Edge computing-based Next-Generation Wireless Networks (NGWN)-IoT offer enhanced bandwidth capacity for large-scale service provisioning but remain vulnerable to evolving cyber threats. Existing intrusion detection and prevention methods provide limited security as adversaries continually adapt their attack strategies. We propose a dynamic attack detection and prevention approach to address this challenge. First, blockchain-based authentication uses the Deoxys Authentication Algorithm (DAA) to verify IoT device legitimacy before data transmission. Next, a bi-stage intrusion detection system is introduced: the first stage uses signature-based detection via an Improved Random Forest (IRF) algorithm. In contrast, the second stage applies feature-based anomaly detection using a Diffusion Convolution Recurrent Neural Network (DCRNN). To ensure Quality of Service (QoS) and maintain Service Level Agreements (SLA), trust-aware service migration is performed using Heap-Based Optimization (HBO). Additionally, on-demand virtual High-Interaction honeypots deceive attackers and extract attack patterns, which are securely stored using the Bimodal Lattice Signature Scheme (BLISS) to enhance signature-based Intrusion Detection Systems (IDS). The proposed framework is implemented in the NS3 simulation environment and evaluated against existing methods across multiple performance metrics, including accuracy, attack detection rate, false negative rate, precision, recall, ROC curve, memory usage, CPU usage, and execution time. Experimental results demonstrate that the framework significantly outperforms existing approaches, reinforcing the security of NGWN-enabled IoT ecosystems
Bitcoin blockchain uses hash-based Proof-of-Work (PoW) that prevents unwanted participants from hogging the network resources. Anyone entering the mining game has to prove that they have expended a specific amount of computational power. However, the most popular Bitcoin blockchain consumes 175.87 TWh of electrical energy annually, and most of this energy is wasted on hash calculations, which serve no additional purpose. Several studies have explored re-purposing the wasted energy by replacing the hash function with meaningful computational problems that have practical applications. Minimum Dominating Set (MDS) in networks has numerous real-life applications. Building on this concept, Chrisimos [TrustCom '23] was proposed to replace hash-based PoW with the computation of a dominating set on real-life graph instances. However, Chrisimos has several drawbacks regarding efficiency and solution quality. This work presents a new framework for Useful PoW, ScaloWork, that decides the block proposer for the Bitcoin blockchain based on the solution for the dominating set problem. ScaloWork relies on the property of graph isomorphism and guarantees solution extractability. We also propose a distributed approach for calculating the dominating set, allowing miners to collaborate in a pool. This enables ScaloWork to handle larger graphs relevant to real-life applications, thereby enhancing scalability. Our framework also eliminates the problem of free-riders, ensuring fairness in the distribution of block rewards. We perform a detailed security analysis of our framework and prove our scheme as secure as hash-based PoW. We implement a prototype of our framework, and the results show that our system outperforms Chrisimos in all aspects.
Silvio Meneguzzo, Claudio Schifanella, Valentina Gatteschi, Giuseppe Destefanis
Decentralised Autonomous Organisations (DAOs) automate governance and resource allocation through smart contracts, aiming to shift decision-making to distributed token holders. However, many DAOs face sustainability challenges linked to limited user participation, concentrated voting power, and technical design constraints. This paper addresses these issues by identifying research gaps in DAO evaluation and introducing a framework of Key Performance Indicators (KPIs) that capture governance efficiency, financial robustness, decentralisation, and community engagement. We apply the framework to a custom-built dataset of real-world DAOs constructed from on-chain data and analysed using non-parametric methods. The results reveal recurring governance patterns, including low participation rates and high proposer concentration, which may undermine long-term viability. The proposed KPIs offer a replicable, data-driven method for assessing DAO governance structures and identifying potential areas for improvement. These findings support a multidimensional approach to evaluating decentralised systems and provide practical tools for researchers and practitioners working to improve the resilience and effectiveness of DAO-based governance models.
