This thesis investigates whether the integration of digital assets, specifically Bitcoin and <br/>Ethereum, into a traditional multi-asset institutional portfolio can produce superior out-of<br/>sample risk-adjusted performance relative to the conventional 60/40 equity-bond benchmark. <br/>Using a Mean-Variance Optimization (MVO) framework enhanced by Ledoit-Wolf <br/>covariance shrinkage, the study evaluates seven distinct portfolio configurations across an <br/>empirical window spanning January 2016 to May 2026, covering multiple market regimes <br/>including the 2020 COVID-19 liquidity shock, the 2022 inflationary spike, and the 2024 <br/>institutional crypto adoption phase. <br/>The empirical design employs a dual-mode out-of-sample validation strategy: a fixed-weight <br/>stability test and a rolling realism test with systematic rebalancing. Results suggest that <br/>portfolios enriched with Bitcoin tended to achieve superior Sharpe and Sortino ratios relative <br/>to the baseline within this sample period and asset universe. The Master Portfolio (Case 7), <br/>combining equities, bonds, Bitcoin, Ethereum, Gold, and Silver, attains a static Sharpe Ratio <br/>of 1.62, a static Sortino Ratio of 3.23 and a Maximum Drawdown of only −6.40%. These <br/>findings are consistent with the primary hypothesis (H₁) that, within this empirical setting, <br/>Bitcoin can improve out-of-sample risk-adjusted performance, while also providing evidence <br/>consistent with the secondary hypothesis (H₂) that Bitcoin acts primarily as a portfolio <br/>diversifier rather than a consistent safe haven. All results should be interpreted as conditional <br/>on the sample period, the chosen asset universe, and the rebalancing assumptions. All asset <br/>price data was sourced from Investing.com and the risk-free rate from the FRED 3-Month <br/>Treasury Bill series (TB3MS).
Shihab Sarar, Ali Imran Mehedi, Fabbiha Tahsin Prova, Saha Reno
The modern metropolis essentially demands the use of state‐of‐the‐art, real‐time surveillance systems, which should be reliable, scalable, and respectful of privacy at the same time. Critical shortcomings in traditional architectures are single points of failure, poor scalability, frequent data breaches, and inadequately managed privacy. These aspects of themselves make it inept for the demands of dynamic, fast‐paced city environments, without which reliability, security, and adaptability cannot be compromised at any cost. This brings to light the critical need for innovative and decentralized solutions that can overcome these challenges comprehensively. In our proposed approach, a decentralized framework integrates private blockchain technology via Ethereum, a hybrid cryptography model combining advanced encryption standard (AES) and Rivest–Shamir–Adleman (RSA) encryption, and state‐of‐the‐art deep learning techniques such as YOLOv8, DeepSort, and ArcFace. Blockchain technology ensures metadata is immutable and transparent, thus saving metadata from unauthorized access and tampering. The hybrid cryptography model encrypts sensitive data through AES and securely shares the key of AES through RSA encryption, while decryption is efficiently done in a key management system (KMS). Furthermore, YOLOv8 and DeepSort can be used for high‐precision object detection and real‐time tracking, and ArcFace can be used for facial recognition, meeting the split‐second decision‐making required in urban surveillance. Extensive experiments are performed, and the results indicate that the proposed framework enhances detection precision, tracking accuracy, real‐time responsiveness (60 FPS), and resistance to tampering (>99% chain quality per quorum Byzantine fault tolerance [QBFT]) without compromising efficiency. The adaptive and reliable solution meets modern urban surveillance demands that are evolving at an ever‐increasing pace. The scalability of the operation further ensures enhanced public safety. This paper discusses a decentralized urban surveillance system that is both tamper‐proof and secure using current blockchain technologies, InterPlanetary file system (IPFS), hybrid AES–RSA, and deep learning technologies to mitigate the risks of a traditional centralized system, such as data tampering and privacy violations. The system uses the Ethereum blockchain to provide immutable metadata, the IPFS protocol to create a fully distributed storage system of video and image frames, and an off‐chain KMS service to distribute the keys to the authorized edge devices. The system utilizes real‐time object detection (YOLOv8), tracking (DeepSort), and face recognition (ArcFace) to perform inference locally on the edge devices. We have performed experiments that demonstrate the tamper‐proof and secure scalability with low latency and secure tamper‐proof data integrity of this urban surveillance system in ever‐changing urban environments.
