Mubashar Iqbal, Henry Marie Mont, Raimundas Matulevičius
Optimizing administrative functions and enhancing the learning experience are ongoing priorities in education. Modernizing student attendance methods is critical for gauging student engagement and academic success. The traditional attendance systems face various challenges ranging from labor-intensive processes to security and privacy concerns. To address these challenges, we propose a blockchain and Decentralized Autonomous Organization (DAO)-based STudent Attendance Management Platform (STAMP). Leveraging blockchain and DAO principles, we establish a decentralized validation and governance mechanism, incentivizing accurate reporting and transferring attendance tracking responsibility from educators to students. Through Proof of Concept (PoC) implementation, we showcase the practical applicability of the proposed system.
N Gobi, M. Balakrishnan, S. R. Indurekaa, A. B. Arockia Christopher
The integration of quantum cryptography and blockchain offers a transformative approach to enhance data security in cloud environments. Quantum cryptography, leveraging principles of quantum mechanics such as quantum key distribution (QKD), ensures unbreakable encryption by enabling secure key exchange. Simultaneously, blockchain technology introduces decentralization, transparency, and immutability, ensuring data integrity and preventing unauthorized tampering. This research explores the synergy between these technologies to develop a novel framework for cloud security. The proposed model leverages QKD for secure communication channels and blockchain-based distributed ledgers for verifying transactions and access control. Key contributions include a hybrid encryption mechanism using quantum keys, a consensus algorithm optimized for cloud operations, and integration strategies to mitigate quantum attacks. Performance evaluations demonstrate enhanced resilience against data breaches, improved key management, and seamless scalability for modern cloud architectures. This study establishes a secure, future-proof infrastructure that addresses emerging threats in the post-quantum era while ensuring trust, confidentiality, and data integrity in cloud ecosystems.
Blockchain deployments require efficient consensus models in order to be scaled for larger networks. Existing consensus models either use stake-levels, trust-levels, authority-levels, etc. or their combinations in order to reduce mining delay while maintaining higher security levels. But these models either have higher energy requirements, lower security, or have linear/exponential relationship between mining delay and length of the chains. Due to these restrictions, the applicability of these models is affected when deployed under real-time network scenarios. To overcome these issues, this text proposes design of an efficient novel trust-based hybrid consensus model for securing blockchain deployments. The proposed model initially uses a hybrid consensus model that fuses Proof-of-Work (PoW), Proof-of-Stake (PoS) with Proof-of-Temporal-Trust (PoTT) for improving security while maintaining higher Quality of Service (QoS) levels. The PoTT Model fuses together temporal mining delay, temporal mining energy, throughput and block mining efficiency in order to generate miner-level trusts. These trust values are fused with Work efficiency and Stake levels and used for selection of miners. The selected miners are used for serving block addition requests, which assists in improving mining speed by 3.2%, reducing energy consumption 4.5%, and improving throughput by 8.5%, while improving block mining efficiency by 2.9% when compared with existing mining optimization models. This performance was validated under Sybil, Finney, Man-in-the-Middle, and Spoofing attacks. Performance of the model was observed to be consistent even under attacks, thereby making it useful for real-time network scenarios.
Avni Rustemi, Fisnik Dalipi, Vladimir Atanasovski, Aleksandar Risteski
Abstract Nowadays, the centralized systems used in higher education institutions are sophisticated and have high security mechanisms, offering secure data transfer and real-time encryption. Despite the prevalent usage of centralized systems in many institutions, particularly those within the realm of higher education, some unresolved concerns persist pertaining to privacy, potential abuse, transparency, and the limited capacity to digitize numerous services. Blockchain systems are considered as a potential solution for addressing these constraints. This study begins by highlighting the significance of implementing blockchain systems in higher education institutions, while also outlining the obstacles encountered by researchers in this domain. Centralized systems and blockchain systems are distinguished, with a description of the challenges related to data transfer and adaptation to different platforms. A thorough explanation of the proposed blockchain system begins with a presentation of the conceptual model, followed by a detailed architecture of the processes that would be executed by the system, with particular emphasis on the generation and authentication of academic credentials. Additionally, an analysis is provided on the significance of smart contracts in the programming of blockchain systems. This includes a detailed explanation of the main smart contract architecture used in the proposed blockchain system. This article aims to explore the development of a proposed blockchain system and its practical implementation for testing purposes using specific scenarios and data in the foreseeable future.
