A decentralized autonomous organization (DAO) is a novel form of blockchain-based organization designed for collective decision-making. As DAOs emphasize a decentralized, democratic decision-making approach, participation serves as the foundation for their sustainable operation and development. Unfortunately, many DAOs struggle with low participation rates, often falling short of the required quorum. To address this critical issue, an increasing number of DAOs have adopted delegated voting, which allows members to transfer their voting rights to others. However, the impact of delegated voting within the DAO context remains unknown. By leveraging variation in the adoption of delegated voting across DAOs, we find that delegated voting increases members’ participation in proposal voting and enhances decision quality. Our results further show that delegated voting stimulates greater participation in proposals with higher participation costs, including those that are more complex, urgent, or operational in nature. However, in the long term, delegated voting also leads to greater voting power concentration and reduces engagement from both new and active voters, potentially harming sustained participation and the growth of the DAO community. Overall, our findings highlight the need for DAOs to balance the short-term gains from higher participation with the potential long-term risks to decentralization.
Modern charitable donation platforms involve multiple stakeholders, including donors, charity organizations, financial institutions, and regulatory authorities. However, traditional centralized systems often suffer from limited transparency, weak trust management, and insufficient traceability, which significantly undermine public confidence in charitable activities. To address these challenges, this study proposes an intelligent blockchain-enabled framework for transparent multi-stakeholder donation management. The proposed system integrates consortium blockchain infrastructure with smart contract mechanisms to support trustworthy transactions, automated governance, and transparent information sharing across participating entities. The framework adopts a modular architecture that facilitates role separation, traceable transaction management, and scalable system evolution. In addition, the system enables transparent supervision and evaluation processes, allowing donors, recipients, and regulatory bodies to participate in collaborative monitoring of charitable activities. A prototype implementation based on the FISCO BCOS consortium blockchain platform is developed to evaluate the feasibility and performance of the proposed framework. Experimental results demonstrate that the system effectively enhances traceability, operational transparency, and trust among participants while maintaining acceptable performance in terms of throughput and latency. The proposed framework provides a practical reference architecture for developing intelligent and trustworthy multi-party platforms and contributes new insights into the design of decentralized expert systems for social good applications.
Adoption of peer-to-peer (P2P) trading is very challenging, mainly due to numerous issues and limitations such as lack of trust in the concept and awareness of the technical, economic and social benefits. This paper aims to understand how blockchain technology can address the current issues/limitations of P2P distributed solar energy (DSE) trading. A series of semi-structured interviews were conducted with 23 community energy stakeholders to confirm and expand the stakeholder issues identified in the literature review. A case representing community energy projects was selected to (1) develop and implement a blockchain system and (2) evaluate its ability to eliminate (or reduce) stakeholder issues and meet stakeholder expectations. A blockchain-enabled P2P trading platform was developed using an Ethereum backend. The system clearly demonstrated its ability to deliver full or partial solutions to 12 stakeholder issues. Two stakeholder issues are unable to be addressed via the blockchain platform since they uncovered the weaknesses of blockchain technology. The P2P trading platform has also demonstrated its ability to facilitate decentralized trading and data management. The outcome of this study indicates the areas of P2P trading projects that can be improved by the application of blockchain technology.
Accurate carbon footprint accounting is fundamental for urban environmental governance. However, multi-stakeholder transit networks struggle with data manipulation, privacy risks, and labor-intensive manual auditing. To resolve these trust and scalability bottlenecks, this paper introduces a tri-layer hybrid blockchain framework based on an “off-chain storage, on-chain evidence” paradigm. The architecture synergizes a relational database (MySQL) for high-throughput structured data, the InterPlanetary File System (IPFS) for decentralized raw evidence, and Hyperledger Fabric to immutably anchor dual-layer cryptographic hashes. We engineer a smart contract auditing pipeline that autonomously executes deterministic verification of hash consistency, emission thresholds, and physical logic integrity. Empirical evaluations utilizing a large-scale urban transit dataset injected with adversarial mutations demonstrate high robustness, achieving F1-scores of 1.000 across multidimensional anomalies. This replaces manual testing with statistically significant verification. Ultimately, this framework provides environmental regulators and transit authorities with a highly scalable, privacy-preserving, and trust-minimized infrastructure for continuous carbon footprint traceability.
