Koosha Esmaeilzadeh Khorasani, Sara Rouhani, Rui Pan, Vahid Pourheidari
Interoperability is a significant challenge in blockchain technology, hindering seamless data and service sharing across diverse blockchain networks. This study introduces Automated Gateways as a novel framework leveraging smart contracts to facilitate interoperability. Unlike existing solutions, which often require adopting new technologies or relying on external services, Automated Gateways framework is integrated directly with a blockchain's core infrastructure to enhance systems with built-in interoperability features. By implementing fine-grained access control mechanisms, smart contracts within this framework manage accessibility and authorization for cross-chain interactions and facilitate streamlining the selective sharing of services between blockchains. Our evaluation demonstrates the framework's capability to handle cross-chain interactions efficiently, significantly reduce operational complexities, and uphold transactional integrity and security across different blockchain networks. With its focus on user-friendliness, self-managed permissions, and independence from external platforms, this framework is designed to achieve broader adoption within the blockchain community.
The chapter highlights the use of blockchain-based timestamping systems as a reliable solution for ensuring the authenticity and immutability of digital materials in various contexts. These systems leverage distributed consensus, cryptography, and smart contracts to guarantee non-modifiable timestamps, which ultimately leads to increased trust and transparency in transactions. The chapter explores the core concepts and applications of blockchain-based timestamping, including its role in verifying historical data and its applications in artificial intelligence, smart contracts, banking, insurance, and legal transactions. Additionally, the chapter discusses recent research, barriers, and future considerations such as scalability, interoperability, regulatory compliance, user experience, and environmental sustainability. Overall, the aim of the chapter is to analyze the power of blockchain-based timestamping in shaping the future of digital trust and security.
In addressing the significant challenges caused by the expansion of data storage needs in blockchain systems, this paper explores the integration of the InterPlanetary File System (IPFS) with Substrate-based blockchain. By leveraging IPFS for off-chain storage and Substrate for on-chain operations, this system addresses the key challenges such as bloated storage, inefficiency, and accessibility while preserving data distribution and privacy. Through a comparative analysis with an Ethereum-based system, this study reveals significant advantages of the Substrate-IPFS solution. There is a significant reduction in data storage size and faster block confirmation times, leading to potentially lower transaction costs. The proposed approach enhances data privacy through the use of the Blake2 hashing algorithm. Overall, this research showcases the potential of Substrate-IPFS integration in overcoming the limitations of traditional blockchain storage approaches. Further exploration into the storage cost optimisation within the Substrate framework and additional functionalities using modular pallets could pave the way for significant advancements in the distributed data storage.
Hulin Yang, Mingzhe Li, Jin Zhang, Alia Asheralieva · 6 authors
The advent of Ethereum 2.0 has introduced significant changes, particularly the shift to Proof-of-Stake consensus. This change presents new opportunities and challenges for arbitrage. Amidst these changes, we introduce BriDe Arbitrager, a novel tool designed for Ethereum 2.0 that leverages Bribery-driven attacks to Delay block production and increase arbitrage gains. The main idea is to allow malicious proposers to delay block production by bribing validators/proposers, thereby gaining more time to identify arbitrage opportunities. Through analysing the bribery process, we design an adaptive bribery strategy. Additionally, we propose a Delayed Transaction Ordering Algorithm to leverage the delayed time to amplify arbitrage profits for malicious proposers. To ensure fairness and automate the bribery process, we design and implement a bribery smart contract and a bribery client. As a result, BriDe Arbitrager enables adversaries controlling a limited (< 1/4) fraction of the voting powers to delay block production via bribery and arbitrage more profit. Extensive experimental results based on Ethereum historical transactions demonstrate that BriDe Arbitrager yields an average of 8.66 ETH (16,442.23 USD) daily profits. Furthermore, our approach does not trigger any slashing mechanisms and remains effective even under Proposer Builder Separation and other potential mechanisms will be adopted by Ethereum.
