Blockchain, as the backbone technology of the current popular Bitcoin digital currency, has become a promising decentralized data management framework. Although blockchain has been widely adopted in many applications (e.g., finance, healthcare, and logistics), its application in mobile services is still limited. This is due to the fact that blockchain users need to solve preset proof-of-work puzzles to add new data (i.e., a block) to the blockchain. Solving the proof of work, however, consumes substantial resources in terms of CPU time and energy, which is not suitable for resource-limited mobile devices. To facilitate blockchain applications in future mobile Internet of Things systems, multiple access mobile edge computing appears to be an auspicious solution to solve the proof-of-work puzzles for mobile users. We first introduce a novel concept of edge computing for mobile blockchain. Then we introduce an economic approach for edge computing resource management. Moreover, a prototype of mobile edge computing enabled blockchain systems is presented with experimental results to justify the proposed concept.
Abstract With the advent of blockchain, smart contracts have become one of the most sought-after technologies because of the high customisability they add to transactions. This has given rise to many smart contract applications in areas ranging from financial services, life sciences and healthcare to energy resources and voting. However, due to their infancy, smart contracts still pose many challenges that encumber the stakeholders who interact with them: users, developers and the organisations that are built on top of smart contracts. This study aims to contribute to the body of knowledge of smart contracts within blockchain technology. Based on a systematic mapping study, we offer a broad perspective on their problems and corresponding solutions, present the research trends within the area and compile the 64 papers identified, grouped by top publication sources, channels, methods and approaches. We conclude that, since 2016, there has been an increasing trend towards the publication of blockchain-based smart contract articles at conferences and journals, mainly reflecting experiments and presenting methods, tools and models. According to the results, the most commonly discussed problems and solutions in the literature are related to the security, privacy and scalability of blockchain and the programmability of smart contracts.
Riya Sapra, Shafiq Ul Rehman, Sugandhi Malhotra
et al.
Blockchain is the evolving technology used by government and private organizations to empower themselves by digitizing and automating their work with the help of distributed computing and cryptography. This technology is being adopted by numerous application areas, including land registry, notarization, civil registration system, and digital certification. In this article, a blockchain-based platform called BirthChain is proposed for digitizing the birth certificate generation and distribution process. Hospitals will use this platform to provide data on newborns, which will be validated by the municipal corporations. The birth certificates will be automatically generated and emailed to the parents in addition to the existing process of certificate generation and handling. The platform will streamline the current birth certificate generation process, remove delays, and provide a secure digital platform. BirthChain has been implemented using Ethereum’s Remix IDE. BirthChain is validated by discussing its implementation on various Ethereum Virtual Machine (EVM)-compatible blockchain networks and comparing it to the existing birth certificate generation process.
Abstract The proliferation of distributed energy resources is reshaping the landscape of power distribution systems, including a network of autonomous microgrids. Networked microgrids transact energy for managing the efficiency, reliability, resilience, security, and sustainability of electric power services. This article offers a vision and analyzes a scheme developed for networked microgrids that utilizes blockchain technologies to optimize the financial and physical operations of power distribution systems. Blockchain provides a powerful and trustworthy path for launching distributed data storage and management, the article explores the possibility of customizing blockchain technologies to meet socioeconomic requirements of transactive energy management at the power distribution level. Then, a set of interoperable blockchains embedded with self-enforcing smart contracts is proposed to manage energy and financial flows among transacting microgrids in a credible manner. The article presents additional smart contract measures for securing optimal energy transactions between networked microgrids and the local distribution grid. It is concluded that blockchain technologies embedded in transactive energy will play a significant role in the evolution of traditional power distribution systems to active distribution networks.
Blockchain technology has been developed for more than ten years and has become a trend in various industries. As the oil and gas industry is gradually shifting toward intelligence and digitalization, many large oil and gas companies were working on blockchain technology in the past two years because of it can significantly improve the management level, efficiency, and data security of the oil and gas industry. This paper aims to let more people in the oil and gas industry understand the blockchain and lead more thinking about how to apply the blockchain technology. To the best of our knowledge, this is one of the earliest papers on the review of the blockchain system in the oil and gas industry. This paper first presents the relevant theories and core technologies of the blockchain, and then describes how the blockchain is applied to the oil and gas industry from four aspects: trading, management and decision making, supervision, and cyber security. Finally, the application status, the understanding level of the blockchain in the oil and gas industry, opportunities, challenges, and risks and development trends are analyzed. The main conclusions are as follows: 1) at present, Europe and Asia have the fastest pace of developing the application of blockchain in the oil and gas industry, but there are still few oil and gas blockchain projects in operation or testing worldwide; 2) nowadays, the understanding of blockchain in the oil and gas industry is not sufficiently enough, the application is still in the experimental stage, and the investment is not enough; and (3) blockchain can bring many opportunities to the oil and gas industry, such as reducing transaction costs and improving transparency and efficiency. However, since it is still in the early stage of the application, there are still many challenges, primarily technological, and regulatory and system transformation. The development of blockchains in the oil and gas industry will move toward hybrid blockchain architecture, multi-technology combination, cross-chain, hybrid consensus mechanisms, and more interdisciplinary professionals.
