Yiwei Gong, Sélinde van Engelenburg, Marijn Janssen
Companies increasingly tender knowledge-intensive tasks using crowdsourcing platforms to gain access to scarce knowledge and skills otherwise out of reach, and in this way, gaining competitive advantage. Despite its potential, existing crowdsourcing platforms encounter several challenges, including (1) fragmentation of expertise, as there are many platforms, (2) distrust between task providers and crowdsourcing participants, as identity and past performance are often not known, and (3) inability to learn from experience due to a lack of openness. A reference architecture for blockchain-based knowledge-intensive crowdsourcing platforms to mediate transactions between demand and supply of knowledge is designed in this paper to overcome these challenges. A design science research method is followed to develop the architecture. The reference architecture shows how blockchain and smart contract components can be integrated to support and coordinate knowledge-intensive crowdsourcing activities. By removing traditional e-commerce intermediaries, blockchain reduces search friction, knowledge transfer costs, and cheating by task providers or crowdsourcing participants.
In order to create a transparent and sound academic communication ecosystem centered on researchers, we developed a system that applied blockchain technology to an open peer review system. In this study, an open peer review system was developed based on Hyperledger Fabric, which is a private blockchain. The system can be operated in connection with the reviewer recommendation module of the existing submission management system. In the reviewer recommendation module, reviewers are recommended by excluding co-authors and colleagues after an expertise test. The blockchain system performs an open peer review process based on smart contracts, while the submission management system selects reviewers for peer review. A service broker intervenes between these two systems for data interchange. The system developed herein is expected to be used as a researcher-centered scholarly communication model in the open science era, in which the intervention of publishers is minimized, and authors and reviewers (as researchers) are centered.
This paper presents the current state of the gaming industry, which provides an important background for an effective serious game implementation in mobile crowdsensing. An overview of existing solutions, scientific studies and market research highlights the current trends and the potential applications for citizen-centric platforms in the context of Cyber–Physical–Social systems. The proposed solution focuses on serious games applied in urban water management from the perspective of mobile crowdsensing, with a reward-driven mechanism defined for the crowdsensing tasks. The serious game is designed to provide entertainment value by means of gamified interaction with the environment, while the crowdsensing component involves a set of roles for finding, solving and validating water-related issues. The mathematical model of distance-constrained multi-depot vehicle routing problem with heterogeneous fleet capacity is evaluated in the context of the proposed scenario, with random initial conditions given by the location of players, while the Vickrey–Clarke–Groves auction model provides an alternative to the centralized task allocation strategy, subject to the same evaluation method. A blockchain component based on the Hyperledger Fabric architecture provides the level of trust required for achieving overall platform utility for different stakeholders in mobile crowdsensing.
A rapid-growing machine learning technique called federated edge learning has emerged to allow a massive number of edge devices (e.g. smart phones) to collaboratively train globally shared models without revealing their private raw data. This technique not only ensures good machine learning performance but also maintains data privacy of the edge devices. However, the federated edge learning still faces the following critical challenges: (i) difficulty in avoiding unreliable edge devices acting as workers for federated edge learning, and (ii) lack of efficient learning task assignment schemes among task publishers and workers. To tackle these challenges, reputation is utilized as a metric to evaluate the trustworthiness and reliability of the edge devices. A many-to-one matching model is proposed to address the task assignment problem between task publishers and reliable workers with high reputation. For stimulating reliable edge devices to join model training and enable secure reputation management, blockchain is employed to store the training records and manage reputation data in a decentralized and secure manner without the risk of a single point of failure. Numerical results show that the proposed schemes can achieve significant performance improvement in terms of reliability of federated edge learning.
