Blockchain Papers

Jun Young Byun, Yosep Na, Daehyun Kim, Hyun Ho Jeon · 6 authors

No abstract is available for this record.

Jasvant Mandloi, Pratosh Bansal

No abstract is available for this record.

Pedro Cosme

No abstract is available for this record.

Subham Kumar, Sushila Soriya

No abstract is available for this record.

Harshit Singh

No abstract is available for this record.

Marion Colas-Blaise

No abstract is available for this record.

Yevhеnii Koroviakа, Volodymyr Khomenko, Oleksandr Pashchenko, Serhii Shevchenko · 5 authors

Objective. To develop, formalize, and evaluate a decentralized multi-agent system for real‑time traffic management in autonomous open‑pit mining operations, aimed at minimizing truck idle time, preventing congestion and deadlocks, and increasing overall haulage efficiency. Methodology. The system is designed using a multi‑agent paradigm where haul trucks, excavators,dumping points, and key infrastructure elements are modeled as autonomous agents. Task allocation is performed via a market‑based Contract Net Protocol with bidding based on estimated time of arrival. Path planning employs an A* algorithm on a dynamic graph, while conflict resolution is achieved through a reservation mechanism managed by infrastructure agents. Reinforcement learning (Q‑learning) is integrated to allow truck agents to adapt their bidding strategies over time. The approach is validated through agent‑based simulations under normal, high‑intensity, and disruption scenarios, and compared against a centralized first‑come, first‑served dispatch system. Results. Under normal operation, the multi‑agent system reduced average truck idle time by 44%, cycle time by 17.5%, increased throughput by 21%, and lowered traffic conflict frequency by 66.5% compared to the baseline. In high‑intensity traffic, it prevented congestion and deadlocks, maintaining smooth flow. During unexpected disruptions (excavator breakdown, road blockage), the system autonomously re‑planned routes and reassigned tasks within minutes, effectively isolating the impact and ensuring operational continuity. Scientific novelty. The novelty lies in the holistic integration of decentralized coordination, market‑based task allocation, dynamic path reservation, and reinforcement learning within a unified multi‑agent framework for real‑time traffic management in open‑pit mining. This approach enables emergent self‑organization, robust adaptation to dynamic conditions, and scalability beyond the capabilities of traditional centralized fleet management systems. Practical significance. The proposed system offers a scalable and resilient solution for autonomous haulage fleets, directly reducing operational costs through lower idle times and higher throughput, improving safety by preventing traffic conflicts, and enhancing resilience to equipment failures or route blockages. It provides a clear pathway for transitioning existing mines to fully autonomous, efficient, and safe operations. Keywords: multi-agent system, traffic management, open-pit mining, autonomous haulage, fleet management, reinforcement learning, Contract Net Protocol, path planning, conflict resolution, simulation.