Decentralization is a foundational principle of permissionless blockchains, with consensus mechanisms serving a critical role in its realization. This study quantifies the decentralization of consensus mechanisms in proof-of-stake (PoS) blockchains using a comprehensive set of metrics, including Nakamoto coefficients, Gini, Herfindahl-Hirschman Index (HHI), Shapley values, and Zipfâs coefficient. Our empirical analysis across ten prominent blockchains reveals significant concentration of stake among a few validators, posing challenges to fair consensus. To address this, we introduce two alternative weighting models for PoS consensus: Square Root Stake Weight (SRSW) and Logarithmic Stake Weight (LSW), which adjust validator influence through non-linear transformations. Results demonstrate that SRSW and LSW models improve decentralization metrics by an average of 51% and 132%, respectively, supporting more equitable and resilient blockchain systems.
Blockchain consensus mechanisms have relied on algorithms such as Proof-of-Work (PoW) and Proof-of-Stake (PoS) to ensure network functionality and integrity. However, these approaches struggle with adaptability for decision-making where the opinions of each matter rather than reaching an agreement based on honest majority or weighted consensus. This paper introduces a novel deliberation-based consensus mechanism where Large Language Models (LLMs) act as rational agents engaging in structured discussions to reach a unanimous consensus. By leveraging graded consensus and a multi-round deliberation process, our approach ensures unanimous consensus for definitive problems and graded consensus for prioritized decision problems and policies. We provide a formalization of our system and use it to show that the properties of blockchains are maintained, while also addressing the behavior in terms of adversaries, stalled deliberations, and confidence in consensus. Moreover, experimental results demonstrate system feasibility, showcasing convergence, block properties, and accuracy, which enable deliberative decision-making on blockchain networks.
Certified randomness can be generated with untrusted remote quantum computers using multiple known protocols, one of which has been recently realized experimentally. Unlike the randomness sources accessible on today's classical computers, the output of these protocols can be certified to be random under certain computational hardness assumptions, with no trust required in the hardware generating the randomness. In this perspective, we explore real-world applications for which the use of certified randomness protocols may lead to improved security and fairness. We identify promising applications in areas including cryptography, differential privacy, financial markets, and blockchain. Through this initial exploration, we hope to shed light on potential applications of certified randomness.
The evaluation of smart contract reputability is essential to foster trust in decentralized ecosystems. However, existing methods that rely solely on code analysis or transactional data, offer limited insight into evolving trustworthiness. We propose a multimodal data fusion framework that integrates code features with transactional data to enhance reputability prediction. Our framework initially focuses on AI-based code analysis, utilizing GAN-augmented opcode embeddings to address class imbalance, achieving 97.67% accuracy and a recall of 0.942 in detecting illicit contracts, surpassing traditional oversampling methods. This forms the crux of a reputability-centric fusion strategy, where combining code and transactional data improves recall by 7.25% over single-source models, demonstrating robust performance across validation sets. By providing a holistic view of smart contract behaviour, our approach enhances the model's ability to assess reputability, identify fraudulent activities, and predict anomalous patterns. These capabilities contribute to more accurate reputability assessments, proactive risk mitigation, and enhanced blockchain security.
Identifying reputable Ethereum projects remains a critical challenge within the expanding blockchain ecosystem. The ability to distinguish between legitimate initiatives and potentially fraudulent schemes is non-trivial. This work presents a systematic approach that integrates multiple data sources with advanced analytics to evaluate credibility, transparency, and overall trustworthiness. The methodology applies machine learning techniques to analyse transaction histories on the Ethereum blockchain. The study classifies accounts based on a dataset comprising 2,179 entities linked to illicit activities and 3,977 associated with reputable projects. Using the LightGBM algorithm, the approach achieves an average accuracy of 0.984 and an average AUC of 0.999, validated through 10-fold cross-validation. Key influential factors include time differences between transactions and received_tnx. The proposed methodology provides a robust mechanism for identifying reputable Ethereum projects, fostering a more secure and transparent investment environment. By equipping stakeholders with data-driven insights, this research enables more informed decision-making, risk mitigation, and the promotion of legitimate blockchain initiatives. Furthermore, it lays the foundation for future advancements in trust assessment methodologies, contributing to the continued development and maturity of the Ethereum ecosystem.
