Reporting on the Non-Fungible Token (NFT) ecosystem overwhelmingly focuses on the community that drove its growth and price volatility, gaining widespread media attention in 2021. This overlooks the communities developing novel creative practices on NFT platforms. Interviews with 16 creatives utilizing NFTs reveal a vast Art World: networks of distinct communities maturing into cooperative ecosystems with unique artistic subcultures, philosophies, and interactions. We observe unique qualities of these decentralized distribution platforms and identify patterns of activity comparable to those of traditional art worlds. We identify how aspects of these systems might subvert, or replicate, existing systems of power, value, and access. The impacts of policy and platform design on online creative communities in the NFT Art World carry valuable lessons for developers of digital interventions into the creative industry, exemplifying pertinent considerations for the future of creative labor and cooperation online.
The proliferation of smart devices, sensors, autonomous robots, drones, and other similar instruments have profoundly changed the way of implementing and deploying systems in industrial and home environments, for diverse scenarios such as smart agriculture, healthcare, or manufacturing. Devices in these settings are not limited to simply observe and acquire data for monitoring, but they are also equipped with actuation capabilities, as well as the possibility of autonomously processing the incoming data through various techniques. However, given their intrinsic limitations regarding the capacity to store and process computations, it is often necessary to delegate some of these processing tasks to intermediary edge nodes in the network. These nodes, given their unique position can act as orchestrators guiding the decentralized work of the interconnected autonomous devices. Beyond static and pre-defined organization structures, in this work we propose the usage of agent and multi-agent-based models for designing and implementing swarms of edge nodes, conceived to dynamically orchestrate other devices, while meeting quality of service conditions. Allowing the control of intelligent edge nodes as conveyors and orchestrators on swarms of devices, we aim at providing intelligence to the self-organization of edge nodes, which may interchange streaming data, and represent their own capabilities through semantic models. Swarm-inspired behavioral patterns would guide the collaborative distribution of their computational tasks. Finally, we will implement and demonstrate the proposed technologies in an elderly home environment powered with a host of edge computing, sensing, and actuating devices.
Long Zhang, Gang Feng, Shuang Qin, Xiaoqian Li · 6 authors
Blockchain is envisioned as one of the promising technologies to address trust concern brought by mobile crowdsensing (MCS), due to its auditability, immutability and decentralization. Nevertheless, blockchain cannot fundamentally guarantee that the valuable sensed data outside the chain can enter the chain, although data integrity and consistency can be ensured once it is confirmed inside the chain. In addition, simply applying blockchain in MCS while ignoring possible abnormal saboteurs hidden in numerous devices may mislead the normal operation of blockchain, resulting in untrustworthy interactions. Consequently, it is highly desirable to build a trust-preserving mechanism (TPM) to fully enjoy the benefits of using blockchain in MCS. To this end, we first resort to a probabilistic trust assessment inferred from the interaction outcomes in blockchain, to incentivize participants to maintain the trustworthiness of interactions. By inferring trust to aid decision-making, trust decision is further made, including leader election and transaction data generation, to filter untrusted nodes from participating in blockchain process. Finally, extensive simulations are conducted to validate the effectiveness and efficiency of TPM, and improve the performance in terms of contribution rate, consensus accuracy and system stability.
The Metaverse, envisioned as the next-generation Internet, will be constructed via twining a practical world in a virtual form, wherein Meterverse service providers (MSPs) are required to collect massive data from Meterverse users (MUs). In this regard, a critical demand exists for MSPs to motivate MUs to contribute computing resources and data while preserving user privacy. Federated learning (FL), as a privacy-preserving collaborative machine learning paradigm, can support distributed intensive computation in the Metaverse. In this work, we first investigate minting the machine learning models into NFT with FL assistance (referred to as FL-NFT), such that MUs as stakeholders can control the ownership and share the economic value of user-generated content (UGC). Specifically, MUs are encouraged to establish a decentralized autonomous organization (i.e., MU-DAO) to aggregate local models and mint FL-NFT. MUs and MSPs optimize the strategies by formulating an imperfect information Stackelberg game to trade off the cost and benefit. We apply the backward induction to derive the equilibrium solution. Then, we construct a privacy-preserving multi-winner sealed-bid auction mechanism (PMS-AM), in which the Hidden Markov Model assists MSPs in choosing rational bidding strategies according to historical bids, and the double auction mechanism determines the winners and price of FL-NFT. Finally, the numerical results based on theoretical analysis and simulations demonstrate that the proposed PMS-AM can increase the quality of FL-NFT and achieve the economic properties of incentive mechanisms such as individual rationality and incentive compatibility.
