Voting methods weighted by stakes are the fundamental governance paradigm in Proof-of-Stake (PoS) blockchains. Such a paradigm is known to be prone to power distortions: a few users possessing large stakes may completely control decision making, even without owning the totality of the stakes. We study this phenomenon through the lens of computational social choice, focusing on the extent of power imbalances in stake-weighted voting when power is quantified using the Penrose-Banzhaf power index. Our work presents both analytical and empirical contributions. Analytically, we demonstrate that while a perfect alignment between power and relative stake ownership is generally unattainable, it can be approximated in expectation under specific conditions. Empirically, using data from a real-world on-chain governance system (Project Catalyst), we provide a more fine-grained understanding of the power imbalances that are likely to occur in current stake-weighted governance systems.
On-chain crowdsourcing leverages blockchain's decentralization, transparency, and tamper-resistance to build trustworthy and verifiable Web3 crowdsourced services. However, existing decentralized reputation frameworks do not reconcile anonymity, reputation binding, and scalability. This paper demonstrates how on-chain crowdsourcing can simultaneously achieve these requirements under a trust-minimized model. We introduce DARTIC, a decentralized, anonymous, and scalable reputation-driven framework for crowdsourcing. DARTIC presents a dual-ledger system that enables requesters and workers to use distinct pseudonyms across interactions, ensuring unlinkability while maintaining accountability. To mitigate Sybil and reputation-reset attacks, we employ zkSNARK-based set membership proofs, cryptographically binding all user pseudonyms to a single access token without revealing the linkage. For scalability, we investigate two aggregation techniques that compress multiple proofs into a single succinct proof to minimize verification overhead. In addition, we design an automated, privacy-preserving reputation model that dynamically evaluates contributions across diverse crowdsourcing contexts. To demonstrate practicality, we instantiate and assess DARTIC in both crowdsensing and federated learning scenarios. Experimental results show that (i) individual proof generation for token spending completes in less than 3s, (ii) aggregation reduces the verification time of 1024 proofs from 8.7s to 0.96s, and (iii) zk-batching lowers gas costs by more than 100x compared to a pure Layer-1 deployment. These results demonstrate that anonymity, robust reputation binding, and scalability can be jointly achieved in fully decentralized crowdsourcing systems.
Crowdfunding is an important mechanism for supporting innovative projects by connecting creators with distributed contributors. Prior research has identified persistent limitations in both traditional and blockchain-based crowdfunding platforms, including limited transparency, centralized control, passive contributor roles, and inflexible fund management processes. These limitations hinder accountability, equitable participation, and effective decision-making throughout the campaign lifecycle. This paper presents a blockchain-enabled crowdfunding framework designed as a decision-support artifact for adaptive fund allocation and participatory governance. The framework enables contributors to engage in spending-request governance through Quadratic Voting, which balances influence across heterogeneous financial stakes and mitigates dominance by large contributors. To support adaptive campaign management, the framework further integrates Ethereum smart contracts with a Markov Decision Process (MDP), enabling campaign-level decisions to respond to evolving contribution patterns and campaign states. The framework is implemented and evaluated through controlled experiments on the Sepolia Ethereum test network. The evaluation includes both an internal ablation of Quadratic Voting and MDP-based adaptive support and an external comparison against representative blockchain-based baselines. The results show that the combined Quadratic Voting and MDP design achieves lower approval latency and higher throughput than partial or static variants of the framework, and that the full proposed platform outperforms the compared baseline systems under increasing campaign workload. Overall, the study demonstrates how participatory governance, adaptive decision support, and transparent smart-contract execution can be systematically integrated into crowdfunding platforms, providing practical guidance for the design of scalable, efficient, and accountable decentralized crowdfunding systems.