Philipp Artur Kienscherf

The dissertation "Essays on Market Design and Algorithms for Distributed and Sustainable Energy Systems" investigates how the large-scale electrification of transport can be coordinated through digital markets and intelligent algorithms. As electric vehicles become ubiquitous, millions of charging decisions intertwine with the real-time dynamics of power grids, creating a cyber-physical system. The dissertation finds that this challenge can be addressed by more means than engineering grid capacity: it proposes new market designs and machine learning methods that translate physical constraints into economic signals, allowing autonomous software agents to coordinate their behavior efficiently and sustainably. The work is built on the insight that large-scale EV charging is a distributed decision problem. Each driver cares about having enough charge when needed, yet the collective charging pattern determines local grid stress, costs, and emissions. Instead of relying on centralized schedulers or detailed travel forecasts, the work develops decentralized mechanisms in which intelligent agents learn to act on real-time prices and limited local information. Through multi-agent reinforcement learning, household charging agents observe only their state of charge, time of day, and previous prices but still learn to bid effectively for charging power in repeated auctions. Simulations on realistic low-voltage grids show that simple linear learning agents can reach near-optimal outcomes within a few percentage points of a full-information benchmark while avoiding the instability and training burden of deep neural networks. This establishes a foundation for self-organizing, bottom-up control of electric loads. Moving beyond individual grid situations, the thesis then explores how market architecture itself can be made trustless and transparent. It develops a blockchain-based bundle trading market that enables EV owners to buy and sell time-specific charging rights without a central auctioneer, using smart contracts that guarantee correctness and auditability. The system achieves the same efficiency as centralized clearing but adds resilience and privacy, illustrating how distributed ledger technology can support critical energy services. The concept of market-based coordination is further generalized to decentralized autonomous organizations (DAOs) that manage both physical and financial resources. Here the dissertation designs a mechanism in which agents not only trade physical bundles but also issue contingent financial claims, allowing them to share risk while preserving autonomy and data privacy. The mechanism converges to the same equilibrium a risk-neutral central planner would choose, demonstrating that even complex, stochastic resource allocation can be governed through decentralized markets. The final part of the dissertation widens the lens to the national scale. Using Germany’s planned Deutschlandnetz of fast-charging stations as a natural laboratory, the dissertation develops a spatial competition model to understand how the location of stations, regional demand, and regulatory price caps shape prices and investment incentives. The analysis shows that uniform national price caps can unintentionally reduce service in sparsely populated areas and that lax ownership constraints can foster market power, harming consumer welfare. Policy options include regionally differentiated price caps, dynamic tendering processes, and real-time data-sharing requirements to foster both competition and equity. What unites these strands is a sociotechnical vision of the future energy system. Rather than treating markets and technology separately, the dissertation demonstrates that market design, machine learning, and physical infrastructure must be co-designed. By coupling incentive-compatible mechanisms with adaptive algorithms, it shows how autonomous agents can collectively manage scarce resources and uncertainty, achieving reliable and cost-effective charging without heavy-handed central control. The work thus provides a conceptual and methodological blueprint for digital energy platforms that are at once economically efficient, technologically scalable, and sustainable.

Swetha Ghanta, Ashok Kumar Pradhan, Prasanthi Boyapati, Sujit Biswas · 5 authors

Federated Learning (FL) enables collaborative training without centralizing sensitive data but faces challenges, including client authenticity, verifiable training participation, and secure aggregation. To overcome these challenges, we propose a novel framework, Zero-Knowledge Reputation-aware Blockchain Federated Learning (ZK-RBFL), which integrates blockchain, FL, Homomorphic Encryption (HE), and zero-knowledge proofs (ZKP). In the proposed ZK-RBFL framework, initially the clients undergo lightweight token-based authentication and then generate ZKP to provide cryptographic evidence of honest local training participation and reported inference accuracy before contributing their model updates. The model updates are encrypted using the CKKS HE mechanism to prevent any potential model inversion attacks. These encrypted model updates are stored on IPFS, with their corresponding CIDs recorded on the blockchain to ensure immutability. Further, ZK-RBFL enables mutual client verification of ZKPs to reduce server bottlenecks and enhance accountability. To ensure fairness and robustness in a distributed environment, we introduce a democratic blockchain consensus mechanism named Proof of Reputation-Weighted Voting (PoRWV) for block acceptance. Once consensus is reached, the encrypted model updates are aggregated using reputation-weighted averaging. We demonstrate the effectiveness of ZK-RBFL for brain tumor classification using a ZKP-compatible LeNet model for proof generation. Despite model simplicity, the global model achieves 94.22% accuracy. In addition, experiments with malicious clients and formal Scyther security analysis demonstrate that ZK-RBFL ensures both security and performance.

