The Intelligent System of Emergent Knowledge (ISEK) establishes a decentralized network where human and artificial intelligence agents collaborate as peers, forming a self-organizing cognitive ecosystem. Built on Web3 infrastructure, ISEK combines three fundamental principles: (1) a decentralized multi-agent architecture resistant to censorship, (2) symbiotic AI-human collaboration with equal participation rights, and (3) resilient self-adaptation through distributed consensus mechanisms. The system implements an innovative coordination protocol featuring a six-phase workflow (Publish, Discover, Recruit, Execute, Settle, Feedback) for dynamic task allocation, supported by robust fault tolerance and a multidimensional reputation system. Economic incentives are governed by the native $ISEK token, facilitating micropayments, governance participation, and reputation tracking, while agent sovereignty is maintained through NFT-based identity management. This synthesis of blockchain technology, artificial intelligence, and incentive engineering creates an infrastructure that actively facilitates emergent intelligence. ISEK represents a paradigm shift from conventional platforms, enabling the organic development of large-scale, decentralized cognitive systems where autonomous agents collectively evolve beyond centralized constraints.
Blockchain's economic value lies in enabling financial and economic transactions without relying on trusted, centralized intermediaries. In practice, however, transactions pass through a fragmented chain of intermediaries before being included on-chain. Because standard blockchain data reveal only the winning block, this process is largely unobservable. We address this limitation by constructing a novel dataset of 15,097 non-winning Ethereum blocks, that is, blocks proposed but not selected for inclusion. We show that 21% of user transactions are delayed: they appear in candidate blocks but not in the winning block, implying that fragmented routing materially affects inclusion time. We further show that execution quality varies substantially across candidate blocks: for the same swap, both execution probability and execution price differ across proposed blocks. To study these differences, we examine competition between two arbitrage bots trading between decentralized and centralized exchanges. We find that, conditional on inclusion in a block that also contains transactions from these bots, user swaps in the same (opposite) direction are less likely (more likely) to execute and receive worse (better) prices. These results show that routing and block composition are central determinants of execution quality and market quality in on-chain markets.
Zhaowei Hu, Jin Rui-fang, HangYi Quan, Shiyun Ni · 5 authors
To address privacy leakage risks arising from low collaborative user engagement, third-party trust deficits, and insufficient collaboration timeliness in location-based services (LBS), this paper proposes a dual-protection framework integrating blockchain technology and threshold cryptography for safeguarding location privacy. The framework employs asymmetric encryption with Shamir's (t, n) secret sharing to encrypt user queries, distributing decryption key fragments to collaborative users while generating n anonymous service requests through location generalization strategies. A temporary private blockchain constructed using smart contracts ensures confidential data transmission, supported by a dynamic privacy parameter configuration system based on Byzantine fault tolerance. The framework implements a priority-response consensus mechanism through Token-based equity proof-of-stake, prioritizing service for users with higher Token values. To mitigate privacy breaches caused by unresponsive collaborators, a competitive incentive mechanism ensures timely information submission. Through ciphertext fragment verification algorithms and Lagrange interpolation-based key reconstruction, the framework enables secure query decryption and service matching in untrusted third-party environments, guaranteeing information security, integrity, and non-repudiation. Experimental validation using real-world datasets confirms the framework's feasibility and operational effectiveness.
Mai Shawkat, Ali El-desoky, Zainab H. Ali, Mofreh Salem
Abstract The Industrial Internet of Things (IIoT) applications have been recognized as an advancement of the conventional wireless network that concentrates on incorporating processes and machines specifically for industrial applications. These Industrial applications frequently use centralized machine learning (ML) approaches not only to enhance their functionality but also to evaluate sensor data for a variety of purposes, including digitizing operations in manufacturers, forecasting maintenance requirements in industrial equipment, and detecting anomalies for security monitoring, they may adversely affect overall system performance due to high cost of computing power and privacy concerns, as so much data is stored on a cloud server. Federated Learning (FL) has emerged as a new benchmark for centralized ML methods. It sends models to user devices without transferring private data to third-party or central servers; it is one of the promising solutions to data leakage issues. This work introduces a comprehensive overview of the advancements, challenges, and future directions in FL adoption with edge devices. It covers security threats and mitigation strategies, emphasizing its categories, privacy and concerns, communication overhead obstacles, heterogeneity issues, aggregation techniques, and associated development tools. This review paper delves into FL-related topics, including system platforms, offering a comprehensive overview of best practice systems in real-world FL applications. To ensure security in IIoT applications, reviewing threats and mitigation strategies by integrating FL with state-of-the-art technologies such as blockchain, federated reinforcement learning, and federated meta-learning has been explored. Finally, the recent research is taking place to determine new future directions and opportunities for FL security defense mechanisms has been considered at the end of this review paper.