In IIoT situations, federated learning (FL) is a way to use industrial data that protects privacy. At the same time, adding blockchain to federated learning training makes it more trustworthy. But there are still some big problems with current blockchain-based FL frameworks: 1) The current consensus mechanisms don't do a good job of filtering out bad devices, which lets low-quality participants mess with global model training and make the model less robust; 2) Current privacy budget strategies are too simple, making it hard to find a balance between protecting privacy during statistical queries and gradient updates. Strong privacy protection lowers model accuracy, while weak protection doesn't protect against poisoning attacks. This paper proposes ShieldDFL, a blockchain-based federated learning framework with dual privacy protection and reputation-driven consensus, to solve these problems. This method uses a hybrid consensus mechanism based on LSTM-based reputation scoring to dynamically assess both short-term and long-term device contributions. This makes it possible to choose the best devices with accuracy. At the same time, it adds a new dual privacy budget mechanism that uses differential privacy for both statistical queries and gradient updates. This keeps privacy strong while keeping the model's performance high. The proposed method lowers the chances of bad devices getting into the consensus pool to 1.5%, lowers the success rates of SAR and BASR attacks to 5.8% and 2.1%, respectively, and keeps the model's accuracy high at 98.1% on MNIST and 87.6% on CIFAR-10. In general, the proposed framework does a good job of getting around the security and privacy problems that come with blockchain-based federated learning. It offers a fast and flexible way for decentralised and trustworthy collaboration in IIoT situations.
Privacy-preserving truth discovery (PPTD) has garnered significant attention in mobile crowdsensing (MCS). However, existing research lacks sufficient privacy protection and is often vulnerable to collusion attacks among malicious participants. Moreover, incorrect data submitted by unreliable users and their weights may reduce the accuracy of truth discovery. To address these issues, this paper proposes a privacy-preserving truth discovery framework resistant to collusion and reputation fraud (TD-RCRF) that is highly resistant to collusion and reputation fraud. The scheme employs additive secret sharing to protect sensing data, weights, intermediate results, and ground truth. To screen trustworthy users who meet reputation requirements under the non-colluding dual-server model, we propose a privacy-preserving reputation verification algorithm that combines Pedersen commitment and zero-knowledge proof to verify the validity of mobile users’ reputation values. Additionally, we propose a homomorphic strategy that converts shares between multiplication and addition and use it to design a lightweight truth discovery algorithm that further improves the accuracy of the “truth” using reputation values. Security analysis proves that TD-RCRF is privacy-preserving and secure under the non-colluding dual-server assumption. Theoretical analysis and experiments show that it is practical and efficient.
This study presents a structured dataset of blockchain-registered artificial intelligence agents under the ERC-8004 standard on Ethereum. The dataset integrates on-chain identity records, minting transactions, transfer events, reputation summaries, and individual feedback records, together with resolved off-chain metadata where available. Data were collected from Ethereum mainnet using Web3 RPC queries and processed into tabular form to enable reproducible analysis. The dataset covers 10,000 agents within a defined block range and includes both event-level records and aggregated summaries. It enables empirical research on agent identity formation, reputation systems, service exposure, and early-stage decentralized AI ecosystems. This resource supports studies in blockchain analytics, decentralized trust infrastructure, and the emerging agentic economy.
Mobile Crowd Sensing (MCS) systems enable large-scale data collection from heterogeneous IoT and mobile devices but face critical challenges related to data reliability, participant trust, and decentralized validation. Existing blockchain-based MCS frameworks often rely on energy-intensive or static consensus mechanisms and lack adaptive intelligence for detecting malicious contributors, limiting their real-world scalability. This paper proposes an intelligent, decentralized trust management framework that integrates a Delegated Proof-of-Stake (DPoS) blockchain with a Dilated RNN–BiGRU deep learning model. The blockchain ensures tamper-proof transaction validation and trust-based consensus, while the deep network dynamically predicts node reliability using temporal behavior patterns. The integration creates a feedback loop where learned trust scores influence validator selection in real time. The proposed hybrid framework was implemented on a Hyperledger Fabric 2.5 network and evaluated using synthetic MCS data representing heterogeneous environmental, noise, and traffic sensing. The system achieved 98.76% accuracy, 57% latency reduction, and 40% computational cost savings compared with existing PoW- and PoA-based models. These results demonstrate that coupling blockchain consensus with adaptive deep trust modeling can significantly enhance the security, scalability, and efficiency of next-generation MCS systems, making the architecture suitable for real-time, large-scale IoT deployments.