Don Gaconnet

Abstract: This paper establishes cross-domain validation of the Echo-Excess Principle (EEP) by demonstrating structural identity with the Riemann Hypothesis of analytic number theory. The central claim is that the EEP conservation constraint—requiring generative cycles to complete only at the membrane interface—was independently discovered in pure mathematics by Bernhard Riemann in 1859, encoded as the constraint that all non-trivial zeros of the zeta function lie on the critical line Re(s) = 1/2. Riemann found the membrane from inside the mathematical formalism. The Echo-Excess Principle names what he found from outside—the structural understanding of what collapse is and where it must occur. The value 1/2 represents the partial zero state: the balance point where inside and outside carry equal weight, the unique location where bilateral completion enables generative cycles to close. The correspondence maps: primes to irreducible triadic structures {I, O, N}; the zeta function to the harmonic cascade; non-trivial zeros to collapse points; the critical line to the membrane; conjugate pairs to inside/outside perspectives of the same collapse event; and the functional equation's symmetry to the conservation integral ∮ε dt = 0. The correspondence is bidirectionally falsifiable: if the Riemann Hypothesis were demonstrated false, the Echo-Excess Principle would be falsified. Both frameworks stake their validity on the same structural claim. This convergence of independent discoveries from different domains validates EEP as a candidate universal law governing generative systems. Keywords: Echo-Excess Principle; Riemann Hypothesis; membrane constraint; conservation law; bilateral completion; cross-domain validation; universal law; collapse dynamics; triadic structure; Cognitive Field Dynamics; critical line; conjugate pairs; structural correspondence; falsifiability; generative systems Resource Type: Publication — Preprint License: Creative Commons Attribution 4.0 International (CC BY 4.0) Related Identifiers: Is supplemented by: DOI: 10.5281/zenodo.18088519 (The Echo-Excess Principle: Foundation Document v2.1) Is part of: OSF Project: https://osf.io/j5836 Subjects: Theoretical Physics Consciousness Studies Mathematics — Number Theory Philosophy of Science Systems Theory Version: 1.0 Publication Date: January 1, 2026 Language: English Additional Notes: Document SHA-256: d561ae2b273850b8f9ce796d109698b0c63a7dbea16b6f040046b9d5e5e578a2 This paper does not claim to constitute a mathematical proof of the Riemann Hypothesis in the formal sense required by mathematics. Rather, it identifies the structural reason why zeros must lie at Re(s) = 1/2—the "outside view" that complements 166 years of work from inside the mathematical formalism—and uses this correspondence to validate the Echo-Excess Principle as a cross-domain structural law. References: Gaconnet, D. L. (2025). The Echo-Excess Principle: Substrate Law of Generative Existence. Foundation Document v2.1. LifePillar Institute. DOI: 10.5281/zenodo.18088519 Riemann, B. (1859). Über die Anzahl der Primzahlen unter einer gegebenen Größe. Monatsberichte der Königlichen Preußischen Akademie der Wissenschaften zu Berlin.

Theodore K. McClendon

No abstract is available for this record.

Rajan Kumar

No abstract is available for this record.

Wanhong Huang

This paper argues that optimization is not a law of the universe, and that morality and the manner of being of intimate relation are not, in their structure, optimization problems. It proceeds in two movements that it is careful to keep apart. The first is a formal proof, carrying no value judgment, that intimate relation furnishes no well-defined optimization problem: the value at stake is in part non-structurable, since the drive, which any complete objective must include, resists representation as a term; it is relational, generated within the relation and existing only there, so that no external standpoint could fix the objective; it is incommensurable, so that to price certain goods is to betray rather than mismeasure them; and the subject is constituted within the relation rather than given before it, so that the optimizer the model presupposes is a product of the process it is meant to stand outside of. Each failure strikes a distinct precondition, and any one leaves the operation of maximization undefined. The second movement takes that result as given and criticizes the imposition of the frame regardless. To optimize where no optimization problem exists converts a value that should circulate into one that can be settled and drawn off, in the manner the theory of generative justice names as extractive computation; it is a symptom of the crisis of the symbolic and of a life-world habituated to the logic of capital, under which a local, historical operation is projected onto the cosmos as its law; and it mistakes the kind of thing morality is, since an optimizing practice, even a successful one, may fail as an ethics, the optimal and the good being orthogonal. The paper then recovers a rationality that responds rather than maximizes, described through response, attunement, and the sustaining of generation, and it closes in polyphony, refusing to name a new master objective, since to do so would reinstate the frame. That refusal is the paper's positive claim, enacted in its form.

