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Enhancing Data Security and Privacy in Energy Applications: Integrating IoT and Blockchain Technologies
Abstract
The integration of blockchain technology with the IoToffers numerous opportunities to enhance the privacy, security, and integrity. This study comprehensively analyze the challenges, scope, and potential solutions associated with integrating blockchain technology and the IoT, with a specific emphasis on nuclear energy applications. We discuss the roles and various aspects of blockchain and the IoT, highlighting their multiple dimensions and applications. Our study develops a secure data management framework that incorporates encryption, integrity verification, an integrated communication network, and a robust data flow architecture. We explore the several aspects of data security, privacy, and integrity, along with the potential solutions in the integration of blockchain and IoT. The study also investigates the secure transaction process, with a specific focus on cryptographic, mathematical, and algorithmic perspectives. We demonstrated the use of blockchain technology in the nuclear energy sector using flow charts, comprehensively addressing the associated security and privacy concerns. While emphasizing the applicability of our methodology to the nuclear sector, we also acknowledge limitations such as requirements for practical validation, challenges with resource-constrained IoT environments, increasing cyberthreats, and limited real-time data availability. The future scope of our study focuses on standardization, scalable blockchain, post-quantum cryptography, privacy, regulations, real-world testbeds, and deep learning for nuclear sector security. Our findings highlight that the integration of blockchain and IoT can significantly enhance the security and privacy of nuclear energy applications, although practical validation and optimization are necessary.
... End Procedure Pre-Processing and Filtering:Before forwarding data to the Core ML Engine, these nodes perform preliminary processing. This may include filtering out irrelevant data, compressing data for efficient transmission, and performing initial categorization [24].This pre-processing step ensures that the Core ML Engine receives data that is already somewhat refined, aiding in more efficient and faster analysis. Secure Data Transmission:The nodes are equipped with secure transmission protocols to ensure that the data collected is transmitted to the Core ML Engine securely, maintaining data integrity and confidentiality [25].Encryption and secure channels prevent potential interception or tampering of the data during transmission. ...
In the current age of digital transformation, the Internet of Things (IoT) has revolutionized everyday objects, and IoT gateways play a critical role in managing the data flow within these networks. However, the dynamic and extensive nature of IoT networks presents significant cybersecurity challenges that necessitate the development of adaptive security systems to protect against evolving threats. This paper proposes the CoralMatrix Security framework, a novel approach to IoT cybersecurity that employs advanced machine learning algorithms. This framework incorporates the AdaptiNet Intelligence Model, which integrates deep learning and reinforcement learning for effective real-time threat detection and response. To comprehensively evaluate the performance of the framework, this study utilized the N-BaIoT dataset, facilitating a quantitative analysis that provided valuable insights into the model's capabilities. The results of the analysis demonstrate the robustness of the CoralMatrix Security framework across various dimensions of IoT cybersecurity. Notably, the framework achieved a high detection accuracy rate of approximately 83.33%, highlighting its effectiveness in identifying and responding to cybersecurity threats in real-time. Additionally, the research examined the framework's scalability, adaptability, resource efficiency, and robustness against diverse cyber-attack types, all of which were quantitatively assessed to provide a comprehensive understanding of its capabilities. This study suggests future work to optimize the framework for larger IoT networks and adapt continuously to emerging threats, aiming to expand its application across diverse IoT scenarios. With its proposed algorithms, the CoralMatrix Security framework has emerged as a promising, efficient, effective, and scalable solution for the dynamic challenges of IoT Cyber Security.
... The selection of RSA encryption is critical because it has been widely adopted in most public-key encryption algorithms because of its strong immunity to attacks, thereby ensuring the safe protection of sensitive data [22,23]. However, in the blockchain paradigm, the RSA performance tends to be inefficient due to the intensive computational operations about the generation, encryption, and decryption operations [24,25]. Thus, this paper presents an advanced governance model of Internet hospitals called alliance blockchain integrated with a novel RSA encryption algorithm named improved RSA. ...
The rise of Internet hospitals has significant issues associated with data security and governance in managing sensitive patient data. This paper discusses an alliance blockchain (i.e., a private blockchain) model for governance innovation in internet hospitals with an improved encryption methodology. We compare our proposed model, improved Rivest-Shamir-Adleman (RSA) encryption, integrated into the blockchain framework. Improved RSA achieves impressive improvements in all key metrics by increasing the throughput by 24.7% and lowering the latency by 19.8% compared to the base model. Thus, the improved model is more optimized for processing transactions related to healthcare data. Memory usage was also reduced by 14.3%. While encryption time remained pretty close, the decryption time remarkably improved by 97.5%. IoT sensors are one of the foundations for Internet hospitals that produce consistent patient data streams, such as physiological and environmental metrics. The proposed alliance blockchain model enables the secure and efficient real-time management of this sensor data. These results demonstrate the capability of alliance blockchain and cryptographic upgrades in creating safe and efficient governance frameworks for Internet hospitals.
... These systems integrate cross-institutional data validation mechanisms and sophisticated behavioral analytics to verify identity claims. The implementation of these technologies has significantly improved the ability to detect and prevent synthetic identity fraud [75]. ...
The proliferation of sophisticated financial fraud and cybersecurity threats in the banking sector necessitates advanced detection and prevention strategies. This comprehensive review examines the current state of artificial intelligence and data science techniques in fraud detection systems within banking institutions, with particular emphasis on enhancing cybersecurity measures. Through systematic analysis of peer-reviewed literature, industry reports, and empirical studies from the past decade, we evaluate the effectiveness of various machine learning algorithms, deep learning architectures, and real-time monitoring systems in fraud detection. Meta-analysis of 47 studies indicates that contemporary AI-powered fraud detection systems achieve detection rates of 87-94% while reducing false positives by 40-60% compared to traditional rule-based methods. Furthermore, integrated AI approaches combining supervised and unsupervised learning techniques consistently demonstrate superior performance in detecting novel fraud patterns and adapting to emerging threats. This review synthesizes current research findings, identifies gaps in existing literature, and provides a comprehensive framework for implementing robust fraud detection systems in banking institutions.