Blockchain technology has emerged as a foundational element of the digital economy, enabling secure, transparent, and decentralized transactions across diverse domains. Although numerous consensus algorithms have been proposed, existing studies often examine them in isolation or from a limited set of metrics. This paper presents a comprehensive and integrated comparative analysis of major consensus mechanisms—Proof of Work (PoW), Proof of Stake (PoS), Delegated PoS (DPoS), Practical Byzantine Fault Tolerance (PBFT), and Federated BFT (FBFT). We systematically evaluate their verification processes, performance metrics, security trade-offs, and application contexts. Our contribution lies in consolidating these aspects into a unified framework that highlights critical design trade-offs and decision criteria for selecting appropriate consensus protocols. This work aims to support researchers and practitioners in developing and deploying more efficient and secure blockchain systems.
The contemporary education landscape is often marred by escalating costs and centralized pedagogical structures, which collectively create significant barriers to entry for millions of potential learners worldwide. This paper presents \textbf{Skill Link}, a sophisticated decentralized platform designed to democratize skill acquisition through a specialized credit-based barter system. Unlike conventional e-learning platforms that rely on traditional currency transactions, Skill Link enables a frictionless exchange of knowledge by utilizing a virtual credit economy where participants earn and spend "learning credits." To address the critical issue of credential fraud in decentralized environments, the platform integrates Ethereum-based blockchain technology to ensure the absolute immutability and verifiable authenticity of all earned certificates. Key innovations include a multi-tiered course classification system, an automated mock assessment framework with negative marking capabilities, an intelligent context-aware AI assistant powered by advanced language models, and a rigorous verification mechanism for professional social links (LinkedIn, GitHub, Indeed). Developed using the robust Django framework, Python-based Web3 utilities, and a secure PostgreSQL/SQLite back-end, Skill Link provides a highly secure, transparent, and scalable ecosystem for peer-to-peer knowledge sharing, ultimately fostering a global community of experts and lifelong learners. The system's architecture emphasizes data integrity through atomic transactions and cryptographic verification, ensuring a trustless environment for global skill exchange.
We study binary decision-making in governance councils of Decentralized Autonomous Organizations (DAOs), where experts choose between two alternatives on behalf of the organization. We introduce an information structure model for such councils and formalize desired properties in blockchain governance. We propose a mechanism assuming an evaluation tool that ex-post returns a boolean indicating success or failure, implementable via smart contracts. Experts hold two types of private information: idiosyncratic preferences over alternatives and subjective beliefs about which is more likely to benefit the organization. The designer's objective is to select the best alternative by aggregating expert beliefs, framed as a classification problem. The mechanism collects preferences and computes monetary transfers accordingly, then applies additional transfers contingent on the boolean outcome. For aligned experts, the mechanism is dominant strategy incentive compatible. For unaligned experts, we prove a Safe Deviation property: no expert can profitably deviate toward an alternative they believe is less likely to succeed. Our main result decomposes the sum of reports into idiosyncratic noise and a linearly pooled belief signal whose sign matches the designer's optimal decision. The pooling weights arise endogenously from equilibrium strategies, and correct classification is achieved whenever the per-expert budget exceeds a threshold that decreases as experts' beliefs converge.