Steve Carroll

No abstract is available for this record.

Kayode S. John

Battery energy storage sits at the centre of Europe’s low-carbon transition, yet financing these assets remains fraught with uncertainty. This thesis asks a pointed question: how do market volatility, shifting regulations, and the threat of asset stranding jointly shape the ability of investors to fund centralised and decentralised storage projects in Germany and Sweden? Drawing on a comparative case study rooted in pragmatist thinking, the analysis pairs discounted cash flow modelling with a careful reading of policy documents, regulatory rulings, and industry commentary. All market data, wholesale electricity prices from ENTSO-E, ancillary-service auction results from national grid operators, cover the period 2019-2024 and are publicly accessible. What emerges is a stark contrast. German centralised battery energy storage systems (BESS) projects carry the heaviest risk burden: frequency containment reserve (FCR) market saturation, confirmed grid-fee hikes, and a massive connection-queue backlog combine to push the internal rate of return from 11.5% down to 2.8% under stress, rendering projects economically unviable. Swedish centralised projects fare better for now, though their dependence on a handful of ancillary-service markets introduces a concentration risk that warrants close monitoring. Across both countries, decentralised storage proves more financially resilient, revenue diversification across retail savings, frequency markets, and peak shaving translates into lower risk premiums and more favourable debt terms, even where headline returns are lower. Monte Carlo simulations confirm that investment feasibility is highly sensitive to revenue cannibalisation and policy shocks. Theoretically, the study extends asset stranding literature by demonstrating that stranding risk in modern storage infrastructure is fundamentally revenue-driven rather than technologically deterministic, with regulatory interventions capable of eroding cash flows as rapidly as market saturation. From a policy perspective, the findings underscore the urgent need for regulatory clarity on grid tariff structures in Germany, the development of a coherent national storage strategy in Sweden, and the effective implementation of the EU Storage Infrastructure Act. For market participants, the analysis establishes that decentralised, revenue-diversified storage configurations offer a more robust risk-return profile, lowering hurdle rates and facilitating capital allocation in Europe’s evolving flexibility markets.

Alicia Amores, Gabriel M. Ramírez V., Fernanda Gutiérrez-Gutiérrez, Jaime Díaz-Arancibia · 5 authors

No abstract is available for this record.

Aparna Singh, Surbhi Sharma, Surabhi Solanki, Mamta Narwaria

Biometric authentication provides high convenience with the drawback of privacy leakage, replay attacks, and centralized control over biometric templates. This paper introduces an Ethereum-based decentralized biometric authentication framework that uses Elliptic Curve Digital Signature Algorithm (ECDSA), InterPlanetary File System (IPFS) storage, and an on-chain challenge–response protocol. In the proposed model, encrypted biometric templates are stored of-chain in IPFS, whereas their content identifiers (CIDs) are registered in Ethereum smart contracts. Every authentication attempt necessitates a new on-chain nonce and an ECDSA signature of the concatenation of the CID and the nonce, authenticated through Ethereum’s built-in method, ecrecover. The design supports explicit replay protection, revocation, and public auditability. Deployment of the prototype on Ganache and MetaMask reveals that the scheme provides secure, transparent, and tamper-proof authentication with minimal gas consumption on FVC2004 datasets and reasonable storage usage on Ethereum.

Robiah Arifin, Wan Azelee Wan Abu Bakar, Mustafa Man, Mohamad Afendee Mohamed · 5 authors

The issue of fake certificates has been widely identified, and their prevalence has increased significantly in recent years. This growing trend has become a global concern due to its adverse impact on educational standards. A key factor contributing to the problem is the continued reliance on manual processes for issuing and verifying certificates. To address these challenges, this study proposes the use of an authority round (AuRa) consensus algorithm for managing certificate data on the Ethereum blockchain. AuRa, a member of the proof of authority (PoA) family, facilitates consensus among nodes distributed across multiple servers and networks. This mechanism plays a vital role in preserving the integrity and decentralization of the blockchain while ensuring the security of transactional data. Furthermore, the study investigates how AuRa enables efficient certificate data transactions within a private Ethereum environment. It also evaluates the algorithm's performance in terms of transaction speed per second (TPS) and throughput per second (TGS), demonstrating its effectiveness for managing certificate transactions on a blockchain network. Then the TPS and TGS results substantiate the suitability of AuRa for digital certificate generation, evidenced by its stable and efficient performance within a controlled private server environment.