Harsha Gowda R, Sahana M Gowda, Chethan J, Gopika R · 5 authors
The healthcare industry continues to have issues regarding the transparency and trust of the financial transactions, and especially in case of handling of insurance claims and the funding of the patient. Intermediaries and centralization is generally accompanied by inefficiencies, delay and lack of accountability. To eliminate these problems, in this paper, Medicare Chain is proposed as a decentralized blockchain-based fund management system in order to ensure the secure and transparent medical transaction. The system utilizes smart contracts of the Ethereum network to automate the process of transfer of funding between the patients, doctors and donors without the need of centralized authority in the process. Data and transaction logs of nurses is set into the InterPlanetary File System (IPFS) to ensure integrity and prevent any kind of tampering. Django-based web interface allows users authentication, access control and access to the blockchain network. By introducing a framework for auditable, secure and efficient management of medical funds using the concepts of decentralization, the proposed framework shows the possibilities of decentralized systems to create more reliability and trust amongst the healthcare ecosystems.
Reentrancy remains one of the most persistent and damaging vulnerabilities in Ethereum smart contracts, enabling adversaries to recursively drain funds despite the presence of static and runtime defenses. Existing studies mainly focus on detection or program analysis, but they do not explain why and under what conditions attackers decide to exploit. In this work, we introduce a decision-theoretic framework that models reentrancy as a profit-maximizing problem under gas, risk, and atomicity constraints. Our framework derives the conditions under which reentrancy attacks are economically viable and provides an algorithm for computing optimal exploit strategies. We further extend the analysis to multi-contract attacks, capturing sequential, parallel, and optimized execution strategies. A simulation environment evaluates profitability across varying balances and gas configurations, highlighting thresholds where attacks become infeasible. Finally, we translate attacker decision profiles into practical defense recommendations for developers, auditors, and DeFi system designers. This framework bridges the gap between exploit detection and adversarial economics, offering a rigorous basis for strengthening smart contract security. This framework establishes a theoretical baseline for adversarial economics in smart contract security, forming a foundation for future MEV-aware exploitability models and Layer-2 risk analysis.
This study focuses on cryptocurrencies. At the beginning it explains what cryptocurrency is, its main features and main areas of its significance for the economy. In this section it deals with the possibility of cryptocurrency one day replacing traditional money, trading opportunities cryptocurrencies offer, possibility to finance a business with digital coins and its availability to people without the access to banking services. A brief overview of cryptocurrency history and a definition of the technology of blockchain are also provided. The practical part of the thesis is analysing cryptocurrencies Bitcoin, Ethereum and Litecoin. Firstly, these are described in terms of their origin, emission, circulation, price development and process of mining. Secondly, the impact of selected factors on the price fluctuation of selected cryptocurrencies is evaluated using statistical methods and econometric models. The analysis showed the cryptocurrency prices are more dependent on the internal factors such as the transaction volume, transaction fee, total supply, demand and hashrate, than on the external factors such as interest rates, exchange rates, stock prices and the price of gold.
In this article, we provide an in-depth analysis of the different causes of food fraud, food safety, and food waste, which are recognized as the main challenges in agri-food supply chains. Leveraging technologies like blockchain- and IoT-enabled traceability systems presents promising solutions to overcome these challenges. Using the PRISMA methodology, we conducted a comprehensive literature review of 41 selected articles to assess the effectiveness of such solutions. The findings reveal that only 48% target primarily improved food safety, while food waste (9%) and food fraud (21%) played a less important role. Only a few papers actively incorporate these attributes into their architectural design and many papers lack practical implementation details, leaving significant gaps in understanding their practical applicability. One finding was that 61% of the proposed solution were build on a public permissionless blockchain (Ethereum) and 39% where build on a permissioned private or consortium blockchain (mainly Hyperledger Fabric and Sawtooth). Critical aspects such as data privacy, confidentiality, final infrastructure governance, or legal frameworks are in most cases missing, e.g., 75% did not discuss the governance of the solution at all. To address the identified limitations, we propose a modular reference architecture that balances transparency and confidentiality through a hybrid blockchain approach. It incorporates a trusted platform layer with secure data storage, publicly verifiable summaries, and role-based access control. The architecture is illustrated through multiple use cases and qualitatively evaluated against characteristics of existing solutions, highlighting its conceptual suitability for regulated agri-food ecosystems.