Intherapidlyevolvingdigitallandscape,freelancing platforms face significant challenges due to a lack of transparency,trust,andcentralizedcontrol.Thispaperpresents the design and implementation of a blockchain-powered web- based project management system integrated with a visual data dashboard. The proposed system leverages Ethereum smart contractstoensuresecure,tamper-proofuserregistration,project posting, bidding,assignment, work submission, payment release, and rating. The backend is developed using Django, while blockchain integration is achieved via Web3.py, enabling secure and transparent interactions. The platform provides real-time analyticsonusers,jobstatus,fundmovement,andratingsthrough a dashboard. The solution enhances trust, transparency,and de- centralization,provingeffectiveforfreelanceprojectecosystems
Vahab Esfandani, Mohammad Amin Borghei, Sara Ravan Ramzani, Peter Konhaeusner · 6 authors
The digital economy has expanded organizations’ ability to source ideas, labor and capital through online participation, making crowdsourcing a strategic mechanism for innovation and problem solving. This chapter conceptualizes strategic crowdsourcing as a socio-technical system rather than ad hoc task outsourcing and synthesizes dispersed theory and evidence into a coherent framework for design and governance. It defines major typologies—micro-tasks, open innovation contests, co-creation, crowdfunding, internal crowdsourcing and citizen science—and situates them relative to outsourcing and open-source collaboration to clarify when each approach fits task uncertainty, required expertise and desired ownership of outputs. Building on open innovation, socio-technical systems and participatory governance perspectives, the chapter proposes an integrated model with five linked layers: contextual drivers; input configuration (task specification, crowd definition and call design); enabling infrastructure (platforms and technologies, including AI and blockchain-based mechanisms); process mechanisms (incentive design, validation and quality assurance, data governance and ethical/legal safeguards); and outputs/outcomes (innovation, organizational learning, governance effects and social value with feedback loops). Cross-sector illustrations from technology, healthcare, education, civic tech and sustainability highlight recurring trade-offs around motivation, quality control, fair compensation, privacy and confidentiality and intellectual property rights. The chapter also evaluates emerging hybrid human–AI crowdsourcing and decentralized autonomous organizations (DAOs), emphasizing that their benefits depend on transparent rules, accountable allocation of rewards and decision rights and human-in-the-loop oversight to mitigate bias, concentration of control and trust failures. Overall, strategic crowdsourcing is positioned as potentially democratizing when aligned with organizational goals and governed responsibly. It concludes by outlining research directions for comparative studies, cross-cultural analysis and regulation-aware design.
Industrial carbon emissions play a major role in environmental pollution and climate change. Because of this, industries are required to continuously monitor their emissions and ensure they follow environmental regulations. Traditional emission monitoring systems generally rely on centralized databases, which can sometimes lead to problems such as delayed reporting, lack of transparency, and the possibility of data being altered. To overcome these issues, this paper introduces CarbonChain, a decentralized carbon emission monitoring system that combines Internet of Things (IoT) sensing technologies with blockchain verification. Environmental parameters such as gas concentration and particulate matter are collected in real time using sensors connected to microcontroller units. The sensor readings are then transmitted to a backend server where the data is validated and categorized. After validation, the emission records are stored on the blockchain through smart contracts, generating secure transaction hashes that ensure the integrity of the data. A web-based dashboard allows regulators and industry stakeholders to monitor emission levels, check compliance status, and verify blockchain records in real time. By combining IoT-based sensing with blockchain technology, CarbonChain creates a transparent and tamper-resistant monitoring platform that can support environmental auditing and carbon credit verification.
Day after day, climate change intensifies, necessitating tracking solutions for carbon emissions that offer transparent operations and efficiency, alongside scalability and sustainable behavioural incentives. The proposition to track carbon emissions is not new, yet standard tracking systems present multiple deficiencies, including double reporting, fraud, high operational costs, and constrained access for small organisations. We have developed a blockchain system that follows a framework to track both carbon emissions and trading activities, using smart contracts and decentralised ledger technologies to establish security, trust, and automation. Our system requires IoT sensor integration and AI analytics to enable continuous monitoring and safe storage, along with direct carbon trading without third-party involvement. The proposed framework addresses blockchain energy consumption issues by examining Proof of Stake (PoS) and hybrid consensus models. The model presented facilitates a massive reduction in carbon emissions and enhances transparency and efficiency.