With the rise of the Internet of Things (IoT) and fifth-generation (5G) networks, which have led to a surge in data processing and increased data transfer time, traditional cloud computing could no longer meet the needs of workers, so edge computing has emerged. Edge computing could meet the demand for low time consumption by processing data at the edge of the network and then transmitting it to a third-party platform. However, since the credibility of the third-party platform is unknown which can easily leak the privacy of workers. For the transparent mechanism of blockchain, a two-stage privacy protection mechanism based on blockchain is proposed to solve this problem. In the first stage, this paper proposes a double disturbance localized differential privacy (DDLDP) algorithm to disturb the location information of workers. In the second stage, all the sensing data are uploaded to the blockchain through edge nodes, processed by the edge cloud, and fed back to the requester. Blockchain technology not only guarantees the integrity of sensing data, but also prevents the possibility of third-party platforms from leaking workers' privacy. Through extensive performance evaluation and comparative experiments on real data sets, the DDLDP algorithm could effectively protect the privacy of workers and has higher service quality and data availability.
Abstract Today, Blockchain technology is emerging to support high secured performance computing. To improve privacy and security factors of allocate, storage, and manage resources in edge computing, blockchain technology can be used to guarantee safe data transmission and communications in Internet of Things (IoT). Due to heterogeneity of resources to cover a range number of IoT applications, resource allocation has a key role for increasing performance of the IoT systems in edge computing. Also, a blockchain‐based resource allocation model can support Quality of Service (QoS) for IoT applications to ensure a high secured communication environment in edge computing. This paper reviews different existing green energy strategies based on blockchain technology in mobile crowd sensing. Mobile crowd sensing refers to all computational aspects related to smart city, smart home‐care, smart grid, smart transportations, smart medical systems, smart industry, manufacturing, and smart farming in IoT environments. This review aims to classify principles and technical aspects of green energy strategies based on blockchain technology in mobile crowd sensing with respect to technical aspects, applied algorithms, evaluation factors, and evaluation environments. Based on Systematic Literature Review (SLR), we selected 37 high potential and key relevant research studies to consider in this review. Based on analytical results, 35% of them were considered as private‐based blockchain methods, 33% of them were applied to the public‐based blockchain methods, 24% of them were considered as consortium‐based blockchain methods, and 8% of them were as hybrid‐based blockchain methods.
Nowadays, sensors inserted in mobile applications are used for gathering data for an explicit assignment that can effectively save cost and time in crowd sensing networks (CSNs). The true value and essence of gathered statistics depend on the participation level from all the members of a CSN, i.e., service providers, data collectors, and service consumers. In comparison with the centralized conventional mechanisms that are susceptible to privacy invasion, attacks, and manipulation, this article proposes a decentralized incentive and reputation mechanism for CSN. The monetary rewards are used to motivate the data collectors and to encourage the participants to take part in the network activities. Whereas the issue of privacy leakage is dealt with using Advanced Encryption Standard (AES128) technique. Additionally, a reputation system is implemented to tackle issues like data integrity, fake reviews, and conflicts among entities. Through registering reviews, the system encourages data utilization by providing correct, consistent, and reliable data. Furthermore, simulations are performed for analyzing the gas consumed by smart contracts. Similarly, the encryption technique is ratified by comparing its execution time with other techniques that are previously used in literature. Lastly, the reputation system is inspected through analyzing the gas consumption and mining time of input string length.
Carlos Núñez‐Gómez, Blanca Caminero, Carmen Carrión
Nowadays, the fog computing paradigm is being consolidated as a solution for processing the explosion of data generated by lots of common IoT devices connected to the Internet. In contrast to cloud computing, fog computing achieves efficient data processing without incurring large latencies or data transfers to/from the cloud. The distributed nature of fog computing makes the resource orchestration a challenge. Commonly used centralized solutions are of limited utility in this context. Moreover, fog nodes are usually resource constrained, so the implementation of the management modules should be carefully designed in order not to cause excessive overheads. On the other hand, blockchain has proven its utility beyond cryptocurrencies, to support distributed and reliable information storage. When combined with smart contracts it can provide a distributed computer where all the nodes independently and equally contribute to a common global system state, which must be agreed by consensus. This also provides inherent desirable features, such as immutability and transparency. In this work, a novel architecture called HIDRA is presented, aimed at resource orchestration in fog computing environments based on a Ethereum blockchain implementation. A prototype implementation on a testbed composed of single-board computers has been carried out. Results show the low overhead introduced into the system and its ability to perform a coordinated action among fog nodes without the intervention of any central authority.