Seyed Amid Moeinzadeh Mirhosseini, Stefan Craß, Bernhard Uhl, Leander B. Hörmann · 5 authors
With the advent of the Internet of Things (IoT), device-generated data has surged significantly. These ever increasing volumes of data need to be trusted and relied on for various purposes. Data consumers heavily depend on the correctness and integrity of the generated data sets, whereas inadequate IoT security measures increase the risk of data contamination. While data quality problems due to technical issues like sensor faults, unstable communication links, and storage failures can be mitigated with simple measures like redundant nodes and checksums, protection against arbitrary attacks by hackers or insiders requires more sophisticated mechanisms. Blockchain technology enables such an integrity protection mechanism as data is stored in a distributed ledger that cannot be controlled by a single entity. However, the main challenge of this approach in the context of resource-constrained IoT devices lies in the potentially high computational overhead of the underlying cryptographic operations as well as additional storage and bandwidth requirements. Therefore, this paper proposes an energy-efficient data anchoring method tailored for IoT sensors that provides end-to-end security guarantees using a combination of advanced hash trees and digital signatures. By checking the corresponding ledger transaction, any interested party can verify the authenticity of arbitrary sensor data items that were shared by the responsible data provider. Our experiments show a negligible energy overhead for sensor nodes compared to the unprotected protocol, thus indicating the practical feasibility of the approach.
A sharding framework has been proposed by Ethereum 2.0, and each shard can execute transactions requested by users, so the number of transactions dealt with by the shard-based blockchain grows as the number of shards increases. Dealing with cross-shard transactions, however, is a major hindrance to blockchain performance, because each such transaction requires cooperation among different shard validators in the network. Given this background, this paper proposes a novel cross-shard architecture in which smart contract functions in different shards are called in a hierarchical manner so as to dramatically reduce the interactions among different shard validators.
Teaching incentives are important in teaching and training. However, in traditional online teaching and training due to the characteristics of non-disclosure and non-transparency of information, students cannot be encouraged to learn well. In order to provide better incentives can be realized by awarding students with Non Fungible Token (NFT) for their achievements. However, traditional NFT methods do not take into account characteristics such as multi-user co-holding, lifetime holding and non-resale, and interaction based on means such as cryptocurrency wallets raises the threshold for users. In order to allow NFT technology to be better applied to the field of education and training, NFT technology is improved by proposing an NFT incentive mechanism based on the key escrow model, which simultaneously realizes the characteristics of multi-user and soulbounding, and designing a framework of teaching and training system based on NFT incentive. Finally, an experimental analysis is conducted under Ethereum blockchain to validate the rationality and effectiveness of the proposed NFT method, and to evaluate its computation and storage overhead to further prove its usability.
O presente artigo emprega a abordagem Kitchenham para realizar um mapeamento sistemático das técnicas de escalonamento presentes na blockchain Ethereum. O estudo focou em analisar as vantagens e desvantagens de sete das soluções mais populares, incluindo: sharding, state channel, sidechains, plasma, validium, rollup zk e otimista. Os resultados indicam que as técnicas mapeadas oferecem benefícios, como aumento da capacidade de transações e redução dos custos. No entanto, também apresentam limitações e riscos que afetam a segurança da rede.
