Data privacy preservation and secure sharing are key technical challenges faced by smart wearable healthcare Internet of Things (IoT) systems. Blockchain technology enables privacy preservation for medical data through encryption. However, conventional data encryption hampers data analysis and sharing, and decrypted data still carries the risk of leakage. Homomorphic encryption is a technique that allows computation directly on encrypted data without decryption, thus reducing the risk of data leakage during sharing. In this article, we propose a blockchain-based privacy preservation and sharing scheme for healthcare IoT data. First, we use an improved homomorphic encryption technique to encrypt and process electronic health records (EHRs), optimizing the modular exponentiation process with a fast exponentiation algorithm, enabling users to efficiently perform data computation and analysis while keeping the data encrypted. Second, we employ symmetric searchable encryption (SSE) to encrypt homomorphic keys and user identity information, and use a Bloom filter as the mapping structure between data keywords and unique identifiers. This approach enhances search efficiency while preserving data privacy, allowing for secure search and analysis on ciphertext. Finally, smart contracts are designed to implement access control during the data-sharing process, increasing the security and transparency of data sharing. Experimental results show that the proposed homomorphic encryption scheme reduces the encryption and decryption time by an average of 34% under different key sizes, while the optimized SSE technique keeps ciphertext retrieval time at a constant level. The proposed scheme provides an effective solution for secure and efficient data analysis and retrieval, ensuring privacy preservation for the secure use and sharing of medical data.
The increasing pervasiveness of digital infrastructures, also extending into marine domains, makes Underwater Wireless Sensor Networks (UWSNs) an essential tool for the development of novel marine sustainability and monitoring paradigms. Applications in sensitive scenarios may require data encryption, non-repudiation, and provenance tracking. Moreover, the broadcast nature of the underwater acoustic channel makes the task of identifying and authenticating nodes of critical importance. To meet such requirements, we introduce AquaID, a protocol for resource-constrained hardware that leverages Distributed Ledger Technologies (DLTs) and Decentralised Identities. It guarantees confidentiality, authentication, and integrity using low bandwidth and CPU usage, while supporting high scalability and interoperability. We validate our solution in a threefold manner: via embedded board implementation, network simulation, and sea trials using commercially-available acoustic modems and underwater nodes. We also include a cost comparison among possible DLT choices. Results show AquaID to be robust to scaling, achieving low authentication delays and overhead, thus proving suitable even for large deployments.
As the Internet of Things (IoT) continues to evolve, the ability to share data has become an integral aspect of cloud computing services.Nevertheless, the persistent issue of data security presents a formidable challenge within the domain.This study introduces a novel blockchain oriented data sharing architecture designed to reinforce data security while optimizing efficiency.The architecture is structured around advanced smart contracts and security mechanisms that register cloud-based data activities on a blockchain ledger.In instances of anomalous activities, the blockchain is scrutinized by a centralized cloud service to identify and hold accountable any malicious gateways.The framework employs robust authentication and secure data transmission protocols to fortify data security.Furthermore, it utilizes sophisticated yet efficient partial decryption algorithms within smart contracts to alleviate the computational load on end users.Blockchain's capability to provide traceable historical records underpins the system's ability to meet stringent data safety standards through transparent and open oversight.Empirical evidence underscores the effectiveness of the proposed system in safeguarding data exchanges across various clients while maintaining high operational efficiency.
As artificial intelligence (AI) systems become increasingly integral to critical infrastructure and global operations, the need for a unified, trustworthy governance framework is more urgent that ever. This paper proposes a novel approach to AI governance, utilizing blockchain and distributed ledger technologies (DLT) to establish a decentralized, globally recognized framework that ensures security, privacy, and trustworthiness of AI systems across borders. The paper presents specific implementation scenarios within the financial sector, outlines a phased deployment timeline over the next decade, and addresses potential challenges with solutions grounded in current research. By synthesizing advancements in blockchain, AI ethics, and cybersecurity, this paper offers a comprehensive roadmap for a decentralized AI governance framework capable of adapting to the complex and evolving landscape of global AI regulation.
