Kentaroh Toyoda, Jun Zhao, Allan N. Zhang, P. Takis Mathiopoulos
Federated learning (FL) is a promising decentralized deep learning technique that allows users to collaboratively update models without sharing their own data. However, due to its decentralized nature, no one can monitor workers' behavior, and they may thus deviate protocols (e.g., participating without updating any models). To solve this problem, many researchers have proposed blockchain-enabled FL to reward workers (or users) with cryptocurrencies to encourage workers to follow the protocols. However, there is a lack of theoretical discussions concerning how such rewards impact workers' behavior and how much should be given to workers. In this article, we propose a mechanism-design-oriented FL protocol on a public blockchain network. Mechanism design (MD) is often used to make a rule intended to achieve a specific goal. With MD in mind, we introduce the concept of competition into blockchain-based FL so that only workers who have contributed well can obtain rewards, which naturally prevents workers from deviating from the protocol. We then mathematically answer the following questions with contest theory, a novel field of study in economics: i) What behavior will workers take?; ii) how much effort should workers exert to maximize their profits?; iii) how many workers should be rewarded?; and iv) what is the best proportion for reward distribution?
Maha Kadadha, Rabeb Mizouni, Shakti Singh, Hadi Otrok · 5 authors
In this paper, a fully distributed auction-blockchain-based crowdsourcing framework is proposed-ABCrowd. In a typical crowdsourcing framework, independent workers compete to be allocated requesters' tasks. These workers advertise their costs to the centralized platform, which then decides the final allocation of tasks. While performing the allocation, centralized platforms face two main challenges: 1) how to ensure trusted execution for the allocation of tasks, and 2) how to motivate workers to declare their truthful costs. To address these challenges, ABCrowd proposes to run the crowdsourcing platform entirely on Ethereum Blockchain while incorporating auctions. Blockchain and smart contracts guarantee trusted execution for the allocation through autonomous and transparent on-Chain execution. ABCrowd uses the Repeated-Single-Minded Bidder (R-SMB) auction mechanism, which motivates workers to bid truthfully before allocating them and calculating their payments. R-SMB is an approximation of the optimized off-Chain Vickrey-Clarke-Groves (VCG) mechanism in terms of maximized profit. It entails repeating the Single-Minded Bidder (SMB) auction mechanism to meet the allocation requirement of crowdsourcing applications. ABCrowd is implemented and evaluated using Solidity on a private Ethereum Blockchain, where a real publicly available dataset is used. The proposed on-Chain R-SMB auction mechanism is compared to the off-Chain VCG mechanism, where the results show that R-SMB provides similar performance to VCG in terms of the average number of allocated tasks. Furthermore, R-SMB outperforms VCG in workers' travelled distance and requesters' costs, at a low execution cost.
Streaming media has been largely used by millions of users every day. The number of customers and programs, e.g., TV series, movies, and various shows, are still growing fast. However, the demand for video transcoding for various personal terminal devices results in the shortage of computing resources and the prolongation of processing delay in centralized video transcoding systems. To solve this issue, we propose a blockchain, especially, smart contract based scheme that can achieve decentralized and on-demand crowdsourcing for video transcoding, which remarkably mitigates the transcoding overhead. Specifically, our scheme consists of four key components such as employers, workers, task allocation, and payment. An employer initializes the smart contract, releases the task, and initiates the smart contract. Workers bid for the task, and the successful bidder will obtain the task and execute the task. The task allocation mechanism and the payment mechanism can guarantee the profits of both and encourage both as well. Moreover, the smart contract consists of the bidding contract and the task execution contract. The extensive analysis of our proposed scheme justified the feasibility, security for defending against typical threats, applicability in realistic situations, and portability for most multimedia such as videos and audios.
Due to the long train marshaling and complex line conditions, the operating modes in heavy haul rail systems frequently change when trains travel. Improper traction or braking operation made by drivers will increase the longitudinal impact force to trains and causes the train decoupling, severely affecting the safe operations of trains. It is quite desirable to replace the manual control with intelligent control in heavy haul rail systems. Traditional machine learning-based intelligent control methods suffer from insufficient data. Due to lacking effective incentives and trust, data from different rail lines or operators cannot be shared directly. In this paper, we propose an approach on blockchain-based federated learning to implement asynchronous collaborative machine learning between distributed agents that own data. This method performs distributed machine learning without a trusted central server. The blockchain smart contract is used to realize the management of the entire federated learning. Using the historical driving data collected from real heavy haul rail systems, the learning agent in the federated learning method adopts a support vector machine (SVM) based intelligent control model. To deal with the imbalanced traction and braking data, we optimize the classic SVM model via assigning different penalty factors to the majority and minority classes. The data set are mapped to a high dimension using kernel functions to make it linearly separable. We construct a mixing kernel function composed of polynomial and radial basis function (RBF) kernel functions, which uses a dynamic weight factor changing with train speeds to improve the model accuracy. The simulation results demonstrate the efficiency and accuracy of our proposed intelligent control method.
