The smart factory is a representative element reshaping conventional computer-aided industry to data-driven smart industry, while it is nontrivial to achieve cost effectiveness, reliability, mobility, and scalability of smart industrial systems. Data-driven industrial systems mainly rely on sensory data collected from statically deployed sensors. However, the spatial coverage of industrial sensor networks is constrained due to the high deployment and maintenance cost. Recently, mobile crowd sensing (MCS) has become a new sensing paradigm owing to its merits, such as cost effectiveness, mobility, and scalability. Nevertheless, traditional MCS systems are vulnerable to malicious attacks and single point of failure due to the centralized architecture. To this end, in this article we integrate MCS with industrial systems without introducing any additional dedicated devices. To overcome the drawbacks of traditional MCS systems, we propose a blockchain-based MCS system (BMCS). In particular, we exploit miners to verify the sensory data and design a dynamic reward ranking incentive mechanism to mitigate the imbalance of multiple sensing tasks. Meanwhile, we also develop a sensory data quality detection scheme to identify and mitigate the data anomaly. We implement a prototype of the BMCS on top of Ethereum and conduct extensive experiments on a realistic factory workroom. Both experimental results and security analysis demonstrate that the BMCS can secure industrial systems and improve the system reliability.
Crowd intelligence has become increasingly popular during recent years, thanks to the exploding global Internet usage. However, most crowd intelligence platforms ceased to operate due to the untrustworthy data or lack of motivated participants. The blockchain technologies propose decentralization as the potential solution to this dilemma. This work introduces reciprocal crowdsourcing, a novel decentralized cooperative crowdsourcing model powered by the blockchain to strengthen trust among crowd intelligence participants, who perform transparent collaborative work in the system thereafter. To validate our proposal, we implemented “Cell Evolution”, a blockchain game, in which the players can build cooperative game worlds on the blockchain with reciprocal crowdsourcing.
Makoto Yano, Chris Dai, Kenichi Masuda, Yoshio Kishimoto
The Japanese Ministry of Economy, Trade, and Industry regards the process of incorporating new information technology, such as artificial intelligence (AI), Internet of Things (IoT), and big data analysis into society as the Fourth Industrial Revolution. This view is reflected in the Fifth Science and Technology Basic Plan. The plan advocates Society 5.0, in which cyber space and physical space are integrated to support an affluent and human-friendly society.
In recent years, cryptocurrencies implemented on top of Blockchains became very popular, with Bitcoin as the most prominent example. However, novel Blockchain-based platforms such as Ethereum also support distributed applications beyond cryptocurrencies through so-called smart contracts. Technically, smart contracts are programs, whose code and execution state is stored in the Blockchain, inherently featuring the ability to transfer (electronic) money during their execution. In this Bachelor thesis, we investigate how smart contracts can be used to implement a distributed crowdsensing application for tracking mobile objects by a crowd of privately owned mobile devices. Such a system could be used, for instance, to nd lost or stolen objects, such as keys, vehicles (cars, bicycles, . . . ), or pets tagged with short-range radio transmitters implemented using readily available Bluetooth or RFID technology. These objects can then be detected by smartphones of private users in the vicinity of the object, effectively implementing a huge sensor network covering many parts of the world without any upfront investments by a central entity. Although highly attractive, implementing a crowdsensing application on top of a Blockchain platform such as Ethereum comes with several challenges. First of all, users need incentives to participate in searching for mobile objects. A natural incentive is a monetary reward that participants automatically receive through the smart contract when reporting sightings (timestamped positions) of wanted objects. However, this directly brings up the problem of malicious participants (attackers) who try to get the reward without actually executing the work of searching for the object by simply reporting fake positions. Therefore, one major goal of this Bachelor thesis is to counter such attacks by proposing effective counter-measures, and implementing and evaluating them for the Ethereum platform. In detail, we propose a basic reputation-based approach for detecting fake positions which judges each sighting made by a mobile devices according to the reputation of that device, implemented by a smart contract. Furthermore, advanced attacks are identified compromising the basic reputation-based approach and effective counter-measures to these advanced attacks are proposed. Identified advanced attacks include reputation farming, where the attacker tries to aggregate reputation first before launching the attack, and the so-called copycat attack, where the attacker simply copies already submitted valid sightings form honest participants, making his fake positions indistinguishable from valid positions. Our evaluations analyses the monetary cost of executing smart contracts with and without our security mechanisms. The results show that the overhead included by our reputation-based approach is at maximum 45% of the cost of a smart contract without implemented security mechanisms.
Spatial crowdsourcing is an effective and novel method. In crowdsourcing systems, a centralized platform is traditionally used to allocate tasks and select workers. Centralized platforms always face following challenges: 1) How to ensure the rationality of tasks allocating; 2) How to ensure the payments of workers in the system when dishonest requesters exist; 3) How to ensure the maximum number of tasks are assigned. 4) How to ensure the integrity and reliability of the centralized platform. To solve these problems, this article proposed a distributed blockchain-based crowdsourcing framework - TSWCrowd (Task Select Worker Crowd). In this framework, tasks are sorted according to specific rules, thus tasks with higher priority are assigned to workers earlier. Workers who are available for a task will be selected and return a result. Then the deployed smart contracts will pay the basic payment automatically. At the same time, relevant contracts also calculate and pay the quality payment according to the proposed quality reward formulation. The proposed TSWCrowd framework on-chain involves a public dataset and uses solidity to compile the smart contracts. The framework was deployed on a local private blockchain. The decentralization property of the blockchain ensures the reliable assignment of tasks. Task-select-worker (TSW) algorithm sorts tasks to ensure reliability. In this paper, the proposed framework was compared with the ABCrowd auction mechanism on-chain and the VCG mechanism off-chain. The results show that the average distance is shorter and the payment is higher, thus reaches the reasonability, reliability and availability.