Cybersecurity challenges pose a significant threat to Healthcare Cyber Physical Systems (CPS) because they heavily rely on wireless communication. Particularly, jamming attacks can severely disrupt the integrity of these CPS networks. This research introduces a decentralized system to address this issue. Therefore, this paper suggested a system that leverages trust and blockchain technology to detect jamming attacks in healthcare CPS effectively. It proposes a layered model to improve CPS networks’ lifetime and performance. In smart healthcare environments, it ensures secure and reliable communication between sensor nodes, wearable sensors, medical devices, and monitoring systems. Results show that the suggested approach outperforms the baseline model in identifying and minimizing jamming assaults, with an average percentage difference of 15.71% more detection rate, 20.21% less packet loss rates, 16.65% less node-level energy consumption, reduced network latency of 8.29%, and 9.63% more network throughput.
The technological advancements in the network technologies that deal with interconnected computing devices has led to the dramatic increase in the number of Internet of Things (IoT) devices. It promises to improve the convenience of our lives by leveraging to modernize the way day to day items are connected to various elements of existence by converting it to smart object. However, with the unprecedented convenience, accessibility, and efficiency, IoT has caused acute security and privacy threats in recent years, and it must be addressed properly. Due to the massive scale and distributed nature of IoT networks, overlooking these privacy and security issues will negatively impact many elements of our existence that involves our surroundings, and these effects will influence our own masses too. The national or jurisdictional boundaries are not restricted to the centralized cloud infrastructure that provides interconnectivity of IoT items, hence maintaining robustness, authentication and security becomes crucial. Recently, the development of blockchain technology is a plausible solution to offer such features. Security, authentication, and maintenance issues can be solved by the secure decentralization of blockchain and overcome the current IoT ecosystem's drawbacks. This paper highlights the IoT features and presents a IoT security and privacy problems are examined and classified in accordance with the IoT layered architecture. Further, we explore the IoT security and privacy challenges and examine blockchain technology towards a solution that help solve many of the IoT privacy and security issues
The Internet of Things (IoT) constitutes a sophisticated network that interconnects devices, optimizing functionality across various domains of human activity. Recent literature projections anticipate a significant increase, with estimates exceeding 50 billion connected devices by 2025. Despite its transformative potential, the IoT landscape confronts formidable privacy and security challenges, encompassing intricate issues such as data acquisition, anonymization, retention, sharing practices, and behavioural profiling. Effectively addressing these challenges mandates the development of scalable solutions, innovative management strategies, and adaptable policy frameworks. In this paper, we conduct an exhaustive examination of major IoT applications, alongside associated privacy and security concerns. We systematically categorize prevalent privacy, security, and interoperability issues within the context of the IoT layered architecture. The review highlights current research initiatives focused on developing energy-efficient devices, optimizing microprocessors, and fostering interdisciplinary collaborations to address the challenges in the IoT landscape. To efficaciously manage risks in this dynamic landscape, stakeholders must implement comprehensive strategies that span stringent data protection legislation, extensive user education initiatives, and the deployment of robust authorization and authentication frameworks. This paper aims to empower industry leaders, policymakers, and researchers by providing actionable solutions, not just insights, to navigate the complexities of the IoT landscape effectively. Future research initiatives should prioritize the fortification of security measures for large-scale IoT deployments, the formulation of user-centric privacy solutions, and the standardization of interoperability protocols. By establishing a robust foundational framework, our paper endeavours to spearhead the discourse on IoT applications, privacy paradigms, and security frameworks, paving the way towards a resilient and interconnected future.
Users and edge servers are not fully mutually trusted in mobile edge computing (MEC), and hence blockchain can be introduced to provide trustable MEC. In blockchain-based MEC, each edge server functions as a node in both MEC and blockchain, processing users’ tasks and then uploading the task related information to the blockchain. That is, each edge server runs both users’ offloaded tasks and blockchain tasks simultaneously. Note that there is a trade-off between the resource allocation for MEC and blockchain tasks. Therefore, the allocation of the resources of edge servers to the blockchain and the MEC is crucial for the processing delay of blockchain-based MEC. Most of the existing research tackles the problem of resource allocation in either blockchain or MEC, which leads to unfavorable performance of the blockchain-based MEC system. In this paper, we study how to allocate the computing resources of edge servers to the MEC and blockchain tasks with the aim to minimize the total system processing delay. For the problem, we propose a computing resource Allocation algorithm for Blockchain-based MEC (ABM) which utilizes the Slater’s condition, Karush-Kuhn-Tucker (KKT) conditions, partial derivatives of the Lagrangian function and subgradient projection method to obtain the solution. Simulation results show that ABM converges and effectively reduces the processing delay of blockchain-based MEC.
Internet of Things (IoT) plays an essential contribution in connecting devices and enabling seamless data exchange, leading to increased efficiency and convenience. However, security concerns in IoT systems are significant, as compromised devices can lead to data breaches and privacy violations. Blockchain technology can enhance IoT security by providing decentralized consensus, immutability, and transparent transaction records, ensuring secure and trustworthy communication and data integrity. This review article gives a succinct but thorough understanding of blockchain technology, covering architecture of blockchain, working principles, types, applications, platforms, and its role in the IoT environment. The study highlights potential benefits of blockchain like enhanced security and privacy, and explores its integration with IoT. Additionally, the study discusses various real-world applications, examines blockchain platforms, and addresses the limitations and challenges associated with blockchain technology. This review serves as a valuable resource for researchers and practitioners seeking a deeper understanding of blockchain’s potential and its implications in the IoT landscape.