La technologie des registres distribués blockchain a perturbé et révolutionné le monde en introduisant un système transactionnel décentralisé traditionnel permettant l'échange de devises et de données de manière sécurisée. Le suivi de la chaîne d'approvisionnement est un domaine qui pourrait grandement bénéficier de cette avancée. Actuellement, les chaînes d'approvisionnement centralisées souffrent d'un manque de transparence et de contrôle des stocks pour vérifier la qualité des produits traités et éviter des problèmes tels que la contrefaçon. Le processus de gestion de la chaîne d'approvisionnement traditionnel insuffisant pour localiser un produit tout au long du processus de développement et de transport et conduit à la nécessité de réorganiser la manière de gérer la traçabilité des biens de consommation. Cette thèse tente d'évaluer la faisabilité de l'utilisation d'une chaîne d'approvisionnement avec un système de registres distributés IOTA pour des applications IoT. Pour valider le concept, une chaîne logistique pharmaceutique est mise en œuvre pour obtenir des données de capteurs de température, d'humidité et GNSS à partir d'un microprocesseur à faible puissance et à faible coût pour stocker et envoyer de manière fiable et sécurisée des données et les envoyer à une nouvelle structure de chaîne de blocs. Nous en faisons la démonstration avec un CC2650 SensorTag en tant que dispositif de systèmes intégrés, le X-NUCLEO-GNSS1A1 pour le module de positionnement et un Raspberry Pi 3, proxy IOTA, pour mesurer le temps, l’énergie et l’efficacité de la création de transactions et le calcul du \guillemotleft proof of work \guillemotright. Nous analysons également les résultats de l'expérience et discutons des avantages et des inconvénients de la mise en œuvre
Medication quality and safety are crucial to the health of the public. Responding to the urgent need for medication information provenance and anti-counterfeiting, this study proposes a blockchain based method for medication information storage, inquiry, and anti-counterfeiting along a medication supply chain. Leveraging the features of decentralization, tamper-proof, traceability, and participative node maintenance of blockchain technology, the proposed method can assure the transparency and openness of medication supply chains. An access control policy model based on smart contract is designed to prevent medication information from being altered or disclosed at nodes of the blockchain. In addition, a point-accumulation upgrade/downgrade mechanism is introduced to improve the consensus mechanism. The proposed solution eliminates the needs for centralized institutions and third-party organizations, and provides a full record of the medication circulation process. Our simulation results show that efficiency and security are enhanced by the improved consensus algorithm and access control mechanism. As a result, our method can render high level of security and privacy protection that is critical to the integrity of a medication information management system.
Justice Odoom, Richlove Samuel Soglo, Samuel Akwasi Danso, Huang Xiao-fang
It is undisputable fact that Coronavirus pandemic will go into the annals of history as one of the devastating plagues. From the healthcare perspective, a lot of efforts are underway geared towards testing, management and vaccination whereas industry and research communities explore innovative solutions. Quite a number of solutions have emerged zooming in on contact tracing, combating misinformation, data aggregation and analysis as well as test result certification with blockchain technology been the core. Aside the reliance on centralized architectures and use of permissioned/consortium blockchain, conspicuously missing in existing solutions based on blockchain is the work around the immutability feature of the technology given the fact that a person's test result is not static but dynamic. In this paper, we propose a solution using blockchain and smart contract that allows for state changes to be made by authorized entities. We leverage distributed storage technology using InterPlanetary File System (IPFS) for storage of user encrypted records and subsequent retrieval for verification purposes. We extend our solution by incorporating vaccination status to provide comprehensive source of information and show proof of concept. The full code of our proposed solution is made publicly available on GitHub.