In the context of smart cities, Non-Fungible Tokens (NFTs) are transforming digital art markets by enabling secure, decentralized transactions. As NFT trading grows, incorporating intelligence and adaptability becomes crucial—making Machine Learning (ML) integration essential. However, existing models, particularly Cooperative Game Theoretic Trading (CoGTT) frameworks, underutilize ML across all trading phases. Key gaps include limited real-time adaptability, suboptimal negotiation strategies, and inadequate buyer–seller matchmaking. This research addresses these gaps by integrating ML into a three-phase CoGTT framework—ML-augmented Naive Trading, Min–Max Price Negotiation, and Equilibrium-Based Trading—to enhance decision-making and pricing. The methodology applies ML algorithms such as decision trees, clustering, and reinforcement learning (Q-learning) within a public blockchain–based simulation environment using smart contracts. The simulation uses a customized dataset reflecting both market dynamics and artist credibility. The dataset is synthetically generated to emulate an NFT marketplace while maintaining controlled experimental conditions, which may limit direct applicability to volatile real-world markets. Zero-knowledge proofs (ZKPs) are employed to preserve privacy. ZKPs are employed to preserve privacy. A comparative analysis of ML models for NFT price estimation and strategic bidding demonstrates the effectiveness of combining predictive algorithms with reinforcement learning. Linear Regression and Random Forest models both accurately estimate NFT prices, with Random Forest achieving higher real-time prediction accuracy (R2 = 0.9920). K-Means clustering effectively segments market participants to support targeted negotiation, achieving a silhouette score of 0.8178. Integrating Q-learning with Random Forest enables dynamic bidding strategies that minimize the gap between recommended and actual prices. The discrete action set (decrease, stay, increase) supports interpretable, real-time bid adjustments. These findings highlight the potential for ML-driven NFT trading systems to support scalable, privacy-compliant digital marketplaces in smart cities, aligning trading behavior with market demands through automated, data-driven processes.
The integrity and scalability of electoral processes within large-scale academic institutions are often compromised by centralized vulnerabilities and high computational overhead. This paper proposes a novel, hierarchical consortium blockchain framework designed for Indian university ecosystem to facilitate secure, transparent, and high-concurrency e-voting. By utilizing tiered architecture comprising establishment-level private sidechains and global university-wide Ethereum ledger, proposed system optimizes trade-off between voter anonymity and transactional throughput by integrating Linkable Ring Signatures and Zero- Knowledge Proofs to ensure the Secret Ballot principle while maintaining public auditability. Experimental evaluations on with N = 4000 participants demonstrate an average gas consumption of 15,580 units per voter and peak throughput of 181 TPS. Experimental results reveal 11.5% reduction in per-voter processing latency compared to state-of-the-art models, showing proposed framework efficacy for high-density academic environments.
Effective traceability management in mineral resource development faces persistent challenges including information asymmetry, data falsification, and verification difficulties across complex value chains. This paper proposes a comprehensive blockchain-enabled traceability evaluation framework integrating distributed ledger technology with systematic assessment methodologies. A four-layer architecture encompassing data acquisition, blockchain storage, analysis processing, and evaluation application is designed to ensure data integrity throughout the mineral lifecycle. A hierarchical indicator system spanning five dimensions-traceability breadth, depth, precision, timeliness, and data credibility-is constructed, with the Analytic Hierarchy Process employed for weight determination and fuzzy comprehensive evaluation applied for performance assessment. Empirical validation through case study analysis of Huaxin Mining Group demonstrates the framework's practical applicability, yielding a comprehensive traceability score of 81.2 (Good grade). Comparative analysis reveals that blockchain-based systems achieve 96.8% data accuracy versus 82.4% for traditional approaches, with trace-back efficiency improving from 127.3 min to 4.7 min. The blockchain technology contribution ratio reaches 47.3% toward maximum traceability improvement. These findings provide theoretical foundations and practical guidance for advancing transparent and accountable mineral resource governance.
D. Hema Lakshmi, B. Prem Sai Siddhik, B. Akhil Kumar, Ch. Siva Venkata Sai Tharun · 5 authors
Resumes are a key part of traditional hiring, but when human reviewers may not accurately identify the true skills of candidates. Sometimes, when checks are done, fraudulent credentials may pass undetected due to limitations in manual verification. A new method is presented here that uses smart algorithms in a distributed ledger system. By connecting machine learning with secure data records, trust in verifying applicants grows a lot. The proposed system improves efficiency by reducing reliance on traditional keyword-based filtering. The software uses natural language tools to look at the applicant's information, extracts relevant skills and generates a performance score for each candidate. Cryptographic hashes of credentials are stored on a distributed ledger, ensuring that validation cannot be altered or hacked. An online model was created using ReactJS, Flask, MongoDB, and connections to the Ethereum Blockchain. The results show that the method automatically sorts job applicants, quickly checks their documents, and consistently finds qualified people in different fields. Combining smart algorithms with decentralised records increases trust, cuts down on manual tasks, and brings more clarity to the hiring process.