Purpose The construction industry, contributing approximately 39% of global carbon emissions, faces challenges to reach net-zero emissions by 2050. Traditional methods for estimating and managing carbon emissions suffer from inaccuracies, low transparency and data integrity issues, highlighting the need for trustworthy and efficient solutions. This paper aims to demonstrate how blockchains can enhance the accuracy of tracking carbon emissions and streamlining carbon trading, providing a robust system to manage and reduce carbon emissions effectively. Design/methodology/approach A case study-based approach is adopted to develop a blockchain-based system (EcoConstruct) to track carbon emissions and circularity of construction materials and facilitate carbon trading in the industry. The implementation uses smart contract technology and the Beneficial Assets Ownership protocol in the Tezos blockchain to validate carbon emission tracking, carbon trading and circularity criteria. The system was evaluated and validated through expert feedback, ensuring its practical applicability and effectiveness. Findings EcoConstruct demonstrates advancements in transparency, data integrity and efficiency in carbon estimation and trading. The system’s immutable ledger securely stores carbon emissions and their compensations using non-fungible tokens called carbon rewards. This system facilitates transparent and accountable carbon trading among stakeholders (clients, contractors and material suppliers). The findings highlight the potential of blockchains to overcome current challenges in carbon emissions management and trading in the construction industry. Originality/value EcoConstruct provides a novel blockchain-based solution for managing carbon emissions and promoting sustainability in construction, moving beyond conceptualisation by leveraging blockchain’s decentralisation, immutability, transparency and security to enhance carbon estimation accuracy and streamline carbon trading.
Seung Eel Oh, Jong‐Hoon Kim, Ji-Young Kim, Jae Hwan Ahn
The complexity of contemporary supply chains and the rise in foodborne illness cases have made ensuring food safety and traceability a top responsibility on a worldwide scale. Traditional traceability systems are prone to data tampering, fragmentation, and limited compatibility. Public blockchains have scalability, latency, and privacy problems that limit their use in real-time food safety systems, despite the fact that blockchain provides a secure data structure. Using Hyperledger Fabric, GS1 EPCIS standards, and Internet of Things-enabled environmental sensors, this paper suggests a private blockchain-based food safety monitoring system. To guarantee fault-tolerant, high-throughput processing in a permissioned blockchain setting, a Raft consensus mechanism was used. Hyperledger Caliper was used to benchmark the system once it was deployed with four nodes. According to experimental data, transaction throughput peaked at 230.2 TPS and averaged 207.4 ± 10.2 TPS. As the network grew from two to four nodes, latency increased somewhat from 259.3 ± 9.5 ms to 278.7 ± 9.1 ms, while block finalization time stayed below 3.184 ± 0.113 s. Over 114,925 documented transactions, data integrity was confirmed to be flawless. These results demonstrate that private blockchain technology can provide effective, scalable, and impenetrable food traceability, boosting openness and confidence throughout food networks.
This study explores the application of Quadratic Voting (QV) and its generalization to improve decentralization and effectiveness in blockchain governance systems. The conducted research identified three main types of quadratic (square root) voting. Two of them pertain to voting with a split stake, and one involves voting without splitting. In split stakes, Type 1 QV applies the square root to the total stake before distributing it among preferences, while Type 2 QV distributes the stake first and then applies the square root. In unsplit stakes (Type 3 QV), the square root of the total stake is allocated entirely to each preference. The presented formal proofs confirm that Types 2 and 3 QV, along with generalized models, enhance decentralization as measured by the Gini and Nakamoto coefficients. A pivotal discovery is the existence of a threshold stakeholder whose relative voting ratio increases under QV compared to linear voting, while smaller stakeholders also gain influence. The generalized QV model allows flexible adjustment of this threshold, enabling tailored decentralization levels. Maintaining fairness, QV ensures that stakeholders with higher stakes retain a proportionally greater voting ratio while redistributing influence to prevent excessive concentration. It is shown that to preserve fairness and robustness, QV must be implemented alongside privacy-preserving cryptographic voting protocols, as voters casting their ballots last could otherwise manipulate outcomes. The generalized QV model, proposed in this paper, enables algorithmic parametrization to achieve desired levels of decentralization for specific use cases. This flexibility makes it applicable across diverse domains, including user interaction with cryptocurrency platforms, facilitating community events and educational initiatives, and supporting charitable activities through decentralized decision-making.
