navigate future technological challenges. The study highlights the advantages of blockchain in educational contexts, including increased security and transparency of academic records, streamlined credentialing processes, and the establishment of decentralized learning platforms that promote collaboration and innovation. It presents case studies of institutions that have successfully implemented blockchain, along with strategies for educators to effectively integrate this technology into their pedagogical approaches. Additionally, the research addresses the challenges and limitations associated with blockchain integration, such as the requisite learning curve and infrastructure demands. The findings indicate that the incorporation of blockchain in computer science education can significantly boost student engagement, provide verifiable skill sets, and align academic outcomes more closely with industry requirements. This study contributes to the ongoing discourse on educational innovation and offers a strategic framework for institutions aiming to utilize blockchain technology in preparing students for the future job market.
Paras Shah, Chetna Patel, Jaykumar Patel, Akash Shah · 6 authors
Blockchain is a decentralized, secure, and immutable public ledger that offers significant benefits over conventional centralized systems by preventing data breaches and cyber-attacks. It has a great potential to improve data security, privacy, and interoperability in healthcare and biomedical research. This review discusses the basic principles and the historical evolution of blockchain and evaluates the implications of blockchain for the existing healthcare infrastructure. It also highlights blockchain technology's advantages in electronic health records, supply chain management, clinical trials, and telemedicine. However, this technology faces several hurdles, including regulatory issues, technical complexity, and economic costs, which suggest a gradual adoption over time. In addition, the review emphasizes its ability to ensure data integrity, enhance collaboration, and protect intellectual property in biomedical research. This review shows that blockchain can enhance healthcare data management by providing secure, efficient, and patient-centric solutions. Furthermore, it also discusses the implications of blockchain for the future of healthcare and biomedical research and suggests that ongoing research and interdisciplinary approaches are essential for overcoming current barriers and realizing the full potential of this technology. Future research should focus on developing privacy-preserving hybrid data storage solutions that comply with international laws and regulations, thus enhancing the sustainability and scalability of this technology in healthcare.
Worldwide diabetes prevalence is rising, which emphasizes the necessity for safe health monitoring methods. The present study investigates the potential of implementing Ring Learning With Errors (RLWE) encryption in a smart glucometer to augment security and privacy in the context of diabetes care. Sensitive health data is encrypted and stored securely with RLWE, a lattice-based cryptography method. The Ethereum blockchain is used by the smart glucometer to provide decentralized and unchangeable data management, and RLWE encryption is included for data security. Ethereum smart contracts offer strong security features by ensuring data integrity and access management. Performance criteria, such as memory utilization and encryption or decryption times, evaluate the usefulness of RLWE in real-world medical equipment. The study highlights RLWE’s effectiveness in preventing unauthorized access to sensitive data and shows notable developments in secure health information systems. The potential of blockchain technology and RLWE efficiency over LWE to improve healthcare data security and the same is highlighted by the results obtained through the various calculations done. The study advances patient care and data security in healthcare settings by promoting safe and reliable medical IoT devices through the use of advanced cryptographic algorithms and decentralized data management.
K. Vijayakumar, A. S. Chethan, M S Muneshwara, M. S. Swetha · 6 authors
In today's rapidly evolving healthcare landscape, the integration of cutting-edge technologies such as IoT and wearable devices has led to significant advancements, particularly in the realm of remote patient monitoring. Despite these advancements, the traditional client/server architecture prevalent in current implementations presents substantial challenges related to security and privacy, leaving healthcare systems vulnerable to various attacks. Consequently, stringent regulatory and security measures are imperative to safeguard health data. To address these challenges and meet regulatory requirements, transitioning to a distributed architecture is essential. Blockchain technology, renowned for its distributed nature and robust security features, offers a promising solution to mitigate the security risks inherent in IoT-based systems. Motivated by these considerations, this study introduces HealthLink—a secure healthcare framework that seamlessly integrates IoT and Blockchain technologies.HealthLink is designed to facilitate remote patient monitoring, particularly for chronic diseases that require continuous oversight. The framework prioritizes security, scalability, and processing efficiency. Security measures include the use of re-encryption proxies combined with Blockchain for hash data storage, while access control is managed through smart contracts. To enhance Blockchain scalability, an off-chain IPFS-based database is employed for data storage, and the Ethereum Blockchain-based proof of authority is leveraged to expedite data storage processes. As a practical demonstration, we apply the HealthLink system to diabetes management, presenting execution results through system interfaces. Experimental findings highlight substantial enhancements in the security of healthcare systems compared to conventional methodologies, underscoring the potential of HealthLink to revolutionize remote patient monitoring and data security in healthcare.