This study evaluates the robustness of Proof of Team Sprint (PoTS) against adversarial attacks through simulations, focusing on both the attacker win rate and computational efficiency under varying team sizes (N) and attacker ratios (α). PoTS is a recently proposed consensus mechanism that relies on randomly formed teams of participants to collaboratively generate blocks. Unlike traditional consensus methods where individual nodes compete independently, PoTS distributes responsibility across multiple nodes in a team, thereby increasing resilience against coordinated attacks. Our simulation results demonstrate that PoTS effectively reduces an attacker’s ability to dominate the consensus process, even under challenging conditions. For instance, when α ＝ 0.5, the attacker win rate decreases from 50.7% at N ＝ 1 to below 0.4% at N ＝ 8, effectively neutralizing adversarial influence. Similarly, at α ＝ 0.8, the attacker win rate drops from 80.47% at N ＝ 1 to only 2.79% at N ＝ 16, highlighting PoTS’s robustness under extreme threat levels. In addition to its strong security properties, PoTS maintains high computational efficiency by synchronizing block generation within each team. We introduce the concept of Normalized Computation Efficiency (NCE) to quantify this efficiency gain, demonstrating that PoTS significantly improves resource utilization as team size increases. As N grows, PoTS not only enhances security but also achieves better computational efficiency due to the averaging effects of execution time variations among team members. These findings underscore PoTS as a promising and practical alternative to traditional consensus mechanisms, such as Proof of Work (PoW) and Proof of Stake (PoS). By leveraging team-based block generation, sequential execution, and randomized participant reassignment in each round, PoTS provides a scalable, resilient, and energy-efficient framework for decentralized consensus in blockchain systems.
Federated learning (FL) enables collaborative model training over distributed private data. However, sustaining open participation requires incentive mechanisms that compensate contributors for their resources and risks. Enabled by Web3 primitives, especially blockchains, recent FL proposals incorporate incentive mechanisms for open participation, yet most focus primarily on algorithmic design and overlook system-level challenges, including coordination efficiency, secure handling of model updates, and practical usability. We present FWeb3, a practical Web3-enabled FL framework for incentive-aware training in open environments. FWeb3 adopts a modular architecture that separates FL functions from Web3 support services, decoupling the off-chain training and data plane from on-chain settlement while preserving verifiable incentive execution. The framework supports pluggable aggregation and contribution evaluation methods and provides a browser-native DApp interface to lower the participation barrier. We evaluate FWeb3 in real-world settings and show that it supports end-to-end incentive-aware FL with transaction and data-transfer overheads of only 21.3% and 3.4% in WAN; FWeb3 also deploys from zero configuration in under 3 minutes and enables user onboarding in under 1 minute.
The integration of distributed data storage, P2P networks, consensus mechanisms, cryptography and other technologies, the application of blockchain technology has expanded from the initial financial field to many other areas, such as logistics and auditing. The consensus mechanism is the soul of blockchain technology, and it is of great significance to conduct a rigorous mathematical analysis. As far as we know, the Proof of Stake (PoS) consensus mechanism is only a qualitative description of the rich and the poor, the rich are richer, the poor are poorer, and there is no quantitative mathematical analysis. This paper presents a novel quantitative framework to quantitatively analyze the PoS consensus mechanism. Under the premise of not carrying out the attack, we use the expected reward and the reward ratio as the evaluation indicators, quantitatively analyze the optimal fund allocation strategy of the two parties game under the PoS consensus mechanism from the perspective of rich miners, and construct the reward function as the objective function. The inequality constrains the optimization problem and solves it using the Karush-Kuhn-Tucker condition. We consider the two schemes of assignment strategy and random strategy, and get the optimal fund allocation strategy. At the same time, it is compared with the general strategy to obtain the optimization effect of the optimal strategy. After that, we compare the situation in which both sides of the game use the optimal strategy. We found that for assignment strategy, the mining activity will not indicate that the rich are richer and the poor are poorer. However, for the random strategy, this will not happen. The random strategy is also the most common strategy in practice. We also use Markov decision process (MDP) to give the optimal strategy calculation method under the rational miner game, which is also applicable to the n-parties game. The work of this paper helps the blockchain developers to analyze the PoS consensus mechanism, and the adoption strategy of the assignment strategy and the random strategy can be used as the future research direction.