Decentralised applications (dApps) that run on public blockchains have the benefit of trustworthiness and transparency as every activity that happens on the blockchain can be publicly traced through the transaction data. However, this introduces a potential privacy problem as this data can be tracked and analysed, which can reveal user-behaviour information. A user behaviour analysis pipeline was proposed to present how this type of information can be extracted and analysed to identify separate behavioural clusters that can describe how users behave in the game. The pipeline starts with the collection of transaction data, involving smart contracts, that is collected from a blockchain-based game called Planet IX. Both the raw transaction information and the transaction events are considered in the data collection. From this data, separate game actions can be formed and those are leveraged to present how and when the users conducted their in-game activities in the form of user flows. An extended version of these user flows also presents how the Non-Fungible Tokens (NFTs) are being leveraged in the user actions. The latter is given as input for a Graph Neural Network (GNN) model to provide graph embeddings for these flows which then can be leveraged by clustering algorithms to cluster user behaviours into separate behavioural clusters. We benchmark and compare well-known clustering algorithms as a part of the proposed method. The user behaviour clusters were analysed and visualised in a graph format. It was found that behavioural information can be extracted regarding the users that belong to these clusters. Such information can be exploited by malicious users to their advantage. To demonstrate this, a privacy threat model was also presented based on the results that correspond to multiple potentially affected areas.
The proliferation of numerous portable mobile devices has made mobile crowd-sensing (MCS) systems a promising new trend. Traditional MCS systems typically outsource sensing tasks to the data aggregator (e.g., cloud server). They collect and analyze the provided sensing data through an appropriate truth discovery (TD) method to identify valuable data sets. However, existing privacy-preserving MCS systems lack transparency, enabling data aggregators to deviate from the specified protocols and allowing malicious users to provide false or invalid sensing data, thereby contaminating the resulting data sets. The lack of transparency and public verifiability in MCS systems undermines widespread adoption by preventing data requesters from confidently verifying data integrity and accuracy. To address this issue, we propose a transparent and privacy-preserving mobile crowd-sensing system with truth discovery (TP-MCS) constructed using zero-knowledge proof (ZKP) and the Merkle commitment tree. This scheme enables data requesters to effectively verify the correctness of the truth discovery service while ensuring data privacy. Furthermore, theoretical analysis and extensive experiments demonstrate that this scheme is secure and efficient.
While the trend of decentralized governance is obvious (cryptocurrencies and blockchains are widely adopted by multiple sovereign countries), initiating governance proposals within Decentralized Autonomous Organizations (DAOs) is still challenging, i.e., it requires providing a low-level transaction payload, therefore posing significant barriers to broad community participation. To address these challenges, we propose a multi-agent system powered by Large Language Models with a novel Label-Centric Retrieval algorithm to automate the translation from natural language inputs into executable proposal transactions. The system incorporates DAOLang, a Domain-Specific Language to simplify the specification of various governance proposals. The key optimization achieved by DAOLang is a semantic-aware abstraction of user input that reliably secures proposal generation with a low level of token demand. A preliminary evaluation on real-world applications reflects the potential of DAOLang in terms of generating complicated types of proposals with existing foundation models, e.g. GPT-4o.
Many blockchain-based crowdsourcing frameworks currently struggle to address the high costs associated with on-chain storage and computation effectively, and they lack a quality-driven incentive mechanism tailored to bounding box annotation scenarios. To address these challenges, this paper proposes CrowdBA: A low-cost, quality-driven crowdsourcing architecture. The CrowdBA utilizes the Ethereum public blockchain as the foundational architecture and develops corresponding smart contracts. First, by integrating Ethereum with the InterPlanetary File System (IPFS), storage and computation processes are shifted off-chain, effectively addressing the high costs associated with data storage and computation on public blockchains. Additionally, the CrowdBA introduces a Dynamic Intersection over the union-weighted bounding box fusion (DWBF) algorithm, which assigns dynamic weights based on IoU to infer true bounding boxes, thereby assessing each worker’s annotation quality. Annotation quality then serves as a key criterion for incentive distribution, ensuring fair and appropriate compensation for all contributors. Experimental results demonstrate that the operational costs of each smart contract function remain within reasonable limits; the off-chain storage and computation approach significantly reduces storage and computation expenses, and the DWBF algorithm shows marked improvements in accuracy and robustness over other bounding box fusion methods.
