Abstract Peer-to-peer (P2P) energy trading represents a viable solution for the transition toward carbon-free energy systems, as they enable prosumers to exchange electricity at lower costs without the need for intermediaries. However, P2P networks require an infrastructure that supports the secure exchange and storage of information. In addition, a key challenge in P2P markets is ensuring economic balance among all network participants. This paper develops an integrated methodology for P2P energy trading by combining distributed optimization with Blockchain technology. To mitigate the challenges of economic balancing and data privacy, we implement an asynchronous distributed algorithm based on Replicator Dynamics within the Ethereum ecosystem. The proposed two-layer architecture utilizes smart contracts to facilitate optimal dispatch among prosumers while maintaining information immutability and security. Experimental implementation shows that the system achieves fast convergence in the optimization process without compromising agent privacy. The study concludes with an automated P2P market framework, demonstrating the potential of Blockchain as a robust infrastructure for decentralized energy management and distributed optimization.
Jose Almarcha-Sanchez, Maria-Jesus Alba-Baena, Volodymyr Dubetskyy
et al.
Abstract Open-source simulators let engineers stress-test blockchain ideas long before field deployment, yet few studies compare tools side-by-side. This tutorial article benchmarks two research-grade simulators, namely, BlockSim and Simewu, and the production-grade IOTA Hornet node under an identical traffic harness that runs on laptop-class hardware. Results show that consensus style dominates capacity. A DAG ledger that finalizes one milestone per second (≈ 6 tx s⁻¹) surpasses the 10 Transactions Per Second (TPS) ceiling of a six-node Bitcoin simulation, while Ethereum-style 12 s blocks lift the same mesh to approximately ~ 20TPS.BlockSim reproduces proof-of-work fairness within ± 3% of theoretical expectations, and a ten-fold increase in propagation delay cuts a miner’s reward roughly in half despite equal hash power. Hornet delivers protocol-truth execution, but at noticeably higher CPU, memory and bandwidth cost than the simulators. All scripts, Docker files and raw logs are released under an open license, providing a one-click baseline for future benchmarking of new distributed-ledger technologies.
Abstract The advent of fourth industrial revolution has brought with it a myriad of advanced technologies, which have simultaneously given rise to new, technologically sophisticated threats for Higher Education Institutions (HEIs). This meant that HEIs had to adopt new cybersecurity strategies incorporating technologies to counter new threats. However, it is not clear to what extent HEIs have adopted and integrated advanced technologies such as Artificial Intelligence/Machine learning (AI/ML) into their traditional cybersecurity strategies for mitigating evolving threats within the HEI context. The study sought to explore current adoption of emerging technologies to enhance cybersecurity posture in HEIs. The study also sought to determine reviewed studies report on the effectiveness of the new emerging technologies in mitigating evolving threats in HEIs. A Systematic Literature Review (SLR) was used to qualitatively examine literature on emerging technologies in HEIs. Guided by the PRISMA framework, the selection process focused on relevant literature from selected databases. A total of 287 studies were retrieved and assessed for eligibility, with 23 studies ultimately included to explore the emerging technologies employed by HEIs to mitigate technological threats. From a thematic analysis of data, findings showed that 4 main new technologies have been adopted and integrated by HEIs, which include AI/ML, cloud services, blockchain and biometric systems. However, the diffusion and adoption of these technologies face challenges related to system integration and resistance or unwillingness to undergo training for new systems. Factors such as lack of integration of systems, resistance to change and the disjointed regulatory environment led to slow adoption and lead to a proliferation of much more aggressive and evolved threats in HEIs. There was also need for training and cybersecurity awareness campaigns to build cybersecurity culture around emerging technologies. Thus, establishing a centralised framework for governance incorporating new technologies to existing cybersecurity controls will address existing challenges of technology adoption in HEIs.
The Internet of Things (IoT) is an emerging technological revolution, where devices communicate with each other over the internet to receive communications and information. These devices generate massive amounts of information. As industries increasingly rely on IoT devices, the need for technologies to enhance data security and privacy has emerged. This data faces significant security challenges, such as cyberattacks or data tampering. Therefore, it is imperative to develop effective protection for this data. This study aims to review the role of artificial intelligence (AI) and blockchain technology in enhancing IoT security by integrating these two technologies. The combination of AI and IoT represents a tremendous revolution in the rapidly evolving field, given its ability to simplify tasks easily and efficiently. AI analyzes and classifies data, detects threats and malicious attacks, while blockchain technology provides an additional layer of protection for the IoT through decentralized storage that prevents data tampering and ensures its integrity and confidentiality. In this study, we present a structured and systematic review of research published between 2021 and 2025, focusing on the role of AI and blockchain in securing IoT data. The results demonstrate that integrating AI with blockchain technology improves IoT security by detecting attacks early, reducing vulnerabilities, and preventing unauthorized access or data tampering. However, the evolving nature of attacks and challenges calls for further research to find or develop solutions capable of addressing future challenges to ensure security and reliability in data exchange between devices.
