Crowdsensing Quality Control and Grading Evaluation Based on a Two-Consensus Blockchain

Jian, An; Liang, Danwei; Gui, Xiaolin; He, Yang; Gui, Ruowei; Xia, He

doi:10.1109/jiot.2018.2883835

Cited by 43 publications

(12 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The reputation is used to select data providers who are more likely to provide high-quality data in crowdsensing. An et al [10] proposed a data provider selection scheme by using credit matching degree and trajectory matching degree for improving data quality in crowdsensing. The credit matching degree is calculated to measure the possibility that the worker submits high-quality data.…”

Section: A Overview Of Reputation Management In Crowdsensingmentioning

confidence: 99%

“…Inspired by the great potential of reputation in solving data quality problems in crowdsensing, we adopt the reputation metric to the selection of trusted and reliable workers for enhancing model training performance in the federated learning. The reputation can reflect how well a worker has performed about model training, which can be measured from its training task completion history with the past behaviors of good or unreliable activities [10]. With the help of reputation, task publishers select trusted and reliable workers to train the global model well, which can prevent the poisoning attacks launched by malicious workers and also remove unreliable data providers for obtaining high accuracy of the global model.…”

Section: A Overview Of Reputation Management In Crowdsensingmentioning

confidence: 99%

See 1 more Smart Citation

Reliable Federated Learning for Mobile Networks

Kang

Xiong

Niyato

et al. 2020

IEEE Wireless Commun.

443

162

View full text Add to dashboard Cite

Federated learning, as a promising machine learning approach, has emerged to leverage a distributed personalized dataset from a number of nodes, e.g., mobile devices, to improve performance while simultaneously providing privacy preservation for mobile users. In the federated learning, training data is widely distributed and maintained on the mobile devices as workers. A central aggregator updates a global model by collecting local updates from mobile devices using their local training data to train the global model in each iteration. However, unreliable data may be uploaded by the mobile devices (i.e., workers), leading to frauds in tasks of federated learning. The workers may perform unreliable updates intentionally, e.g., the data poisoning attack, or unintentionally, e.g., low-quality data caused by energy constraints or high-speed mobility. Therefore, finding out trusted and reliable workers in federated learning tasks becomes critical. In this article, the concept of reputation is introduced as a metric. Based on this metric, a reliable worker selection scheme is proposed for federated learning tasks. Consortium blockchain is leveraged as a decentralized approach for achieving efficient reputation management of the workers without repudiation and tampering. By numerical analysis, the proposed approach is demonstrated to improve the reliability of federated learning tasks in mobile networks.

show abstract

Section: A Overview Of Reputation Management In Crowdsensingmentioning

confidence: 99%

Section: A Overview Of Reputation Management In Crowdsensingmentioning

confidence: 99%

Reliable Federated Learning for Mobile Networks

Kang

Xiong

Niyato

et al. 2020

IEEE Wireless Commun.

443

162

View full text Add to dashboard Cite

show abstract

“…Recently, An et al [24] have proposed a node matching method based on the idea of matching degree calculation, improving the quality of the sensing data acquired from the workers. Research on location privacy preserving for mobile location services in MCS has been widely published in related literature [25].…”

Section: Related Workmentioning

confidence: 99%

CrowdBLPS: A Blockchain-Based Location-Privacy-Preserving Mobile Crowdsensing System

Zou

Wang

et al. 2020

IEEE Trans. Ind. Inf.

View full text Add to dashboard Cite

With the popularization of intelligent terminals, especially current trends, such as "Industrie 4.0" and the Internet of Things, mobile crowdsensing is becoming one of the promising applications built on smart devices in mobile networks. However, the existing mobile crowdsensing models are mostly based on a centralized platform, which is not fully trusted in reality and results in the existence of fraud and other security problems. Furthermore, the data quality collected through crowdsensing is varied, and the location privacy is difficult to guarantee, especially at the worker selection stage. To solve these two problems, an effective blockchain-based location-privacy-preserving crowdsensing model, CrowdBLPS, is proposed in this article. First, the idea of a blockchain is introduced into this model. The decentralized structure and the consensus approach are applied to realize the nonrepudiation and nontampering of information. Second, to improve the data sensing quality and protect worker privacy, a two-stage approach, including the preregistration stage and the final selection stage, is proposed. Finally, we further implement a prototype on the Ethereum public testing network, and the experimental results show the feasibility, availability, and reliability of CrowdBLPS.

show abstract

“…The results supported the effectiveness of blockchain as a tool for crowdsourcing. [34] proposed a crowdsensing quality control model based on blockchain, designing the two-consensus process to achieve a means to evaluate data quality. [35][36][37] considered the privacy issue by utilizing blockchain.…”

Section: Blockchain Backgroundmentioning

confidence: 99%

A Blockchain-Based Hybrid Incentive Model for Crowdsensing

Wei

Long

2020

Electronics

View full text Add to dashboard Cite

Crowdsensing is an emerging paradigm of data aggregation, which has a pivotal role in data-driven applications. By leveraging the recruitment, a crowdsensing system collects a large amount of data from mobile devices at a low cost. The critical issues in the development of crowdsensing are platform security, privacy protection, and incentive. However, the existing centralized, platform-based approaches suffer from the single point of failure which may result in data leakage. Besides, few previous studies have addressed the considerations of both the economic incentive and data quality. In this paper, we propose a decentralized crowdsensing architecture based on blockchain technology which will help improve the attack resistance. Furthermore, we present a hybrid incentive mechanism, which integrates the data quality, reputation, and monetary factors to encourage participants to contribute their sensing data while discouraging malicious behaviors. The effectiveness our of proposed incentive model is verified through a combination of the theory of mechanism design. The performance analysis and simulation results illustrate that the proposed hybrid incentive model is a reliable and efficient mean to promote data security and incentivizing positive conduct on the crowdsensing application.

show abstract

Crowdsensing Quality Control and Grading Evaluation Based on a Two-Consensus Blockchain

Cited by 43 publications

References 25 publications

Reliable Federated Learning for Mobile Networks

Reliable Federated Learning for Mobile Networks

CrowdBLPS: A Blockchain-Based Location-Privacy-Preserving Mobile Crowdsensing System

A Blockchain-Based Hybrid Incentive Model for Crowdsensing

Contact Info

Product

Resources

About