Smart cities are data driven and collect data from a variety of sources. Certain types of data such as building data is under-represented and remains harder to find despite its value. Our goal is to incentivise the stakeholders to make building data easier to avail by turning it into an asset. We use tokenized building data assets on a blockchain to improve data accessibility. This is achieved by connecting building data owners with the consumers of building information via tokens (fungible and non-fungible), which serves the purpose of coordinating the activities of the built ecosystem. Further, we present our system architecture designed to sustain the economic incentives for interested parties and individuals.
Regardless of which community, incentivizing users is a necessity for well-sustainable operations. In the blockchain-backed Web3 communities, known for their transparency and security, airdrop serves as a widespread incentive mechanism for allocating capital and power. However, it remains a controversy on how to justify airdrop to incentive and empower the decentralized governance. In this paper, we use ParaSwap as an example to propose a role taxonomy methodology through a data-driven study to understand the characteristic of community members and the effectiveness of airdrop. We find that users receive more rewards tend to take positive actions towards the community. We summarize several arbitrage patterns and confirm the current detection is not sufficient in screening out airdrop hunters. In conjunction with the results, we discuss from the aspects of interaction, financialization, and system design to conclude the challenges and possible research directions for decentralized communities.
Founded in 2017, Algorand is one of the world's first carbon-negative, public blockchains inspired by proof of stake. Algorand uses a Byzantine agreement protocol to add new blocks to the blockchain. The protocol can tolerate malicious users as long as a supermajority of the stake is controlled by non-malicious users. The protocol achieves about 100x more throughput compared to Bitcoin and can be easily scaled to millions of nodes. Despite its impressive features, Algorand lacks a reward-distribution scheme that can effectively incentivize nodes to participate in the protocol. In this work, we study the incentive issue in Algorand through the lens of game theory. We model the Algorand protocol as a Bayesian game and propose a novel reward scheme to address the incentive issue in Algorand. We derive necessary conditions to ensure that participation in the protocol is a Bayesian Nash equilibrium under our proposed reward scheme even in the presence of a malicious adversary. We also present quantitative analysis of our proposed reward scheme by applying it to two real-world deployment scenarios. We estimate the costs of running an Algorand node and simulate the protocol to measure the overheads in terms of computation, storage, and networking.
In crowdsourcing systems, requesters publish tasks, and interested workers provide answers to get rewards. Worker anonymity motivates participation since it protects their privacy. Anonymity with unlinkability is an enhanced version of anonymity because it makes it impossible to ``link'' workers across the tasks they participate in. Another core feature of crowdsourcing systems is worker quality which expresses a worker's trustworthiness and quantifies their historical performance. Notably, worker quality depends on the participation history, revealing information about it, while unlinkability aims to disassociate the workers' identities from their past activity. In this work, we present AVeCQ, the first crowdsourcing system that reconciles these properties, achieving enhanced anonymity and verifiable worker quality updates. AVeCQ relies on a suite of cryptographic tools, such as zero-knowledge proofs, to (i) guarantee workers' privacy, (ii) prove the correctness of worker quality scores and task answers, and (iii) commensurate payments. AVeCQ is developed modularly, where the requesters and workers communicate over a platform that supports pseudonymity, information logging, and payments. In order to compare AVeCQ with the state-of-the-art, we prototype it over Ethereum. AVeCQ outperforms the state-of-the-art in three popular crowdsourcing tasks (image annotation, average review, and Gallup polls). For instance, for an Average Review task with $5$ choices and $128$ participating workers AVeCQ is 40\% faster (including overhead to compute and verify the necessary proofs and blockchain transaction processing time) with the task's requester consuming 87\% fewer gas units.
Blockchain can ensure data security and reliability during the stage of building operation and maintenance (BOM), provide reliable data for decision-making. However, existing schemes based on single-chain architecture have the problems of storage limitation and scalability, and ignore the impact of event's priority and real-time on blockchain transaction. Therefore, for BOM, this paper provides a BOM framework based on sharding blockchain (SBC-BOMF), which constructs two-layer architecture based on master-chain and multiple shards, relieves the storage pressure of blockchain nodes and improves the concurrency capability. Priority-based transaction handling strategy is designed to achieve reasonable and rapid response for multi-level transactions. Finally, an actual BOM project is taken as example to illustrate the effectiveness of proposed scheme; experiments are conducted for performance testing and evaluation. Results show that proposed scheme can effectively solve the scalability problem caused by the application of blockchain in BOM, reduce storage overhead, and realize efficient handling for blockchain transactions.