William C. Quigley, Mohamed Rahouti, Gary M. Weiss
The maritime industry is governed by stringent environmental regulations, most notably the International Convention for the Prevention of Pollution from Ships (MARPOL). Ensuring compliance with these regulations is difficult due to low inspection rates and the risk of data fabrication. To address these issues, this paper proposes a secure blockchain-assisted framework for real-time maritime environmental compliance monitoring. By integrating IoT and shipboard sensors with blockchain technology, the framework ensures immutable and transparent record-keeping of environmental data. Smart contracts automate compliance verification and notify relevant authorities in case of non-compliance. A proof-of-concept case study on sulfur emissions demonstrates the framework's efficacy in enhancing MARPOL enforcement through real-time data integrity and regulatory adherence. The proposed system leverages the Polygon blockchain for scalability and efficiency, providing a robust solution for maritime environmental protection. The evaluation results demonstrate that the proposed blockchain-enhanced compliance monitoring system effectively and securely ensures real-time regulatory adherence with high scalability, efficiency, and cost-effectiveness, leveraging the robust capabilities of the Polygon blockchain.
While a plethora of machine learning (ML) models are currently available, along with their implementation on disparate platforms, there is hardly any verifiable ML code which can be executed on public blockchains. We propose a novel approach named LMST that enables conversion of the inferencing path of an ML model as well as its weights trained off-chain into Solidity code using Large Language Models (LLMs). Extensive prompt engineering is done to achieve gas cost optimization beyond mere correctness of the produced code, while taking into consideration the capabilities and limitations of the Ethereum Virtual Machine. We have also developed a proof of concept decentralized application using the code so generated for verifying the accuracy claims of the underlying ML model. An extensive set of experiments demonstrate the feasibility of deploying ML models on blockchains through automated code translation using LLMs.
In a world where data is the new currency, wearable health devices offer unprecedented insights into daily life, continuously monitoring vital signs and metrics. However, this convenience raises privacy concerns, as these devices collect sensitive data that can be misused or breached. Traditional measures often fail due to real-time data processing needs and limited device power. Users also lack awareness and control over data sharing and usage. We propose a Privacy-Enhancing Technology (PET) framework for wearable devices, integrating federated learning, lightweight cryptographic methods, and selectively deployed blockchain technology. The blockchain acts as a secure ledger triggered only upon data transfer requests, granting users real-time notifications and control. By dismantling data monopolies, this approach returns data sovereignty to individuals. Through real-world applications like secure medical data sharing, privacy-preserving fitness tracking, and continuous health monitoring, our framework reduces privacy risks by up to 70 percent while preserving data utility and performance. This innovation sets a new benchmark for wearable privacy and can scale to broader IoT ecosystems, including smart homes and industry. As data continues to shape our digital landscape, our research underscores the critical need to maintain privacy and user control at the forefront of technological progress.
As robotic systems (RS) become more autonomous, they are becoming increasingly used in small spaces and offices to automate tasks such as cleaning, infrastructure maintenance, or resource management. In this paper, we propose iTrash, an intelligent trashcan that aims to improve recycling rates in small office spaces. For that, we ran a 5 day experiment and found that iTrash can produce an efficiency increase of more than 30% compared to traditional trashcans. The findings derived from this work, point to the fact that using iTrash not only increase recyclying rates, but also provides valuable data such as users behaviour or bin usage patterns, which cannot be taken from a normal trashcan. This information can be used to predict and optimize some tasks in these spaces. Finally, we explored the potential of using blockchain technology to create economic incentives for recycling, following a Save-as-you-Throw (SAYT) model.