Distributed ledger technology such as blockchain is considered essential for supporting large numbers of micro-transactions in the Machine Economy, which is envisioned to involve billions of connected heterogeneous and decentralized cyber-physical systems. This stresses the need for performance and scalability of distributed ledger technologies. Addressing this, sharding techniques that divide the blockchain network into multiple committees are a common approach to improve scalability. However, with current sharding approaches, costly cross-shard verification is needed to prevent double-spending. This article proposes a novel and more scalable distributed ledger method named ScaleGraph that implements dynamic sharding by using routing and logical proximity concepts from distributed hash tables. ScaleGraph addresses cybersecurity in terms of integrity and availability to support frequent micro-transactions between autonomous devices. Benefits of ScaleGraph include a total storage space complexity of \(O(t)\) , where \( t \) is the global number of transactions (assuming a constant replication degree). This space is sharded over \( N \) nodes so that each node needs \(O(t/N)\) storage in expectation, which provides a high level of concurrency and data localization as compared to other delegated consensus proposals. ScaleGraph allows for a dynamic grouping of validators that are selected based on a distance metric. We analyze the consensus requirements in such a dynamic setting and show that a synchronous consensus protocol allows shards to be smaller than an asynchronous one, and likely yields better performance. Moreover, we provide an experimental analysis of security aspects regarding the required size of the consensus groups with ScaleGraph. Our analysis shows that dynamic sharding based on proximity concepts brings attractive scalability properties in general, especially when the fraction of corrupt nodes is small.
Sharding is an important technology that utilizes group parallelism to enhance the scalability and performance of blockchain. However, the existing solutions use a historical transaction-based approach to reallocate shards, which cannot handle temporary overload and incurs additional overhead during the reallocation process. To this end, this paper proposes LMChain, an efficient load-migratable beacon-based sharding blockchain system. The primary goal of LMChain is to eliminate reliance on historical transactions and achieve the high performance. Specifically, we redesign the state maintenance data structure in Beacon Shard to effectively manage all account states at the shard level. Then, we innovatively propose a load-migratable transaction processing protocol built upon the new data structure. To mitigate read-write conflicts during the selection of migration transactions, we adopt a novel graph partitioning scheme. We also adopt a relay-based method to handle cross-shard transactions and resolve inter-shard state read-write conflicts. We implement the LMChain prototype and conducted experiments in a real network environment comprising 17 cloud servers. Experimental results show that, compared with state-of-the-art solutions, LMChain effectively reduces the average transaction wait latency of overloaded transactions by 30% to 48% in different cases within 16 transaction shards, while improving throughput by 3% to 10%.
Abstract: In the ever-evolving landscape of distributed systems, ensuring safety, efficiency, and scalability remains a paramount challenge. BlockEdge emerges as a pioneering framework designed to address these critical issues by leveraging the principles of blockchain technology and advanced consensus mechanisms. This abstract outlines the key features, innovations, and potential impacts of BlockEdge on the realm of distributed computing. BlockEdge integrates blockchain's immutable ledger properties with a novel consensus algorithm tailored for distributed systems. Unlike traditional blockchain applications that prioritize decentralization for financial transactions, BlockEdge focuses on enhancing the performance and reliability of distributed applications. The framework employs a hybrid consensus model that combines Byzantine Fault Tolerance (BFT) with Proof-of-Stake (PoS), optimizing both security and energy efficiency. A standout feature of BlockEdge is its modular architecture, which allows seamless interoperability between different types of distributed networks. This modularity facilitates the integration of various consensus protocols, catering to the specific needs of diverse applications, from IoT networks to large-scale data processing systems. By enabling secure and efficient cross-chain communication, BlockEdge effectively mitigates the silo effect prevalent in current distributed system designs.