Gabriel Fernández-Blanco, Iván Froiz-Míguez, Paula Fraga‐Lamas, Tiago M. Fernández‐Caramés
The educational system manages extensive documentation and paperwork, which can lead to human errors and sometimes abuse or fraud, such as the falsification of diplomas, certificates or other credentials. In fact, in recent years, multiple cases of fraud have been detected, representing a significant cost to society, since fraud harms the trustworthiness of certificates and academic institutions. To tackle such an issue, this article proposes a solution aimed at recording and verifying academic records through a decentralized application that is supported by a smart contract deployed in the Ethereum blockchain and by a decentralized storage system based on Inter-Planetary File System (IPFS). The proposed solution is evaluated in terms of performance and energy efficiency, comparing the results obtained with a traditional Proof-of-Work (PoW) consensus protocol and the new Proof-of-Authority (PoA) protocol. The results shown in this paper indicate that the latter is clearly greener and demands less CPU load. Moreover, this article compares the performance of a traditional computer and two Single-Board Computers (SBCs) (a Raspberry Pi 4 and an Orange Pi One), showing that is possible to make use of the latter low-power devices to implement blockchain nodes but at the cost of higher response latency. Furthermore, the impact of Ethereum gas limit is evaluated, demonstrating its significant influence on the blockchain network performance. Thus, this article provides guidelines, useful practical evaluations and key findings that will help the next generation of green blockchain developers and researchers.
Paula Fraga‐Lamas, Sérgio Ivan Lopes, Tiago M. Fernández‐Caramés
Decentralized Metaverses, built on Web 3.0 and Web 4.0 technologies, have attracted significant attention across various fields. This innovation leverages blockchain, Decentralized Autonomous Organizations (DAOs), Extended Reality (XR) and advanced technologies to create immersive and interconnected digital environments that mirror the real world. This article delves into the Metaverse of Everything (MoE), a platform that fuses the Metaverse concept with the Internet of Everything (IoE), an advanced version of the Internet of Things (IoT) that connects not only physical devices but also people, data and processes within a networked environment. Thus, the MoE integrates generated data and virtual entities, creating an extensive network of interconnected components. This article seeks to advance current MoE, examining decentralization and the application of Opportunistic Edge Computing (OEC) for interactions with surrounding IoT devices and IoE entities. Moreover, it outlines the main challenges to guide researchers and businesses towards building a future cyber-resilient opportunistic MoE.
The Internet of Medical Things (IoMT) is revolutionising the healthcare landscape by seamlessly integrating medical devices, sensors, and healthcare information systems. This interconnected network of devices is designed to improve patient outcomes, enhance healthcare delivery, and streamline medical processes. However, as IoMT continues to evolve, it introduces new challenges related to data security, privacy, and interoperability. Blockchain technology has emerged as a promising solution to address these challenges, offering a decentralised and secure framework for managing health-related data in IoMT applications. This research aims to implement a blockchain-enabled network within a Federated Learning-based Internet of Medical Things (IoMT) environment. The proposed framework features a centralized server hosting a global machine learning model. IoMT devices operate with local models that run concurrently with the global model, incorporating device-specific data. Simulations and comparisons have been conducted on the predominant consensus models, namely Proof of Stake and Proof of Work. These ongoing initiatives aspire to play a role in enhancing the security and privacy aspects of the latest developments in the Internet of Medical Things.
Zi Hau Chin, Vishnu Monn Baskaran, Chee Keong Tan, Ian K. T. Tan · 5 authors
Abstract This study examines the potential of BIP-152’s Compact Block Relay (CBR) to enhance the Bitcoin network. This work explores the block propagation efficiency through dynamic prefilling of transactions. In addition, an enhanced CBR model is proposed to reduce superfluous transaction requests, thus improving the block distribution process. The analysis considers the impact of the dynamically prefilled transactions on Bitcoin network scalability, comparing the advantages and disadvantages of this approach. We also conduct a comparative study of fixed-size and dynamically sized prefilled transactions to highlight the importance of adapting to network demands. Prefilling a fixed number of transactions without considering demand can cause inefficiencies and strain the network with unnecessary bandwidth use. Indiscriminate prefilling exacerbates these issues by inflating data packets unnecessarily, increasing latency and reducing network responsiveness. Our research indicates that the proposed solution can significantly reduce the number of round-trips between network nodes by an average of 29.77% and block reconstruction latency by 39.10% when compared with the CBR.