It is increasingly popular to leverage the wisdom of crowd for knowledge discovery and monetization. Among others, crowdsensing with truth discovery has emerged as a promising way for leveraging the crowd wisdom, which can mine reliable knowledge from the generally unreliable sensory data contributed collected from diverse sources. Building a knowledge marketplace based on crowdsensing with truth discovery for knowledge discovery and monetization, however, is non-trivial and has to overcome several challenges. First, the sensory data should be protected as they may carry sensitive information. Second, many real crowdsensing applications usually yield sensory data in a streaming fashion, posing the demand that truth discovery should be conducted over data streams to continuously mine reliable knowledge in each data collection epoch. Third, knowledge monetization should be well treated, fully addressing the practical needs of parties in the monetization ecosystem. In this article, we take the first research attempt and propose a new full-fledged framework for building a secure knowledge marketplace over crowdsensed data streams. Our marketplace supports secure monetization of reliable knowledge mined privately from data streams in crowdsensing applications. Our framework leverages lightweight cryptographic techniques like additive secret sharing to enable privacy-preserving streaming truth discovery, continuously producing reliable knowledge over data streams. For monetization of the learned truth, i.e., knowledge, we resort to the emerging blockchain technology and deliver a tailored and full-fledged design, which promises monetization fairness, knowledge confidentiality, and streamlined processing. Extensive experiments on Amazon cloud and Ethereum blockchain demonstrate the practically affordable performance of our design.
Summary Smart cities aim to provide smart governance with the emphasis on gaining high transparency and trust in public services and enabling citizen participation in decision making processes. This means on the one hand data generated from urban transactions need to be open and trustworthy. On the other hand, security and privacy of public data needs to be handled at different administrative and geographical levels. In this paper, we investigate the pivotal role of blockchain in providing privacy, self‐verification, authentication, and authorization of participatory transactions in open governance. We also investigate up to what extent edge computing can contribute toward management of permissioned sharing at specific administrative levels and enhance privacy and provide an economic approach for resource utilization in a distributed environment. We introduce a novel architecture that is based on distributed hybrid ledger and edge computing model. The architecture provides refined and secure management of data generated and processed in different geographical and administrative units of a city. We implemented a proof of concept of the architecture and applied it on a carefully designed use case, ie, citizen participation in administrative decisions through consensus. This use case highlights the need to keep and process citizen participation data at local level by deploying district chaincodes and only share consensus results through permissioned chaincodes. The results reveal that proposed architecture is scalable and provide secure and privacy protected environment for citizen participatory applications. Our performance test results are promising and show that under control conditions, the average registration time for a citizen transaction is about 42 ms, whilst the validation and result compilation of 100 concurrent citizens' transactions took about 2.4 seconds.
Recently, crowd sensing has been intensively researched, due to the rapid growth of sensor-integrated mobile devices. Crowd sensing is a participatory sensing service where a server gathers and analyzes sensing data submitted from mobile devices of lots of users. In crowd sensing, the user's anonymity is desired, since the server gathers sensitive data from the participants including their GPS locations and moving path. However, the anonymous data submission may compromise the trust of the sensing data, because anonymous users may submit inappropriate sensing data, but they cannot be traced. Therefore, as the system to achieve both anonymity and trust in crowd sensing, ARTSense has been proposed. In the system, the trust of the sensing data is assessed on the sensed environment, other users' sensing, and the reputation of the user, and furthermore the reputation of the user is anonymously managed on the feedback from the trust assessment for the data. However, the anonymous reputation system of ARTSense has the efficiency problem, i.e., the user needs to wait a random time after the data submission phase before requesting the reputation update, which causes the communication delay. In this paper, we propose an efficient anonymous reputation system for crowd sensing, which can be integrated to the trust assessment in ARTSense. In the proposed system, during the data submission, the reputation update is anonymously completed. This is because the server does not manage the reputation of each user, but each user manages his/her reputation in the user side, where the the validity of the reputation is ensured by a certificate and anonymously checked by zero-knowledge proofs. Therefore, the proposed system achieves the better efficiency with no delay.
With the rapid development of Internet of Things (IoT), mobile crowdsensing (MCS), i.e., outsourcing sensing tasks to mobile devices or vehicles, has been proposed to address the problem of data collection in the scenarios such as smart city. Despite its benefits for a wide range of applications, MCS lacks an efficient incentive mechanism, restricting the development of IoT applications, especially for Internet of Vehicles (IoV)-a typical example of IoT applications; this is because vehicles are usually reluctant to participate these sensing tasks. Moreover, in practice, some sensing tasks may arrive suddenly (called an emergent task) in the IoV environment, but the resources of a single vehicle may be insufficient to handle, and thus multivehicles collaboration is required. In this case, the incentive mechanisms for the participation of multiple vehicles and the task scheduling for their collaborations are collectively needed. To address this important problem, we first propose a new model for the scenario of two vehicles collaboration, considering the situation of the emergent appearance of a task. In this model, for a general sensing task, we propose a bidding mechanism to better encourage vehicles to contribute their resources, and the tasks for those vehicles are scheduled accordingly. Second, for an emergent task, a novel time-window-based method is devised to manage the tasks among vehicles and to incent the vehicles to participate. Finally, we develop a blockchain framework to achieve the secured information exchange through smart contract for the proposed models in IoV.