Sharding is one of the most promising techniques for improving blockchain scalability. In blockchain state sharding, account migration across shards is crucial to the low ratio of cross-shard transactions and cross-shard workload balance. Through reviewing state-of-the-art protocols proposed to reconfigure blockchain shards via account shuffling, we find that account migration plays a significant role. From the literature, we only find a related work that utilizes the lock mechanism to realize account migration. We call this method the SOTA Lock, in which both the target account’s state and its associated transactions need to be locked when migrating this account between shards. Thereby, SOTA Lock causes a high makespan to the associated transactions. To address these challenges of account migration, we propose a dedicated Fine-tuned Lock protocol. Unlike SOTA Lock, Fine-tuned Lock enables real-time processing of the affected transactions during account migration. Thus, the makespan of associated transactions can be lowered. We implement Fine-tuned Lock protocol using an open-sourced blockchain testbed (i.e., BlockEmulator) and deploy it in Tencent cloud. The experimental results show that the proposed Fine-tuned Lock outperforms the SOTA Lock in terms of transaction makespan. For example, the transaction makespan of Fine-tuned Lock achieves around 30% the makespan of SOTA Lock.
Zheyuan He, Zihao Li, Ao Qiao, Xiapu Luo · 9 authors
Blockchains, with intricate architectures, encompass various components, e.g., consensus network, smart contracts, decentralized applications, and auxiliary services. While offering numerous advantages, these components expose various attack surfaces, leading to severe threats to blockchains. In this study, we unveil a novel attack surface, i.e., the state storage, in blockchains. The state storage, based on the Merkle Patricia Trie, plays a crucial role in maintaining blockchain state. Besides, we design Nurgle, the first Denial-of-Service attack targeting the state storage. By proliferating intermediate nodes within the state storage, Nurgle forces blockchains to expend additional resources on state maintenance and verification, impairing their performance. We conduct a comprehensive and systematic evaluation of Nurgle, including the factors affecting it, its impact on blockchains, its financial cost, and practically demonstrating the resulting damage to blockchains. The implications of Nurgle extend beyond the performance degradation of blockchains, potentially reducing trust in them and the value of their cryptocurrencies. Additionally, we further discuss three feasible mitigations against Nurgle. At the time of writing, the vulnerability exploited by Nurgle has been confirmed by six mainstream blockchains, and we received thousands of USD bounty from them.
The closed architecture of prevailing blockchain systems renders the usage of this technology mostly infeasible for a wide range of real-world problems. Most blockchains trap users and applications in their isolated space without the possibility of cooperating or switching to other blockchains. Therefore, blockchains need additional mechanisms for seamless communication and arbitrary data exchange between each other and external systems. Unfortunately, current approaches for cross-blockchain communication are resource-intensive or require additional blockchains or tailored solutions depending on the applied consensus mechanisms of the connected blockchains. Therefore, we propose an oracle with an off-chain aggregation mechanism based on Zero-Knowledge Succinct Non-interactive Arguments of Knowledge (zk-SNARKs) to facilitate cross-blockchain communication. The oracle queries data from another blockchain and applies a rollup-like mechanism to move state and computation off-chain. The zkOracle contract only expects the transferred data, an updated state root, and proof of the correct execution of the aggregation mechanism. The proposed solution only requires constant 378 kgas to submit data on the Ethereum blockchain and is primarily independent of the underlying technology of the queried blockchains.
Today, blockchain technology has found a special role in the energy industry and has various applications. In this paper a token-based peer-to-peer energy trading model is presented. Since the peer-to-peer trading of energy leads to the growth of micro-grids and smart grids, which in turn play a vital role in the development of renewable energies, using blockchain is the best and safest way to develop peer-to-peer networks. The definition of a special token can lead to balance and relative independence of the price of energy supply, which means that the price of network energy is related to the price of the token, and if the token is accepted, the growth of the network is inevitable. This can be one of the best ways for the growth of renewable energies, considering the renewable energy sources in the network. After reviewing the model, a sample smart contract corresponding to this model has been implemented on the Ethereum local blockchain platform.