The Secure Women Safety Platform envisions a decentralised application on the Ethereum blockchain, merging AI, IoT, and blockchain technologies to tackle societal safety challenges. Through Ethereum’s blockchain, it ensures tamper-proof identity management, empowering users with control over their data. IoT wearables equipped with GPS and sensors enable real-time tracking and swift response to emergencies, while AI-driven anomaly detection analyses user behaviour patterns for proactive threat identification. An emergency alert system triggers timely notifications to contacts and authorities, enhancing user safety. User-centric data management safeguards privacy, with Ethereum’s blockchain facilitating secure access permissions. Crowdsourced safety ratings and incident reporting foster a collaborative ecosystem, with blockchain’s immutability ensuring data integrity. This holistic solution advances women’s safety, setting a precedent for future innovations in personal security by combining AI, IoT, and cloud computing within a decentralised framework.
The growing consumer interest in product quality and originality has made the drug supply chain management system complex and critical. Moreover, fraudulent activities, such as drug contamination, have become global issues in the drug supply chain. Traditional centralized systems struggle to maintain transparency and traceability throughout the supply chain, often resulting in inefficiencies and vulnerabilities. Here, consumer electronics devices can be integrated with the ecosystem to extend the reach of the drug supply chain. In this paper, a solution for traditional drug supply chain systems is presented by using blockchain technology, Internet of Things (IoT), and consumer electronics. Here, blockchain supports to store drug records immutably, while keeping information transparent to all entities involved in the network. This proposed scheme is deployed over the Ethereum blockchain network and uses IoT technology to provide the key features of blockchain, such as drug traceability, security, and transparency. Here, smart contracts automate the entire process in the proposed supply chain network, and all the drug batch’s transfer details are stored on the blockchain network. The security analysis and performance analysis of the proposed scheme are performed to evaluate the efficiency of the proposed scheme over some existing schemes.
This paper surveys the landscape of security and data attacks on machine unlearning, with a focus on financial and e-commerce applications. We discuss key privacy threats such as Membership Inference Attacks and Data Reconstruction Attacks, where adversaries attempt to infer or reconstruct data that should have been removed. In addition, we explore security attacks including Machine Unlearning Data Poisoning, Unlearning Request Attacks, and Machine Unlearning Jailbreak Attacks, which target the underlying mechanisms of unlearning to manipulate or corrupt the model. To mitigate these risks, various defense strategies are examined, including differential privacy, robust cryptographic guarantees, and Zero-Knowledge Proofs (ZKPs), offering verifiable and tamper-proof unlearning mechanisms. These approaches are essential for safeguarding data integrity and privacy in high-stakes financial and e-commerce contexts, where compromised models can lead to fraud, data leaks, and reputational damage. This survey highlights the need for continued research and innovation in secure machine unlearning, as well as the importance of developing strong defenses against evolving attack vectors.
Afnan Alsadhan, Areej Alhogail, Hessah A. Alsalamah
The Internet of Medical Things (IoMT) is a rapidly expanding network comprising medical devices, sensors, and software that collect and exchange patient health data. Today, the IoMT has the potential to revolutionize healthcare by offering more personalized care to patients and improving the efficiency of healthcare delivery. However, the IoMT also introduces significant privacy concerns, particularly regarding data privacy. IoMT devices often collect and store large amounts of data about patients’ health. These data could be used to track patients’ movements, monitor their health habits, and even predict their future health risks. This extensive data collection and surveillance could be a major invasion of patient privacy. Thus, privacy-preserving research in an IoMT context is an important area of research that aims to mitigate these privacy issues. This review paper comprehensively applies the PRISMA methodology to analyze, review, classify, and compare current approaches of preserving patient data privacy within IoMT blockchain-based healthcare environments.