Purpose: The study sought to explore the impact of edge computing on real-time data processing. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to the impact of edge computing on real-time data processing. Preliminary empirical review reveled that edge computing significantly reduced latency and enhanced efficiency in real-time data processing across various industries by bringing computational resources closer to data sources. It highlighted the technology's ability to handle large volumes of IoT-generated data, improve security by localizing data processing, and drive innovation and economic growth through new applications and services. Edge computing's decentralized approach proved essential for reliable and robust data handling, particularly in critical sectors like healthcare and finance, ultimately solidifying its importance in the digital transformation landscape. Unique Contribution to Theory, Practice and Policy: The Diffusion of Innovations Theory, Resource-Based View (RBV) and Sociotechnical Systems Theory may be used to anchor future studies on edge computing on real-time data processing. The study recommended expanding theoretical frameworks to include the unique aspects of edge computing, investing in robust edge infrastructure, and developing standardized protocols and best practices. It emphasized the need for government incentives and supportive regulatory frameworks to promote adoption, and suggested that academic institutions incorporate edge computing into curricula. Additionally, the study called for ongoing research to address emerging challenges and opportunities, ensuring continuous advancement and effective implementation of edge computing technologies.
Natalia Dashkevich, Steve Counsell, Giuseppe Destefanis
The complexity and interconnection within the financial ecosystem demand innovative solutions to improve transparency, security, and efficiency in financial reporting and liquidity management, while also reducing accounting fraud. This paper presents Blockchain Financial Statements (BFS), an innovative accounting system designed to address accounting fraud, reduce data manipulation, and misrepresentation of company financial claims, by enhancing availability of the real-time and tamper-proof accounting data, underpinned by a verifiable approach to financial transactions and reporting. The primary goal of this research is to design, develop, and validate a blockchain-based accounting prototype—the BFS system—that can automate transformation of transactional data, generated by traditional business activity into comprehensive financial statements. Incorporating a Design Science Research Methodology with Domain-Driven Design, this study constructs a BFS artefact that harmonises accounting standards with blockchain technology and business orchestration. The resulting Java implementation of the BFS system demonstrates successful integration of blockchain technology into accounting practices, showing potential in real-time validation of transactions, immutable record-keeping, and enhancement of transparency and efficiency of financial reporting. The BFS framework and implementation signify an advancement in the application of blockchain technology in accounting. It offers a functional solution that enhances transparency, accuracy, and efficiency of financial transactions between banks and businesses. This research underlines the necessity for further exploration into blockchain’s potential within accounting systems, suggesting a promising direction for future innovations in tamper-evident financial reporting and liquidity management.
This study investigates the impact of consensus mechanism changes on cryptocurrency markets within the framework of the efficient market hypothesis, focusing on Ethereum’s transition from Proof-of-Work to Proof-of-Stake consensus, known as the Ethereum 2.0 ‘The Merge’ update. Two main hypotheses guide the enquiry: (i) ‘The Merge’ update will significantly enhance market efficiency and (ii) Ethereum’s updates will have a greater impact on market efficiency compared to other cryptocurrencies. Using the Hurst exponent’s R/S statistic, changes in Ethereum’s long-term memory characteristics before and after major hard forks are quantified. The analysis reveals substantial improvements in Ethereum’s market efficiency following the Ethereum 2.0 hard fork, attributed to the introduction of Proof-of-Stake, which enhanced transaction speed and built trust. These findings suggest a positive trajectory towards improved efficiency in Ethereum’s market, particularly with ‘The Merge’ update. In conclusion, this study contributes to understanding the role of consensus mechanisms in cryptocurrencies and provides insights into future market trends resulting from such changes.
Blockchain is a technology that is rapidly gaining prominence and finding applications in various sectors such as banking, supply chain, healthcare, and e-governance. The consensus algorithm employed in a blockchain network is crucial as it directly impacts the network's performance and security. Different consensus techniques exist, including Proof of Work (PoW), Proof of Stake (PoS), Robust Proof of Stake (RPoS), and Delegate Proof of Stake (DPoS), each with its own set of advantages and disadvantages. In this work, we propose a new consensus algorithm called Delegated Proof of Stake with Exponential Back-off (DPoSEB). DPoSEB utilizes a stake-based selection of delegates and employs an exponential back-off technique to mitigate collisions among nodes within the network. Each delegate is assigned a random sleep time, and the node with the shortest wake-up time is chosen to mine the block for that particular round. However, collisions among nodes can still occur. To provide a fair chance for each delegate node, collided nodes are assigned an exponential back-off time. We implement our proposed algorithm on an Ethereum-based private blockchain network. To evaluate the effectiveness of our proposed work, we compare it with existing consensus mechanisms such as PoS (version 2) and Delegated RPOS with downgrading (DDRPOS) using different scenarios in terms of transaction latency, waiting time, and fairness as evaluation metrics. The results reveal that DPoSEB performs better than POS and DDRPOS.