Open access
Blockchain Technology Applications and Security
Mobile Crowdsensing and Crowdsourcing
Advanced Steganography and Watermarking Techniques
John Stephan, Matej Pavlovic, Antonio Locascio, Benjamin Livshits
Zero-Knowledge (ZK) rollups have become a popular solution for scaling blockchain systems, offering improved transaction throughput and reduced costs by aggregating Layer 2 transactions and submitting them as a single batch to a Layer 1 blockchain. However, the computational burden of generating validity proofs, a key feature of ZK rollups, presents significant challenges in terms of performance and decentralization. Current solutions rely on centralized infrastructure to handle the computational tasks, limiting the scalability and decentralization of rollup systems. This paper proposes CrowdProve, a prover orchestration layer for outsourcing computation to unreliable commodity hardware run by a broad community of small provers. We apply CrowdProve to proving transaction batches for a popular ZK rollup. Through our experimental evaluation, we demonstrate that community proving can achieve performance comparable to, and in some cases better than, existing centralized deployments. Our results show that even systems utilizing modest hardware configurations can match the performance of centralized solutions, making community-based proof generation a viable and cost-effective alternative. CrowdProve allows both the rollup operator and community participants to benefit: the operator reduces infrastructure costs by leveraging idle community hardware, while community provers are compensated for their contributions.
A user-friendly program called “Mini Wallet for Sending Ethers” was created to make utilizing private keys to send ethers (ETH) between wallets easier. This application, which prioritizes user-friendliness, enables users to connect to their Ethereum wallets—including well-known choices like MetaMask— via the Infura API. It enables customers to effectively manage their Ethereum transactions by reducing crypto currency transactions to a few clicks, doing away with a requirement to browse complicated wallet interfaces. Notably, the program guarantees the security and integrity of transactions by adhering to accepted block chain transaction protocols. The pertinent information is safely entered into a database following every transaction, creating a complete record of all transactions. Furthermore, the sender has easy access to details about the transaction, which improves Ethereum transactions’ accountability and transparency. For Ethereum aficionados looking for a hassle-free method to handle their crypto currency transactions, this cutting-edge technology offers an effective and user-friendly option.
The public ledger characteristic of blockchain grants data immutability but simultaneously introduces privacy leakage risks, making association analysis between on-chain behaviors and real-world identities possible. Existing privacy protection schemes struggle to balance the anonymity of the querier with the traceability of malicious behaviors. On one hand, legitimate inquiry behaviors are easily reverse-tracked by third parties through on-chain records (i.e., "human flesh search" targeting the querier); on the other hand, a completely anonymous environment may lead to data abuse without the possibility of accountability.To address this issue, this paper proposes an anti-"human flesh search" privacy protection system based on blockchain and zero-knowledge proofs. Addressing the aforementioned contradictions, this paper presents a blockchain data sharing scheme that balances privacy and regulation. The scheme utilizes IPFS to implement graded encrypted storage for large files. The core innovation lies in combining the Schnorr protocol and Chameleon Hash to construct a Blockchain Designated Verifier Proof (BDVP). While verifying user query permissions through blockchain smart contracts, the system utilizes the trapdoor property of the Chameleon Hash to achieve the non-transferability of proofs, preventing third parties from reverse-tracking the querier's identity by analyzing on-chain records<sup>[<xref ref-type="bibr" rid="R2">2</xref>]</sup>. Furthermore, the system introduces a threshold private key held by regulatory agencies to ensure that, in the event of data abuse, malicious users can be de-anonymized and held accountable according to the law.
Consensus protocols used today in blockchains often rely on computational power or financial stakes - scarce resources. We propose a novel protocol using social capital - trust and influence from social interactions - as a non-transferable staking mechanism to ensure fairness and decentralization. The methodology integrates zero-knowledge proofs, verifiable credentials, a Whisk-like leader election, and an incentive scheme to prevent Sybil attacks and encourage engagement. The theoretical framework would enhance privacy and equity, though unresolved issues like off-chain bribery require further research. This work offers a new model aligned with modern social media behavior and lifestyle, with applications in finance, providing a practical insight for decentralized system development.