Open access
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
The safe and smooth transfer of data across intercepting nodes is a crucial component of data processing in the medical field. Transmitting error-free, unduplicated data is achievable when third-party entities are effectively eliminated and direct connections between the patient and healthcare provider are maintained. Blockchain technology offers a secure method for exchanging information through nodes and connections, ensuring the safety of transactions and potentially addressing current limitations. Currently, the medical data exchange is provider-centric, insecure, sluggish, and often incomplete. These issues arise from fundamental, structural, and semantic inoperability, which impede data interchange. By utilizing blockchain technology with the appropriate markers, patient data security during transfer can be ensured. This research assesses the possibility of future use of distributed ledger technology in mobile healthcare settings.
According to the current situation of deep aging globally, how to provide low-cost and high-quality medical services has become a problem that the whole society needs to consider. To address these challenges, we propose an e-healthcare management system leveraging the integration of the Internet of Things (IoT) and blockchain technologies. Our system aims to provide comprehensive, reliable, and secure one-stop services for patients. Specifically, we introduce a blockchain-based searchable encryption scheme for decentralized storage and real-time updates of electronic health records (EHRs). This approach ensures secure and efficient data traceability across medical equipment, drug supply chains, patient health monitoring, and medical big data management. By improving information processing capabilities, our system aspires to advance the digital transformation of e-healthcare services.
Mohamed Moetez Abdelhamid, Layth Sliman, Raoudha Ben Djemaa, Guido Perboli
Blockchain provides several advantages, including decentralization, data integrity, traceability, and immutability. However, despite its advantages, blockchain suffers from significant limitations, including scalability, resource greediness, governance complexity, and some security related issues. These limitations prevent its adoption in mainstream applications. Artificial Intelligence (AI) can help addressing some of these limitations. This survey provides a detailed overview of the different blockchain AI-based optimization and improvement approaches, tools and methodologies proposed to meet the needs of existing systems and applications with their benefits and drawbacks. Afterward, the focus is on suggesting AI-based directions where to address some of the fundamental limitations of blockchain.
In the pursuit of sustainable supply chains, the importance of data interoperability and fusion has become increasingly evident. Distributed Ledger Technologies (DLT) have emerged as a transformative solution, enabling enhanced data sharing, transparency, and accountability among diverse stakeholders. This review explores the role of DLT in improving data interoperability within sustainable supply chains, addressing key challenges and opportunities that arise from its implementation. The first section provides an overview of sustainable supply chains, highlighting the necessity for effective data interoperability to achieve operational efficiency and meet sustainability goals. The challenges related to data fragmentation, disparate formats, and security concerns are discussed, emphasizing the need for a cohesive approach to data management. Next, the review delves into the core principles of DLT, including decentralization, immutability, and consensus mechanisms. It outlines how these principles facilitate the development of standardized data formats and promote secure, transparent data sharing among supply chain participants. The integration of DLT with legacy systems and its capacity to enhance cross-border data exchange are also examined, showcasing how DLT can bridge existing gaps in data interoperability. Moreover, the benefits of DLT in sustainable supply chains are explored, including enhanced traceability, increased efficiency, better compliance with sustainability standards, and the establishment of trust among stakeholders. Real-world case studies from sectors such as food, textiles, and energy illustrate successful implementations of DLT and the resultant improvements in sustainability outcomes. The review also discusses future directions and innovations in the application of DLT. The potential integration of artificial intelligence (AI) for predictive analytics and decision-making, as well as the incorporation of Internet of Things (IoT) devices for real-time data capture, are highlighted as pivotal developments that can further enhance data interoperability. This review underscores the critical role of DLT in fostering data interoperability and fusion within sustainable supply chains. It calls for collaborative efforts among stakeholders to harness the full potential of DLT, paving the way for more resilient, efficient, and sustainable supply chain systems in the future. Through this exploration, the review aims to contribute to the ongoing discourse on the intersection of technology and sustainability in supply chain management. Keywords: Ledger Technologies, Data Interoperability, Supply Chains, Review.