Júlia Almeida Valadares, Saulo Moraes Villela, Heder Soares Bernardino, Alex Borges Vieira

No abstract is available for this record.

Shraddha M. Naik, Huned Materwala, Davor Svetinović

Maximal Extractable Value (MEV) poses significant threats to the security and fairness of Ethereum's decentralized finance ecosystem by enabling participants to exploit transaction ordering to extract profits at the expense of others. Heuristic-based detection methods have been widely adopted to identify MEV transactions such as sandwich, arbitrage, and liquidation. However, a lack of standardized evaluation across heuristics limits the ability to compare their detection behavior and computational characteristics. This paper presents a unified experimental framework to systematically evaluate the detection capabilities, agreement rates, and resource efficiency of existing heuristic approaches. An agreement metric is introduced to quantify consistency across detection methods. Additionally, we assessed resource utilization and execution time to evaluate computational scalability. Our empirical findings reveal that while these approaches exhibit scalability, their reliability, measured in terms of detection consistency, varies significantly across different MEV types. Agreement rates averaged 0.53 for sandwich detection, 0.40 for arbitrage, and 0.81 for liquidation, highlighting substantial differences in detection capabilities and heuristic formulations. These findings offer valuable insights into the practical challenges of achieving consistent MEV detection and highlight the need for developing more robust security countermeasures.

Gopal Ojha

The Ethereum Virtual Machine (EVM) is a stack-based virtual processor that executes smart contract bytecode sequentially. While this design ensures determinism and correctness, it inherently limits instruction throughput. This paper presents a feasibility study of instruction-level pipelining within the EVM interpreter architecture. By analyzing the internal execution flow of the EVM as implemented in the Go-Ethereum (geth) client, the study identifies the program counter dependency, particularly under jump instructions, as the principal control hazard preventing naïve pipelining. A two-stage pipelined execution model is proposed, separating opcode fetch and decode from execution and program counter update, with a feedback mechanism to preserve EVM semantics. The work focuses on architectural feasibility rather than performance evaluation and optimization, demonstrating that pipelining inside the EVM interpreter is conceptually possible under controlled synchronization. Limitations, design challenges, and future research directions are discussed.

Karina Mavletova, Yash Madhwal, Yury Yanovich

No abstract is available for this record.

Mingzheng Lv, Chen Liang, Baokun Zheng, Tianqing Zhu · 7 authors

The exponential growth of connected devices and embodied intelligent systems in B5G and 6G networks demands secure, adaptive, and autonomous communication among distributed agents. However, ensuring privacy-preserving coordination among these Agentic AI systems remains a major challenge, particularly in decentralized environments where transparency conflicts with confidentiality. To address this issue, we propose a Smart-contract-based Embodied Covert Agent Communication Architecture (SECA), which integrates Ethereum election contracts with image steganography to enable covert, high-bandwidth communication among intelligent agents. In this framework, Blockchain-based agents utilize candidate images as visual carriers to embed encrypted messages, achieving imperceptible data exchange during on-chain interactions. We further design a Stackelberg Minimal Control Algorithm (SMCA) that enables adaptive manipulation of voting agents to ensure communication reliability with minimal control cost. Experimental results demonstrate that our approach achieves a transmission bandwidth up to 103× higher than ORIM-based covert channels and passes multiple detection benchmarks (K-S and χ2tests), all without incurring additional gas costs. This work provides a foundational perspective for secure Agentic AI communication frameworks, bridging embodied intelligence, decentralized networking, and covert information transmission in emerging 6G environments.

Said Tkatek, Ayoub Azzayani, Amine Fannan, Hamza Ettakifi

No abstract is available for this record.