The dynamic and unpredictable nature of network environments poses a significant challenge for distributed systems, particularly those relying on consensus algorithms for state management and fault tolerance. To address this challenge, this article introduces a novel simulation model designed to study the impact of unstable network connections on clusters running consensus algorithms. The model is engineered to mimic varying degrees of network instability, including latency fluctuations and connection disruptions, which are characteristic of real-world distributed systems. Our proposed model represents a significant advancement in the simulation of distributed networks. It employs a sophisticated network emulation layer capable of generating a wide spectrum of unstable network conditions. The core of the model is a highly configurable consensus mechanism simulator that allows for the adjustment of key parameters such as heartbeat intervals, election timeouts, and message loss rates. This level of configurability enables a comprehensive analysis of consensus behaviors under different network scenarios. The article focuses on the methodology behind the development of the model, detailing the theoretical underpinnings and the implementation strategies used to ensure a realistic representation of network instability. We also discuss the potential applications of the model, which extend beyond academic research into practical domains where distributed ledger technologies and distributed databases are prevalent. Through the deployment of this model, researchers and system architects can gain deeper insights into the resilience and adaptability of consensus algorithms. The model serves as a tool for preemptively identifying and addressing potential issues in distributed systems, facilitating the development of more robust and reliable technologies. In summary, the article showcases the design and capabilities of a new model that enables an in-depth understanding of the delicate interplay between network instability and consensus efficiency. By focusing on the model itself, the article aims to lay a foundation for future studies and improvements in the field of distributed systems.
Jummai Enare Abang, Haifa Takruri, Rabab Al-Zaidi, Mohammed Al-Khalidi
Blockchain is a decentralized and distributed ledger technology that enables secure and transparent recording of transactions across multiple participants. Hyperledger Fabric (HLF), a permissioned blockchain, enhances performance through its modular design and pluggable consensus. However, integrating HLF with enterprise applications introduces latency challenges. Researchers have proposed numerous latency performance modelling techniques to address this issue. These studies contribute to a deeper understanding of HLF’s latency by employing various modelling approaches and exploring techniques to improve network latency. However, existing HLF latency modelling studies lack an analysis of how these research efforts apply to specific use cases. This paper examines existing research on latency performance modelling in HLF and the challenges of applying these models to HLF-enabled Internet of Things (IoT) use cases. We propose a novel set of criteria for evaluating HLF latency performance modelling and highlight key HLF parameters that influence latency, aligning them with our evaluation criteria. We then classify existing papers based on their focus on latency modelling and the criteria they address. Additionally, we provide a comprehensive overview of latency performance modelling from various researchers, emphasizing the challenges in adapting these models to HLF-enabled IoT blockchain within the framework of our evaluation criteria. Finally, we suggest directions for future research and highlight open research questions for further exploration.
Sergio Demian Lerner, Ramon Amela, Shreemoy Mishra, Martin Jonáš · 5 authors
BitVMX is a new design for a virtual CPU to optimistically execute arbitrary programs on Bitcoin based on a challenge response game introduced in BitVM. Similar to BitVM1 we create a general-purpose CPU to be verified in Bitcoin script. Our design supports common architectures, such as RISC-V or MIPS. Our main contribution to the state of the art is a design that uses hash chains of program traces, memory mapped registers, and a new challenge-response protocol. We present a new message linking protocol as a means to allow authenticated communication between the participants. This protocol emulates stateful smart contracts by sharing state between transactions. This provides a basis for our verification game which uses a graph of pre-signed transactions to support challenge-response interactions. In case of a dispute, the hash chain of program trace is used with selective pre-signed transactions to locate (via $n$-ary search) and then recover the precise nature of errors in the computation. Unlike BitVM1, our approach does not require the creation of Merkle trees for CPU instructions or memory words. Additionally, it does not rely on signature equivocations. These differences help avoid complexities associated with BitVM1 and make BitVMX a compelling alternative to BitVM2. Our approach is quite flexible, BitVMX can be instantiated to balance transaction cost vs round complexity, prover cost vs verifier cost, and precomputations vs round complexity.