Malicious assaults and information leakage have grown in importance in practically every area of information and communication technology (ICT) in recent years. Enterprise ledger organization, preservation, security, and protection are all greatly aided by information security (IS). To maintain their positions in the market, industries must safeguard their data and other vital assets. The main goals of this paper’s systematic review, which covers the entire process of privacy protection and security, are to introduce cryptographic IS for Industry 5.0 (the Fifth Industrial Revolution) as a comprehensive solution that provides regulatory compliance and IS policies in addition to the industrial goals. The review covers the four main areas: (i) recognizing the need for frameworks to be developed for a secure Industry 5.0 lifecycle; (ii) emphasizing security guidelines, procedures, and countermeasures for Industry 5.0; (iii) applying control, access, and availability in real-time; and (iv) proposing a futuristic architecture for industrial information security. Furthermore, the analysis of previously published state-of-the-art techniques is presented in this paper. These techniques survive with various limitations and challenges, including intercommunication exchange, fine-grained control access, and interconnectivity-related issues, which affect the adoption of an IS in industrial programmes. At the end of this systematic review, we investigated a few open research problems and mentioned those that are involved in the design of cryptographic re-encryption-enabled IS for future Industry 5.0 developments.
Low-speed internet can negatively impact incident response by causing delayed detection, ineffective response, poor collaboration, inaccurate analysis, and increased risk. Slow internet speeds can delay the receipt and analysis of data, making it difficult for security teams to access the relevant information and take action, leading to a fragmented and inadequate response. All of these factors can increase the risk of data breaches and other security incidents and their impact on IoT-enabled communication. This study combines virtual network function (VNF) technology with software -defined networking (SDN) called virtual network function software-defined networking (VNFSDN). The adoption of the VNFSDN approach has the potential to enhance network security and efficiency while reducing the risk of cyberattacks. This approach supports IoT devices that can analyze large volumes of data in real time. The proposed VNFSDN can dynamically adapt to changing security requirements and network conditions for IoT devices. VNFSDN uses threat filtration and threat-capturing and decision-driven algorithms to minimize cyber risks for IoT devices and enhance network performance. Additionally, the integrity of IoT devices is safeguarded by addressing the three risk categories of data manipulation, insertion, and deletion. Furthermore, the prioritized delegated proof of stake (PDPoS) consensus variant is integrated with VNFSDN to combat attacks. This variant addresses the scalability issue of blockchain technology by providing a safe and adaptable environment for IoT devices that can quickly be scaled up and down to pull together the changing demands of the organization, allowing IoT devices to efficiently utilize resources. The PDPoS variant provides flexibility to IoT devices to proactively respond to potential security threats, preventing or mitigating the impact of cyberattacks. The proposed VNFSDN dynamically adapts to the changing security requirements and network conditions, improving network resiliency and enabling proactive threat detection. Finally, we compare the proposed VNFSDN to existing state-of-the-art approaches. According to the results, the proposed VNFSDN has a 0.08 ms minimum response time, a 2% packet loss rate, 99.5% network availability, a 99.36% threat detection rate, and a 99.77% detection accuracy with 1% malicious nodes.
The manufacturing industry comprises various departments, and the supply chain is crucial in ensuring uninterrupted commodity flow during production. However, traditional supply chain systems face transparency, data visibility, and security challenges. This scientific article presents a Proof of Concept (PoC) that explores integrating IoT devices with blockchain technology to address these issues. Our PoC focuses on enabling IoT devices to autonomously sign transactions to the blockchain using IoT devices’ authenticated private keys, eliminating the need for external wallets. This approach offers scalability, efficiency, and real-time responsiveness benefits. Leveraging the devices’ transaction processing capabilities enhances scalability, allowing for a higher transaction volume. Automation of transaction signing streamlines the process, improving efficiency and eliminating delays caused by human interaction. Real-time responsiveness is ensured, eliminating any latency introduced by external wallets. We provide a detailed workflow of the use case and simulation results, making our research findings more accessible. Through this work, we demonstrate the feasibility and advantages of this method in scenarios where continuous, automated interaction with the blockchain is required through IoT devices. The PoC code is publicly available on GitHub.
The overall purpose of this study is to explore the potential of quantum-based communication networks, leveraging the unique properties of quantum entanglement and superposition, to address critical applications, such as image processing, machine learning, data mining, and encryption. Additionally, this research aims to overcome the limitations of traditional binary systems in the field of computing. The basic methodology of this research involves a comprehensive investigation into the establishment of end-to-end entanglement between two quantum computers, which is fundamental for building robust quantum networks. Performance measures for quantum computers within the context of quantum networks are defined and evaluated, with a specific focus on network design, reliability, security, and trust. Quantum-dot cellular automata (QCA) devices are introduced as a technological advancement to enhance security and trust mechanisms within quantum communication networks.
During the COVID-19 pandemic, the urgency of effective testing strategies had never been more apparent. The fusion of Artificial Intelligence (AI) and Machine Learning (ML) models, particularly within medical imaging (e.g., chest X-rays), holds promise in smart healthcare systems. Deep Learning (DL), a subset of AI, has exhibited prowess in enhancing classification accuracy, a crucial aspect in expediting COVID-19 diagnosis. However, the journey to harness DL’s potential is rife with challenges: notably, the intricate landscape of medical data privacy. Striking a balance between utilizing patient data for insights while upholding privacy is formidable. Federated Learning (FL) emerges as a solution by enabling collaborative model training across decentralized data sources, thus bypassing data centralization and preserving data privacy. This study presents a tailored, collaborative FL architecture for COVID-19 screening via chest X-ray images. Designed to facilitate cooperation among medical institutions, the framework ensures patient data remain localized, eliminating the need for direct data sharing. Addressing imbalanced and non-identically distributed data, the architecture is a robust solution. Implementation entails localized and fog-computing-based FL models. Localized models utilize Convolutional Neural Networks (CNNs) on institution-specific datasets, while the FL model, refined iteratively, takes precedence in the final classification. Intriguingly, the global FL model, fortified by fog computing, emerges as the frontrunner in classification after weight refinement, surpassing local models. Validation within the COLAB platform gauges the model’s performance through metrics such as accuracy, precision, recall, and F1-score. Remarkably, the proposed model excels across these metrics, solidifying its efficacy. This research navigates the confluence of AI, FL, and medical imaging, unveiling insights that could reshape healthcare delivery. The study enriches scientific discourse by addressing data privacy in collaborative learning and carries potential implications for enhanced patient care.
