ABSTRACT Smart contracts are the cornerstone of decentralized applications and financial protocols, extending digital currency transactions but also introducing serious security challenges that cause substantial economic losses. Existing solutions primarily target code‐level vulnerabilities, which constitute only a portion of all security incidents. Though prior research conducts static analysis across contract lifecycles, they lack the characteristic analysis of vulnerabilities in each stage and the distinction between the vulnerabilities. This paper presents the first empirical study on the security of smart contracts throughout their lifecycle, including deployment and execution, upgrade, and destruction stages. We propose two feature categories: transaction features (contract lifespan and four dynamic features: transaction numbers, transaction amounts, neighbour numbers, and the number of old and new neighbours) and ego network properties (temporal network density and local clustering coefficient). These seven features capture behavioural patterns across dimensions, including activity duration, transaction frequency, financial liquidity, interaction breadth, dynamic interaction changes, and structural properties of the transaction network. Finally, five machine learning models—logistic regression, random forest, support vector machine, decision tree, and K‐nearest neighbours—are used to identify vulnerabilities at different stages. Results show that vulnerable contracts exhibit distinct transaction features and ego network properties at different lifecycle stages.

ABSTRACT Blockchain faces problems such as network congestion, high data propagation latency, and low throughput. These problems seriously affect the large‐scale application of blockchain, so the performance optimization of blockchain networks is imminent. To reduce the propagation delay of transactions and blocks in the blockchain network and create conditions for shortening the block interval to increase throughput, we design a novel blockchain network structure and transaction propagation method. We propose a neighbouring node selection algorithm based on location and delay, introduce clusters and supernodes, and construct a low‐latency network structure. In the new network structure, we design a transaction propagation method based on random linear network coding to reduce the latency and bandwidth consumption of syncing transactions across the entire network. The improvement in transaction propagation delay minimizes the probability of missing transactions in compact block, thus indirectly optimizing the block propagation delay. Simulation results show that our network structure can reduce block propagation delay by 19.7%. The new network structure combined with the transaction propagation method based on network coding enables the overall scheme to shorten the time it takes for a block to reach consensus in the network by 65.1%.

Abstract Blockchain technology ensures accountability, transparency, and redundancy, but its reliance on public‐key cryptography makes it vulnerable to quantum computing threats. This article addresses the urgent need for quantum‐safe blockchain solutions by integrating post‐quantum cryptography (PQC) into blockchain frameworks. Utilizing algorithms from the NIST PQC standardization process, it is aimed to fortify blockchain security and resilience, particularly for IoT and embedded systems. Despite the importance of PQC, its implementation in blockchain systems tailored for embedded environments remains underexplored. A quantum‐secure blockchain architecture is proposed, evaluating various PQC primitives and optimizing transaction sizes through techniques such as public‐key recovery for Falcon, achieving up to 17% reduction in transaction size. The analysis identifies Falcon‐512 as the most suitable algorithm for quantum‐secure blockchains in computer‐based environments and XMSS as a viable but unsatisfactory stateful alternative. However, for embedded‐based blockchains, Dilithium demonstrates a higher transactions‐per‐second (TPS) rate compared to Falcon, primarily due to Falcon's slower signing performance on ARM CPUs. This highlights the signing time as a critical limiting factor within embedded blockchains. Additionally, smart contract functionality is integrated, assessing the impact of PQC on smart contract authentication. The findings demonstrate the feasibility and practicality, paving the way for robust and future‐proof IoT applications.

ABSTRACT As decentralised applications (dApps) and decentralised finance (DeFi) gain widespread adoption on Ethereum, scalability and transaction costs have emerged as critical challenges. Layer 2 (L2) rollups are promising solutions for improving scalability, but they suffer from significant gas costs due to the need for data availability on Ethereum Layer 1 (L1). Proto‐Danksharding ( EIP‐4844) introduces blob transactions to reduce calldata costs, providing a new avenue for cost optimisation. This paper presents a comprehensive gas efficiency and economic analysis of EIP‐4844's impact on Layer 2 rollups, providing mathematical models, simulation results, and architectural diagrams to illustrate the improvements in gas costs and system design.

Abstract Blockchain trilemma is a considerable obstacle for today's decentralized systems. It is hard to achieve a perfect balance among decentralization, security, and scalability. Many popular blockchain platforms sacrifice scalability to preserve decentralization and security, resulting in low speed, reduced throughput, and poor real‐time performance (the time from transaction initiation to confirmation). Currently, there are several technologies, such as sharding, directed acyclic graph technology, sidechains, off‐chain state channels etc., that aim to improve throughput and real‐time performance. However, most of these solutions compromise the core feature of blockchain, which is decentralization, and introduce new security risks. In this paper, the authors propose a novel method, called MEchain, based on the Proof of Time Series Algorithm. MEchain consists of two models: the multi‐chain model and the elastic‐chain model. The authors’ experimental results show that these two models can enhance real‐time performance and throughput to a higher level in the industry.

Abstract Folding at Home community gathers pecuniary crypto interest to an altruist cause (medical drugs research participation). There are many possibilities of ‘teams’ for folding, some of which allow compensation in cryptomonies. The authors will therefore study the three major communities in order to compare them, in a cost‐benefit study logic. A cost‐benefit analysis was performed between CURECOIN, BANANO, DOGECOIN folding, DOGECOIN mining and their communities on social platforms based on several outcomes: ‘Points Per Day (PPD)’, ‘Whattomine Mh/s Equivalent’, ‘Graphics Processing Units (GPU) Thermal Design Power/Typical Board Power Watts’, ‘Coins per 1,000,000 PPD’, ‘Coins per Day’, ‘Cost Per Coin’, ‘Cost Per Day’, ‘kWh Used Per Day’. Actually, BANANO, thanks to a large community and bots, has the highest PPD production and the lowest energetic cost on Central Processing Unit per week. On GPUs, DOGECOIN folding has the lowest weekly cost. However, the DOGECOIN community cannot produce as many PPD as the Banano team. CURECOIN offers a good compromise between the environmental point of view and the profitability one. To ensure fairness, the choice of the crypto and the way to earn it that has to be solved should be a pure public good, that is, perfectly non‐rival in consumption and non‐excludable. Indeed, the primary goal of those three communities is to allow scientific progress (thanks to Folding at Home) before allowing a return on investment.

Abstract The modern world has its foundation built on data. Data is generated all the time, and many aspects of the society and economy are built upon it. In today's world, data is employed in better decision‐making, innovation, and improving the efficiency of systems. It becomes essential to protect it from being tampered with or misused. Encryptions based on cryptographic systems are used in mainstream data protection these days. There have been newer iterations of mechanisms based on new techniques using blockchain and the zero trust model and even systems that employ fog computing along with content delivery networks. The advent of computation through quantum technologies also helped in developing data security with post‐quantum mechanisms. The critical conditions for the working of these mechanisms hinder their adoption primarily. This also leads current systems to lack one or more of these technologies. Thus, the importance of a unified system that can employ quantum technologies effectively becomes prominent and requires a definitive investigation. In this article, a Quantum‐IoT‐based data protection scheme that can be of value to Industry 4.0 is analyzed. Light is also shed on innovations in quantum computing that can help solve not just current problems but also the future.