R. Sabitha, M Madhini, Priya Sethuraman, S. Vijayalakshmi · 5 authors
A key component of a blockchain is its distributed digital ledger of transactions, which is effectively a record of all transactions recorded in the network. A key component of a blockchain is its distributed digital ledger of transactions, which is effectively a record of all transactions recorded in the network. The process of altering images is been simplified. In a web-based program, a validation system for student certificates is created. The issue statement suggests that universities keep records of students who are unable to attend class in the form of a certificate. To skip class and get a doctor’s note is just too simple. A few pupils have been caught using forged certificates to skip class. Many people nowadays are dishonest and would buy or make fake certificates from websites that claim to provide them. Having to verify and validate certificates is a pain for the company and the institution. For safekeeping of certificates on the blockchain, we provide a method we term Blockchain Powered Student Certificate Validation (BPSCV). A standard Optical Character Recognition (OCR) model is used for cross-validation in order to assess how well the suggested task works. Digitization of the paper certificates is the initial step. When creating the certificate’s hash code, the suggested algorithm is utilized. Certificates are then recorded in the blockchain. Furthermore, the mobile app verifies these credentials. The use of blockchain technology allows us to validate digital certificates in a more efficient and safe manner.
Sharding provides an opportunity to overcome the inherent scalability challenges of the blockchain, which is the infrastructure for the next generation of the Web. In a sharding blockchain, the state is partitioned into smaller groups known as "shards." Since the states are placed on different shards, cross-shard transactions are inevitable, which is detrimental to the performance of the sharding blockchain. Existing solutions place states based on heuristic algorithms or redistribute states via graph-partitioning-based methods, which are either less effective or costly. In this paper, we present SPRING, the first deep-reinforcement-learning(DRL)-based sharding framework for state placement. SPRING formulates the state placement as a Markov Decision Process, which considers the cross-shard transaction ratio and workload balancing and employs DRL to learn the effective state placement policy. Experimental results based on real Ethereum transaction data demonstrate the superiority of SPRING compared to other state placement solutions. In particular, it decreases the cross-shard transaction ratio by up to 26.63% and boosts throughput by up to 36.03%, all without unduly sacrificing the workload balance among shards. Moreover, updating the training model and making decisions takes only 0.1s and 0.002s, respectively, which shows the overhead is acceptable.
The adoption of blockchain technology has catalyzed the expansion of decentralized finance (DeFi), leading to the harnessing of blockchain platforms. However, the decentralization of blockchain has given rise to blockchain extractable value (BEV) activities, influenced by consensus mechanisms. This study centers on BEV, unveiling a real-time discovery and mining system (RDMS) tailored for arbitrage-based DeFi activities. The system employs innovative methodologies for localized computation and execution. It establishes a comprehensive monitoring system for arbitrage and liquidation activities, contributing positively to the DeFi ecosystem. Leveraging round-the-clock on-chain data indexing and event-driven parsing methods, the RDMS enables automated and periodic analysis of BEV activities. This system provides valuable insights for BEV research, particularly in the context of arbitrage and liquidation activities. And we are able to consistently extract value using arbitrage strategies on blockchains, using RDMS that monitors the chain in real time and applies gas cost reduction mechanisms. Experimental testing and comparative analysis validate the RDMS’s effectiveness, showcasing minimal latency and remarkable gas optimization capabilities.
Ethereum 2.0 is the second-largest cryptocurrency by market capitalization and a widely used smart contract platform. Therefore, examining the reliability of Ethereum 2.0's incentive mechanism is crucial, particularly its effectiveness in encouraging validators to adhere to the Ethereum 2.0's protocol. This paper studies the incentive mechanism of Ethereum 2.0 and evaluates its robustness by analyzing the interaction between block proposers and attesters in a single slot. To this end, we use Bayesian games to model the strategies of block proposers and attesters and calculate their expected utilities. Our results demonstrate that the Ethereum 2.0 incentive mechanism is incentive-compatible and promotes cooperation among validators. We prove that a Bayesian Nash equilibrium and an ex ante dominant strategy exist between the block proposer and attesters in a single slot. Our research provides a solid foundation for further analysis of Ethereum 2.0's incentive mechanism and insights for individuals considering participation as a validator in Ethereum 2.0.