This paper presents a novel architecture for developing Decentralized Applications (DApps) on Blockchain that integrate Artificial Intelligence (AI) and decentralized storage for the age of Web3 applications. As these emerging technologies continue to evolve, the synergy among them offers not only unprecedented opportunities for innovation and advancement but also raises confusions, incompatibilities and unreliability. The proposed architecture aims to harness the strengths of Blockchain's distributed computation and ledger technology for transparency and security, AI's capabilities for fraud detection and penalization, and Social Media's network effects for user engagement and decentralized storage's reliability for trust. Through the integration of these technologies, DApps can offer enhanced privacy, autonomy, and trust while fostering inclusive and participatory ecosystems. The paper discusses the design principles, components, and potential use cases of such a hybrid architecture, highlighting its potential to revolutionize various domains, including content creation, social networking, land registration, and property market. To verify and validate the architecture, we have developed two DApps- one for social media and another for land registration and property market. Our developed DApps provided upto 100 fold gains in speed, 10 folds gains in cost, more reliably and automation than existing similar centralized applications.
Smart contracts, primarily written in Solidity, are Turing-complete programs on platforms like Ethereum, requiring gas fees for deployment and execution. Gas quantifies computational costs, and inefficient contracts result in unnecessary expenses for developers and users. Gas optimization at the source code level has been studied in various related works; however, existing methods for summarizing gas-inefficient patterns primarily rely on author-defined rules or heuristic approaches, and their evaluations lack a labeled dataset.In this paper, we conduct a comprehensive empirical study on the issue of gas optimization in smart contracts. We begin by gathering audit reports from Code4rena, a well-known smart contract audit platform. These reports include both expert evaluations, conducted by professionals known as Wardens, and automated analyses generated by the platform’s static analysis tool, 4naly3er. After filtering out false-positive gas optimization instances from the automated reports, we identify 2,095 instances of gas-inefficient patterns across 54 projects. We categorize these inefficiencies into 24 types using thematic analysis and find that static analysis tools often produce false positives and negatives. To address this, we propose a hybrid method combining static analysis and large language models (LLMs) to detect and repair gas inefficiencies. The static analysis tool identifies potential optimization opportunities, while the LLM refines these findings and suggests effective repairs. Our evaluation shows that our approach achieves a precision rate of 82.28% and a recall rate of 88.46%, and can save 919 units of gas per function on average during execution.
The gig economy faces significant challenges with centralized platforms like Upwork and Fiverr, including high service fees (10–20%), opaque algorithms, unreliable reviews, and frequent payment disputes. To address these issues, this work proposes Work Bounty, a decentralized freelancing marketplace powered by Web3 and blockchain technologies. Built on the Ethereum blockchain, the platform utilizes smart contracts to automate critical processes such as job creation, bidding, work delivery, and escrow-based payments, thereby eliminating intermediaries and enhancing trust. Authentication is streamlined using MetaMask wallets, enabling secure, passwordless access tied to unique cryptographic addresses. Job details and deliverables are stored on the InterPlanetary File System (IPFS) to ensure immutable and tamper-resistant data storage, while a blockchain-based reputation system provides transparent, unalterable user ratings. Experimental evaluation on the Ethereum test network demonstrates that the system achieves a 100% success rate in smart contract executions and reduces overall transaction costs to 2–3% equivalent gas fees, compared to the 10–20% fees on centralized platforms. MetaMask authentication achieved a 98.7% success rate, and beta testing with freelancers and clients revealed 92% user satisfaction with the platform&s;s ease of use and trustworthiness. These results highlight the system&s;s potential to provide a secure, transparent, and cost-effective alternative to traditional freelancing platforms, fostering a more equitable and globally accessible gig economy aligned with Web3 principles.