The Internet of Things (IoT) is rapidly becoming one of the most talked-about and essential components of any digitization process. The IoT is comprised of several key necessary components, the most important of which are sensors, communication (the internet), and user interfaces for data processing. IoTs are currently finding applications in virtually every industry, including healthcare, where they are known as the internet of medical things (IoMT), industry, where they are known as the industrial internet of things (IIoT), and interconnection between people, where they are known as the internet of everything (IoE). The challenge is to leverage the Internet of Things (IoT), technology, and data to create smarter and more sustainable cities that enhance the quality of life for residents. Therefore, in this article; we have demonstrated the use of the IoT in a variety of applications for smart communities. These applications include smart transportation, smart water management, smart garbage management, smart house illumination, smart parking, smart infrastructure, etc. This research also includes an explanation of the flow process of implementing the IoT in different applications of smart communities, as well as their characteristics and particular applications. Along with their flow illustration, the stages involved in the implementation of smart city applications and the components they consist of are also displayed here. We have also taken into consideration the instances of particular cases and their implementation utilizing IoT. Some of these cases include the automated water collection methods of smart water management systems as well as the condition of the water. Based on the findings of the research, we came to the conclusion that IoT devices play an essential role in each and every one of the smart city project implementations.
Deep learning algorithms have a wide range of applications, including cancer diagnosis, face and speech recognition, object recognition, etc. It is critical to protect these models since any changes to them can result in serious losses in a variety of ways. This article proposes the consortium blockchain-enabled conventional neural network (CBCNN), a four-layered paradigm for detecting malicious vehicles. Layer-1 is a convolutional neural network-enabled Internet-of-Things (IoT) model for the vehicle; Layer-2 is a spatial pyramid polling layer for the vehicle; Layer-3 is a fully connected layer for the vehicle; and Layer-4 is a consortium blockchain for the vehicle. The first three layers accurately identify the vehicles, while the final layer prevents any malicious attempts. The primary goal of the four-layered paradigm is to successfully identify malicious vehicles and mitigate the potential risks they pose using multi-label classification. Furthermore, the proposed CBCNN approach is employed to ensure tamper-proof protection against a parameter manipulation attack. The consortium blockchain employs a proof-of-luck mechanism, allowing vehicles to save energy while delivering accurate information about the vehicle’s nature to the “vehicle management system.” C++ coding is employed to implement the approach, and the ns-3.34 platform is used for simulation. The ns3-ai module is specifically utilized to detect anomalies in the Internet of Vehicles (IoVs). Finally, a comparative analysis is conducted between the proposed CBCNN approach and state-of-the-art methods. The results confirm that the proposed CBCNN approach outperforms competing methods in terms of malicious label detection, average accuracy, loss ratio, and cost reduction.
Privacy and verifiability are crucial security requirements in e-voting systems and combining them is considered to be a challenge given that they seem to be contradictory. On one hand, privacy means that cast votes cannot be traced to the corresponding voters. On the other hand, linkability of voters and their votes is a requirement of verifiability which has the consequence that} a voter is able to check their vote in the election result. These two contradictory features can be addressed by adopting privacy-preserving cryptographic primitives, which at the same time as achieving privacy, achieve verifiability. Many end-to-end schemes that support verifiability and privacy have the need for some voter action. This makes ballot casting more complex for voters. We propose the PVPBC voting system, which is an e-voting system that preserves privacy and verifiability without affecting voter usability. The PVPBC voting system uses an effective and distributed method of authorization, which is based on revocable anonymity, by making use of a permissioned distributed ledger and smart contract. In addition, the underlying PVPBC voting system satisfies election verifiability using the Selene voting scheme. The Selene protocol is a verifiable e-voting protocol. It publishes votes in plaintext accompanied by tracking numbers. This enables voters to confirm that their votes have been captured correctly by the system. Numerical experiments support the claim that PVPBC scales well as a function of the number of voters and candidates. In particular, PVPBC's authorization time increases linearly as a function of the population size. The average latency associated with accessing the system also increases linearly with the voter population
size. The latency incurred when a valid authentication transaction is
created and sent on the DLT network is 6.275 ms. Empirical results suggest that the cost in GBP for casting and storing an encrypted ballot alongside a tracker commitment is a linear function of the number of candidates, which is an attractive aspect of PVPBC.
Blockchain technology in the healthcare industry has potential to enable enhanced privacy, increased security, and an interoperable data record. Blockchain technology is being implemented in dental care systems to store and share medical information, improve insurance claims, and provide innovative dental data ledgers. Because the healthcare sector is a large and ever-growing industry, the use of blockchain technology would have many benefits. To improve dental care delivery, researchers advocate using blockchain technology and smart contracts due to their numerous advantages. In this research, we concentrate on blockchain-based dental care systems. In particular, we examine the current research literature, pinpoint issues with existing dental care systems, and consider how blockchain technology may be used to address these issues. Finally, the limitations of the proposed blockchain-based dental care systems are discussed which may be regarded as open issues.