This study assesses incident response and recovery (IRR) tasks and their integration with smart technologies in port cybersecurity, with a focus on Laem Chabang Port (LCP), Thailand. Using the Delphi method, expert consensus was gathered to identify essential IRR tasks, challenges in their implementation, and smart technologies for improvement. The findings emphasize three key components for effective IRR: well-structured tasks, enhanced communication mechanisms, and the integration of emerging technologies such as the Internet of Things (IoT), artificial intelligence (AI), and blockchain. These technologies facilitate real-time monitoring, incident classification, and recovery coordination, thus enhancing both proactive and reactive cybersecurity measures. While the study focuses on LCP, its findings are applicable to ports globally, offering actionable insights for improving IRR frameworks, enhancing system integrity, and optimizing recovery efficiency. The study also highlights the need for transparent communication strategies and suggests the adoption of smart technologies to align with the global digital transformation of port operations. Future research should explore broader stakeholder involvement, comparative studies across ports, and empirical validation of smart technology effectiveness in real-world cybersecurity incidents.
In response to the credibility of ESG reporting, this study analyzes the shortcomings of the existing framework, such as the fragmentation of standards, the low applicability for SMEs, and the lack of technical tools, and proposes optimization solutions, including promoting global standards harmonization, introducing technology-driven approaches (such as IoT, blockchain, and AI), and strengthening the coverage of emerging issues. The study discusses the challenges in the implementation of the framework, such as the balance between data transparency and compliance costs, the complexity of technology integration, and the difficulty of harmonizing international standards, and proposes solutions such as policy support, technology standardization, and international cooperation. Through case analysis and international experience, the study reveals the key path of ESG audit optimization and points out that the new ISSB standard provides an opportunity for global standardization, but its implementation still needs to overcome multiple obstacles. This study provides theoretical and practical guidance for improving the credibility of ESG reporting and is of great significance for promoting the sustainable development of ESG ecology.
Smart contracts are pieces of code that execute automatically on the blockchain,and the safety problem is critical due to their irreversibility and close links to financial transactions.However,the current smart contract vulnerability detection technology still faces problems such as low feature extraction efficiency,low detection accuracy,and over-reliance on expert rules.In order to solve these problems,this paper proposes a vulnerability detection method based on multi-dimensional feature deep fusion of heterogeneous contract graph.Firstly,the code of smart contract data is denoised,and the data set is expanded by data enhancement method of code function exchange,and represented as heterogeneous contract graph.Secondly,the high-dimensional semantic representation of nodes in the smart contract graph is efficiently obtained by combining graph embedding technology and code pre-training technology.Finally,the dual heterogeneous graph attention network is designed to deeply integrate the node features learned in two dimensions to achieve more accurate vulnerability detection.The experimental results for different types of vulnerabilities show that the overall performance of the proposed method has been improved,and the average F1 index is higher than 77.72%.In the case of denial of service vulnerability detection,the F1 value is up to 84.88%,which is significantly improved by 10.62% and 22.34% compared with the traditional deep learning method and the graph topology detection method respectively.The proposed method not only improves the detection efficiency and accuracy,but also reduces the dependence on expert rules by learning node characteristics,providing a more reliable guarantee for the security of smart contracts.
Introduction The study explores blockchain technology as a disruptive innovation in digital advertising, emphasizing its role in enhancing transparency, minimizing advertising clutter, and improving return on investment among Indian firms. Methods Primary data were collected through a structured questionnaire from 220 technology-oriented firms in Jammu and Kashmir. The study employed exploratory factor analysis (EFA) and canonical correlation analysis to examine relationships among incremental innovation, decision aid, technology trust, perceived disruptive value, and technology adoption. Results Findings reveal that incremental innovation, decision aid, and technology trust significantly influence perceived disruptive value, which in turn correlates positively with technology adoption. Minimizing advertising clutter and improving control and transparency emerged as key adoption factors. Discussion The results highlight blockchain’s potential to reduce inefficiencies and enhance credibility in online advertising. The study contributes to early empirical understanding of blockchain adoption and its disruptive potential in digital marketing.
Luca Olivieri, Luca Negrini, Vincenzo Arceri
et al.
In concurrent transactional systems, a phantom read occurs when a transaction retrieves a set of data, and simultaneously, new data is inserted, updated, or removed from that set by one or more other transactions, leading to unexpected data being read. In Hyperledger Fabric (HF), a popular enterprise-grade framework for developing permissioned blockchain platforms, phantom reads are detected during the transaction validation phase. It inspects the values from read operations and checks their consistency, also re-executing some domain-specific read operations called range queries. However, being HF based on an optimistic concurrency control model, managing an excessive number of conflicts related to phantom reads could result in sudden system slowdowns. Additionally, some kind of range queries are not considered in the validation and verification process. For the latter, the re-execution is not performed and checks are not provided leading to undetected phantom reads when the values returned from them are written to the ledger. Hence, the burden of implementing phantom read-free applications (i.e., smart contracts) is on the developers, who need to correctly manage the read instructions in the code and use automatic verification tools to detect any unsafe implementations leading to system slowdowns and undetectable phantom reads. In this paper, we explore the phantom reads detection problem at the smart contract level and demonstrate how a verification approach through formal methods can identify possible bottlenecks caused by phantom reads and mitigate range query risks, outperforming the current state-of-the-art and state-of-the-practice for their detection. Our approach is implemented with GoLiSA, a semantic static analyzer based on abstract interpretation for Go applications.