Tehseen Mazhar, Syed Faisal Abbas Shah, Syed Azeem Inam, Joseph Bamidele Awotunde · 6 authors
The incorporation of Artificial Intelligence (AI) into the fields of Neurosurgery and Neurology has transformed the landscape of the healthcare industry. The present study describes seven dimensions of AI that have transformed the way of providing care, diagnosing, and treating patients. It has exhibited unparalleled accuracy in analyzing complex medical imaging data and expediting precise diagnoses of neurological conditions. It has also enabled personalized treatment plans by harnessing patient-specific data and genetic information, promising more effective therapies. For instance, AI-powered surgical robots have brought precision and remote capabilities to neurosurgical procedures, reducing human error. In AI, machine learning models predict disease progression, optimizing resource allocation and patient care, whereas wearable devices with AI provide continuous neurological monitoring, and enable early intervention for chronic conditions. It has also accelerated drug discovery by analyzing vast datasets, potentially leading to breakthrough therapies. Chatbots and virtual assistants powered by AI, enhance patient engagement and adherence to treatment plans. It holds promise in further personalization of care, augmented decision-making, earlier intervention, and the development of groundbreaking treatments. The present study mainly focuses on the incorporation of blockchain technology and provides a reasonable understanding of the associated issues and challenges along with its solutions. It will allow AI and healthcare professionals to advance the field and contribute towards the improvement of an individual's well-being when facing neurological challenges.
This comprehensive article explores the transformative integration of edge computing and hybrid cloud storage, a technological convergence that is reshaping data processing architectures in the era of exponential data growth. The research delves into the fundamental principles of edge computing and hybrid cloud storage, examining their synergistic relationship in addressing the limitations of traditional centralized cloud computing. By bringing computational resources closer to data sources, this integrated approach significantly reduces latency, enhances processing efficiency by up to 50%, and improves overall system reliability. The article presents detailed case studies in autonomous driving and smart city infrastructure, showcasing real-world applications and benefits. It critically analyzes the challenges inherent in this integration, including security concerns in decentralized architectures, data consistency issues, and cost implications. Furthermore, the article explores future directions, discussing emerging technologies such as AI-powered edge devices, evolving hybrid cloud solutions, and the potential for further optimization. This research provides valuable insights for organizations and researchers navigating the complex landscape of distributed computing, offering a roadmap for leveraging edge computing and hybrid cloud storage to achieve unprecedented levels of performance, scalability, and flexibility in data management and processing.
Kuan Hay Chua, Muhammad Ehsan Rana, Vazeerudeen Abdul Hameed, Muhammad Hussain Rana
Counterfeit products pose a significant threat to global economies, undermining consumer trust and damaging brand reputations. Traditional verification methods often prove ineffective in combating the proliferation of counterfeit goods. This research explores the integration of QR codes and Ethereum blockchain technology to enhance product verification within supply chains, offering a more secure and transparent solution. The proposed research aims to foster trust, transparency, and consumer confidence by generating unique QR codes linked to digital counterparts on the blockchain. The system enables dynamic interactions between consumers and the authentication process through a user-friendly mobile interface, where QR codes serve as secure anchors for product information verified against blockchain entries. The research encompasses the development of smart contracts, a user-friendly interface, and comprehensive testing to ensure the system’s scalability and reliability. Overall, this research demonstrates the potential of combining QR codes and blockchain technology to revolutionise product verification within supply chains, providing a more secure, efficient, and reliable means of combating counterfeiting.