This thesis presents a comprehensive predictive maintenance system and application interface that integrates deep learning and blockchain technologies in order to enhance maintenance strategies in industrial systems. Traditional predictive maintenance systems have significant issues regarding data security and decentralization. This study aims to address these limitations by leveraging blockchain technology, with a specific focus on improving the reliability and verifiability of predictive maintenance processes. In this study, an LSTM-CNN hybrid model was developed to evaluate complex patterns in both time and features, thereby enabling high-accuracy fault prediction. The proposed model is designed to perform binary classification for fault prediction in industrial equipment. During the implementation phase of the study, an open-source dataset was used to train and test the developed model. The Randomized Search method was used in the hyperparameter optimization process to increase the prediction success of the proposed model. The hybrid model was trained with 5-fold cross-validation, and class weighting and threshold value optimization methods were applied to eliminate the class imbalance problem. In the threshold optimization phase, F1-score-based methods are applied to maximize recall at three predefined minimum precision levels (0.05, 0.2, and 0.85), while identifying the most balanced trade-off between precision and recall. In the proposed system, sensor data are stored in a database (SQLite3), and cryptographic proofs generated using zero-knowledge techniques are transmitted to the Ethereum network. The Poseidon hash function is used to ensure data integrity, and the Groth16 protocol is used for Zk-Snark proof generation. This approach enables secure verification of data validity without publicly disclosing sensor data and simultaneously addresses scalability concerns. The system architecture is designed to include manager, operator, and engineer nodes, and all smart contracts are implemented using Solidity. In addition, a graphical user interface is developed using the Tkinter library in Python. The experimental results demonstrate that the proposed LSTM–CNN hybrid model produces successful outcomes in terms of fault prediction performance. According to scenario where the decision threshold is optimized based on the F1-score, the model achieves an accuracy of 0.987, an AUC value of 0.979, and an F1-score of 0.794. In future studies, the proposed system is planned to be implemented on the Ethereum mainnet instead of a test network, with a comprehensive evaluation of on-chain operational costs. However, instead of Zk-Snark proofs, which have a centralized structure, the use of Zk-Stark proofs, which are transparent and do not violate the principle of decentralization, is planned.
The smart contracts facilitated by blockchains allow the decentralized and automated implementation of digital contracts, yet the current security measures in this space are mostly geared towards vulnerability detection and post-implementation functionality, which do not provide much defence against runtime attacks. The paper analyses the concept of smart contracts as a unified approach to cybersecurity, and provides a Hardened Smart Contract Model (HSCM) as a proactive and runtime security quotient model. The suggested model places policy-conscious logic, formal safety requirements, risk aversive execution, upgradability under control by governance, and unchangeable auditability directly in the design of contracts. The framework guarantees the elimination of unauthorized access, re-entrancy and logic abuse by providing runtime verification and automated response measures that avert such violations even before state transitions take place. A fair amount of experimental confirmation on an Ethereum-compatible system proves that there is a high security guarantee with tolerable load overhead, the deployed smart contracts could be hardened.
Abhay Kumar R J, Dr. Bhavya Vikas, Dr. Sharath Ambrosse
The financial sector has been revolutionized by blockchain technology and digital assets, offering novel investment opportunities. Stable coins, in particular, have risen to the fore for their blend of blockchain benefits and moderate price fluctuations. Stable coins differ from other cryptocurrencies like Bitcoin (BTC) and Ethereum (ETH), which are known for their volatile price swings, with their stable value, meaning they can be employed in payment, trading, decentralized finance (De Fi), and portfolio management. This study aims to assess USDT and DAI's contribution to investment strategies in the current investment landscape between 2022 and 2026 alongside Bitcoin and Ethereum. The secondary data was analysed via time series analysis, 3 year moving average, rolling volatility, market capitalization, and correlation analysis of data obtained from Coin Market Cap, Coin Gecko, Reserve Bank publications and other financial databases. The results show that USDT and DAI possessed less volatility, more price stability and better capital preservation when compared to traditional cryptocurrencies. The study also finds that inflation, interest rates and US Dollar Index (DXY) affect the performance of stable coins and market demand. While there are regulatory, transparency, and market trust issues to address, stable coins have proven to be a potentially low-risk digital asset. In conclusion, according to the study, USDT and DAI are good investment alternatives for those who are looking for stability in the cryptocurrency market and are either conservative or new investors.