As the number of smart cars grows rapidly, vehicular crowdsensing (VCS) is gradually becoming popular. In a VCS infrastructure, sensing devices and computing units hold on smart cars as well as cloud servers form an IoT-edge-cloud continuum to perform real-time sensing tasks. In order to encourage the smart cars to participate in the real-time VCS process, blockchain technology can be combined with VCS to provide an automated incentive for VCS data trading without relying on trusted third parties. However, directly using blockchain to enforce the VCS data trading process incurs expensive service fees and participants still can conduct various misbehavior. In this article, we propose a secure blockchain-based data trading system for VCS named BTT system to address the above issues. In particular, we first integrate the blockchain-based data trading process with a lightweight privacy-preserving truth discovery algorithm to ensure the accuracy of sensing data while preserving data privacy. We then propose a gas-aware optimization mechanism to minimize the gas consumption of the data trading process. Finally, we carefully design a distributed judgment mechanism to regulate all participants to behave correctly in the data trading process. To demonstrate the practicability of our design, we implement a prototype of the BTT system deployed on an Ethereum test network and conduct extensive simulations.
Hussain Ahmad Madni, Rao Muhammad Umer, Gian Luca Foresti
Federated Learning (FL) is a machine learning technique, where collaborative and distributed learning is performed, while the private data reside locally on the client. Rather than the data, only gradients are shared among all collaborative nodes with the help of a central server. To ensure the data privacy, the gradients are prone to the deformation, or the representation is perturbed before sharing, ultimately reducing the performance of the model. Recent studies show that the original data can still be recovered using latent space (i.e., gradient leakage problem) by Generative Adversarial Network and different optimization algorithms such as Bayesian and Covariance Matrix Adaptation Evolution Strategy. To address the issues of data privacy and gradient leakage, in this paper, we train deep neural networks by exploiting the blockchain-based Swarm Learning (SL) framework. In the SL scheme, instead of sharing perturbed or noisy gradients to the central server, we share the gradients among authenticated (i.e., blockchain-based smart contract) training nodes. To demonstrate the effectiveness of the SL approach, we evaluate the proposed approach using the standard CIFAR10 and MNIST benchmark datasets and compare it with the other existing methods.
Crowdsourcing task success depends on the contributions of developers. How to identify capable developers and motivate them to actively contribute to a task is a challenging issue. This study investigates how the use of cryptocurrency rewards, i.e., the choices of stablecoins and unstablecoins affects the crowdsourcing task success, and how the relationship depends on task difficulty. Based on 3858 crowdsourcing tasks, we find that the use of unstablecoins reduces the number of participating contributors and extends the time period of having the first contributor, but has no significant effect on the likelihood of task success. In addition, task difficulty alleviates the negative effect of the unstablecoins on the number of participants. Our study potentially provides important implications for the use of cryptocurrency tokens as task rewards.
We introduce DARSAN, a decentralized review system designed for Non-Fungible Token (NFT) marketplaces, to address the challenge of verifying the quality of highly resalable products with few verified buyers by incentivizing unbiased reviews. DARSAN works by iteratively selecting a group of reviewers (called ``experts'') who are likely to both accurately predict the objective popularity and assess some subjective quality of the assets uniquely associated with NFTs. The system consists of a two-phased review process: a ``pre-listing'' phase where only experts can review the product, and a ``pre-sale'' phase where any reviewer on the system can review the product. Upon completion of the sale, DARSAN distributes incentives to the participants and selects the next generation of experts based on the performance of both experts and non-expert reviewers. We evaluate DARSAN through simulation and show that, once bootstrapped with an initial set of appropriately chosen experts, DARSAN favors honest reviewers and improves the quality of the expert pool over time without any external intervention even in the presence of potentially malicious participants.
In developing countries, funding is a significant obstacle to receiving higher education. Brilliant but needy students cannot complete their studies since their parents are unemployed and their countries’ economies are poor. As a result, the students’ talents are not harnessed to their full potential. Blockchain technology is a growing platform for making and storing secure and trustworthy transactions on distributed ledgers. In this work, we employ blockchain technology coupled with crowdsourcing to provide a loan platform to offer financial assistance to students in tertiary institutions in developing countries. This platform will allow students to apply for loans through registered financial institutions from registered investors and pay them back with interest when they start working. We perform a thorough security analysis and back the efficiency of our work with numerical results.