With the rapid growth of renewable energy resources, energy trading has been shifting from the centralized manner to distributed manner. Blockchain, as a distributed public ledger technology, has been widely adopted in the design of new energy trading schemes. However, there are many challenging issues in blockchain-based energy trading, e.g., low efficiency, high transaction cost, and security and privacy issues. To tackle these challenges, many solutions have been proposed. In this survey, the blockchain-based energy trading in the electrical power system is thoroughly investigated. Firstly, the challenges in blockchain-based energy trading are identified and summarized. Then, the existing energy trading schemes are studied and classified into three categories based on their main focuses: energy transaction, consensus mechanism, and system optimization. Blockchain-based energy trading has been a popular research topic, new blockchain architectures, models and products are continually emerging to overcome the limitations of existing solutions, forming a virtuous circle. The internal combination of different blockchain types and the combination of blockchain with other technologies improve the blockchain-based energy trading system to better satisfy the practical requirements of modern power systems. However, there are still some problems to be solved, for example, the lack of regulatory system, environmental challenges and so on. In the future, we will strive for a better optimized structure and establish a comprehensive security assessment model for blockchain-based energy trading system.
The rapid advancement of Blockchain technology has significantly benefited banks with more efficiency, highly secured activities, compliance, fraud prevention, and risk control. All previous studies focused on stakeholders’ perceptions and ignored measuring the value of blockchain adoption. This study addresses this gap by quantifying and rating blockchain’s impact on reducing banking transaction costs. The data has been collected from 17 of 20 United Arab Emirates national banks over 2017–2023 and analyzed using the random forest method to assess the association between blockchain adoption and four transaction cost elements. The random forest technique accurately quantifies and classifies blockchain’s role in cost reduction. The findings indicate that blockchain adoption significantly reduces processing, transfer, and fraud costs. This study has a visible practical and theoretical contribution as it shifts focus to quantifying blockchain’s impact, providing useful insights for managers, and suggesting future research across different sectors and countries.
M. Kuzlu, M. Pipattanasomporn, Levent Gurses
et al.
Focusing on one of the most popular open source blockchain frameworks-Hyperledger Fabric, this paper evaluates the impact of network workload on performance of a blockchain platform. In particular, the performance of the Hyperledger Fabric platform is evaluated in terms of: (a) throughput, i.e., successful transactions per second; (b) latency, i.e., response time per transaction in seconds; and (c) scalability, i.e., number of participants serviceable by the platform. The results indicate that the instance of Hyperledger Fabric platform being implemented can support up to 100,000 participants on the selected AWS EC2 instance. As long as the transaction rate is maintained within 200 transactions per seconds, the network latency is in the order of fraction of a second.
Introduction Applications of Blockchain technology (BT) offer transformative innovations in organizations. Because of its effectiveness as an intermediary-free platform, researchers consider this technological platform to adopt disruptive developments. In banking sector, BT has been adopted massively for significant disruptions, but their landscape of studies to develop general understanding are still at its emergent stage, therefore it is imperative to define existing landscape of BT for greater benefits in the research community. This paper examines existing studies of BT adoption in banking sector, with a special focus to reveal on how BT architectures can bring disruptions. Methods The analysis has scrutizised 214 relevant articles from peer-reviewed journals across four vital databases (coverage from 2021 to 15 July 2025), through an intelligent review that represents a combined iterative approach adopting both methods of Latent Dirichlet Allocation (LDA) topic modelling and content analysis. Results From an information systems viewpoint, the study divided the findings into three phases: pre-adoption, adoption, and post-adoption, highlighting blockchain’s dimensions, applications in banking, the current banking landscape, and the challenges that inhibit widespread adoption of BT in banking systems. Discussion/Conclusion The synthesized findings indicate interesting directions for future research.