Proof-of-work blockchains purchase their security through the expenditure of compute and energy — yet the work performed is itself discarded entirely. Decentralized AI networks provide useful compute but secure no ledger. Myelin unifies both functions: miners jointly operate a large agentic language model (the network model) via pipeline parallelism, and the same cryptographically attested inference work (“Proof of Inference”, PoI) determines compensation and feeds the voting weight of consensus. The native coin MYL closes the value cycle: users burn MYL for inference credits, and miners receive newly minted MYL in proportion to verified work (burn-and-mint equilibrium). We specify (i) a layered architecture that decouples consensus latency from inference latency, (ii) a three-tier verification model combining deterministic redundancy, optimistic sampling with a bisection game, and optional zkML anchors, (iii) a token economy with a quantifiable security condition (S_min = g/p²), and (iv) core data types and reference algorithms of an open-source implementation. We name the open core problems — deterministic cross-hardware inference, the latency–collusion trade-off of pod formation, and the 50% redundancy overhead — explicitly and propose measurement procedures. Bilingual release: this record contains the English and German editions of the whitepaper (PDF + Markdown each). In case of discrepancies, the German original prevails.
The rapid proliferation of IoT devices in metropolitan environments poses critical challenges for heterogeneous device management under minimal centralized control. This paper presents DCRO, a Distributed Coalition-based Resource Orchestration framework enabling IoT devices to self-organize into dynamic coalitions for cooperative resource management. Unlike traditional hierarchical approaches that suffer from scalability bottlenecks, DCRO integrates three core components: a Self-Organizing Device Clustering Algorithm (SODCA) that adapts to topology changes without global coordination; a Game-Theoretic Coalition Formation Mechanism (GT-CFM) that drives fair resource allocation through Shapley value-based negotiation; and a Lightweight Hierarchical Consensus Protocol (LHCP) coupled with a Merkle-DAG security architecture that ensures tamper-resistant coordination without blockchain overhead. Experiments across three metropolitan testbeds demonstrate 26.2% latency reduction and 31.4% energy savings over centralized baselines, only 11.3% throughput degradation under continuous fault injection, and stable coalition convergence at 5,000 devices within 15 iterations.
Distributed ledger technology (DLT) initiatives are frequently designed from a predominantly technical and functional perspective. Such a narrow focus often overlooks stakeholder-oriented non-functional requirements, thereby limiting adoption in complex socio-technical environments. To address this gap, this paper develops the SERS framework, a stakeholder-oriented framework that structures four key dimensions for the design and evaluation of decentralized systems: Security, Efficiency, Resiliency, and Sustainability. Following a Design Science Research approach, the framework is derived from a systematic literature review and refined through expert validation using the fuzzy Delphi technique. The resulting framework provides a domain-independent typology of stakeholder-oriented performance dimensions and associated assessment criteria. To demonstrate its applicability, the framework is used to guide the design of a multi-layer architecture integrating distributed ledger technologies, IoT, and Cloud/Edge/Fog computing within a healthcare context. The architecture is subsequently evaluated using the Fuzzy Analytic Network Process (Fuzzy-ANP) based on the SERS dimensions. The results indicate that the proposed architecture performs particularly strongly in terms of security and resiliency, while highlighting efficiency and sustainability as areas requiring further improvement. Beyond healthcare, the SERS framework provides a reusable mechanism for designing and evaluating decentralized socio-technical systems across a broad range of technology adoption contexts.
Open access
IoT and Edge/Fog Computing
Mobile Crowdsensing and Crowdsourcing
Innovative Approaches in Technology and Social Development
When is honest Bitcoin mining rational? This question is central to the incentive design of proof-of-work blockchains. Sapirshtein et al. computationally derived near-tight lower and upper bounds on the incentive-compatibility threshold using a Markov Decision Process. Kiayias et al.'s Blockchain Mining Games instead derived theoretical lower and upper bounds. However, this theoretical approach has two limitations: its model restricts miners to a narrow action space and assumes idealized tie behavior, and its lower and upper bounds are far from tight. We resolve both limitations. We develop a more realistic model with a broader miner action space and asymmetric tie-breaking parameters $γ^-$ and $γ^+$. We then propose an algorithm that computes lower and upper bounds on the incentive-compatibility threshold with a maximum error of $9.98006\times10^{-4}$.