Currently, data sharing for the Internet of Vehicles (IoV) applications has drawn much attention in the framework of developing smart cities and smart transportation. A critical challenge for data sharing is to incentivize users to participate in collecting and sharing data. The traditional incentive mechanism of crowdsourcing is not practical for IoV because of its trust issues. Although blockchain technology has been introduced to address trust issues and security challenges, ensuring trust in off-chain data for the blockchain-based approaches is still an open issue. In this article, we propose a quality-driven auction-based incentive mechanism based on a consortium blockchain that guarantees trust in both on-chain data and off-chain data. We first introduce a consortium blockchain that is used as an open and distributed hyperledger to address the security issue of on-chain data. Then, we formulate the problem as a reverse auction in which the platform acts as an auctioneer that purchases data from users. By utilizing a data quality-driven auction model, the evaluated data quality via expectation maximization is used to ensure the trust in off-chain data. The quality-driven, auction-based incentive mechanism can obtain the high-quality data and optimal social welfare with low social cost. Otherwise, we design a smart contract to perform the data sharing automatically. Finally, the extensive simulations show that our proposed algorithm achieves maximum social welfare, outperforms other solutions, and scales well when the number of users or tasks increase. Moreover, the performance of the smart contract shows its low computing cost.
Given the devastating outcomes of the recent hurricanes, earthquakes, and floods across the globe, governments all over the world started to pay serious attention to their disaster management protocols. The current reliance on centralized, infrastructure-dependent solutions is a serious threat to the whole management process. The entire process is mainly about processing help requests based on real-time information about the disaster, and efficiently responding to such requests by dispensing a set of limited resources available at the management's disposal. Relying on independent anonymous help givers can expand the resource availability and facilitate help provisioning. Further, Crowdsourcing can be a valuable source of information that can better guide help provisioning process. However, establishing dynamic trust relationship between help requesters, information providers, and help givers is the key enabler to such enhancement. Help requests should be probably authenticated not to waste the valuable resources, help provisioning should be rewarded to motivate help givers, and Data sharing should be verified not to act on false information. In this paper, we present a decentralized Blockchain based trust management framework to facilitate cooperative anonymous help provisioning at disaster time considering infrastructure-supported scenario.
Blockchain, a promising decentralized para-digm, can be exploited not only to overcome the shortcomings of the traditional crowdsourcing systems, but also to bring technical innovations, such as decentralization and accountability. Nevertheless, some critical inherent limitations of blockchain have been rarely addressed in the literature when it is incorporated into crowdsourcing, which may yield the performance bottleneck in the crowdsourcing systems. To further leverage the superiority of combining blockchain and crowdsourcing, in this article, we propose an innovative hybrid blockchain crowdsourcing platform, named zkCrowd. Our zkCrowd integrates with a hybrid blockchain structure, smart contract, dual ledgers, and dual consensus protocols to secure communications, verify transactions, and preserve privacy. Both the theoretical analysis and experiments are performed to evaluate the advantages of zkCrowd over the state of the art.
Dimitrios G. Kogias, Helen C. Leligou, Michael G. Xevgenis, Maria Polychronaki · 8 authors
Crowdsourcing has been pursued as a way to leverage the power of the crowd for many different purposes in diverse sectors from collecting information, aggregating funds, and gathering employees to perform tasks of different sizes among other targets. Data integrity and nonrepudiation are of utmost importance in these systems and are currently not guaranteed. Blockchain technology has been proven to improve on these aspects. In this article, we investigate the benefits that the adoption of Blockchain technology can bring in crowdsourcing systems. To this end, we provide examples of real-life crowdsourcing use cases and explore the benefits of using Blockchain, mainly as a database.
Naman Goel, Cyril van Schreven, Aris Filos-Ratsikas, Boi Faltings
Blockchain based systems allow various kinds of financial transactions to be executed in a decentralized manner. However, these systems often rely on a trusted third party (oracle) to get correct information about the real-world events, which trigger the financial transactions. In this paper, we identify two biggest challenges in building decentralized, trustless and transparent oracles. The first challenge is acquiring correct information about the real-world events without relying on a trusted information provider. We show how a peer-consistency incentive mechanism can be used to acquire truthful information from an untrusted and self-interested crowd, even when the crowd has outside incentives to provide wrong informations. The second is a system design and implementation challenge. For the first time, we show how to implement a trustless and transparent oracle in Ethereum. We discuss various non-trivial issues that arise in implementing peer-consistency mechanisms in Ethereum, suggest several optimizations to reduce gas cost and provide empirical analysis.