With the widespread adoption of Zero-Knowledge Proof systems, particularly ZK-SNARK, the efficiency of proof generation, encompassing both the witness generation and proof computation phases, has become a significant concern. While substantial efforts have successfully accelerated proof computation, progress in optimizing witness generation remains limited, which inevitably hampers overall efficiency. In this paper, we propose Yoimiya, a scalable framework with pipeline, to optimize the efficiency in ZK-SNARK systems. First, Yoimiya introduces an automatic circuit partitioning algorithm that divides large circuits of ZK-SNARK into smaller subcircuits, the minimal computing units with smaller memory requirement, allowing parallel processing on multiple units. Second, Yoimiya decouples witness generation from proof computation, and achieves simultaneous executions over units from multiple circuits. Moreover, Yoimiya enables each phase scalable separately by configuring the resource distribution to make the time costs of the two phases aligned, maximizing the resource utilization. Experimental results confirmed that our framework effectively improves the resource utilization and proof generation speed.
H.C. Zhang, Shike Li, Shike Li, Hang Bao ¡ 6 authors
The rapid development of blockchain technology has driven the widespread application of decentralized applications (DApps) across various fields. However, DApps cannot directly access external data and rely on oracles to interact with off-chain data. As a bridge between blockchain and external data sources, oracles pose potential risks of malicious behavior, which may inject incorrect or harmful data, leading to trust and security issues. Additionally, with the surge in data requests, the disparity in oracle trustworthiness and costs has increased, making the dynamic selection of the most suitable oracle for each request a critical challenge. To address these issues, this paper proposes a Trust-Aware and Cost-Optimized Blockchain Oracle Selection Model with Deep Reinforcement Learning (TCO-DRL). The model incorporates a comprehensive trust management mechanism to evaluate oracle reputation from multiple dimensions and employs an improved sliding time window to monitor reputation changes in real time, enhancing resistance to malicious attacks. Moreover, TCO-DRL uses deep reinforcement learning algorithms to dynamically adapt to fluctuations in oracle reputation, ensuring the selection of high-reputation oracles while optimizing node selection, thereby reducing costs without compromising data quality. We implemented and validated TCO- DRL on Ethereum. Experimental results show that, compared to existing methods, TCO-DRL reduces the allocation rate to malicious oracles by more than 39.10% and saves over 12.00% in costs. Furthermore, simulated experiments on various malicious attacks further validate the robustness and effectiveness of TCO-DRL
Ethereum has adopted a rollup-centric roadmap to scale by making rollups (layer 2 scaling solutions) the primary method for handling transactions. The first significant step towards this goal was EIP-4844, which introduced blob transactions that are designed to meet the data availability needs of layer 2 protocols. This work constitutes the first rigorous and comprehensive empirical analysis of transaction- and mempool-level data since the institution of blobs on Ethereum on March 13, 2024. We perform a longitudinal study of the early days of the blob fee market analyzing the landscape and the behaviors of its participants. We identify and measure the inefficiencies arising out of suboptimal block packing, showing that at times it has resulted in up to 70% relative fee loss. We hone in and give further insight into two (congested) peak demand periods for blobs. Finally, we document a market design issue relating to subset bidding due to the inflexibility of the transaction structure on packing data as blobs and suggest possible ways to fix it. The latter market structure issue also applies more generally for any discrete objects included within transactions.
Scalable and secure data management is important in Internet of Things (IoT) applications such as smart water meters, where traditional blockchain storage can be restrictive due to high data volumes. This paper investigates a hybrid blockchain and InterPlanetary File System (IPFS) approach designed to optimise storage efficiency, enhance throughput, and reduce block time by offloading large data off-chain to IPFS while preserving on-chain integrity. A substrate-based private blockchain was developed to store smart water meter (SWM) data, and controlled experiments were conducted to evaluate blockchain performance with and without IPFS. Key metrics, including block size, block time, and transaction throughput, were analysed across varying data volumes and node counts. Results show that integrating IPFS significantly reduces on-chain storage demands, leading to smaller block sizes, increased throughput, and improved block times compared to blockchain-only storage. These findings highlight the potential of hybrid blockchain-IPFS models for efficiently and securely managing high-volume IoT data.