Shampa Rani Das, N. Z. Jhanjhi, David Asirvatham, Farzeen Rizwan · 5 authors
The installation of the blockchain into artificial intelligence (AI)-driven healthcare systems is explored to prevent security breaches and optimize the patient's well-being. The digital revolution in healthcare brings with it substantial challenges related to data security, notably privacy invasions and data compromises, and even shortcomings with data interoperability. These limitations, which are rendered severe by advanced cyberattacks, indicate how inadequate conventional security precautions are and how this sector must move beyond more constantly evolving and predictive security approaches. Integrated Blockchain-AI concerning the healthcare sector is revolutionizing the management of patient data by utilizing an intricate, multifaceted infrastructure extending from data collection to service delivery. This ingenious incorporation represents a crucial breakthrough when it comes to healthcare systems and research as it not only ensures stringent data privacy and compliance but also greatly improves diagnostic, predictive, and individualized healthcare services. Meanwhile, it streamlines supply chain management (SCM) by offering an opaque, traceable system that inhibits the commercialization of counterfeit medicinal products and upholds quality control (QC), sustaining the wellness of patients and making certain legal compliance. The adoption of blockchain platforms to track pharmaceutical lifecycles and AI to personalize healthcare is demonstrated through usage scenarios in drug provenance and secured medical records management. The unified infrastructure must deal with ethical dilemmas and legal concerns including regulatory compliance, intellectual property (IP) protection, and liability formulation, crucial to providing reliable, legally compliant healthcare options. A concerted effort from all stakeholders is required to navigate and optimally utilize the prospective benefits of these advancements, as the healthcare industry looks forward to a subsequent of more secure operations, individualized treatment, and operational efficiency via an amalgamation of AI and blockchain.
Blockchain Technology Applications and Security
Artificial Intelligence in Healthcare and Education
Ugochukwu O. Mathew, Demóstenes Zegarra RodrÃguez, Renata Lopes Rosa, Muhammad Shoaib Ayub · 5 authors
As Healthcare 5.0 becomes more widely accepted, the healthcare industry in general is changing, leading to better patient care and more system efficiency. The degree of digital automation that introduced cutting-edge technology applications to the health sector is seriously challenged by this shift. Concerns over patient’s privacy and security are heightened by the rising digitization of healthcare institutions, which makes it harder to collaborate and share data seamlessly. The exponential growth of healthcare data necessitates effective processing and analysis in order to maximize patient outcomes and healthcare delivery. To fully realize the potential of Healthcare 5.0, healthcare practitioners, information technology specialists, data scientists, and researchers must address these complex security difficulties. Healthcare 5.0 provides a comprehensive solution to the pressing issues facing the digitalized healthcare industry by thoroughly examining its foundational principles and exploring its practical implementation through cloud computing, data analytics, and federated learning. Through healthcare federated learning, the national electronic health (e-health) policy and strategy will be legitimized. In this paper, the authors categorized and combined healthcare technology with federated learning artificial intelligence (AI) at various automation stages. The paper discussed the issues that are now facing the healthcare industry, such as security, privacy and dependability. The paper provided readers with guidance on how to use AI and federated learning to solve healthcare information system synchronization. In conclusion, the authors discussed broad security topics and future research in the healthcare management system utilizing federated learning and blockchain approach.
P. Chinnasamy, G. Charles Babu, Ramesh Kumar Ayyasamy, S. Amutha · 6 authors
6G mobile network technology will set new standards to meet performance goals that are too ambitious for 5G networks to satisfy. The limitations of 5G networks have been apparent with the deployment of more and more 5G networks, which certainly encourages the investigation of 6G networks as the answer for the future. This research includes fundamental privacy and security issues related to 6G technology. Keeping an eye on real-time systems requires secure wireless sensor networks (WSNs). Denial of service (DoS) attacks mark a significant security vulnerability that WSNs face, and they can compromise the system as a whole. This research proposes a novel method in blockchain 6G-based wireless network security management and optimization using a machine learning model. In this research, the deployed 6G wireless sensor network security management is carried out using a blockchain user datagram transport protocol with reinforcement projection regression. Then, the network optimization is completed using artificial democratic cuckoo glowworm remora optimization. The simulation results have been based on various network parameters regarding throughput, energy efficiency, packet delivery ratio, end-end delay, and accuracy. In order to minimise network traffic, it also offers the capacity to determine the optimal node and path selection for data transmission. The proposed technique obtained 97% throughput, 95% energy efficiency, 96% accuracy, 50% end-end delay, and 94% packet delivery ratio.
E. S. Vani, K. Raja Kumar, Keshav R Poojary, Praveen Naik
Online trading requires trust between unfamiliar parties, often facilitated by third-party services. This thesis explores leveraging blockchain technology to enhance trust in such transactions, building on its decentralized nature introduced by Bitcoin for peer-to-peer money transfers.A decentralized application (dApp) is developed for storing product tracking and agreement data on a blockchain, aiming to reduce fraud and enhance trust while enabling real-time product tracking. This blockchain-based system demonstrates a significant reduction in fraud risks and an improvement in overall trust compared to traditional third-party services by establishing a Decentralized Autonomous Organization (DAO) environment [12]. Importantly, it eliminates transaction value limitations.However, the inherent limitations of blockchain technology, such as data storage and transfer speed, present usability challenges. Integrating logistics companies into blockchain contracts exacerbates these issues. While blockchain solutions may not address all aspects of online trading services, their potential improvements make them appealing to certain users. This research underscores the importance of considering both the technical capabilities and limitations when implementing blockchain-based trade agreements and highlights the tradeoff between scalability and security.