Diese Masterarbeit untersucht, ob Posts von Elon Musk auf Twitter (jetzt: X) die Bitcoin-Volatilität beeinflussen können. Einige meinen, dass Musk in der Lage sei, den Bitcoin-Kurs mit einem einzigen Tweet zu beeinflussen. Deshalb untersuche ich diese Frage, indem ich die Volatilität von Bitcoin modelliere und prognostiziere. Dafür verwende ich ein heterogenes autoregressives Modell der realisierten Volatilität (HARRV) basierend auf Hochfrequenz-Daten von Bitcoin-Preisen. Das Modell erweitere ich nicht nur durch Variablen, die für die Tweets von Musk stehen, sondern auch durch andere. Beispielsweise eine Variable, die zwischen Wochentagen und Wochenenden unterscheidet und eine Variable, die die Häufigkeit der Google-Suchen nach dem Wort Bitcoin widerspiegelt. In der Masterarbeit zeige ich, dass Tweets von Elon Musk, die Interaktionen über dem Durchschnitt aufweisen, einen starken signifikanten Effekt auf die realisierte Volatilität haben. Außerdem zeigt sich, dass das Hinzufügen der Tweets-Variablen zum HAR-RV-Modell dazu beiträgt, die Modellierung und Vorhersage der Volatilität von Bitcoin zu verbessern.
Abdulhadi Sahin, Kemal Akkaya, Sukumar Ganapati, A. Selcuk Uluagac
While blockchain technology has the potential to become one of the enablers of distributed applications and autonomous organizations (DAOs) with its decentralized structure, practical implementations reveal the existence of power and resource concentration at various levels. This concentration raises serious security concerns regarding the core consensus mechanisms, which regulate block creation and the interaction between the nodes of blockchain, potentially preventing the realization of the technology’s full potential. To this end, in this paper, we analyze one of the most commonly deployed consensus protocols, namely Proof of Stake (PoS), from a security perspective. We demonstrate how PoS inadvertently encourages the concentration of power, which may cause some users to leave, thereby endangering blockchain security. Specifically, in PoS, nodes with higher stakes have a greater likelihood of being selected as block proposers, leading to power concentration among a small group of users. This situation increases the risk of collusion and compromise of the consensus mechanism. To address these issues, we propose and analyze an alternative incentive mechanism for PoS by utilizing theories on optimal taxation from behavioral economics. Our proposed model bases the block proposer selection process on a modified and relatively more balanced distribution. It offers preferential treatment to low-stake holders while also integrating a reputation mechanism to mitigate potential Sybil attacks encouraged by equitable rewards. Using a game-theoretic model, we demonstrate the optimal balance of incentives between earning potential and blockchain network sustainability for both high- and low-stake holders. We also conducted extensive performance analyses, including real data from Uniswap, which showed that our proposed mechanism can mitigate inequality and provide an appealing solution for keeping the users in the network to sustain the security of the consensus mechanisms and thus the secure and sustainable operation of blockchain applications.
Sai Srikanth Madugula, Peplluis Esteva De La Rosa, Daya Shankar
The rapid proliferation of Agentic Artificial Intelligence fundamentally disrupts traditional customer loyalty paradigms. As AI evolves from passive recommendation algorithms to autonomous, goal-directed agents capable of executing purchasing decisions, the conventional understanding of consumer-brand relationships requires a structural reevaluation. By synthesizing extant literature across human-machine teaming, consumer decision-making, and algorithmic trust dynamics, we demonstrate that traditional loyalty models fail to account for algorithmic bounded rationality and constructed autonomy. To address this, we introduce the Dynamic Verifiable Multi-Agent Human Agentic Loyalty Loop (DVM-HALL) model. We formalize brand choice via a softmax probability formulation where human emotional equity, agentic machine-experience utility, calibrated trust, delegated authority, and verifiable execution jointly determine selection. The model features recursive updating mechanisms to dynamically calibrate trust and delegation after each interaction. Crucially, the framework integrates a verifiable execution layer for Decentralized Finance (DeFi) and tokenized loyalty settings, incorporating execution risks -- such as gas costs, slippage, MEV exposure, and smart-contract vulnerabilities -- as core predictors of agentic brand preference. Furthermore, we introduce the Net Human-Agent Score (NHAS), an auditable, risk-weighted metric designed to measure human-agent alignment using human feedback, execution logs, benchmark comparisons, and verifiable receipts. Finally, we propose a comprehensive three-stage empirical validation plan spanning controlled shopping experiments, multi-agent market simulations, and DeFi testbeds. This framework provides the foundational theory required for brands to navigate the impending transition toward machine customers.