K C Ankit, Rhishav Pandey, Deepesh Bhandari, Birendra Khadka · 5 authors
The growing use of digital certificates across various sectors demands a more reliable and efficient method for verifying their authenticity. Current verification processes are hampered by manual verification, delays, human error, and insufficient security, leaving room for fraud, especially in critical areas like education. This paper proposes a novel blockchain-based certificate authentication system that offers enhanced security and transparency. The proposed system makes use of a private blockchain to validate the certificates and a public blockchain to store the certificates without losing their integrity. The designed system is equipped with an effective and fast searching mechanism while validating the certificates. The proposed system is tested in a Sepolia Testnet environment run on Ethereum. An analysis has been carried out on the cost incurred for the done transactions on the proposed architecture. The experiments are done for both cases using search optimization methodologies and without using the search optimizations. It can be observed from the results that the search optimization techniques are highly effective for searching the false certificates.
The pervasive issue of degree fraud poses a signif-icant challenge to the credibility of academic credentials, neces-sitating innovative solutions to ensure the integrity of diploma verification processes. This paper proposes an integrated solution leveraging Blockchain, InterPlanetary File System (IPFS), and Advanced Encryption Standard (AES) encryption technologies to establish a secure and transparent diploma management system. IPFS offers a decentralized approach to file storage and distribution, eliminating reliance on centralized authorities and enhancing data availability and accessibility. Similarly, Blockchain technology provides a tamper-resistant ledger for storing diploma data, ensuring transparency and immutability through distributed consensus mechanisms. Ethereum, with its smart contract functionality, facilitates the automation of trans-actional processes, further enhancing the system's efficiency and reliability, finally, AES ensures the confidentiality of certificate contents through strong encryption, preventing unauthorized access to sensitive information. The loading process for university diplomas involves encrypting PDF files with AES encryption, uploading them to the IPFS cluster, and recording their unique hash values on the Ethereum blockchain. Subsequently, the verification process requires accurate specification of both the file and transaction hash values, ensuring the authenticity of diplomas through blockchain-based verification. The implemen-tation of the proposed system demonstrates its efficacy in securely uploading and verifying diplomas, utilizing technologies such as MetaMask for Ethereum authentication and AES encryption for data confidentiality. By ensuring the integrity and authenticity of diplomas, the system mitigates the risk of fraud and upholds the integrity of educational qualifications.
Giovanni Quattrocchi, Filippo Scaramuzza, Damian A. Tamburri
Bitcoin and Ethereum, respectively the first and the second generations of blockchains, exhibit two main problems, mostly connected to the increase of network traffic and load onto their respective networking and service models: scalability and interoperability. To solve these issues, several technologies have been introduced—thus paving the way to the so-called third-generation blockchains—which are divided into three main categories: (1) Layer 1 solutions, (2) rollups, and (3) side-chains. We present a validated framework for the evaluation and comparison of these categories, based on the three main non-functional aspects—reflecting therefore a trilemma—that discriminate their use for the design and orchestration of complex blockchain-oriented service applications, namely: scalability, decentralization, and security.
Optimistic rollup has emerged as a promising Layer 2 (L2) scaling solution for blockchain; however, its existing protocols are vulnerable to front/back-running activities, where an opportunistic rollup operator can strategically alter the transactions' order to create an arbitrage opportunity. Specifically, in the limited edition ERC-721 standardized non-fungible tokens (NFTs), the re-ordering of transactions introduces a lucrative threat landscape due to its scarcity-driven pricing and market volatility. In this work, we introduce PAROLE, a novel attack technique on optimistic rollup systems, where an adversarial aggregator re-orders the NFT transactions in an optimal way, leveraging model-free deep reinforcement learning (DRL) to maximize the balance of a target account. We create our own NFT called the “PAROLE Token” (PT) and deploy it in the OpenSea marketplace via Optimism Goerli to validate the attack impact. Furthermore, we collect NFT snapshots from rollup mainchains to analyze the impact in real-world NFT marketplaces.