We propose OmniLytics, a blockchain-based secure data trading marketplace for machine learning applications. Utilizing OmniLytics, many distributed data owners can contribute their private data to collectively train an ML model requested by some model owners, and receive compensation for data contribution. OmniLytics enables such model training while simultaneously providing 1) model security against curious data owners; 2) data security against the curious model and data owners; 3) resilience to malicious data owners who provide faulty results to poison model training; and 4) resilience to malicious model owners who intend to evade payment. OmniLytics is implemented as a blockchain smart contract to guarantee the atomicity of payment. In OmniLytics, a model owner splits its model into the private and public parts and publishes the public part on the contract. Through the execution of the contract, the participating data owners securely aggregate their locally trained models to update the model owner's public model and receive reimbursement through the contract. We implement a working prototype of OmniLytics on Ethereum blockchain and perform extensive experiments to measure its gas cost, execution time, and model quality under various parameter combinations. For training a CNN on the MNIST dataset, the MO is able to boost its model accuracy from 62% to 83% within 500ms in blockchain processing time.This demonstrates the effectiveness of OmniLytics for practical deployment.
In today’s world, crowdsourcing is a highly rising paradigm where mass people are engaged in solving a problem. Though this system has a lot of advantages, yet people are not interested in working on this platform. Thus, we survey people to find out the constraints of this platform and the main reason behind their unwillingness. 59% of people think that security and privacy is the major challenge of a crowdsourcing platform. Therefore, we propose a blockchain-based crowdsourced system which can provide security and privacy to the user’s information. We also have used a smart contract to verify the task so that the users get the exact output that they have wanted. We implemented our system and compared the performance with the existing systems. Our proposed approach outperforms the current methods in terms of cost and properties.
Baoyi An, Mingjun Xiao, An Liu, Yun Xu · 6 authors
Crowdsensed Data Trading (CDT) is a novel data trading paradigm, where each data consumer can publicize its data demand as some crowdsensing tasks, and some mobile users (i.e., data sellers) can compete for these tasks, collect the corresponding data, and sell the results to the consumers. Existing CDT systems generally depend on a data trading broker, which will inevitably cause consumers concerns on the trustworthiness of the systems and truthfulness of the data. To address this problem, we propose a Blockchain-based Crowdsensed Data Trading (BCDT) system, mainly containing a smart contract, called BCDToken. First, we replace the broker with blockchain to guarantee the trustworthiness of data trading. Meanwhile, BCDToken adopts Blockchain-based Reverse Auction (BRA) to assign tasks to data sellers. BRA holds truthfulness and individual rationality, which can ensure the sellers to report costs honestly and prevent sellers to manipulate the auction. Moreover, we implement a Secure Truth Discovery and reliability Rating (STDR) mechanism in BCDToken based on homomorphic cryptography, which can incentivize sellers to upload the truthful data and consumers to rate truthfully the reliabilities of sellers without revealing any privacy of data. Additionally, we also deploy BCDToken to the test network to demonstrate its practicability.
Peer-review is a necessary and essential quality control step for scientific publications but lacks proper incentives. Indeed, the process, which is very costly in terms of time and intellectual investment, not only is not remunerated by the journals but is also not openly recognized by the academic community as a relevant scientific output for a researcher. Therefore, scientific dissemination is affected in timeliness, quality, and fairness. Here, to solve this issue, we propose a blockchain-based incentive system that rewards scientists for peer-reviewing other scientists' work and that builds up trust and reputation. We designed a privacy-oriented protocol of smart contracts called Ants-Review that allows authors to issue a bounty for open anonymous peer-reviews on Ethereum. If requirements are met, peer-reviews will be accepted and paid by the approver proportionally to their assessed quality. To promote ethical behavior and inclusiveness the system implements a gamified mechanism that allows the whole community to evaluate the peer-reviews and vote for the best ones.