During the recent decade, two variants of evolving computing networks have augmented the Internet: (i) The Internet of Things (IoT) and (ii) Blockchain Network(s) (BCNs). The IoT is a network of heterogeneous digital devices embedded with sensors and software for various automation and monitoring purposes. A Blockchain Network is a broadcast network of computing nodes provisioned for validating digital transactions and recording the “well-formed” transactions in a unique data storage called a blockchain ledger. The power of a blockchain network is that (ideally) every node maintains its own copy of the ledger and takes part in validating the transactions. Integrating IoT and BCNs brings promising applications in many areas, including education, health, finance, agriculture, industry, and the environment. However, the complex, dynamic and heterogeneous computing and communication needs of IoT technologies, optionally integrated by blockchain technologies (if mandated), draw several challenges on scaling, interoperability, and security goals. In recent years, numerous models integrating IoT with blockchain networks have been proposed, tested, and deployed for businesses. Numerous studies are underway to uncover the applications of IoT and Blockchain technology. However, a close look reveals that very few applications successfully cater to the security needs of an enterprise. Needless to say, it makes less sense to integrate blockchain technology with an existing IoT that can serve the security need of an enterprise. In this article, we investigate several frameworks for IoT operations, the applicability of integrating them with blockchain technology, and due security considerations that the security personnel must make during the deployment and operations of IoT and BCN. Furthermore, we discuss the underlying security concerns and recommendations for blockchain-integrated IoT networks.
In today’s scenario, blockchain technology is an emerging area and promising technology in the field of the food supply chain industry (FSCI). A literature survey comprising an analytical review of blockchain technology with the Internet of things (IoT) for food supply chain management (FSCM) is presented to better understand the associated research benefits, issues, and challenges. At present, with the concept of farm-to-fork gaining increasing popularity, food safety and quality certification are of critical concern. Blockchain technology provides the traceability of food supply from the source, i.e., the seeding factories, to the customer’s table. The main idea of this paper is to identify blockchain technology with the Internet of things (IoT) devices to investigate the food conditions and various issues faced by transporters while supplying fresh food. Blockchain provides applications such as smart contracts to monitor, observe, and manage all transactions and communications among stakeholders. IoT technology provides approaches for verifying all transactions; these transactions are recorded and then stored in a centralized database system. Thus, IoT enables a safe and cost-effective FSCM system for stakeholders. In this paper, we contribute to the awareness of blockchain applications that are relevant to the food supply chain (FSC), and we present an analysis of the literature on relevant blockchain applications which has been conducted concerning various parameters. The observations in the present survey are also relevant to the application of blockchain technology with IoT in other areas.
In recent years, the global Internet of Medical Things (IoMT) industry has evolved at a tremendous speed. Security and privacy are key concerns on the IoMT, owing to the huge scale and deployment of IoMT networks. Machine learning (ML) and blockchain (BC) technologies have significantly enhanced the capabilities and facilities of healthcare 5.0, spawning a new area known as “Smart Healthcare.” By identifying concerns early, a smart healthcare system can help avoid long-term damage. This will enhance the quality of life for patients while reducing their stress and healthcare costs. The IoMT enables a range of functionalities in the field of information technology, one of which is smart and interactive health care. However, combining medical data into a single storage location to train a powerful machine learning model raises concerns about privacy, ownership, and compliance with greater concentration. Federated learning (FL) overcomes the preceding difficulties by utilizing a centralized aggregate server to disseminate a global learning model. Simultaneously, the local participant keeps control of patient information, assuring data confidentiality and security. This article conducts a comprehensive analysis of the findings on blockchain technology entangled with federated learning in healthcare. 5.0. The purpose of this study is to construct a secure health monitoring system in healthcare 5.0 by utilizing a blockchain technology and Intrusion Detection System (IDS) to detect any malicious activity in a healthcare network and enables physicians to monitor patients through medical sensors and take necessary measures periodically by predicting diseases. The proposed system demonstrates that the approach is optimized effectively for healthcare monitoring. In contrast, the proposed healthcare 5.0 system entangled with FL Approach achieves 93.22% accuracy for disease prediction, and the proposed RTS-DELM-based secure healthcare 5.0 system achieves 96.18% accuracy for the estimation of intrusion detection.
Blockchain technology is a highly regarded technology that possesses a plethora of exciting features. This paper analyzes trends and highlights the potential benefits of blockchain deployment in IoT and healthcare. According to the literature, blockchain technology is mostly utilized for data management operations in healthcare and IoT, specifically to improve data security, which includes data integrity, access control, and privacy preservation. In both areas, six distinct types of data security preservation strategies are applied. Additionally, publications highlight how blockchain and IoT, including health IoT, can be used in an integrative way. Three integration mechanisms were seen to accomplish this goal. These solutions range from fully integrating blockchain into data exchanges between IoT devices to using it solely for metadata storage. The most frequently covered area of IoT is a smart city, where blockchain is utilized to improve real-time data sharing, and electricity trading, and so on. Additionally, it is learned that, despite the numerous benefits of blockchain in healthcare, authors typically use it for drug supply chain management and data management purposes in order to avoid counterfeiting and empower patients with regard to their data, respectively.
Inspection of mining assets is a crucial part of the maintenance process and is of interest to several stakeholders (e.g., OEMs, owners, users, and inspectors). Inspections require an inspector to verify several characteristics of the assets onsite, typically using legacy and poorly digitized procedures. Thus, many research opportunities arise from the adoption of digital technologies to make these procedures more efficient, reliable, and straightforward. In addition to cloud computing, the ubiquitous presence of modern mobile devices, new measurement tools with embedded connectivity capabilities, and blockchain technologies could greatly improve trust and transparency between the stakeholders interested in the inspection. However, there has been little discussion on integrating these technologies into the mining domain. This paper presents and evaluates an end-to-end system to conduct inspections using mobile devices that directly interact with constrained IoT sensor devices. Furthermore, our proposal provides a method to integrate constrained IoT devices as smart measuring tools that directly interact with a blockchain system, guaranteeing data integrity and increasing the trustworthiness of the data. Finally, we highlight the benefits of our proposed architecture by evaluating a real case study in a mining inspection scenario.