The 1st International Workshop on Requirement Engineering for Web3 Systems (RE4Web3), held at the 32nd IEEE RE Conference 2024, fills in the space between traditional Requirements Engineering (RE) and particular challenges posed by Web3 technologies. The workshop discussed changing RE artifacts, processes, and practices to efficiently build and operate emerging Web3 systems. The accepted papers showcase the diversity and depth of research in this emerging field, addressing key topics such as smart contract compatibility with Central Bank Digital Currencies, RE challenges in rollup construction, privacy and security in blockchain-based federated learning, and infrastructure requirements for blockchain-native information systems. The new findings described in these industry-focused papers add to the formation of the discipline and lay the cornerstone for future research and practice in RE integration with Web3 technologies.
In global grid-based cloud computing settings, performance optimization depends on effective data scheduling. The usefulness of the absolute distributed data scheduling function in controlling resource allocation, load balancing, and data dissemination across heterogeneous cloud infrastructures is assessed in this study. By taking into account variables including data locality, processing capacity, and network latency, we evaluate the function's capacity to increase system throughput while reducing scheduling overhead. Simulations that compare to current scheduling models show gains in fault tolerance, scalability, and efficiency. High-performance cloud computing is advanced by the findings, which offer insights on optimizing distributed scheduling systems. Cloud security is crucial for attracting customers and protecting data privacy. Online attackers disrupt cloud services, leading to financial growth for cloud-based organizations. Various methodologies are reviewed to develop strong security mechanisms for cloud computing, but machine learning is not enough. This research focuses on high-level technologies like Block chain and Quantum computing with Machine Learning (ML) concepts and algorithm conceptions like deep neural networks and quantum neural networks. These models reduce attacks and increase user trust, benefiting cloud service providers. The research aims to eradicate issues and promote end-to-end protection and secrecy in the cloud environment. Cloud computing is an on-demand technology that provides various services like vast computing power, unlimited storage, and on-demand web services over the internet without the need for internal infrastructure. This research focuses on data security and privacy of cloud customers using various experiments. Cyber-attacks can be Denial of Services (DoS), Distributed Denial of Services (DDoS), Man In The Middle (MITM), and malware attacks. To protect the cloud system from cyber-attacks, deep learning is used to train an intelligent honeynet system that not only protects the system from DDoS attacks but also redirects attacks towards another direction. Another approach is the Quantum Neural Network (QNN) approach, which helps identify attack patterns and categorizes them into different classes of DoS/DDoS attacks. The QNN training process addresses slowing down of the cloud system and allows valid cloud customers to access their private data in cloud storage. Another approach is Zero Knowledge Proof (ZKP) technology, which verifies the authenticity of cloud users by polarizing photons at a specific angle. This verifier model allows cloud customers to access sensitive data and only cloud services provided by the cloud service provider. Blockchain, a powerful security framework, is used to address increasing security vulnerabilities. The Quantum-Blockchain framework incorporates the quantum superimposition principle to prevent data tampering, ensuring data privacy and data security. This research aims to address intrusion detection and data storage security challenges in the cloud computing environment using collaborative efforts from Machine Learning and advanced technologies like Quantum Computing and Blockchain. The cloud manifesto and security alliance need to be standardized to ensure privacy and security. Current research is limited due to lack of security and privacy standards between cloud vendors and users. Future studies should focus on advanced technologies like hybrid cloud, artificial intelligence, quantum computing, data mining, machine learning, big data, and cryptography to enhance security and prevent cyber-attacks.
Blockchain technology, known for its decentralized and immutable nature, serves as the foundation for various applications. As a prominent application of blockchain, decentralized storage is powered by blockchain technology and is expected to provide a reliable and cost-effective alternative to traditional centralized storage. A major challenge in blockchain-powered decentralized storage is how to guarantee the quality of storage services in decentralized storage nodes (DSNs). Storage auditing can ensure the integrity and security of the stored data. Unfortunately, it incurs additional computational costs for data owners and extra storage overheads for DSNs, which thereby cannot be directly applied to decentralized storage networks consisting of nodes with various computation and storage capacity. In this article, we overcome these problems and minimize additional burdens in storage auditing. We propose EDCOMA, a computation and storage efficient auditing scheme for blockchain-based decentralized storage, in which a double compression method is designed to compress data authenticators using both data and polynomial commitment. To prevent replay attacks on double compression launched by DSNs, we introduce zero knowledge proof and design a compression arithmetic circuit to guarantee the execution of compression operations in DSNs. We analyze the security of EDCOMA under the random oracle model and conduct extensive experiments to evaluate the performance of EDCOMA. Experimental results affirm that EDCOMA outperforms state-of-the-art approaches in both computational and storage efficiency.