Farming is a major sector required for any nation to become self-sustainable. Quality seeds heavily influence the effectiveness of farming. Seeds cultivated by breeders pass through several entities in order to reach farmers. The existing seed supply chain is opaque and intractable, which not only hinders the growth of crops but also makes the life of a farmer miserable. Blockchain has been widely employed to enable fair and secure transactions between farmers and buyers, but concerns related to transparency and traceability in the seed supply chain, counterfeit seeds, middlemen involvement, and inefficient processes in the agricultural ecosystem have not received enough attention. To address these concerns, a blockchain-based solution is proposed that brings breeders, farmers, warehouse owners, transporters, and food corporations to a single platform to enhance transparency, traceability, and trust among trust-less parties. A smart contract updates the status of seeds from a breeder from <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>s</mi><mi>u</mi><mi>b</mi><mi>m</mi><mi>i</mi><mi>t</mi><mi>t</mi><mi>e</mi><mi>d</mi></mrow></semantics></math></inline-formula> to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>a</mi><mi>p</mi><mi>p</mi><mi>r</mi><mi>o</mi><mi>v</mi><mi>e</mi><mi>d</mi></mrow></semantics></math></inline-formula>. Then, a non-fungible token (NFT) corresponding to approved seeds is minted for the breeder, which records the date of cultivation and its owner (breeder). The NFT enables farmers to keep track of seeds right from the date of their cultivation and their owner, which helps them to make better decisions about picking seeds from the correct owner. Farmers directly interact with warehouses to purchase seeds, which removes the need for middlemen and improves the trust among trust-less entities. Furthermore, a tender for the transportation of seeds is auctioned on the basis of the priority location <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>l</mi><mi>o</mi><msub><mi>c</mi><mi>p</mi></msub></mrow></semantics></math></inline-formula>, <i>Score</i>, and <i>bid_amount</i> of every transporter, which provides a fair chance to every transporter to restrict the monopoly of a single transporter. The proposed system achieves immutability, decentralization, and efficiency inherently from the blockchain. We implemented the proposed scheme and deployed it on the Ethereum network. Smart contracts deployed over the Ethereum network interact with React-based web pages. The analysis and results of the proposed model indicate that it is viable and secure, as well as superior to the current seed supply chain system.
Federated Learning (FL) has gained prominence as a machine learning framework incorporating privacy-preserving mechanisms. However, challenges such as poisoning attacks and free rider attacks underscore the need for advanced security measures. Therefore, this paper proposes a novel framework that integrates federated learning with blockchain technology to facilitate secure model aggregation and fair incentives in untrustworthy environments. The framework designs a reputation evaluation method using quality as an indicator, and a consensus method based on reputation feedback. The trustworthiness of nodes is dynamically assessed to achieve an efficient and trustworthy model aggregation process while avoiding reputation monopolisation. Furthermore, the paper defines a tailored contribution calculation process for nodes in different roles in an untrusted environment. A reward and punishment scheme based on the joint constraints of contribution and reputation is proposed to attract highly qualified workers to actively participate in federated learning tasks. Theoretical analysis and simulation experiments demonstrate the framework's ability to maintain efficient and secure aggregation under a certain degree of attack while achieving fair incentives for each role node with significantly reduced consensus consumption.
Large files cannot be efficiently stored on blockchains. On one hand side, the blockchain becomes bloated with data that has to be propagated within the blockchain network. On the other hand, since the blockchain is replicated on many nodes, a lot of storage space is required without serving an immediate purpose, especially if the node operator does not need to view every file that is stored on the blockchain. It furthermore leads to an increase in the price of operating blockchain nodes because more data needs to be processed, transferred and stored. IPFS is a file sharing system that can be leveraged to more efficiently store and share large files. It relies on cryptographic hashes that can easily be stored on a blockchain. Nonetheless, IPFS does not permit users to share files with selected parties. This is necessary, if sensitive or personal data needs to be shared. Therefore, this paper presents a modified version of the InterPlanetary Filesystem (IPFS) that leverages Ethereum smart contracts to provide access controlled file sharing. The smart contract is used to maintain the access control list, while the modified IPFS software enforces it. For this, it interacts with the smart contract whenever a file is uploaded, downloaded or transferred. Using an experimental setup, the impact of the access controlled IPFS is analyzed and discussed.
The blockchain is the distributed ledger technology, and the unchangeability is the main feature. However, the scalability is hindered to satisfy the characteristic. Nowadays, there are more and more attempts to put many applications on block-chains, but there are problems in actual use, for example, the low number of processing transactions per second (tps). Since solving these problems can increase the performance of the blockchain and reduce the cost, to solve them is one of the most important issues in the blockchain; And it is called the scalability issue. In this paper, we analyze methods that various attempts to solve it into following categories: On-chain, Off-chain, Side-chain, Child-chain and Inter-chain. We will analyze how these methods in each category work, what their pros and cons are, and finally compare which scalability issue has been resolved and their own features.