Web3 represents the next-generation value-driven Internet built on blockchain technology, whose realization heavily relies on mobile devices. However, the limited resources of these devices significantly restrict their ability to participate in transaction verification and ledger maintenance in blockchain networks. Existing offloading schemes often overlook storage offloading or adopt oversimplified joint strategies, failing to adequately consider the synergistic effects of storage and computation offloading on network performance. To address this issue, this paper proposes MEChain, a Mobile Edge Computing (MEC)-aided blockchain network that implements a two-layer joint computation-storage offloading mechanism involving edge service providers (ESPs) and cloud service providers (CSPs). The joint computation offloading, ledger storage, and resource pricing problem is formulated as a three-stage Stackelberg game to capture the complexity of multi-party interactions. An iterative algorithm based on backward induction is designed to efficiently solve the Nash equilibrium, thereby ensuring system stability. Theoretical analysis and numerical experiments demonstrate that the MEChain framework not only significantly improves the profit per unit time of mobile devices by 11.3% but also exhibits rapid convergence of the proposed algorithm, providing a practical and theoretical foundation for resource optimization in mobile blockchain systems.
Decentralized Autonomous Organizations (DAOs) offer a novel approach to collectively governing projects through a democratic mechanism facilitated by blockchain. DAOs allow members to put forward and vote on proposals, thereby shaping the organization’s future. However, low voter turnout is common in DAO decision-making, particularly in large and active DAOs, where the high volume of proposals makes it unrealistic to expect members to track all proposals. Abstentionism threatens the voting system's effectiveness and the legitimacy of the results. We consider that recommender systems can help boost voter engagement. This article details the design of a recommendation approach tailored to aid DAO members in identifying proposals of interest, alongside its implementation and evaluation. The design accommodates the domain constraints that render off-the-shelf recommendation approaches inadequate, namely that proposals are short-lived and can only be recommended while available for voting. To the best of our knowledge, this is the first study to examine recommendation in DAO governance. To carry out our research, we have compiled a dataset, made publicly available, covering 12 of the most active DAOs. We compare a baseline, specifically designed to accommodate DAO-specific constraints, against a range of recommendation techniques. The findings confirm that personalized recommenders can often anticipate voting preferences, significantly outperforming the baseline. In turn, given the limitations of offline evaluation, an online evaluation using A/B testing would also be needed to fully assess their impact on participation. We also discuss how their implementation must carefully incorporate fairness and transparency to ensure community trust and adoption. We believe that proposal recommender systems in DAOs not only could drive engagement improvements similar to those observed in other online collaborative projects, but can also provide insights for collective governance settings beyond blockchain, such as cooperative organizations or participatory budgeting platforms.
Despite the promise of decentralization, measurement studies have identified a conspicuous lack of decentralization in blockchains. Centralization has been observed in almost all layers of the blockchain, in decentralized applications, and in decentralized autonomous organizations. In many cases, it is practically impossible to definitively determine the extent of centralization in the system. While multiple works have proposed methods to decrease centralization, by and large blockchains continue to be significantly centralized. In this paper, we develop a general framework for building verifiably decentralized blockchain systems. Our framework is motivated by the core observation that the richness and diversity of collaborative interactions between users -- rather than resource uniformity -- captures the essence and extent of decentralization in a blockchain system. Existing blockchains do not have any incentive mechanisms to encourage inter-coalition collaboration, which directly contributes to centralization. We propose a novel reward design that incentivizes users to collaborate with other users without forming isolated coalitions. Technically, our method uses a Sybil-resistant asymmetric Shapley value for reward attribution within a collaboration group, and the theory of expander graphs for measuring and enforcing decentralization. Our framework is general and can be adapted to alleviate centralization in any layer, application, or decentralized organization. It also has important implications beyond the topic of centralization. For example, we show that our solution can naturally address the blockchain scalability problem. We also identify a new class of decentralized collaborative applications that have hitherto been unexplored in blockchains.
To map and synthesize original research on the Ethereum blockchain, emphasizing the dominant study themes, practical implications, recurring technical findings, and future research needs across security, performance, decentralized applications, markets, privacy, governance, and domain-specific implementations. The review uses 250 references and builds its evidence map from 488 original studies with 242345462 total participants/sample observations (topic-deduplicated ΣN). This review suggests that Ethereum is best understood not as a single application but as a programmable settlement layer whose real-world value is consistently conditioned by security, transaction-cost, and governance constraints rather than by immutability alone. The most recurrent result-level signal indicates that openness creates measurable adversarial surfaces, with blockchain extractable value reaching $540.54M over 32 months and censoring actors producing 46% of blocks while delaying affected transactions by an average of 85%. In parallel, machine learning and graph-based methods were repeatedly associated with strong fraud and phishing detection performance, with reported accuracies exceeding 96% in several settings. These patterns support a practical emphasis on gas-aware design, contract assurance, and continuous monitoring, often realized through hybrid on-chain/off-chain and permissioned architectures. Because most evidence comes from experimental and feasibility studies, future work should prioritize longitudinal, real-world deployment studies that measure security incidents, cost, and resilience beyond controlled testnets.