Bitcoin's rise has put blockchain technology into the mainstream, amplifying its potential and broad utility. While Bitcoin has become incredibly famous, its transaction rate has not match such a corresponding increase. It still takes approximately 10 minutes to mine a block and add it to the chain. This limitation highlights the importance of seeking scale-up solutions that solve the low throughput transaction rates. Blockchain's consensus mechanisms make peer-to-peer transactions becomes feasible and effectively eliminate the need for centralized control. However, the decentralized systems also causes a lower speed and throughput compared to centralized networks as we mentioned Bitcoin's block creation rates. Two mainstreams scale-up solutions, Layer 1 scale-up and Layer 2 scale-up have been implemented to address these issues. Layer 1 level scalability enhancements happen at where traditional blockchain operates. This paper provides a deep examination of the components of the Layer 1 protocol and the scale-up methods that directly improve the lower level blockchain. We also address that Layer 1 solutions encounter inherent limitations although improvements were applied due to layer 1 storage costs and latency are high. In addition, we discuss layer 2 protocols, advanced scalability techniques, that elevate blockchain performance by handling transactions off the mainnet. Our findings indicate that Layer 2 protocols, with their various implementations such as rollups and channels, significantly outperform Layer 1 solutions in terms of transaction throughput and efficiency. This paper discusses these Layer 2 scaling methods in detail, aiming to provide readers with a comprehensive understanding of these protocols and the underlying logic that drives their effectiveness.
Since its conceptualization in 2008, blockchain technology has advanced rapidly and been applied in multiple domains. In higher education, blockchain can be applied to develop ICT systems that can revolutionize student accreditation through certificate verification and micro-accreditations, which represent skills and other learning outcomes, in the form of digital/smart badges. While there are multiple studies that highlight the significance of blockchain in higher education and propose digital systems, few of those studies include the evaluation of such proposed systems by real users. As such, the research question of how useful a higher education blockchain system would be for its relevant stakeholders remains largely unanswered. In the research publication at hand, a blockchain-powered higher education platform was applied in the School of Electrical and Computer Engineering of the National Technical University of Athens, where it was used and evaluated by students and professors at the school. The evaluation of the platform was positive, and participants found that the smart badge functionality was among the most useful. Finally, the execution and evaluation of the pilot led to several lessons learned and policy recommendations towards dealing with existing barriers and further promoting blockchain in higher education.
Context: Scientific research, increasingly reliant on data and computational analysis, confronts the challenge of integrating collaboration and data sharing across disciplines. Collaborative frameworks that support decentralized decision-making and knowledge-sharing are essential, yet integrating them into computational environments presents technical challenges, such as decentralized identity, user-centered policy-making, flexible asset management, automated provenance, and distributed collaborative workflow management. Solution: This study introduces a conceptual framework and its prototype implementation called Decentralized Virtual Research Environment (D-VRE). This approach enhances seamless, trusted data sharing and collaboration within research lifecycles. It incorporates custom sharing policies, secure asset management, collaborative workflows, and research activity tracking, all without centralized oversight. Evaluation: Demonstrated through a real-world case study in the CLARIFY project, the prototype of the decentralized virtual research environment proved effective in enabling advanced data sharing and collaborative scenarios, showcasing its adaptability in scientific research. Results: Integrated into JupyterLab, D-VRE supports custom collaboration agreements and smart contract-based automated execution on the Ethereum blockchain. This ensures secure, verifiable transactions and promotes trust and reliability in shared research findings. Contribution: D-VRE addresses barriers to scientific research collaboration and data sharing, offering a scalable and adaptable decentralized model. This model promotes a more inclusive, efficient, and trustworthy research ecosystem, paving the way for future advancements in virtual research environments.