Vehicular crowdsensing is attracting more and more attention because of its wide sensing coverage and diverse usage in smart cities. However, privacy issues that stem from traditional vehicular crowdsensing scenarios, violate the participant's privacy. Although some privacy-preserving schemes have been designed that aim to protect the sensitive information of sensed data, the reliability cannot be guaranteed because of the system's centralized structure. The introduction of blockchain in crowdsensing applications provides reliable data storage, however, the reliability of data sources remains an open challenge. Under these circumstances, the crowdsourcing service requester may not be able to obtain quality data. To solve these problems, we propose a novel Privacy-preserving and Reliable Vehicular crowdsensing via Blockchain oracle, called PRVB. More specifically, a privacy-preserving vehicular data aggregation scheme is presented to protect the data privacy and unlinkability between participant vehicles and sensed data. Besides, two protocols are designed to protect data privacy and to achieve fair rewards for data providers. Thorough theoretical analysis and experimental evaluations have proved that the proposed PRVB achieves privacy protection, reliability, and fairness with significant computation & communication efficiency.
Qilie Liu, Yinyi Xu, Bin Cao, Lei Zhang · 5 authors
The forking problem plays a key role in the security issue, which is a major concern in the blockchain system. Although many works studied the attack strategy, consensus mechanism, privacy-protecting and security performance analysis, most of them only address the intentional forking caused by a malicious attacker. In fact, without any attacker, unintentional forking still remains due to transmission delay and failure, especially in wireless network scenarios. To this end, this paper investigates the reason for generating unintentional forking and derives the forking probability expression in Wireless Blockchain Networks (WBN). Furthermore, in order to illustrate the unintentional forking on the blockchain system, the performances in terms of resource utilization rate, block generation time, and Transaction Per Second (TPS) are investigated. The numerical results show that the target difficulty of hash algorithm in generating a new block, the delay time of broadcasting, the network scale, and the transmission failure probability would affect the unintentional forking probability significantly, which can provide a reliable basis for avoiding forking to save resource consumption and improving system performance.
As more and more organizations deploy their sensing devices in the industrial Internet of Things network, it becomes increasingly important for the organizations to share data with others, so that the value of the data can be fully explored. However, individuals' privacy may be compromised because of data sharing. In this article, we study the differentially private data publishing problem, which aims at balancing between privacy and data utility. Specifically, two blockchain-based data publishing protocols are proposed. For histogram publishing, we propose a protocol where the Laplace noise added in the query result is verified by the blockchain. For anonymized data publishing, we propose a protocol, which can prevent the publisher and the recipient from lying about the utility of the published data. With the blockchain acting as a reliable intermediary between the publisher and the recipient, the proposed protocols can help to realize fair and auditable data sharing.
Crowdsourcing aims to deliver services and content by aggregating contributions from a large user population. For mobile networks and IoT systems, crowdsourcing is used to gather and process sensor data from mobile devices (crowdsensing), in order to deliver real-time, context-aware services and possibly support user collaboration in extended geographic areas. In applications like geonsensitive navigation, location-based activity sharing and recommendations, the challenge of adequate service quality and user experience may be at stake, as the services are provided securely to an ever-growing user population. This happens due to the inherent trade-off between security and real-time performance that ultimately sets in doubt any scalability prospect beyond a certain user-interaction load. This work introduces a publish-subscribe architecture for mobile crowdsensing systems, which can be transparently scaled up to higher usage load, while retaining adequate performance and security by load balancing into multiple MQTT brokers. The security support combines a lightweight TLS implementation with an integrated mechanism for two-level access control: user-device interactions and message topics. We provide proof-of-concept measurements that show how our solution scales to increasing interaction loads through load-balancing the processing cost that includes the overhead of the security mechanisms applied. The system architecture was implemented in a vehicular crowdsensing navigation network that allows to exchange navigation information at real-time, for improved routing of vehicles to their destination.