Civic intelligence (CI) represents the collective capacity of communities to address challenges, yet its integration with smart city infrastructure remains limited. This study bridges CI theory with technical implementation through a novel framework combining blockchain and AI technologies. Our approach maps core CI components (knowledge capital, system capital, and relational capital) to specific technical solutions: a civic engagement index for measuring participation quality, a tokenization framework for incentivizing meaningful engagement, and a governance optimization function for resource allocation. Using mixed-methods research, we developed and validated the conceptual CI governance (CIG) framework, which satisfies CI principles through smart contracts and AI-assisted interfaces. The empirical evaluation demonstrates both social and technical improvements: 40% increased civic participation rates, 85% governance efficiency maintenance, and significant gains in engagement quality metrics (knowledge sharing +32%, collective decision making +28%). While technical implementation shows promise, success requires the careful integration of social dynamics, digital literacy initiatives, and regulatory compliance. This research contributes to smart city development by providing a theoretically grounded, feasible framework that introduces the fusion of blockchain and AI technologies to enhance civic participation while preserving governance effectiveness.
Roozbeh Sarenche, Alireza Aghabagherloo, Svetla Nikova⋆, Bart Preneel
The security of Bitcoin protocols is deeply dependent on the incentives provided to miners, which come from a combination of block rewards and transaction fees. As Bitcoin experiences more halving events, the protocol reward converges to zero, making transaction fees the primary source of miner rewards. This shift in Bitcoin's incentivization mechanism, which introduces volatility into block rewards, leads to the emergence of new security threats or intensifies existing ones. Previous security analyses of Bitcoin have either considered a fixed block reward model or a highly simplified volatile model, overlooking the complexities of Bitcoin's mempool behavior. This paper presents a reinforcement learning-based tool to develop mining strategies under a more realistic volatile model. We employ the Asynchronous Advantage Actor-Critic (A3C) algorithm, which efficiently handles dynamic environments, such as the Bitcoin mempool, to derive near-optimal mining strategies when interacting with an environment that models the complexity of the Bitcoin mempool. This tool enables the analysis of adversarial mining strategies, such as selfish mining and undercutting, both before and after difficulty adjustments, providing insights into the effects of mining attacks in both the short and long term. We revisit the Bitcoin security threshold presented in the WeRLman paper and demonstrate that the implicit predictability of valuable transaction arrivals in this model leads to an underestimation of the reported threshold. Additionally, we show that, while adversarial strategies like selfish mining under the fixed reward model incur an initial loss period of at least two weeks, the transition toward a transaction-fee era incentivizes mining pools to abandon honest mining for immediate profits. This incentive is expected to become more significant as the protocol reward approaches zero in the future.
Rafael Marin Machado de Souza, A. I. S. Holm, Márcio Biczyk, Leandro Nunes de Castro
Federated learning (FL) and bioinspired computing (BIC), two distinct, yet complementary fields, have gained significant attention in the machine learning community due to their unique characteristics. FL enables decentralized machine learning by allowing models to be trained on data residing across multiple devices or servers without exchanging raw data, thus enhancing privacy and reducing communication overhead. Conversely, BIC draws inspiration from nature to develop robust and adaptive computational solutions for complex problems. This paper explores the state of the art in the integration of FL and BIC, introducing BIC techniques and discussing the motivations for their integration with FL. The convergence of these fields can lead to improved model accuracy, enhanced privacy, energy efficiency, and reduced communication overhead. This synergy addresses inherent challenges in FL, such as data heterogeneity and limited computational resources, and opens up new avenues for developing more efficient and autonomous learning systems. The integration of FL and BIC holds promise for various application domains, including healthcare, finance, and smart cities, where privacy-preserving and efficient computation is paramount. This survey provides a systematic review of the current research landscape, identifies key challenges and opportunities, and suggests future directions for the successful integration of FL and BIC.