Decentralized physical infrastructure networks (DePINs) are an emerging vertical within "Web3" replacing the traditional method that physical infrastructures are constructed. Yet, the boundaries between DePIN and traditional method of building crowd-sourced infrastructures such as citizen science initiatives or other Web3 verticals are not always so clear cut. In this work, we systematically analyze the differences between DePIN and other Web2 and Web3 verticals. For this, the study proposes a novel decision tree for classifying systems as DePIN. This tree is informed by prior studies and differentiates DePIN from related concepts using criteria such as the presence of a three-sided market, token-based incentives for supply, and the requirement for physical asset placement in those systems. The paper demonstrates the application of the decision tree to various blockchain systems, including Helium and Bitcoin, showcasing its practical utility in differentiating DePIN systems. This research offers significant contributions towards establishing a more objective and systematic approach to identifying and categorizing DePIN systems. It lays the groundwork for creating a comprehensive and unbiased database of DePIN systems, which will inform future research and development within this emerging sector.
Blockchained federated learning (BFL) combines the concepts of federated learning and blockchain technology to enhance privacy, security, and transparency in collaborative machine learning models. However, implementing BFL frameworks poses challenges in terms of scalability and cost-effectiveness. Reputation-aware BFL poses even more challenges, as blockchain validators are tasked with processing federated learning transactions along with the transactions that evaluate FL tasks and aggregate reputations. This leads to faster blockchain congestion and performance degradation. To improve BFL efficiency while increasing scalability and reducing on-chain reputation management costs, this paper proposes AutoDFL, a scalable and automated reputation-aware decentralized federated learning framework. AutoDFL leverages zk-Rollups as a Layer-2 scaling solution to boost the performance while maintaining the same level of security as the underlying Layer-1 blockchain. Moreover, AutoDFL introduces an automated and fair reputation model designed to incentivize federated learning actors. We develop a proof of concept for our framework for an accurate evaluation. Tested with various custom workloads, AutoDFL reaches an average throughput of over 3000 TPS with a gas reduction of up to 20X.
Ahmed Ayoub Bellachia, Mouhamed Amine Bouchiha, Yacine Ghamri-Doudane, Mourad Rabah
Blockchain-based Federated Learning (BFL) is an emerging decentralized machine learning paradigm that enables model training without relying on a central server. Although some BFL frameworks are considered privacy-preserving, they are still vulnerable to various attacks, including inference and model poisoning. Additionally, most of these solutions employ strong trust assumptions among all participating entities or introduce incentive mechanisms to encourage collaboration, making them susceptible to multiple security flaws. This work presents VerifBFL, a trustless, privacy-preserving, and verifiable federated learning framework that integrates blockchain technology and cryptographic protocols. By employing zero-knowledge Succinct Non-Interactive Argument of Knowledge (zk-SNARKs) and in-crementally verifiable computation (IVC), VerifBFL ensures the verifiability of both local training and aggregation processes. The proofs of training accuracy and aggregation are verified on-chain, guaranteeing the integrity and auditability of each participant's contributions. To protect training data from inference attacks, VerifBFL leverages differential privacy. Finally, to demonstrate the efficiency of the proposed protocols, we built a proof of concept using emerging tools. The results show that generating proofs for local training and aggregation in VerifBFL takes less than 81s and 2s, respectively, while verifying them on-chain takes less than 0.6s.
Danilo Rafael de Lima Cabral, Pedro Antonino, Augusto Sampaio
The Ethereum blockchain has a \emph{gas system} that associates operations with a cost in gas units. Two central concepts of this system are the \emph{gas limit} assigned by the issuer of a transaction and the \emph{gas used} by a transaction. The former is a budget that must not be exhausted before the completion of the transaction execution; otherwise, the execution fails. Therefore, it seems rather essential to determine the \emph{minimum gas limit} that ensures the execution of a transaction will not abort due to the lack of gas. Despite its practical relevance, this concept has not been properly addressed. In the literature, gas used and minimum gas limit are conflated. This paper proposes a precise notion of minimum gas limit and how it can differ from gas used by a transaction; this is also demonstrated with a quantitative study on real transactions of the Ethereum blockchain. Another significant contribution is the proposition of a fairly precise estimator for each of the two metrics. Again, the confusion between these concepts has led to the creation of estimators only for the gas used by a transaction. We demonstrate that the minimum gas limit for the state of the Ethereum blockchain (after the block) $t$ can serve as a near-perfect estimation for the execution of the transaction at block $t + Î$, where $Î\leq 11$; the same holds for estimating gas used. These precise estimators can be very valuable in helping the users predict the gas budget of transactions and developers in optimising their smart contracts; over and underestimating gas used and minimum gas limit can lead to a number of practical issues. Overall, this paper serves as an important reference for blockchain developers and users as to how the gas system really works.