With the accelerated marketization of data factors, achieving fair contribution evaluation, privacy-preserving verification, and dynamic incentives in decentralized environments has emerged as a critical challenge. Existing studies exhibit a structural tension between privacy protection and verification transparency, while lacking adaptive mechanisms for non-independent and identically distributed (Non-IID) data scenarios. To address these issues, this paper proposes a collaborative trading framework integrating zero-knowledge proofs, personalized federated learning, and reinforcement learning. The framework employs zk-SNARKs to construct non-interactive proofs, thereby resolving the verification-privacy dilemma. A meta-learning–driven personalized aggregation scheme is introduced to correct valuation bias under Non-IID data distributions, and a deep Q-network (DQN) agent is deployed to enable dynamic incentive responses to market supply–demand fluctuations. Experiments conducted on Ethereum and Farcaster datasets demonstrate that the proposed mechanism improves the Contribution Fairness Index (CFI) by 19.7%–22.4% over the strongest baseline, achieving a Verification-Utility Ratio (VER) of 24.6. Under a collaboration scale of N = 20, market vitality entropy increases to 0.75 (baseline: 0.41), effectively suppressing monopolistic tendencies. Moreover, despite the introduction of proof mechanisms, the estimated additional on-chain verification and consensus latency per round is approximately 13 s, calibrated against empirical benchmarks. This work provides a verifiable trading mechanism for data factor markets that jointly ensures privacy, fairness, and efficiency, supporting secure data circulation in domains such as healthcare and finance.
This paper presents a blockchain-based electronic voting system designed to address the persistent challenges of transparency, security, and integrity in democratic electoral processes. Traditional voting systems in countries like Nepal suffer from vote manipulation, ballot rigging, logistical inefficiencies, and limited public trust. To overcome these limitations, this work proposes a decentralized e-voting application built on the Ethereum blockchain, leveraging smart contracts for tamper-proof vote recording and enforcement of voting rules. The system incorporates multi-factor authentication, combining facial recognition via OpenCV with Voter ID and Date of Birth verification to ensure only eligible voters participate. MetaMask wallet integration enables secure blockchain transactions, while Web3.js facilitates real-time interaction between the frontend and the deployed smart contracts on Ganache. The methodology encompasses data collection, voter authentication, smart contract deployment, and result retrieval. This paper offers a scalable and cost-effective alternative to conventional voting methods, with future scope for public Ethereum deployment and expanded biometric authentication.
Bitcoin is permissionless and does not rely on any central administrator, which gives it strong censorship resistance. At the same time, it is important to incentivize miners to behave in ways that align with the interests of the system as a whole. This paper asks whether miners are individually incentivized to propagate blocks, one of the most fundamental processes in Bitcoin. Miners collectively maintain the blockchain by generating blocks and disseminating them across the network. If miners have an incentive not to propagate some blocks, this would indicate a fundamental flaw in Bitcoin's incentive design. Although prior work has studied how propagation delays affect forks and mining rewards, it has not fully characterized miners' incentives to improve block propagation under different tie-breaking rules. To address this gap, we derive analytical reward expressions for each tie-breaking rule based on a blockchain network model that captures the effect of forks on mining fairness. These expressions explicitly characterize how block propagation delays, hashrate distribution, and tie-breaking rules jointly determine mining rewards. We then use them to analyze miners' incentives to improve block propagation. Our results show, for example, that miners have no mining-reward incentive to relay blocks generated by other miners. By contrast, under the first-seen rule, every non-majority miner is incentivized to receive other miners' blocks more quickly and to propagate its own blocks more quickly. Finally, we compare tie-breaking rules and identify a trade-off between propagation incentives and mining fairness. In particular, the first-seen rule provides the strongest incentives to reduce propagation delays, but it also worsens mining fairness the most.