Naoki Kobayakawa, Mitsuyoshi Imamura, Kei Nakagawa, Kenichi Yoshida
Open source software (OSS) has become indispensable to our society. The success of OSS depends on the participation of a large number of developers or maintainers (contributors). Shedding light on the mechanisms of their participation has been an important academic and practical matter. One aspect to decide participation is the future prospects of a project. However, the causal mechanism behind participation has yet to be studied exhaustively and remains unclear. In this study, we used cryptocurrency projects, many of them were developed on GitHub, to better understand this mechanism. Both GitHub and cryptocurrencies are highly transparent, i.e., information is fully disclosed; we can analyze relevant information on a project, such as the contributors' activities, financial information, and development status. We adopted market capitalization as the substitution index of future prospects and the number of contributors and analyzed the relationship using time series analysis techniques, such as the Granger causality test and regression. We found that the number of contributors increases two months after market capitalization increases. This quantifies the impact of the future prospects of the project, i.e., of the market capitalization of a cryptocurrency, on the participation of contributors.
Jiawen Kang, Zehui Xiong, Chunxiao Jiang, Yi Liu · 9 authors
The emerging Federated Edge Learning (FEL) technique has drawn considerable attention, which not only ensures good machine learning performance but also solves "data island" problems caused by data privacy concerns. However, large-scale FEL still faces following crucial challenges: (i) there lacks a secure and communication-efficient model training scheme for FEL; (2) there is no scalable and flexible FEL framework for updating local models and global model sharing (trading) management. To bridge the gaps, we first propose a blockchain-empowered secure FEL system with a hierarchical blockchain framework consisting of a main chain and subchains. This framework can achieve scalable and flexible decentralized FEL by individually manage local model updates or model sharing records for performance isolation. A Proof-of-Verifying consensus scheme is then designed to remove low-quality model updates and manage qualified model updates in a decentralized and secure manner, thereby achieving secure FEL. To improve communication efficiency of the blockchain-empowered FEL, a gradient compression scheme is designed to generate sparse but important gradients to reduce communication overhead without compromising accuracy, and also further strengthen privacy preservation of training data. The security analysis and numerical results indicate that the proposed schemes can achieve secure, scalable, and communication-efficient decentralized FEL.
Crowdsourcing is a process wherein an individual or an organisation utilizes the talent pool present over the Internet to accomplish their task. The existing crowdsourcing platforms and their reputation computation are centralised and hence prone to various attacks or malicious manipulation of the data by the central entity. A few distributed crowdsourcing platforms have been proposed but they lack a robust reputation mechanism. So we propose a decentralised crowdsourcing platform having an immutable reputation mechanism to tackle these problems. It is built on top of Ethereum network and does not require the user to trust a third party for a non malicious experience. It also utilizes IOTAs consensus mechanism which reduces the cost for task evaluation significantly.
Federated learning (FL) is an emerging collaborative machine learning method to train models on distributed datasets with privacy concerns. To properly incentivize data owners to contribute their efforts, Shapley Value (SV) is often adopted to fairly assess their contribution. However, the calculation of SV is time-consuming and computationally costly. In this paper, we propose FedCoin, a blockchain-based peer-to-peer payment system for FL to enable a feasible SV based profit distribution. In FedCoin, blockchain consensus entities calculate SVs and a new block is created based on the proof of Shapley (PoSap) protocol. It is in contrast to the popular BitCoin network where consensus entities "mine" new blocks by solving meaningless puzzles. Based on the computed SVs, a scheme for dividing the incentive payoffs among FL clients with nonrepudiation and tamper-resistance properties is proposed. Experimental results based on real-world data show that FedCoin can promote high-quality data from FL clients through accurately computing SVs with an upper bound on the computational resources required for reaching consensus. It opens opportunities for non-data owners to play a role in FL.
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.
Participatory sensing is gaining popularity as a method for collecting and sharing information from distributed local environments using sensor-rich mobile devices. There are a number of participatory sensing applications currently in wide use, such as location-based service applications (e.g., Waze navigation). Usually, these participatory applications collect tremendous amounts of sensing data containing personal information, including user identity and current location. Due to the high sensitivity of this information, participatory sensing applications need a privacy-preserving mechanism, such as anonymity, to secure and protect personal user data. However, using anonymous identifiers for sensing sources proves difficult when evaluating sensing data trustworthiness. From this perspective, a successful participatory sensing application must be designed to consider two challenges: (1) user privacy and (2) data trustworthiness. To date, a number of privacy-preserving reputation techniques have been proposed to satisfy both of these issues, but the protocols contain several critical drawbacks or are impractical in terms of implementation. In particular, there is no work that can transparently manage user reputation values while also tracing anonymous identities. In this work, we present a blockchain-based privacy-preserving reputation framework called BPRF to transparently manage user reputation values and provide a transparent tracing process for anonymous identities. The performance evaluation and security analysis show that our solution is both practical and able to satisfy the two requirements for user privacy and data trustworthiness.
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.