The Metaverse is like another world functioning parallel to the actual physical world. It merges physical reality with digital virtuality by the convergence of different technologies that enable interactions with virtual objects and environment. The complete immersive experience with metaverse generates huge data which comes with its own set of challenges, a major one being the security and storage of user centric data. Considerably, blockchain offers a significant solution due to its exceptional features of decentralization, immutability, and transparency. For a detailed insight into the role of blockchain in the metaverse, the paper explores its usage in the healthcare industry. The paper then proposes a healthcare system named MetaBlock that is a fusion of both the metaverse and blockchain techniques. The paper discusses the components of the system from a detailed technical interpretation, such as data acquisition, data storage, virtual object and space simulation, data sharing and data privacy protection. Finally, an important research direction in terms of distributed consensus mechanism is discussed.
Puneeta Singh, Shrddha Sagar, Sofia Singh, Haya Mesfer Alshahrani · 6 authors
The Crucial and costly process of verifying medical documents frequently depends on centralized databases. Nevertheless, manual validation of document verification wastes a great deal of time and energy. The application of Blockchain technology could potentially alleviate the problem by reducing fraud and increasing efficiency. Non-transferable Soul-bound tokens (SBTs) can be a safe and unbreakable way to authenticate medical records by generating encrypted code, which allows the user to authenticate a portion of data. Within the paper, we provide a blockchain-based SBT-based automatic mechanism for authentication and verification of records. Soul-bound tokens generate a decentralized, immutable identity or credential system that is tied to a record. Through cloud computing, the system can reduce the verification time by accessing a decentralized database. Blockchain systems can lower platform costs and determine the optimal allocation of resources across a dispersed network by utilizing deep learning algorithms. Two advantages of utilizing blockchain technology are less fraud and increased efficiency. SBTs and cloud computing enable the procedure to be expedited and decentralized databases to be readily available. The suggested system's scalability and potential uses in other industries may be the subject of future research.
In the rapidly evolving landscape of distributed computing, the integration of fog computing, cloud paradigms, and blockchain technology has emerged as an innovative approach to enhance the efficiency and security of task scheduling. This article introduces a novel method using the Sparrow Search Algorithm (SSA), along with its decomposition variant SSA/D, to optimize task scheduling within this integrated system. This paper presents a comprehensive framework that outlines the unique characteristics of fog, cloud, and blockchain environments, addressing their individual constraints while leveraging their combined strengths. The adaptability and robustness of SSA/D are employed to address the multi-objective nature of the scheduling problem, focusing on minimizing latency, energy consumption, and financial cost while ensuring data integrity through the blockchain's immutable ledger. We have employed the Sparrow Search Algorithm (SSA), a recent optimization method proven effective in other applications. Our main contribution is the integration of a decomposition strategy to enhance the exploration of the search space and enable parallel execution of the SSA process. Experimental results show that the proposed approach outperforms existing algorithms in terms of both efficiency and effectiveness, providing significant improvements over popular task scheduling metaheuristics. This paper not only offers a new algorithmic solution but also sets a precedent for future research in integrated fog-cloud-blockchain systems, with a view towards integrating machine learning techniques to further optimize these problems.
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.
Sixth generation (6G) networks deploy unmanned aerial vehicles and mobile edge computing to provide collaborative computing and reliable connectivity for resource-limited mobile devices (MDs). However, due to the untrusted and broadcast nature of wireless transmission among communicating MDs and computing resource providers, ensuring the security of resource transactions will be challenging. Blockchain-based resource-sharing systems have been proposed to address security issues. However, these systems use existing consensus mechanisms like Proof-of-Work that consume massive amounts of system resources. In addressing this, some studies attempted to use single-agent deep reinforcement learning (DRL) in leader selection. Nevertheless, these solutions overlooked the intelligence and flexibility of blockchain configuration, and a single-point of failure can cause the system to fail. We propose a multiagent distributed deep deterministic policy gradient (MAD3PG)-assisted consensus mechanism for blockchain-based collaborative resource sharing to address these issues. First, we propose a stochastic game-based incentive-mechanism to encourage consensus nodes to participate in transaction validation. Then, we formulate the optimization problem of node selection and blockchain configuration as a Markov decision process and solve it with the MAD3PG algorithm. With MAD3PG, the agents select consensus nodes based on their experience and available resources and dynamically adjust blockchain settings. The simulation results show that MAD3PG outperforms the benchmarks in maximizing throughput and incentive while minimizing block production latency.