Huijuan Zhu, Lei Yang, Liangmin Wang, Victor S. Sheng
Smart contracts have gained extensive adoption across diverse industries, including finance, supply chain, and the Internet of Things. Nevertheless, the surge in security incidents of smart contracts over recent years has led to substantial economic losses. Therefore, ensuring the security of smart contracts has become a critical and complex challenge in both academic and industrial domains. Based on 539 real-world security incidents in the Ethereum platform and audit reports from 10 authoritative auditing institutions, we summarize 27 types of exploited security vulnerabilities and draw insights into their principles, typical cases, relevant research and recommended prevention strategies. Besides, we also gather 7 other potentially threatening vulnerability types as supplements. On this basis, we conduct an in-depth analysis of the root causes of vulnerabilities and further formulate eight safety practical rules. Moreover, we perform a comprehensive review of 178 recent papers on smart contract security analysis, classifying detection methods into formal verification, fuzz testing, machine learning, program analysis, and others. For each category, we seize the specific detection tools and analyze them comprehensively. Then, we conduct an extensive analysis and synthesis from various angles, presenting a comprehensive overview of the current research landscape in smart contract security detection. We also discuss current on-chain and off-chain repair methods. Finally, this review outlines major challenges and highlights potential areas for future research in this field.
Humaira Ashraf, Uswa Ihsan, Ata Ullah, Sayan Kumar Ray · 5 authors
Blockchain and AI have revolutionized how distributed systems can work. Combining blockchain and generative ai to enhance data integrity and cooperation in the treacherous field may find innovative ways blockchain and generative AI can develop distributed systems in terms of data integrity, transparency, scalability, security, cooperation, and decision-making. Through an investigation, it is understood how blockchain secures AI models, makes them more transparent and traceable, decentralizes training, and makes distributed models more efficient and scalable. Examples from real applications across worlds will guide how blockchain and generative AIs are used in fields such as health, finance, film, supply chains, and electrical transmission.As a result, different sources' knowledge is adopted as proven to propel the process of supplementing blockchain and AI towards a future of intelligent, flexible, and secure distributed systems.
Mithul Raaj A T, B. Saravana Balaji, Sai Arun Pravin R R, Rani Chinnappa Naidu · 10 authors
In response to the growing need for enhanced energy management in smart grids in sustainable smart cities, this study addresses the critical need for grid stability and efficient integration of renewable energy sources, utilizing advanced technologies like 6G IoT, AI, and blockchain. By deploying a suite of machine learning models like decision trees, XGBoost, support vector machines, and optimally tuned artificial neural networks, grid load fluctuations are predicted, especially during peak demand periods, to prevent overloads and ensure consistent power delivery. Additionally, long short-term memory recurrent neural networks analyze weather data to forecast solar energy production accurately, enabling better energy consumption planning. For microgrid management within individual buildings or clusters, deep Q reinforcement learning dynamically manages and optimizes photovoltaic energy usage, enhancing overall efficiency. The integration of a sophisticated visualization dashboard provides real-time updates and facilitates strategic planning by making complex data accessible. Lastly, the use of blockchain technology in verifying energy consumption readings and transactions promotes transparency and trust, which is crucial for the broader adoption of renewable resources. The combined approach not only stabilizes grid operations but also fosters the reliability and sustainability of energy systems, supporting a more robust adoption of renewable energies.
P. Sivaprakash, R. M. Dilip Charaan, J Vimala Ithayan, M Sankar · 6 authors
Healthcare is among the industries that are very interested in blockchain technology due to its potential. Blockchain and the interplanetary file system are emerging technologies that include distributed fault tolerance, decentralization, flexible security features, and effective data management. By providing a decentralized solution, the blockchain and Interplanetary file system integration addresses the issues of data security, integrity, and accessibility in healthcare systems. The healthcare industry electronically maintains medical data, including prescriptions, diagnostic results, and personal patient information. When there is a risk of a data breach or loss, many healthcare institutions store patient data utilizing centralized models and third-party applications. The current centralized system has a 75% accuracy rate. To guarantee the integrity and security of medical data, the suggested system would demonstrate the qualities of blockchain technology, including immutability, transparency, and decentralization. The suggested system's methodology stores and hashes data using Ethereum smart contracts and consensus mechanisms. Approaches based on consensus algorithms are used to combine data upload and storage authentication and validation. Following data mining, the uploaded data will be verified and saved in the Interplanetary File System with unique content identifiers. Only those who have registered are able to access the saved data. The risk of unwanted tampering or data breaches is decreased because all transactions pertaining to the storage and access of data are recorded in an unchangeable and transparent manner. The suggested distributed and decentralized system has a 90% accuracy rate. Moreover, the decentralized nature of blockchain eliminates the necessity for a central authority, so reducing the likelihood of a single point of failure and enhancing data resilience.