Blockchain-based crowdsourcing logistics is a promising decentralized paradigm for solving the “last-mile delivery” problem, in which smart contracts automatically execute the business logic. Since crowdsourcing logistics inherently involves frequent fund transfers, its smart contracts are particularly susceptible to reentrancy vulnerabilities. Existing works address reentrancy by inserting a lock mechanism at design-time, which lacks dynamic responsiveness and incurs additional gas overhead. To overcome this limitation, we propose RE4SC, the first runtime enforcement framework for vulnerable smart contracts. RE4SC contains two components: off-Blockchain granularity segmentation and on-Blockchain granular block reordering. At the off-Blockchain level, bytecode is segmented into granular blocks through control flow analysis. This yields a finer granularity than conventional basic blocks in a control flow graph. These granular blocks are then organized into a tree structure that captures their hierarchical nesting relationships. A data flow analysis further ensures data dependency consistency after reordering. At the on-Blockchain level, a runtime enforcer retrieves the pre-computed reordering specifications from off-Blockchain analysis. It applies a depth-first reordering algorithm to reposition key state variable assignments before transfer operations, eliminating reentrancy vulnerabilities without introducing additional bytecode. We implement a prototype tool and make it open-source. Experiments on self-constructed crowdsourcing logistics contracts and three public datasets demonstrate that RE4SC repairs vulnerable contracts with zero gas overhead, outperforming existing approaches.
Crowdfunding platforms have democratized access to capital for creators and startups, yet centralized models face high fees, limited transparency, fraud risks, and inter-mediary control over funds. Blockchain addresses these issues through decentralized ledgers, smart contracts, and tokenomics, enabling trustless and transparent funding. This survey exam-ines blockchain-based crowdfunding systems, focusing on DAO implementations, auction mechanisms, social voting models, and milestone-based fund releases on Ethereum and IPFS. We review key works such as VCG auctions, social DAOs like LikeStarter, and the CrowdChain prototype with automated refunds, along with a comparative analysis of voting-, auction-, and token-staked models. Research gaps include scalability, oracle dependencies, Sybil resistance, and regulatory challenges. We propose an enhanced CrowdChain++ system with community verification, multi-milestone campaigns, governance tokens, and Layer-2 optimiza-tion, implemented using Solidity, Hardhat, React, and IPFS. Simulation results show improved automation, reduced gas costs, and stronger fraud resistance, with future directions including ZK-proofs, cross-chain interoperability, and AI-driven anomaly detection.
In this article, we focus on personal data management in service exchange networks, where members meet each other to share services based on their skills. Through the case study of the Accorderie (a Quebec solidarity cooperative), we propose an innovative protocol designed to reinforce the confidentiality of data relative to members’ addresses and service intervention locations. Using distributed ledger and peer-to-peer interaction, our proposal minimizes the Accorderie’s direct involvement while keeping its position as a trusted authority, allowing members to engage in direct interactions without reliance on a centralized platform. We present three versions of private set membership protocols specially designed to manage locations in the sharing economy. Finally, our findings suggest that decentralized solutions could be a relevant support for solidarity communities, in particular by enhancing member privacy and security, but also by facilitating and reducing maintenance costs.
Li Li, X R Wang, Hong‐Ning Dai, Fang Li · 6 authors
As a paradigm for encouraging users to contribute data spontaneously, mobile crowdsensing (MCS) has received considerable attention recently. It is crucial to evaluate the truthfulness of MCS data by proper truth discovery mechanisms. Although recent truth discovery schemes can determine truthful information, they either provide limited privacy preservation or have heavy computation and communication overheads. Moreover, most of them are not resilient to malicious faults and active attacks. To tackle the above problems, we propose two fault-tolerant and privacy-preserving truth discovery solutions. Our first scheme is mainly used for scenarios with a relatively stable number of users, where participants do not frequently join or leaves. Integrating ring signature with the perturbation technique, we design an anonymous and privacy-preserving truth discovery scheme, namely RsAnonTD, which can achieve privacy preservation and resist active attacks. To address the challenge with dynamically changed workers, we devise a multi-client inner product functional encryption scheme with a lightweight zero-knowledge proof protocol (namely McFeKDeTD) for defending against active attacks. The security analysis shows that both schemes can preserve the privacy of sensory data, weights, and estimated truths while resisting active attacks, thereby guaranteeing fault tolerance. Extensive experiments demonstrate that our designs achieve superior performance than other schemes in terms of accuracy, convergence speed, and system overheads. For example, compared with the state-of-the-art approach RPTD-II, which has a security level comparable to ours, our proposed schemes, RsAnonTD and McFeKDeTD, reduce the computational overheads approximately by 98% and 69%, respectively.