The suspension of institutions around the world in early 2020 due to the COVID-19 virus did not stop the learning process. E-learning concepts and digital technologies enable students to learn from a safe distance while continuing their educational pursuits. Currently, the Internet of Things (IoT) is one of the most rapidly increasing technologies in today’s digital world; and e-learning is one of the most powerful learning methods available. In today’s world, smart devices and new technologies assist teachers in concentrating on new models of student learning while avoiding time wastage. By examining the characteristics of the Internet of Things and the challenges that exist in the field of e-learning, the potential functions, benefits, and advancements of utilizing the Internet of Things in online education are identified and discussed. This article examines the existing and future condition of the Internet of Things world as it pertains to the topic of education and sophisticated capabilities available through the Internet of Things that enable the application of e-learning after an architecture has been designed. Students’ pulse rates, brain waves, and skin resistance are measured in real time by a collection of IoT sensors, including cameras, microphones, and wearable gadgets. By utilizing the proposed architecture, universities can change their distance learning tactics to maximize resources and boost efficiency without changing their overall academic activities. According to the study’s findings, e-learning has a favorable and statistically significant impact on students’ flexibility, learning experience, educational productivity, and overall quality of education.
Internet of Vehicles (IoV) has emerged as an advancement over the traditional Vehicular Ad-hoc Networks (VANETs) towards achieving a more efficient intelligent transportation system that is capable of providing various intelligent services and supporting different applications for the drivers and passengers on roads. In order for the IoV and VANETs environments to be able to offer such beneficial road services, huge amounts of data are generated and exchanged among the different communicated entities in these vehicular networks wirelessly via open channels, which could attract the adversaries and threaten the network with several possible types of security attacks. In this survey, we target the authentication part of the security system while highlighting the efficiency of blockchains in the IoV and VANETs environments. First, a detailed background on IoV and blockchain is provided, followed by a wide range of security requirements, challenges, and possible attacks in vehicular networks. Then, a more focused review is provided on the recent blockchain-based authentication schemes in IoV and VANETs with a detailed comparative study in terms of techniques used, network models, evaluation tools, and attacks counteracted. Lastly, some future challenges for IoV security are discussed that are necessary to be addressed in the upcoming research.
Blockchain has recently attracted significant academic attention in research fields beyond the financial industry. In the Internet of Things (IoT), blockchain can be used to create a decentralized, reliable, and secure environment. The use of blockchain in IoT applications is still in its early stages, particularly at the low end of the computing spectrum. As a result, the future roadmap is hazy, and several challenges and questions must be addressed. Several articles combining blockchain technology with IoT have recently been released, but they are limited to shallow technological potential discussions, with very few providing an in-depth examination of the complexities of implementing blockchain technology for IoT. Therefore, this paper aims to coherently and comprehensively provide current cutting-edge efforts in this direction. It provides a literature review of IoT and blockchain integration by examining current research issues and trends in the applications of blockchain-related approaches and technologies within the IoT security context. We have surveyed published articles from 2017 to 2021 on blockchain-based solutions for IoT security, taking into consideration different security areas and then, we have organized the available articles according to these areas. The surveyed articles have been chronologically organized in tables for better clarity. In this paper, we try to investigate the vital issues and challenges to the integration of IoT and blockchain, and then investigate the research efforts that have been conducted so far to overcome these challenges.
This study aims to build smart supply chains for the first time using the internet of things (IoT) and blockchain. Classification and clarification of causal relationships can provide a useful framework for researchers and professionals who seek to implement an intelligent supply chain using IoT tools in a blockchain platform, and it also demonstrates the intensity of communications indicating such relationships. The research methodology is mixed method, comprised of qualitative and quantitative methods. The qualitative method includes the Delphi method used for selecting indigenous components and features proper for the pattern. The quantitative method is the Dematel method used for assessing the relationships between the available concepts in the pattern and accessing the network structure between components. Interpretative Structural Modeling is also employed to classify the network structure obtained from the Dematel technique. The findings of the study identify indicators of IoT and blockchain as causes based on Dematel, application of tools, components interconnectedness, optimal decision making, automatedness, integration, innovation and learning, which are indicators of smart supply chain, are the effects in this study.
In this paper we investigate the essential minimum functionality of the autonomous blockchain, and the minimum hardware and software required to support it in the micro-scale in the IoT world. The application of deep-blockchain operation in the lower-level activity of the IoT ecosystem, is expected to bring profound clarity and constitutes a unique challenge. Setting up and operating bit-level blockchain mechanisms on minimal IoT elements like smart switches and active sensors, mandates pushing blockchain engineering to the limits. “How deep can blockchain actually go?” “Which is the minimum Thing of the IoT world that can actually deliver autonomous blockchain functionality?” To answer, an experiment based on IoT micro-controllers was set. The “Witness Protocol” was defined to set the minimum essential micro-blockchain functionality. The protocol was developed and installed on a peer, ad-hoc, autonomous network of casual, real-life IoT micro-devices. The setup was tested, benchmarked, and evaluated in terms of computational needs, efficiency, and collective resistance against malicious attacks. The leading considerations are highlighted, and the results of the experiment are presented. Findings are intriguing and prove that fully autonomous, private micro-blockchain networks are absolutely feasible in the smart dust world, utilizing the capacities of the existing low-end IoT devices.
Context
Data breaches caused by insiders are on the rise, both in terms of frequency and financial impact on organizations. Insider threat originates from within the targeted organization and users with authorized access to an organization’s network, applications, or databases commit insider attacks.
Motivation
Insider attacks are difficult to detect because an attacker with administrator capabilities can change logs and login records to destroy the evidence of the attack. Moreover, when such a harmful insider attack goes undetected for months, it can do a lot of damage. Such data breaches may significantly impact the affected data owner’s life. Developing a system for rapidly detecting data breaches is still critical and challenging. General Data Protection Regulation (GDPR) has defined the procedures and policies to mitigate the problems of data protection. Therefore, under the GDPR implementation, the data controller must notify the data protection authority when a data breach has occurred.
Problem Statement
Existing data breach detection mechanisms rely on a reliable third party. Because of the presence of a third party, such systems are not trustworthy, transparent, secure, immutable, and GDPR-compliant.
Contributions
To overcome these issues, this study proposed a GDPR-compliant data breach detection system by leveraging the benefits of blockchain technology. Smart contracts are written in Solidity and deployed on a local Ethereum test network to implement the solution. The proposed system can generate alert notifications against every data breach.