Protocol-level validity mechanisms, such as validity rollups and STARK-based proving systems, improve blockchain scalability, but do not remove the cost of executing computation-intensive application logic inside smart contracts. At the application layer, a meaningful class of structured arithmetic workloads—including polynomial evaluations, cryptographic primitive computations, and signal-processing operations over finite fields—arises naturally in contract logic, yet remains subject to the on-chain fee model regardless of protocol-level optimizations. This paper investigates whether outsourcing such workloads and replacing direct execution with transparent on-chain verification can provide a practical cost advantage under realistic smart-contract fee models. We present a cross-platform implementation of GKR-based transparent on-chain verification for outsourced structured arithmetic circuits on the Ethereum Virtual Machine (Solidity) and Starknet (Cairo). GKR requires no trusted setup, which makes it particularly suitable for deployment in permissionless public blockchain environments where trust assumptions must be minimized. The design targets layered circuits with regular structure and incorporates implementation techniques that reduce verifier-side overhead and eliminate the linear input-handling bottleneck in naive deployments. These workloads can be represented as layered arithmetic circuits with regular wiring and logarithmic depth. Our evaluation shows that direct execution cost increases rapidly with input size, whereas GKR-based verification scales much more favorably. At size 4096, verification reduces execution cost by approximately 84% on the EVM and 91% on Starknet. These results identify outsourced execution with on-chain GKR verification as a practical design point for application-layer verifiable computation, especially in public blockchain environments where transparency and the absence of trusted setup are important.
Andreu Pere Isern-Deyà, M. Francisca Hinarejos, Josep Lluís Ferrer Gomila
Online transactions are becoming increasingly popular, and the purchase and delivery of digital assets is a prominent example. In these transactions, buyers are hesitant to pay for an asset until they receive it, whereas sellers are reluctant to send the asset until they are paid. Unfortunately, actual solutions do not always meet all the requirements to conduct a secure exchange, with fairness being one of the requirements that needs more attention. Historically, solutions to this problem have relied on trusted third parties (TTPs) serving as trusted intermediaries among participants, but the advent of blockchain has enabled the reduction or elimination of TTP involvement in many cases. In this paper, we present a fair blockchain-based solution that does not require any TTP for the secure delivery of digital assets, proving its technical feasibility and cost-effectiveness through assessments on blockchains based on the Ethereum Virtual Machine.
The cryptocurrency has represented a revolutionary force in the financial market, with a wide variety of available digital assets that can serve different technological and financial needs. Cryptocurrencies vary considerably. It, therefore, goes without saying that this paper should focus on the wide array of cryptocurrencies, grouping them according to their underlying technology, use cases, and functionalities. It refers to the major classification, including Bitcoin, the first digital currency designed primarily as a unit of store and medium of exchange; altcoins, including alternative cryptocurrencies like Ethereum and Ripple, that should introduce new features and functions such as smart contracts and fast processing of transactions; and tokens, which can be issued and managed on existing blockchain platforms and may range from utility in decentralized applications to representing assets. Such categories of analysis are intended to make it possible to distinguish between the roles and technological innovations connected with each type of cryptocurrency. This research adventure offers insight into how the digital currency landscape is emerging and will impact financial systems, investment strategies, and the regulatory approach. This research goes into a comprehensive review of current literature and case studies, engaged with all types of diverse functionalities and applications of cryptocurrencies, providing foundational understanding to stakeholders and policymakers entering this dynamic field. DOI - https://doi.org/10.65525/SVUP.9788199651548.2026.130-141
Decentralized finance (DeFi) is an emerging financial service on blockchain, enabling automatic and anonymous transactions.Within DeFi, decentralized exchanges (DEXs) maintain reserves of a pair of tokens and determine the exchange rate to swap tokens.However, DEXs also create opportunities for Maximal Extractable Value (MEV), where attackers include, exclude, or reorder DEX transactions to exploit price discrepancies of tokens and extract profit.Uncovering MEV opportunities requires high throughput, as the 12-second block interval and the vast search space impose strict time constraints.However, existing tools suffer from low throughput, as they rely on CPU-bound execution, which is hindered by frequent state forking and slow DEX execution.In this paper, we take the first step in leveraging GPU parallel computing power to boost MEV-search throughput in arbitrage and sandwich strategies.More precisely, we compile an MEV bot into a GPU application and then launch thousands of GPU threads to search for profit in parallel.To this end, we design new solutions to address three major challenges: designing cheatcodes to simulate transactions on GPU, proposing a memory manager to reduce GPU memory usage, and designing strategyaware mutations to improve input diversity.We implement a prototype named MeVisor that runs DEXs on GPUs and searches for MEV using a parallel genetic algorithm.Evaluated on 3,941 real MEV cases from Ethereum, MeVisor achieves 3.3M-5.1Mtransactions per second, outperforming the CPU baseline by 100,000x.In a large-scale study of Q1 2025 data, MeVisor estimates MEV opportunities ranging from 2 to 14 transactions, yielding at most $1.1 million in MEV profit.