This paper provides a brief overview of the ongoing financial revolution, which extends beyond the emergence of cryptocurrencies as a digital medium of exchange. At its core, this revolution is driven by a paradigm shift rooted in the technological advancements of blockchain and the foundational principles of Islamic economics. Together, these elements offer a transformative framework that challenges traditional financial systems, emphasizing transparency, equity, and decentralized governance. The paper highlights the implications of this shift and its potential to reshape the global economic landscape.
The convergence of humans and artificial intelligence systems introduces new dynamics into the cultural and intellectual landscape. Complementing emerging cultural evolution concepts such as machine culture, AI agents represent a significant techno-sociological development, particularly within the anthropological study of Web3 as a community focused on decentralization through blockchain. Despite their growing presence, the cultural significance of AI agents remains largely unexplored in academic literature. Toward this end, we conceived hybrid netnography, a novel interdisciplinary approach that examines the cultural and intellectual dynamics within digital ecosystems by analyzing the interactions and contributions of both human and AI agents as co-participants in shaping narratives, ideas, and cultural artifacts. We argue that, within the Web3 community on the social media platform X, these agents challenge traditional notions of participation and influence in public discourse, creating a hybrid marketplace of ideas, a conceptual space where human and AI generated ideas coexist and compete for attention. We examine the current state of AI agents in idea generation, propagation, and engagement, positioning their role as cultural agents through the lens of memetics and encouraging further inquiry into their cultural and societal impact. Additionally, we address the implications of this paradigm for privacy, intellectual property, and governance, highlighting the societal and legal challenges of integrating AI agents into the hybrid marketplace of ideas.
Quantum computing poses fundamental risks to classical blockchain systems by undermining widely used cryptographic primitives. In response, two major research directions have emerged: post-quantum blockchains, which integrate quantum-resistant algorithms, and quantum blockchains, which leverage quantum properties such as entanglement and quantum key distribution. This survey reviews key developments in both areas, analyzing their cryptographic foundations, architectural designs, and implementation challenges. This work provides a comparative overview of technical proposals, highlight trade-offs in security, scalability, and deployment, and identify open research problems across hardware, consensus, and network design. The goal is to offer a structured and comprehensive reference for advancing secure blockchain systems in the quantum era.
Mark C. Ballandies, Guangyao Li, Claudio J. Tessone
Abstract This paper investigates whether Bitcoin can be regarded as a decentralized autonomous organization (DAO), what insights it may offer for the broader DAO ecosystem, and how Bitcoin governance can be improved. First, a quantitative literature analysis reveals that Bitcoin is increasingly overlooked in DAO research, even though early works often classified it as a DAO. Next, the paper applies a DAO viability framework, centering on collective intelligence, digital democracy, and adaptation, to examine Bitcoinâs organizational and governance mechanisms. Findings suggest that Bitcoin instantiates key DAO principles by enabling open participation and employing decentralized decision-making through Bitcoin Improvement Proposals (BIPs), miner signaling, and user-activated soft forks. However, this governance carries potential risks, including reduced clarity on who truly âvotesâ due to the concentration of economic power among large stakeholders. The paper concludes by highlighting opportunities to refine Bitcoinâs deliberation process and reflecting on broader implications for DAO design, such as the absence of a legal entity. In doing so, it underscores Bitcoinâs continued relevance as an archetype for decentralized governance, offering important findings for future DAO implementations.