The ability of tracing states of logistic transportations requires an efficient storage and retrieval of the state of logistic transportations and locations of logistic objects. However, the restriction of sharing states and locations of logistic objects across organizations from different countries makes it hard to deploy a centralized database for implementing the traceability in a cross-border logistic system. This paper proposes a semantic data model on Blockchain to represent a logistic process based on the Semantic Link Network model where each semantic link represents a logistic transportation of a logistic object between two parties. A state representation model is designed to represent the states of a logistic transportation with semantic links. It enables the locations of logistic objects to be derived from the link states. A mapping from the semantic links to the blockchain transactions is designed to enable schema of semantic links and states of semantic links to be published in blockchain transactions. To improve the efficiency of tracing a path of semantic links on blockchain platform, an algorithm is designed to build shortcuts along the path of semantic links to enable a query on the path of a logistic object to reach the target in logarithmic steps on the blockchain platform. A reward-penalty policy is designed to allow participants to confirm the state of links on blockchain. Analysis and simulation demonstrate the flexibility, effectiveness and the efficiency of Semantic Link Network on immutable blockchain for implementing logistic traceability.
Blockchain technology has reshaped numerous industries by providing secure and transparent transactional platforms. This paper delves into the intersection of blockchain analytics and artificial intelligence (AI) to advance transaction analysis. The primary aim is to bolster fraud detection and enhance transaction efficiency. Through a comprehensive literature review, we identify gaps in existing knowledge and lay the groundwork for our research. We introduce a novel transaction-hybrid model developed using machine learning (ML) algorithms, including support vector machines (SVMs), K-nearest neighbors (KNNs), and random forest (RF). This transact-hybrid model aims to fortify fraud detection capabilities by harnessing the strengths of each algorithm. We curate a unique dataset comprising 1000 instances, incorporating critical transaction features such as transaction hash, block number, transaction fee and gas limit, with binary classification indicating fraudulent transactions. Meticulous preprocessing, including feature engineering and data splitting for training and testing, is conducted. Visualization techniques, including seaborn-based graphs, correlation plots and violin plots, elucidate the dataset’s characteristics. Additionally, a spring colormap correlation map enhances the understanding of feature relationships. Transaction fee distributions before and after preprocessing are visually presented, highlighting the impact of data preparation. We introduce the novel transact-hybrid classifier (THC) with detailed mathematical equations, emphasising its contribution to transactional fraud detection. The classifier integrates SVM, KNN and RF outputs using an exclusive OR operation, showcasing innovation in model development. To evaluate model performance, we conduct a comparative analysis, incorporating SVM, KNN, RF and a voting classifier. Bar plots for accuracy, precision, recall and F1 score, with a custom plasma colormap, offer a visual summary of each model’s metrics. Furthermore, a receiver operating characteristics (ROC) curve analysis is presented, highlighting the area under the curve (AUC) for SVM, KNN, RF and voting models, providing a comprehensive view of their performance in distinguishing between true positive and false positive rates. Our proposed method demonstrates over 99% efficacy in fraud detection, underscoring its potential impact in transaction analysis.
Abstract Anti-money laundering has been an issue in our society from the beginning of time. It simply refers to certain regulations and laws set by the government to uncover illegal money, which is passed as legal income. Now, with the emergence of cryptocurrency, it ensures pseudonymity for users. Cryptocurrency is a type of currency that is not authorized by the government and does not exist physically but only on paper. This provides a better platform for criminals for their illicit transactions. New algorithms have been proposed to detect illicit transactions. Machine learning and deep learning algorithms give us hope in identifying these anomalies in transactions. We have selected the Elliptic Bitcoin Dataset. This data set is a graph data set generated from an anonymous blockchain. Each transaction is mapped to real entities with two categories: licit and illicit. Some of them are not labeled. We have run different algorithms for predicting illicit transactions like Logistic Regression, Long Short Term Memory, Support Vector Machine, Random Forest, and a variation of Graph Neural Networks, which is called Graph Convolution Network (GCN). GCN is of special interest in our case. Different evaluation parameters such as accuracy, ROC and F1 score are analyzed for different models. Our experimental results show that the proposed GCN model gives the accuracy $$98.5\%$$ , the AUC 0.9444 and the RMSE 0.1123, which concludes that our GCN is better than the existing models, in particular with the model proposed in Weber et al. (Anti-money laundering in bitcoin: experimenting with graph convolutional networks for financial forensics, 2019. http://arxiv.org/abs/1908.02591 ).
Rana Hassam Ahmed, Jabeen Sultana, Samraiz Zahid
et al.