Xichun Cai, Lixing Chen, Yang Bai, Xi Lin · 6 authors
Web 3.0 and Edge computing are inherently compatible, making them an ideal combination for building a secure and efficient distributed service platform to support decentralized applications (DApps). This paper investigates an elastic hybrid computing architecture in Edge Web 3.0, allowing DApp tasks to be executed in a hybrid manner by integrating on-chain and off-chain execution. The principle is to transfer a portion of DApp to an off-chain execution environment, along with an appropriate result verification process, to enhance computing efficiency and reduce blockchain overhead. We formulate a DApp task scheduling problem that jointly optimizes the execution pattern and offloading decision of user tasks. A learning-based DApp task scheduling scheme is designed based on Proximal Policy Optimization (PPO) to minimize the gas cost and service delay of DApps. Particularly, we tailor PPO to handle the hard constraints of service delay, gas consumption, and computing capacity in Edge Web 3.0 by adding regularization terms in the learning objective function. We establish an Edge Web 3.0 testbed based on Goerli, ZkSync, and Ethereum to evaluate the proposed method. The experimental results show that our method outperforms state-of-the-art benchmarks.
Soosan Naderi Mighan, Jelena Mišić, Vojislav B. Mišić, Xiaolin Chang
Implementing a consensus protocol in a Proof-of-Stake context requires a delicate tradeoff between different system parameters. Ethereum 2.0, probably the most popular PoS system today, uses a large number of validators to achieve decentralization, but long time windows, during which both blocks and attestations for those blocks are considered valid, open up the possibility for a number of attacks that target the process of consensus. A possible remedy would be to try to achieve single-slot finality similar to that obtained in Practical Byzantine Fault Tolerance (PBFT). In this paper, we develop a Markov chain model of validator lifecycle in an Ethereum 2.0-like system with single-slot finality which includes penalties and rewards, as well as the possibility of voluntary exit and waiting to rejoin the validator pool. Using the model, we obtain the probability of achieving consensus as the function of probabilities of different events, most notably the probability of truthful voting by the validator. Our results indicate that consensus is rather sensitive to false voting, and that low probability of waiting and low probability of voluntary exit help improve the probability of consensus.
David Melo, Saúl E. Pomares Hernández, Lil María Rodríguez-Henríquez, Julio César Pérez-Sansalvador
Blockchain technology ensures record-keeping by redundantly storing and verifying transactions on a distributed network of nodes. Permissionless blockchains have pushed the development of decentralized applications (DApps) characterized by distributed business logic, resilience to centralized failures, and data immutability. However, storage scalability without sacrificing throughput is one of the remaining open challenges in permissionless blockchains. Enhancing throughput often compromises storage, as seen in projects such as Elastico, OmniLedger, and RapidChain. On the other hand, solutions seeking to save storage, such as CUB, Jidar, SASLedger, and SE-Chain, reduce the transactional throughput. To our knowledge, no analysis has been performed that relates storage growth to transactional throughput. In this article, we delve into the execution of the Bitcoin and Ethereum transactional models, unlocking patterns that represent any transaction on the blockchain. We reveal the trade-off between transactional throughput and storage. To achieve this, we introduce the spent-by relation, a new abstraction of the UTXO model that utilizes a directed acyclic graph (DAG) to reveal the patterns and allows for a graph with granular information. We then analyze the transactional patterns to identify the most storage-intensive ones and those that offer greater flexibility in the throughput/storage trade-off. Finally, we present an analytical study showing that the UTXO model is more storage-intensive than the account model but scales better in transactional throughput.
Abstract The introduction of blockchain technology has brought about significant transformation in the realm of digital transactions, providing a secure and transparent platform for peer-to-peer interactions that cannot be tampered with. The decentralised and distributed nature of blockchains guarantees the integrity and authenticity of the data, eliminating the need for intermediaries. The applications of this technology are not limited to the financial sector, but extend to various areas, such as supply chain management, identity verification, and governance. At the core of these blockchains is the consensus mechanism, which plays a crucial role in ensuring the reliability and integrity of a system. Consensus mechanisms are essential for achieving an agreement amongst network participants regarding the validity of transactions and the order in which they are recorded on the blockchain. By incorporating consensus mechanisms, blockchains ensure that all honest nodes in the network reach a consensus on whether to accept or reject a block, based on predefined rules and criteria. The aim of this study is to introduce a novel consensus mechanism named Erdos, which seeks to address the shortcomings of existing consensus algorithms, such as the Proof of Work and Proof of Stake. Erdos emphasises security, decentralisation, and fairness. One notable feature of this mechanism is its equitable node-selection algorithm, which ensures equal opportunities for all nodes to engage in block creation and validation. In addition, Erdos implements a deterministic block finalisation process that guarantees the integrity and authenticity of the blockchain. The main contribution of this research lies in its innovative approach to deterministic block finalisation, which effectively mitigates the various security risks associated with blockchain systems.