Results
We tested and deployed our proposed system, and the findings indicate that it can accomplish the insider threat mitigation objective. Furthermore, the GDPR compliance analysis of our system was also evaluated to make sure that it complies with the GDPR principles (such as right to be forgotten, access control, conditions for consent, and breach notifications). The conducted analysis has confirmed that the proposed system offers capabilities to comply with the GDPR from an application standpoint.
Recent developments in the Internet of Vehicles (IoV) and Vehicular Adhoc Networks (VANET) have revolutionized our infrastructure, making it safer, more convenient, and efficient. VANET provide smart traffic control, event allocation, and real-time information. Existing vehicles in VANET are now equipped with intelligent navigation, entertainment, and emergency applications. However, the highly connected nature of these vehicles poses a significant safety and security risk to drivers and assets which can result in life-threatening consequences. Location privacy is critical, and robust network security techniques should be used to counter threats in VANET environments. Existing schemes like obfuscation, mix-zones, and silent periods have preserved location privacy to some extent but have poor Quality of Service (QoS) and lack efficiency and security. To address these issues, a shadowing scheme is introduced, which is an improvement of earlier schemes used for location privacy. This approach ensures better service to the vehicle by allowing precise Location Based Service (LBS) requests to the LBS server and uses blockchain technology for storing vehicular certificates. The inclusion of a group leader significantly reduces the time taken for implementing the scheme, improving efficiency and scalability. The anonymity set size increases over time, offering better privacy protection especially in densely populated areas. The proposed scheme overcomes drawbacks of existing techniques which includes reduced location accuracy and low-quality service in spatial obfuscation techniques, limited applicability and high tracking rate in shadow-based approaches, and reduced utility in distance-based schemes. Moreover, single point of failure and resource-intensive group formation in group-based schemes, and dependency on additional infrastructure in mix-zone-based schemes are also overcome. The proposed scheme’s experimental results validate it, showing that it outperforms current state-of-the-art schemes based on metrics such as anonymity set size, entropy, and Tracking Success ratio.
Data privacy in e-health deals with the protection of sensitive medical information that is collected, stored, and analyzed in electronic health systems. Several organizations publish sensitive person-specific data for research purposes. E-health data and related domains are the loci of research. First, in publishing sensitive person-specific datasets, ensuring the privacy of user sensitive information is an issue. Secondly, ascertaining both privacy-preservation and data utility simultaneously are contradictory to each other. In addition, all transactions have the same prior belief that may result in erroneous modeling and privacy breaches. To refrain from the belief of an adversary and to solve the above discussed issues, a semantic privacy guarantee must be ensured before publishing data by any organization. This paper proposes a solution to the former issue, a framework for privacy preservation of structured datasets in ascertaining that an adversary has low confidence in extrapolation. The latter problem is also tackled by the proposed framework that combines stratified sampling with generalization to achieve representative semantic privacy-preservation with high data utility. Moreover, this study presents a mathematical proof that the proposed framework achieves differential privacy. Our experimental results show that our algorithm provides better data utility and privacy simultaneously. The proposed framework achieves 3% and 0.04% higher classification accuracy and low relative error, respectively, compared to state-of-the-art existing privacy-preservation approaches.
T-spherical fuzzy set (T-SFS) is emerged as one of the effective tools for dealing uncertainty in decision-making process. Whereas, power aggregation operators help us in normalizing the impact of extreme values and capture the interconnectedness of the arguments. On the other hand, in multi-attribute decision-making (MADM) problems, one of the most important contributing factors is the lack of awareness of biasness. Neutral operators emphasize fair and unbiased character of the decision makers. Thus, for the first time, taking advantages of these operators, a hybrid form of operators, weighted power partitioned neutral average operator and weighted power partitioned neutral geometric operator under T-SFS environment are developed. Beside these, power weighted neutral average, power ordered weighted neutral average, power hybrid neutral average operators, and their dual forms are initiated under T-SFS environment. A new modified score function for T-SFS is formulated. Based on the developed operators and score function, we constitute an MADM algorithm and utilize in solving a hypothetical case study problem on hydrogen () refuelling station site selection. Finally, comparative study of the developed operators with other operators is carried out to explore the applicability and supremacy of our designed MADM algorithm.
The Internet of Things (IoT) typically comprises sensors that have inadequate computational resources. On the contrary, blockchain technology is thought to be expensive in terms of resource consumption due to the consensus procedures involved. As a result, the application of blockchain to the IoT environment is a challenging task. The most appropriate solution lies in replacing the computationally heavy consensus algorithms with lightweight counterparts. Proof of authentication (PoAh) is the most suited replacement for such an environment. Several modifications have been proposed for PoAh during the last few years, and they are reviewed here.
Blockchain technology has revolutionized the ways of processing and storing data in terms of reliability and security. Blockchain plays a pivotal role in transferring the processing hurdle from the client-server to a decentralized and secured platform. Blockchain is deemed to be an efficient technology in a forthcoming era that would beneficiate multifarious industries. An issue that becomes a bottleneck for blockchain-based applications is their restricted ability to process in comparison to distributed database systems. In this paper, we present intelligent traffic control using a hybrid model based on the PSO-based optimization algorithm and fuzzy logic in order to improve blockchain performance. The real-time network feedback model is designed and used as an input to control the transaction traffic across the entire network in a robust way without human intervention. In order to evaluate the effectiveness of the designed model, a clinical trial service framework as a test network is implemented on top of Hyperledger Fabric. The case study is further compared with baseline network, network with fuzzy approach, and network with optimized parameter. The experiments show that the proposed model not only enhanced the network by maximizing the network throughput and minimizing the network latency. A smart contract is implemented to automate the transaction flow as per real-time data of network conditions. An open-source blockchain framework, Hyperledger Fabric, is harnessed for implementation of the experiment environment in order to signify the potential of the proposed model. The outcome of this study indicated a remarkable increase in transaction throughput (i.e., 38.5%) and a decrease in transaction latency of 40.5%. Moreover, the proposed model can easily be integrated with other existing blockchain-based performance-enhancing tools.