The convergence of Artificial Intelligence (AI) and Large Language Models (LLMs) with blockchain technology is transforming information systems by enhancing their efficiency, security, and decision-making capabilities. This research explores the integration of AI and LLMs, such as GPT and BERT, into blockchain-based information systems to address challenges related to data integrity, transaction processing, and smart contract automation. A layered architecture is proposed, comprising an AI-powered query engine, an LLM-enhanced decision-making layer, and a data synchronisation module bridging on-chain and off-chain environments. The system evaluation highlights significant improvements in transaction efficiency, query accuracy, energy savings, and detection rates of security threats. Experimental results demonstrate a 32% reduction in transaction latency, a 20.5% increase in fraud detection accuracy, and a 23% reduction in energy consumption. These findings underscore the viability of integrating AI and LLMs with blockchain technology for developing intelligent, secure, and scalable information systems.
This extensive research investigates the integration of blockchain, MQTT and machine learning on the Internet of Things (IoT), a field ripe for transformation with technologies. These three technologies are blockchain and Message Queuing Telemetry Transport (MQTT). Machine learning is a foundational pillar, each offering unique benefits to enhance data exchange, security and decision making in interconnected IoT environments. Our study aims to explore the synergies among these technologies and the implications of their combined usage on the IoT. I delve into how their integration strengthens data security, enables communication, and facilitates data-driven decision-making across IoT scenarios. The study examines types of blockchain technology and the significance of MQTT in IoT communication. Additionally, I explore the implementation of machine learning models. Our primary focus is on exploring how combining blockchain and MQTT can enhance data sharing. I address challenges such as privacy concerns, scalability issues and consensus processes. To illustrate the impact of this convergence, I present practical examples from industries like supply chain management, healthcare services, and finance. Furthermore, this research also encompasses themes such as interoperability, among systems standardization measures, edge computing applications, and privacy-oriented machine learning approaches.
Electronic computers. Computer science, Information technology
V. Sitharamulu, G. Sucharitha, Srihari Babu Gole
et al.
Abstract Web 3.0 represents an advanced internet framework that combines technologies such as blockchain and semantic communication to enable decentralized, transparent data exchange. While semantic communication reduces redundancy by transmitting contextual meaning rather than raw data and existing systems lack a cohesive integration strategy, further, the centralized models struggle with efficient semantic sharing and valuation. The work proposed integration framework of semantic web communication with blockchain to enhance reliable web 3.0 data sharing. A novel “Proof of Semantic” mechanism tackles data integrity issues, ensuring only high-quality semantic inputs. The approach also elucidates the implementation of the bottleneck method and state channels. The computational load is optimized using a semantic shared protocol across the web. Further, a hierarchical Stackelberg game model optimizes semantic pricing, balancing stakeholder incentives. Evaluations confirm the framework’s efficacy, achieving reduction in network load without compromising accuracy compared to conventional approaches.
Muhammad Adnan Khan, Muhammad Zahid Hussain, Muhammad Farhan Khan
et al.
Abstract For detecting and diagnosing a wide range of ophthalmological diseases, fundus images are used as a primary and basic tool. Early and accurate diagnosis of these ocular diseases can substantially improve the quality of treatment as well as important for preventing permanent vision loss. Changes in the anatomical structures like the optic disc, macula, blood vessels, and fovea show the presence of diseases like age-related macular degeneration, glaucoma, diabetic retinopathy, cataracts, myopia, and hypertension. In the proposed work, six different automated convolutional neural network architectures based on the Internet of Medical Things (IoMT) using transfer learning techniques were implemented for the classification of fundus images that can detect ocular diseases. These pre-trained neural networks were tuned by modifying the four layers before training them on the dataset. The proposed models incorporate blockchain technology-based private clouds for the security of the patient’s data. Transfer learning provides a promising framework with the combination of IoMT technologies and blockchain technology layers to enhance the diagnosing capabilities of Ocular disease. An ocular disease dataset was used, which was classified into four classes Cataract, Glaucoma, Myopia, and AMD. Class-wise Accuracy, Precision, Sensitivity, F1 Score, Specificity, and Misclassification Rate were computed with up to 96.88% training and 95.40% testing accuracy. The second part of this research is a comparative analysis of implemented models. The performance of AlexNet, GoogleNet, MobileNetV2, DarkNet-19, VGG-19, and DenseNet-201 compared in terms of accuracy. GoogleNet yielded unquestionably impressive results when compared to AlexNet and MobileNetV2.