With the explosive growth of online education resources, traditional education platforms that rely on centralized servers for resource distribution and storage gradually expose issues such as inefficient resource management, lack of trust in sharing, high storage costs, and a high risk of single-point failure. In response, this study designs a decentralized education resource sharing platform by integrating blockchain technology and distributed file systems. It leverages the distributed ledger, immutability, and traceability features of blockchain, along with the high data availability and low storage cost advantages of distributed file systems. Results show that the proposed education platform reaches 1,388 transactions per second when the number of nodes is 200, with a latency response time of 9.8 seconds and an average memory overhead of 268 MB. In practical performance evaluation, the platform achieves a top-10 hit rate of 97.2%, a business interruption probability as low as 4.8% under unexpected conditions, and an average resource throughput efficiency of 176 Mbps. Overall, the platform performs well in education resource sharing and demonstrates strong algorithm fault tolerance, practicality, robustness, and service stability, providing reliable technical support for global education resource sharing.
This paper explores the application of blockchain technology to manage distributed edge computing resources. The core claim is that blockchain can facilitate dynamic resource allocation and efficient utilization within edge computing environments. The proposed mechanism involves constructing a blockchain-based resource management system leveraging smart contracts to automate and optimize resource distribution. This approach addresses the challenges of centralized control, inefficient resource utilization, and security vulnerabilities commonly found in traditional edge computing models. The research investigates the potential benefits of blockchain's decentralized, transparent, and immutable ledger for enhancing edge computing resource management, ultimately leading to improved performance, scalability, and trust within distributed edge systems. The key focus is on establishing a secure and automated framework for resource sharing and access control, significantly improving the overall efficiency and reliability of edge computing deployments. ---
This paper proposes a novel approach to distributed trusted computation leveraging the inherent properties of blockchain technology. The core claim is that blockchain's consensus mechanisms and data integrity guarantees can facilitate a secure and trustworthy distributed computing environment, effectively addressing the trust issues prevalent in cloud computing. The proposed mechanism involves decomposing computational tasks into smaller sub-tasks, which are then collaboratively executed by nodes within a blockchain network. Smart contracts are employed to manage task scheduling and validate the results. This system offers an alternative to traditional trust models, utilizing cryptographic techniques and distributed consensus for enhanced security and transparency. The research explores the potential of blockchain to fundamentally transform the landscape of distributed computing, providing a robust solution for sensitive computations and data processing. The paper focuses on the technical design and theoretical underpinnings of this approach, outlining key components and potential challenges.
Smart contract deployment costs constitute an economic consideration in blockchain ecosystems, yet existing gas optimization approaches primarily focus on execution efficiency while neglecting deployment gas reduction. We analyze Solidity storage internals and identify that conventional variable packing–despite reducing storage slots–often increases deployment costs due to compiler-generated masking and shifting operations. This finding motivates State Variable Mutation , the first systematic approach designed to reduce smart contract deployment costs through guided reordering of state variable declarations. Our approach explores variable orderings to identify layouts that minimize gas-expensive storage operations while preserving semantic equivalence and storage efficiency. We implement our approach in DGRed , an open-source tool, and evaluate it on 300 real-world smart contracts. Results demonstrate deployment gas reductions of up to 32.72%, with an average reduction of 15.65% (52,850 gas units per contract), translating to total savings of 15,854,883 gas units across all contracts. Under high network congestion (200 gwei), these savings correspond to $12,381.4. Compared with state-of-the-art gas optimization tools, DGRed achieves superior deployment gas reductions (15.65% average vs. 4.91% for GasSaver and 2.83% for GASOL) while maintaining 100% semantic preservation and producing valid bytecode for all 300 contracts. In contrast, GasSaver introduces compilation errors in 148 contracts, while GASOL generates invalid bytecode in 234. DGRed 's state variable mutation produces identical execution gas to the original contract in all 100 contracts evaluated for execution gas impact, confirming that deployment optimization does not affect runtime efficiency. Additionally, DGRed 's 15.65% reduction is over 8 \(\times\) larger than the best achievable through Solidity compiler flag tuning alone, demonstrating that the two approaches are complementary. DGRed provides developers with a practical, semantic-preserving solution for deployment cost optimization without modifying contract logic or functionality. Because DGRed only reorders state variable declarations, it introduces no runtime trade-off, and entails no risk of behavioral regression.
Abstract The integration of Internet of Things (IoT) and blockchain technologies enables secure, decentralized data management for real-time applications. However, limitations in processor, memory, and energy resources restrict the direct processing of large datasets. Notably, Garbage Collector (GC) mechanisms in high-level languages increase variance in P99 queue latencies, while expanding data volumes can result in system crashes due to Out-of-Memory (OOM) errors. This study introduces a hybrid Rust-Python Simplified Payment Verification (SPV) native hashing engine deployed on resource-constrained edge devices (Raspberry Pi Zero W) and high-capacity gateways (Raspberry Pi 5). Laboratory evaluations demonstrate that the hybrid system achieves a verification capacity of 34,000 Merkle nodes per second, representing a 4.35-fold speed improvement over pure Python on the Pi Zero W. Additionally, the system reduces GC-induced latency fluctuations and offers up to 75% potential energy savings, as indicated by theoretical modeling based on active processor cycle analyses. Bottleneck analyses on Raspberry Pi 5 indicate that Foreign Function Interface (FFI)-related data transfer costs limit parallel processing benefits for low-volume datasets. In contrast, the scalability of the hybrid architecture is evident with datasets containing 1.5 million records. The memory-mapped streaming architecture minimizes OOM risks and achieves a cache miss rate of 0.34%. Memory safety was assessed using the MIRI tool.
This paper presents and evaluates a hybrid blockchain architecture for organizational information systems in which PostgreSQL remains the operational database while Ethereum Sepolia is used as an externally verifiable transaction-recording layer. The implemented proof-of-concept is a university wallet system combining a React and TypeScript frontend, an Express.js backend, PostgreSQL with Drizzle ORM, and an OpenZeppelin-based ERC-20 smart contract deployed on Ethereum Sepolia. The system associates successful application transactions with corresponding Ethereum transaction hashes stored in a dedicated relational table. The experimental evaluation uses sequential workloads of 10, 50, and 100 transactions, comprising 160 measured application transactions in total, together with a separate 30-transaction database-mutation experiment. The performance evaluation measures database insertion time, blockchain transaction time, end-to-end execution time, success rate, and gas consumption. All 160 performance-test transactions completed successfully. Mean database insertion time remained below 32 ms, while blockchain transaction time ranged from approximately 15.26 to 21.93 seconds and dominated end-to-end execution time. Mean gas consumption was approximately 40,324 gas per successful transfer. The database-mutation experiment modified the amount field of 7 of 30 successfully recorded transactions after their blockchain references had been established. All seven modified records retained their corresponding blockchain transaction references. However, the experiment did not perform field-level comparison between the modified database records and blockchain event contents and therefore is not presented as a complete cryptographic tamper-detection validation. The implementation and experimental artifacts are publicly available through the associated project repository. The paper presents the work as a proof-of-concept implementation and empirical evaluation of a hybrid database-to-blockchain transaction architecture.