Open-source systems help to manage the rapid development of software, while governing open-source systems properly can effectively promote software and software engineering. However, some significant problems, such as code controls, incentives, interaction and cooperation, automation, transparency and fairness of rights and responsibilities, cannot be properly solved by traditional methodologies. The decentralization, immutability, change in trust mode and smart contract programming of blockchain provide new solutions. In order to solve the problems of traditional centralized open-source governance, this paper proposes a decentralized open-source coordination management system using a novel triple-blockchain architecture. Through the analysis of traditional and blockchain-based research, the business and technical issues that need to be addressed in decentralized open-source governance systems have been emphatically studied. Combined with triple-blockchain architecture, smart contracts, oracles and continuous integration tools, we study the decentralization of open-source businesses and make them more trustworthy, automated and coordinated. An identity authentication mechanism is designed for permission control and inter-community collaboration. A decentralized open-source reputation is proposed for incentive and reference. We also improved the DPoS (Delegated Proof of Stake) consensus under triple-blockchain architecture to reduce repeated elections. By constructing the OSCMS prototype based on the proposed architecture model, many comparative experiments were conducted under different parameters and conditions and showed good feasibility, scalability, reliability and performance. The OSCMS not only solves the shortcomings of previous research but also provides a comprehensive and feasible reference for the decentralized practice of open-source governance.
The recent explosive development of new forms of the digital currency creates unprecedented opportunities while posing significant governance challenges.These challenges call into question whether bitcoin should adopt an institutionalized framework.It also raises concerns about whether bitcoin governance following the path of internet governance will be a viable solution to the challenges.This paper investigates the concept of the bitcoin ecosystem, the governance challenges that the ecosystem faces, stakeholder roles in governance, and whether having an institutionalized framework will effectively minimize the challenges that bitcoin network users face.
The term ‘Web3’ refers to the practices of participating in digital infrastructures through the ability to read, write and coordinate digital assets. Web3 is hailed as an alternative to the failings of big tech, offering a participatory mode of digital self-organizing and shared ownership of digital infrastructure through software-encoded governance rules and participatory practices. Yet, very few analytical frameworks have been presented in academic literature by which to approach Web3. This piece draws on the theoretical lens of infrastructure studies to offer an analytical framework to approach the emergent field of Web3 as an exploration in ‘how to infrastructure’ through prefigurative self-infrastructuring. Drawing on qualitative examples from digital ethnographic methods, I demonstrate how the origins of Web3 reveal the intentions of its creators as a political tool of prefiguration, yet its practices reveal the inherent tension of expressing these ideals in coherent technical and institutional infrastructure. Thus, I argue that one of the fundamental challenges Web3 is negotiating through technical and governance experiments is ‘how to self-infrastructure?’.
Web 3.0 is the next-generation Internet that enables participants to read, write, and own contents in a decentralized manner. It is mainly driven by blockchain, semantic communication, edge computing, and artificial intelligence, which can construct value networks to achieve participatory economics based on participatory decision making. Web 3.0 can capture the characteristics of blockchain, semantic extraction, and communication to achieve decentralized semantic sharing and transfer information precisely. However, current Web 3.0 solutions focus on the blockchain while overlooking other new technologies' roles in Web 3.0. To further unleash the advantages of semantic extraction and communication in Web 3.0, in this article, we propose a blockchain-based semantic exchange framework to realize fair and efficient interactions. In this framework, we first attempt to tokenize semantic information into Non-Fungible Token (NFT) for semantic exchange. Then we utilize a Stackelberg game to maximize buying and pricing strategies for semantic trading. We also leverage Zero-Knowledge Proof to share authentic semantic information without publishing it before receiving payments, which can achieve a fair and privacy-preserving trading compared with current NFT marketplaces. A case study about urban planning is given to show clearly the proposed mechanisms. Finally, several challenges and opportunities are identified.
Javier Arroyo, David Davó, Elena MartÃnez-Vicente, Youssef Faqir-Rhazoui · 5 authors
Decentralized Autonomous Organizations (DAOs) are a new kind of organization that relies on blockchain software to govern their projects. Typically, DAO members may put forward and vote on proposals. For instance these proposals may consist on someone doing some tasks in exchange for a share of the DAO crypto-funds. In recent times, DAOs have gained a remarkable adoption, and yet they are still understudied by the academic literature. In this work, we present a visual analytics tool to study DAO activity focusing on their participation and temporal evolution. Our tool will hopefully help to stimulate research on this new kind of online community and collaborative software.