ABSTRACT The perishable food cold chain is a vital part of the global food system, mainly ensuring the quality of temperature‐sensitive products such as milk, seafood, fruits, and vegetables. However, this system still faces several challenges related to real‐time monitoring, data transparency, and proactive demand planning, which often lead to spoilage, food safety violations, and disruptions. In this work, we propose a comprehensive end‐to‐end system that includes Internet of Things (IoT)‐based environmental sensors, blockchain‐enabled immutable logging, and a hybrid ARIMA–LSTM model for demand forecasting and spoilage risk detection. The system uses ESP32 microcontrollers with DHT11 sensors at key cold‐chain points to collect temperature and humidity data, streamed to Blynk dashboards for real‐time visualization and anomaly alerts. Anomalies and product metadata are permanently recorded through smart contracts on a private Ethereum blockchain, while payloads are stored off‐chain on IPFS for traceability and auditability. Additionally, historical blockchain logs and external sales data are used to train a hybrid ARIMA–LSTM model that predicts future demand and spoilage risks more accurately. Experimental results show that the proposed model achieves a forecasting accuracy of 90% ( R 2 = 0.90), outperforming baseline approaches on multiple metrics including RMSE, MAE, MAPE, and R 2 . The framework is scalable and data‐driven, aiming to improve supply availability and reduce food waste.
Rajesh Bose, Shrabani Sutradhar, Arfat Ahmad Khan, Sandip Roy · 7 authors
ABSTRACT The promise of blockchain applications is transformative in terms of certificate verification and managing digital identities in the various fields, such as education, healthcare and land records. Nevertheless, current blockchain‐based certificate solutions have serious shortcomings: most are based on simple cryptography protection with no privacy‐preserving systems, have low throughput (16.67 TPS in typical Ethereum‐based systems), have unpredictable response times under varying loads, are not standardised across industries and are expensive to operate due to gas fees. Besides, the current implementations are mostly either theoretical or without performance tests in practice. This paper fills these gaps by suggesting a Plonk‐based system that incorporates zero‐knowledge proofs, digital signatures and trusted identity verification to improve the efficiency, security, and privacy of the verifiable credential digital identity verification and management systems (VC DIVMS). It was implemented at JIS University, India, with 50 transactions per minute (an improvement of 200% over Ethereum), an error rate of 244–1277ms in response times under load conditions (24‐33 faster than Ethereum) and high‐level privacy through ZKP. Contrary to currently used models that are sector‐specific, the offered Plonk framework offers a single, scalable, privacy‐focused model that can be applied in areas of education, healthcare, or credit verification. Intense testing ensured both resilience, scalability and compatibility with a demanding environment, making Plonk a strong and secure substitute to decentralised identity verification and credential management that is resistant to tampering.
This study introduces a blockchain-based system for digital identification and verification of educational documents in Pakistan. The solution tackles ongoing issues like document fraud, slow manual verification, lack of transparency, and poor interoperability among institutions. The system uses the Ethereum blockchain for smart contracts as well as IPFS for decentralized storage to provide secure, tamper-proof, and highly efficient access to academic records. A prototype with a simple web interface that was user-friendly was designed for students, institutions, and recruiters. The questionnaire USE was used to assess usability and 204 respondents claimed to have high satisfaction with ease of use, learnability, usefulness, and overall experience. The system not only saves the organizations of the administrative overhead but also inspires stakeholders to trust digital means and at the same time, the paperless process of environmental exposure ultimately leads to sustainability. By making use of the most up-to-date technology, it deals with historic issues in the educational systems. The findings of the study underscore the potential of the blockchain in the transformation of educational document verification in least developed areas whereby global academic ecosystems can scale up become transparent, affordable, and reliable.