Dinh C. Nguyen, Pubudu N. Pathirana, Ming Ding, Aruna Seneviratne
\n\t\t\t\t\tBlockchain technology with its secure, transparent and decentralized nature has been recently employed in many mobile applications. However, the process of executing extensive tasks such as computation-intensive data applications and blockchain mining requires high computational and storage capability of mobile devices, which would hinder blockchain applications in mobile systems. To meet this challenge, we propose a mobile edge computing (MEC) based blockchain network where multi-mobile users (MUs) act as miners to offload their data processing tasks and mining tasks to a nearby MEC server via wireless channels. Specially, we formulate task offloading, user privacy preservation and mining profit as a joint optimization problem which is modelled as a Markov decision process, where our objective is to minimize the long-term system offloading utility and maximize the privacy levels for all blockchain users. We first propose a reinforcement learning (RL)-based offloading scheme which enables MUs to make optimal offloading decisions based on blockchain transaction states, wireless channel qualities between MUs and MEC server and user’s power hash states. To further improve the offloading performances for larger-scale blockchain scenarios, we then develop a deep RL algorithm by using deep Q-network which can efficiently solve large state space without any prior knowledge of the system dynamics. Experiment and simulation results show that the proposed RL-based offloading schemes significantly enhance user privacy, and reduce the energy consumption as well as computation latency with minimum offloading costs in comparison with the benchmark offloading schemes.\n\t\t\t\t
The blockchain-empowered mobile-edge computing (MEC) is a promising solution for enhancing the computation capabilities of mobile equipments (MEs) to process computation-intensive tasks such as the real-time data processing tasks and mining tasks. However, because of the “cold start” and “long return” problems, efficient computation offloading cannot be achieved in blockchain-empowered MEC because the MEs do not always have enough coins to afford the offloading service cost. In this article, we study the joint computation-offloading and coin-loaning problem for blockchain-empowered MEC to minimize the total cost of all MEs. We introduce the banks that can provide loan services to the MEs to address the above two issues. We formulate the problem as a noncooperative game to model the competitions between the myopic MEs. By using a potential game method, we prove the existence of a pure-strategy Nash equilibrium (NE) and design a distributed algorithm to achieve the NE point with low computational complexity. We also provide an upper bound on the price of anarchy of the game by theoretical proof. Besides, two smart contracts are designed to automatically perform the computing resource trading and coin loaning processes. Lastly, our simulation results show that our proposed algorithm can significantly reduce the total cost of all MEs, has better performance compared with other solutions, and scales well as the number of MEs increases. Moreover, the financial cost for executing the two smart contracts on the Ethereum network is low.
Blockchain is a decentralized digital ledger to capture and record economic transactions of mobile applications. In blockchain operation process, mining tasks play a key role and pose intensive computation demands on resource constrained mobile devices. Mobile edge computing (MEC) is a promising solution to alleviate the heavy burden on the devices through task offloading. Since only the miner device that first obtains a block can get rewards, there are risks of the devices for consuming edge computing resources without any profit. However, traditional task offloading schemes that purely rely on expected utilities of the devices always ignore the difference of various devices in balancing risks and rewards, thus make the offloading inefficient. To address this problem, we adopt a prospect theoretic approach to design an optimal offloading scheme for MEC-empowered blockchain, where diverse preferences of devices for profits and risks are explicitly taken into account. Specifically, we formulate the offloading process as a Stackelberg game that incorporates notions from prospect theory. We design an efficient algorithm to obtain the optimal offloading strategies, which maximize the utilities of both the miner devices and the MEC service providers. Numerical results are presented to illustrate the performance of the proposed offloading schemes.
Blockchain technique has been widely applied in various fields, such as finance, Internet of Things, law, etc. However, it is a challenge to apply blockchain technique for mobile applications, as the mobile devices cannot afford computing resources required by the mining processes. This paper proposes a mechanism based on a combinatorial double auction to offload the mining process of miners to the edge servers. The mechanism is formulated as a resource allocation problem. The corresponding allocation algorithms and payment scheme are proposed to allocate resources and calculate trade prices, respectively. Moreover, this paper proves that the proposed mechanism is efficient in terms of computation, and it satisfies three properties of economic auction which are budget balance, individual rationality and truthfulness. Experimental results show that the proposed mechanism is able to yield higher total utility, together with good scalability. For the case of 900 miners in the auction, the total utility of the proposed mechanism is higher than two existing works by 39.2% and 569.9% on average, respectively.