Blockchain, with its immutability and decentralization, drives innovation in finance and supply chain, but the growing data volume makes storing complete ledger replicas impractical for users, especially in the resource-constrained Internet of Thing (IoT) scenarios. Existing solutions focus on nodes storing only a partial ledger to alleviate storage burdens. Nonetheless, these approaches prioritize storage optimization by minimizing the query cost and lack control over storage cost. Furthermore, these approaches overlook the relationships between network users, thus failing to fully measure the future query cost. Thus, this article proposes BSSN, a blockchain storage technology based on social networks. The combined use of storage cost and query cost is introduced for the first time to formulate the node allocation optimization (NAO) problem, and the multipopulation genetic ant colony (MGAC) algorithm will be employed to derive node allocation strategies. Specifically, we address three technical challenges: 1) to predict the transactions that nodes will participate in the future, we employ the social ties to obtain the access frequencies among users; 2) to strike a balance between the storage cost and query cost, we jointly model the two costs as a multiobjective optimization problem to formulate the NAO problem; and 3) to solve the NP-hard NAO problem, we use the MGAC algorithm, where the storage and query populations collaboratively search for solutions based on four operations. Extensive experiments indicate that compared with existing work, BSSN can reduce the average query cost to 67% with its adjustable storage cost, ensuring a balanced data storage among users.
Redmond R. Shamshiri, Abdullah Kaviani Rad, Maryam Behjati, Siva K. Balasundram
The challenges and drawbacks of manual weeding and herbicide usage, such as inefficiency, high costs, time-consuming tasks, and environmental pollution, have led to a shift in the agricultural industry toward digital agriculture. The utilization of advanced robotic technologies in the process of weeding serves as prominent and symbolic proof of innovations under the umbrella of digital agriculture. Typically, robotic weeding consists of three primary phases: sensing, thinking, and acting. Among these stages, sensing has considerable significance, which has resulted in the development of sophisticated sensing technology. The present study specifically examines a variety of image-based sensing systems, such as RGB, NIR, spectral, and thermal cameras. Furthermore, it discusses non-imaging systems, including lasers, seed mapping, LIDAR, ToF, and ultrasonic systems. Regarding the benefits, we can highlight the reduced expenses and zero water and soil pollution. As for the obstacles, we can point out the significant initial investment, limited precision, unfavorable environmental circumstances, as well as the scarcity of professionals and subject knowledge. This study intends to address the advantages and challenges associated with each of these sensing technologies. Moreover, the technical remarks and solutions explored in this investigation provide a straightforward framework for future studies by both scholars and administrators in the context of robotic weeding.
This review proposes a comprehensive framework that integrates data fusion with Distributed Ledger Technologies (DLT) to enhance sustainability in supply chain management. In today’s global supply chains, ensuring transparency, efficiency, and environmental responsibility is critical, yet the lack of real-time visibility and data fragmentation presents significant challenges. The framework addresses these issues by merging data from multiple sources, including IoT devices, operational databases, and external environmental factors, using advanced data fusion algorithms. DLT, with its decentralized, immutable, and transparent nature, ensures the integrity and security of the data, allowing all stakeholders to access accurate and tamper-proof information. The fusion of data within a DLT infrastructure not only improves traceability and accountability but also enables the automation of sustainability checks via smart contracts. These contracts can trigger actions based on predefined sustainability metrics such as carbon emissions, energy consumption, and resource efficiency. Furthermore, predictive analytics and machine learning algorithms integrated into the system provide real-time monitoring and optimization of sustainability performance throughout the supply chain. The proposed review offers numerous benefits, including enhanced transparency, reduced operational costs, improved sustainability outcomes, and risk mitigation. It also addresses challenges such as scalability, data privacy, and regulatory compliance, offering solutions to overcome these hurdles. By exploring case studies of successful implementations, this review demonstrates the practical applications and future potential of combining DLT and data fusion for sustainable supply chain management, positioning it as a critical tool for organizations aiming to meet environmental and regulatory demands in an increasingly digital and eco-conscious world.