This thesis addresses two research areas: scalable distributed ledgers for micro-transactions, and the automation of assembly planning in manufacturing industries. Established blockchain solutions are robust and reliable. Being distributed and decentralized, they avoid a single point of failure, and fault-tolerant consensus mechanisms ensure that the system works as intended even when some participants are faulty or malicious. However, their main weakness is scalability. The two most popular and well-known blockchain solutions, Bitcoin and Ethereum, require all nodes to store all transactions, and their transaction throughput is far too low to compete with traditional, centralized transaction processing systems. To improve scalability, systems have been developed that split the network nodes into groups that can process transactions in parallel, a technique known as sharding. We propose a sharded system called ScaleGraph that uses a novel architecture with one transaction per block and one shard per account, designed to maximize parallelism. The design is inspired by concepts from distributed hash tables, particularly to define shards based on a logical distance metric for node IDs and account IDs. Nodes store and process only transactions involving those accounts that are close to the node according to the distance metric. This greatly reduces the storage burden on each node and allows any number of transactions involving distinct accounts to be validated in parallel. We also design a new cross-shard transaction commit protocol for this architecture. The protocol offers global serializability and inevitable atomic commit, without the need for an abort path. This is achieved using only shard-local consensus and certificate exchange, rather than global or joint cross-shard consensus. Manufacturing is a highly complex process in many industries and involves many different planning problems where increasing automation has the potential to make manufacturing more efficient. This thesis presents a proof-of-concept solution to the kitting layout problem, where a list of parts has to be placed on a kitting wagon for delivery to an assembly line station. However, some problems have proven difficult to automate in practice, despite decades of research. One such problem, assembly line balancing, is analyzed in depth. We identify fundamental challenges that make the goal of complete automation implausible in some industries, such as automotive manufacturing. Human intervention is thus unavoidable, suggesting that bridging the gap between theory and practice requires decision support systems for assisted, iterative, and interactive planning. The thesis also includes preliminary work on the product sequencing problem, limited to framing the use case, assumptions, and requirements. Subsequent ongoing work suggests strong parallels to assembly line balancing, indicating that the identified challenges and possibilities for addressing them reflect a broader pattern in industrial planning automation.
This article is devoted to the contribution of cryptocurrencies and blockchain to the transformation of the world economy. It describes the basic principles of blockchain functioning, the evolution of major cryptocurrencies such as Bitcoin and Ethereum, as well as their practical application in the areas of decentralized finance, cross-border payments, and supply chain management. The study also analyzes the advantages of these technologies and their current limitations.
Open access
Security, Politics, and Digital Transformation
Digitalization and Economic Development in Agriculture
Traditional and electronic voting systems face significant challenges in ensuring transparency, security, and voter trust. Issues such as centralized control, lack of auditability, vulnerability to tampering, and potential for fraud undermine the integrity of electoral processes. This paper proposes a novel blockchain-based electronic voting system designed to address these shortcomings through decentralized ledger technology and smart contracts. The system ensures vote integrity, voter anonymity, and public verifiability while preventing double voting and eliminating single points of failure. By employing cryptographic techniques such as zero-knowledge proofs and ring signatures, voter privacy is maintained without compromising transparency. The proposed architecture is evaluated through simulation, demonstrating scalability, reduced transaction costs, and robustness against common cyber threats. This work contributes to the advancement of trustworthy digital democracy and provides a feasible framework for real-world electoral deployment.
Abstract Many compilation stages of smart contracts on the Ethereum blockchain have been transitioned to the intermediate language . Tasks such as smart contract optimization and bytecode generation are—or will soon be—performed directly at the level in the compilers for the higher-level languages such as Solidity. In this paper, we develop a formal semantics of programs in Rocq, suitable for verification, which allows formal reasoning at the level of code or generation tools processing programs. Our semantics is expressive enough to be the basis for formal verification tools, and simple enough to make the development of such tools feasible. In order to prove its adequacy for verification, we develop in Rocq a checker (and associated soundness proofs), based on our semantics, able to verify the results of the liveness analysis stage of the official Solidity compiler , which opens the door towards formally verified Ethereum’s smart contracts compilation. Experiments on more than 1,500 smart contracts show that we are able to automatically verify ’s liveness analysis results in negligible time.