The healthcare industry has witnessed a transformative impact due to recent advancements in sensing technology, coupled with the Internet of Medical Things (IoMTs)-based healthcare systems. Remote monitoring and informed decision-making have become possible by leveraging an integrated platform for efficient data analysis and processing, thereby optimizing data management in healthcare. However, this data is collected, processed, and transmitted across an interconnected network of devices, which introduces notable security risks and escalates the potential for vulnerabilities throughout the entire data processing pipeline. Traditional security approaches rely on computational complexity and face challenges in adequately securing sensitive healthcare data against evolving threats, thus necessitating robust solutions that ensure trust, enhance security, and maintain data confidentiality and integrity. To address these challenges, this paper introduces a two-phase framework that integrates blockchain technology with IoMT to enhance trust computation, resulting in a secure cluster that supports the quality-of-service (QoS) for sensitive data. The first phase utilizes the decentralized interplanetary file system and hashing functions to create a smart contract for device registration, establishing a resilient storage platform that encrypts data, improves fault tolerance, and facilitates data access. In the second phase, communication overhead is optimized by considering power levels, communication ranges, and computing capabilities alongside the smart contract. The smart contract evaluates the trust index and QoS of each node to facilitate device clustering based on processing capabilities. We implemented the proposed framework using OMNeT++ simulator and C++ programming language and evaluated against the cutting-edge IoMT security approaches in terms of attack detection, energy consumption, packet delivery ratio, throughput, and latency. The qualitative results demonstrated that the proposed framework enhanced attack detection by 6.00%, 18.00%, 20.00%, and 27.00%, reduced energy consumption by 6.91%, 8.19%, 12.07%, and 17.94%, improved packet delivery ratio by 3.00%, 6.00%, 9.00%, and 10.00%, increased throughput by 7.00%, 8.00%, 11.00%, and 13.00%, and decreased latency by 4.90%, 8.81%, 11.54%, and 20.63%, against state-of-the-art methods and was supported by statistical analysis.
The Internet of Medical Things (IoMT) is a compelling networking paradigm integrating wireless communications sensors, connected devices, and embedded computing technologies. The IoMT involves the collection of real-time health data using sophisticated medical sensors. In recent years, the IoMT has become increasingly significant within the broader context of the Internet of Things (IoT). It provides accessibility for health monitoring and poses security obstacles to safeguarding the confidentiality and privacy of patient data. Therefore, this article presents a blockchain-integrated quantum authentication scheme in sensor-assisted IoMT networks. The proposed concept utilizes blockchain technology to achieve efficient patient authentication without the need for third-party entities. In addition, a secure quantum authentication scheme is designed not to require patients to authenticate themselves when communicating with multiple doctors simultaneously. This protocol explicitly addresses how clinicians can misuse their professional roles toward patients in IoMT networks. An evaluation analysis assesses the proposed technique’s efficacy compared to existing authentication schemes. The performance analyses demonstrate that the proposed protocol is resilient against various security attacks. Also, the practical usability of the quantum authentication scheme proved its importance as a significant improvement in communication security for IoMT networks.
In essence, blockchain is a distributed, secure ledger that contains a hierarchical network of blocks that maintains a record of all transactions. Bringing cloud capabilities closer to computation tasks is the goal of edge computing. It is possible to overcome existing security and scalability issues with blockchain and edge computing. Security is strengthened by the integration of Blockchain technology into Industrial IoT (IIoT) intrusion detection frameworks. In the proposed framework, the decentralized and tamper-proof nature of Blockchain is leveraged to enhance trust and integrity at the edge of the network in the detection of malicious activities. It aims to identify and mitigate cyber threats in IIoT systems for a robust security solution. The framework's performance is measured using various IIoT scenarios, including detection accuracy, response time, delay time, and overall performance score. A combination of blockchain technology and edge-based intrusion detection significantly improves the security posture of IIoT networks, producing high detection accuracy and minimal delays. A more resilient, trustworthy and secure IIoT network can be achieved with this study, which advances secure IIoT architectures.