A. Bendada, Mouhamed Amine Bouchiha, Mourad Rabah, Yacine Ghamri-Doudane
Current blockchain-based reputation solutions for crowdsourcing fail to tackle the challenge of ensuring both efficiency and privacy without compromising the scalability of the block chain. Developing an effective, transparent, and privacy-preserving reputation model necessitates on-chain implementation using smart contracts. However, managing task evaluation and reputation updates alongside crowdsourcing transactions on-chain substantially strains system scalability and performance. This paper introduces RollupTheCrowd, a novel blockchain-powered crowdsourcing framework that leverages zkRollups to enhance system scalability while protecting user privacy. Our framework includes an effective and privacy-preserving reputation model that gauges workers' trustworthiness by assessing their crowdsourcing interactions. To alleviate the load on our blockchain, we employ an off-chain storage scheme, optimizing RollupTheCrowd's performance. Utilizing smart contracts and zero-knowledge proofs, our Rollup layer achieves a significant 20x reduction in gas consumption. To prove the feasibility of the proposed framework, we developed a proof-of-concept implementation using cutting-edge tools. The experimental results presented in this paper demonstrate the effectiveness and scalability of RollupTheCrowd, validating its potential for real-world application scenarios.
In the domain of spatial crowdsourcing drone services, which includes tasks like delivery, surveillance, and data collection, secure communication is paramount. The Public Key Infrastructure (PKI) ensures this by providing a system for digital certificates that authenticate the identities of entities involved, securing data and command transmissions between drones and their operators. However, the centralized trust model of traditional PKI, dependent on Certificate Authorities (CAs), presents a vulnerability due to its single point of failure, risking security breaches. To counteract this, the paper presents D2XChain, a blockchain-based PKI framework designed for the Internet of Drone Things (IoDT). By decentralizing the CA infrastructure, D2XChain eliminates this single point of failure, thereby enhancing the security and reliability of drone communications. Fully compatible with the X.509 standard, it integrates seamlessly with existing PKI systems, supporting all key operations such as certificate registration, validation, verification, and revocation in a distributed manner. This innovative approach not only strengthens the defense of drone services against various security threats but also showcases its practical application through deployment on a private Ethereum testbed, representing a significant advancement in addressing the unique security challenges of drone-based services and ensuring their trustworthy operation in critical tasks.
Ahmed Alagha, Hadi Otrok, Shakti Singh, Rabeb Mizouni · 5 authors
Deep Reinforcement Learning (DRL) has emerged as a powerful paradigm for solving complex problems. However, its full potential remains inaccessible to a broader audience due to its complexity, which requires expertise in training and designing DRL solutions, high computational capabilities, and sometimes access to pre-trained models. This necessitates the need for hassle-free services that increase the availability of DRL solutions to a variety of users. To enhance the accessibility to DRL services, this paper proposes a novel blockchain-based crowdsourced DRL as a Service (DRLaaS) framework. The framework provides DRL-related services to users, covering two types of tasks: DRL training and model sharing. Through crowdsourcing, users could benefit from the expertise and computational capabilities of workers to train DRL solutions. Model sharing could help users gain access to pre-trained models, shared by workers in return for incentives, which can help train new DRL solutions using methods in knowledge transfer. The DRLaaS framework is built on top of a Consortium Blockchain to enable traceable and autonomous execution. Smart Contracts are designed to manage worker and model allocation, which are stored using the InterPlanetary File System (IPFS) to ensure tamper-proof data distribution. The framework is tested on several DRL applications, proving its efficacy.
Georgios Birmpas, Philip Lazos, Evangelos Markakis, Paolo Penna
In this paper, we investigate the impact of reward schemes and committee sizes motivated by governance systems over blockchain communities. We introduce a model for elections with a binary outcome space where there is a ground truth (i.e., a "correct" outcome), and where stakeholders can only choose to delegate their voting power to a set of delegation representatives (DReps). Moreover, the effort (cost) invested by each DRep positively influences both (i) her ability to vote correctly and (ii) the total delegation that she attracts, thereby increasing her voting power. This model constitutes the natural counterpart of delegated proof-of-stake (PoS) protocols, where delegated stakes are used to elect the block builders. As a way to motivate the representatives to exert effort, a reward scheme can be used based on the delegation attracted by each DRep. We analyze both the game-theoretic aspects and the optimization counterpart of this model. Our primary focus is on selecting a committee that maximizes the probability of reaching the correct outcome, given a fixed monetary budget allocated for rewarding the delegates. Our findings provide insights into the design of effective reward mechanisms and optimal committee structures (i.e., how many DReps are enough) in these PoS-like governance systems.