Blockchain is now in the news, and a lot of companies are working on blockchain technology. The first time the blockchain come into existence in the case of cryptocurrency where the decentralized architecture is followed for mining and storage. The trend of cryptocurrency is increasing day by day and, in other fields, the blockchain is in development. In this book, the chapter author has discussed the future and recent trends, history of blockchain technology. How to make a block and what is the initial requirement to build up the blockchain. The Book chapter gives an overview of the challenges associated with this technology. Later in the section, you will learn about how the blockchain work and the basic functionality of the block with some case study like smart farming, credit fraud detection, etc.
Blockchain technology has taken on a leading position in today’s industrial applications by providing salient features and showing significant performance since its beginning. Blockchain began its journey from the concept of cryptocurrency and is now part of a range of core applications to achieve resilience and automation between various tasks. However, with the integration of Blockchain technology into different industrial applications, many application designs, security, and privacy challenges present themselves, posing serious threats to users and their data. Although several approaches have been proposed to address the specific application, security and privacy challenges of targeted applications with limited security enhancement solutions, there is still a need for a comprehensive research study on the application design, security and privacy challenges, and requirements of Blockchain-based industrial applications, along with possible security threats and countermeasures. This study presents a comprehensive and state-of-the-art survey of Blockchain-based Industry 4.0 applications, focusing on potential application design, security and privacy requirements, as well as corresponding attacks on Blockchain systems with potential countermeasures. We also analyse and provide the classification of security and privacy techniques used in these applications to enhance the advancement of security features. Furthermore, we highlight some open issues of integrating Blockchain technology into industrial applications that help design secure Blockchain-based applications as future directions.
The research study focused on integrating blockchain technology with the internet of things. The study is necessitated by the need to come up with practical and feasible means to improve the accuracy, accountability and trust among various parties involved in blockchain transactions. The research recognizes the fact that there has been an increasing using of the cryptocurrency in the global market. This has lead to the emergence of blockchain, which is one of the disruptive technologies in the information and technology sector. The research study indicates that there is a need to improve on the architecture of blockchain and the IoT system. It shows that there is a need to develop a new model to improve its efficiency and accuracy. This research paper recommends the use of decentralized model to enhance the efficiency of the integration of the blockchain technology and IoT.
The objective of this study was to determine the effectiveness of a particular convergencemodel for IoT and blockchain. Multiple regression model was selected to determine the effectiveof a specific convergence model. Three convergence models were selected for this study, includingthe hybrid approach, the IoT-blockchain approach, and the IoT-IoT approach. The findingsindicated that there are issues with convergence between two different technologies. The otherfinding was that the hybrid model provided the best convergence platform for integrating IoT withblockchain. Regarding the IoT and blockchain, convergence challenges included the limitedcapacity of IoT devices to handle the nature of distributed ledgers. The recommendation is that theaspects of traditional blockchain should be redesigned because of new requirements of IoT,including smart contracts, consensus protocol, data privacy, and security.
For the data-intensive applications, resource planning and scheduling has become an important part for smart cities. The cloud computing techniques are being used for planning and scheduling of resources in data-intensive applications. The regular methodologies being used are adequately successful for giving the asset allotment yet they do not provide time effectiveness during task execution. This article presents an effective and time prioritization based smart resource management platform employing the Cuckoo Search based Optimized Resource Allocation (CSO-RA) methodology. The opensource JStorm platform is utilized for dynamic asset planning while using big data analytics and the outcomes of the experimentation are observed using various assessment parameters. The proposed (CSO-RA) system is compared with the current methodologies like particle swarm optimization (PSO), ant colony optimization (ACO) and genetic algorithm (GA) based optimization methodologies and the viability of the proposed framework is established. The percentage of optimality observed for CSO-RA algorithm is 97\% and overall resource deployment rate of 28\% is achieved using CSO-RA method which is comparatively much better than PSO, GA and ACO conventional algorithms. Feasible outcomes are obtained by using the CSO-RA methodology for cloud computing based large scale optimization-based data intensive industrial applications.
A non-traditional type of financial asset, cryptocurrencies based on public blockchains are still little understood in their real-world behavior. Exogenous events such as cyber-attacks and their media coverage can strongly affect their supply and demand, adoption and usage, efficiency, and infrastructural development, thus influencing their price stability and market valuation. Given the great technical complexity of blockchains, we believe that a pure economic analysis of risks associated with cryptocurrencies is simply not sufficient to convey the relationships between attacks and the disruptive effects that these can bring on the operation of cryptocurrencies.On these grounds, we survey the most common types of attacks for PoW cryptocurrencies and evaluate their impact on the returns of a number of real-world cryptocurrencies for which market data are available. Due data availability, our event study analysis focuses on instances of 51% attacks, hard forks, and wallet attacks. The main goal of our analysis is to understand the relationship between technical events (cyber-attacks and coordinated user/miner behavior) and the economic impacts surrounding them. We aim to develop a deeper understanding of these systems that are object of great research interest in separate disciplines, supporting policy makers in their regulatory decisions concerning crypto-assets and associated cyber-related financial risks.
Blockchain and Internet of Things (IoT) technologies are used in many domains, predominantly for electronic-healthcare. Here, IoT devices has the ability to provide real-time sensor data from patients to get processed and analyzed. As a single point of failure, mistrust, data manipulation and tampering, and privacy avoidance may all occur as a result of such a method. Through offering shared computing and storage for IoT data, blockchain can help solve such issues.Maintaining and sharing Medical data is necessary here.If there occurs loss of confidence means it threatens the medical data and loss of integrity creates impact on the life of patient. So, the first objective is to protect the medical records. Also, a central server to the records will pretend the hackers to attack and continuous fetching is difficult.Therefore, combining Blockchain and IoT will be a threat breaker for computerized medical records.