Federated learning (FL) is a promising way to allow multiple data owners (clients) to collaboratively train machine learning models without compromising data privacy. Yet, existing FL solutions usually rely on a centralized aggregator for model weight aggregation, while assuming clients are honest. Even if data privacy can still be preserved, the problem of single-point failure and data poisoning attack from malicious clients remains unresolved. To tackle this challenge, we propose to use distributed ledger technology (DLT) to achieve FLock, a secure and reliable decentralized Federated Learning system built on blockchain. To guarantee model quality, we design a novel peer-to-peer (P2P) review and reward/slash mechanism to detect and deter malicious clients, powered by on-chain smart contracts. The reward/slash mechanism, in addition, serves as incentives for participants to honestly upload and review model parameters in the FLock system. FLock thus improves the performance and the robustness of FL systems in a fully P2P manner.
Igor Y. Mazurok, Yevhen Leonchyk, Sergii Grybniak, Oleksandr S. Nashyvan · 5 authors
Decentralized public platforms are becoming increasingly popular due to a growing number of applications for various areas of business, finance, and social life. Authorless nodes can easily join such networks without any confirmation, making a transparent system of rewards and punishments crucial for the self-sustainability of public platforms. To achieve this, a system for incentivizing and punishing Workers' behavior should be tightly harmonized with the corresponding consensus protocol, taking into account all of its features, and facilitating a favorable and supportive environment with equal rights for all participants. The main purpose of rewards is to incentivize Workers to follow the protocol properly, and to penalize them for any type of misbehavior. The issues of block rewarding and punishing in decentralized networks have been well studied, but the DAG referential structure of the distributed ledger forces us to design methods that are more relevant. Since referential structures cannot be reliably validated due to the fact that they are built on the basis of the instantaneous visibility of blocks by a certain node, we propose to set rewards for blocks in the DAG network based on the degree of confidence of topological structures. In doing so, all honest nodes make common decisions based only on information recorded into the ledger, without overloading the network with additional interactions, since such data are always identical and available. The main goal of this work is to design a fair distribution of rewards among honest Workers and establish values for penalties for faulty ones, to ensure the general economic equilibrium of the Waterfall platform. The proposed approach has a flexible and transparent architecture that allows for its use for a wide range of PoS-based consensus protocols. The core principles are that Workers' rewards depend on the importance of the conducted work for block producing and achieving consensus and their penalties must not be less than the potential profit from possible attacks. The incentivizing system can facilitate protection from various kinds of attacks, namely, so-called Nothing-at-stake, Rich-get-richer, Sybil, and Splitting attacks, and from some specific threats related to a DAG structure.
The sharing of high-quality traffic information plays a crucial role in enhancing the driving experience and safety performance for vehicular networks, especially in the development of electric vehicles (EVs). The crowdsourcing-based real-time navigation of charging piles is characterized by low delay and high accuracy. However, due to the lack of an effective incentive mechanism and the resource-consuming bottleneck of sharing real-time road conditions, methods to recruit or motivate more EVs to provide high-quality information gathering has attracted considerable interest. In this paper, we first introduce a blockchain platform, where EVs act as the blockchain nodes, and a reputation-based incentive mechanism for vehicular networks. The reputations of blockchain nodes are calculated according to their historical behavior and interactions. Further, we design and implement algorithms for updating honest-behavior-based reputation as well as for screening low-reputation miners, to optimize the profits of miners and address spatial crowdsourcing tasks for sharing information on road conditions. The experimental results show that the proposed reputation-based incentive method can improve the reputation and profits of vehicle users and ensure data timeliness and reliability.
Daniel Montero Hernández, Jorge Peña Queralta, Tomi Westerlund
The increased use of Internet of Things (IoT) devices -- from basic sensors to robust embedded computers -- has boosted the demand for information processing and storing solutions closer to these devices. Edge computing has been established as a standard architecture for developing IoT solutions, since it can optimize the workload and capacity of systems that depend on cloud services by deploying necessary computing power close to where the information is being produced and consumed. However, as the network scale in size, reaching consensus becomes an increasingly challenging task. Distributed ledger technologies (DLTs), which can be described as a network of distributed databases that incorporate cryptography, can be leveraged to achieve consensus among participants. In recent years DLTs have gained traction due to the popularity of blockchains, the most-well known type of implementation. The reliability and trust that can be achieved through transparent and traceable transactions are other key concepts that bring IoT and DLT together. We present the design, development and conducted experiments of a proof-of-concept system that uses DLT smart contracts for efficiently selecting edge nodes for offloading computational tasks. In particular, we integrate network performance indicators in smart contracts with a Hyperledger Blockchain to optimize the offloading on computation under dynamic connectivity solutions. The proposed method can be applied to networks with varied topologies and different means of connectivity. Our results show the applicability of blockchain smart contracts to a variety of industrial use cases.