Unintended behavior in smart contracts can lead to major financial losses. Due to the immutable nature of blockchains, it is of utmost importance to ensure the functional correctness of smart contracts before deployment. Formal verification is a powerful technology for such critical applications, as it can show the absence of errors. Current approaches focus on verifying programs on specific blockchains, such as the Ethereum Virtual Machine (EVM). Consequently, the SmartML smart contract modeling language was developed to design smart contracts independently of any particular blockchain. In this work, we present a novel approach for formally verifying SmartML contracts via an automatic translation to Java Card and the Java Modeling Language (JML). We extend SmartML with SmartJML, a JML-like specification language, and describe how SmartML and SmartJML can be automatically translated into Java Card and JML. With this, the established deductive verification tool KeY can be used for conducting proofs on the generated Java Card program. The faithfulness of our translation ensures that the obtained guarantees hold for the original SmartML models. In addition to the theoretical work, we provide a prototypical implementation of the automatic translation and evaluate it with a case study of an escrow.
This paper proposes a polar-coordinate framework for Bitcoin prices around the halving cycle, where each revolution is one halving epoch and radial distance represents log price. This alignment reveals recurring patterns conventional time-series charts obscure. We develop five statistics, spanning seasonality amplitude, phase concentration, intercycle repeatability, radial growth, and bull-bear asymmetry, each paired with an explicit null hypothesis and, given the small number of completed cycles, block-bootstrap or randomization inference. To test whether the structure is specific to Bitcoin's supply schedule, we apply the identical clock to Ethereum and the S&P 500 as placebo assets. Bitcoin shows significant halving-phase seasonality (p
Electromobility requires transactive coordination that respects distribution-network limits while preserving auditability and privacy. This study presents a reproducible peer-to-peer energy trading system that integrates a network-constrained market with permissioned blockchain settlement. The market solves a convex welfare program with linearized power-flow limits and recovers nodal prices from dual variables to match bids and offers and determine clear quantities. Settlement uses Hyperledger Fabric via the Gateway API, including proposal endorsement, ordering, validation, and commit notifications. Meter evidence is hashed and, when necessary, stored with private data collections. A co-simulation harness links MATLAB/Simulink and MATPOWER for feeder dynamics and price formation with chaincode and client logic for settlement. Three case studies are evaluated: an urban microgrid, a suburban microgrid, and a mobile electric-vehicle swarm. An Ethereum testnet serves as a public-chain baseline. In the testbed, a tuned Fabric configuration sustained approximately 1.6 to 1.7 thousand transactions per second with 99th-percentile submit-to-commit latency near one second and full deadline compliance at a one-second clearing cadence. Energy delivery accuracy remained tight, Multi-Version Concurrency Control conflicts were low, and dynamic nodal prices reduced EV charging cost relative to a flat tariff while signaling congestion through predictable rent patterns. The contribution is a deployable blueprint that connects network economics to verifiable settlement, with an open repository, benchmarking artefacts, and practical targets for endorsement width, block size, and timeouts, and clear pathways to field trials, stochastic and robust clearing, zero-knowledge meter proofs, and city-scale deployment.
Seema C K, Sharan V Talwar, subhash Chandra K R Patel, S. E. R. Sacha Emile R.
The Internet of Things (IoT) presents critical security challenges including device identity spoofing, replay attacks, and data tampering across billions of deployed endpoints. This work presents ZK-IoTChain, a blockchain-enabled security framework that integrates zk-SNARK-based device authentication with Merkle-anchored data integrity in a unified architecture. The proposed system employs a three-layer design consisting of device-side proof generation, on-chain Groth16 verification, and IPFS-based off-chain storage. This architecture ensures privacy-preserving authentication while maintaining scalability and cost efficiency. Experimental evaluation on the Ethereum Sepolia testnet demonstrates a mean proof generation latency of 3.21 seconds and on-chain verification latency of approximately 125 milliseconds, with an average gas cost of 278,400 per authentication. The framework achieves a 99.98% reduction in storage cost compared to full on-chain approaches. Security analysis under the Dolev–Yao adversary model confirms effective mitigation of identity spoofing, replay attacks, data tampering, and man-in-the-middle (MITM) attacks. The results highlight ZK-IoTChain as a practical and efficient solution for secure, scalable, and privacy-preserving IoT ecosystems.