A Quality-Aware Attribute-Based Filtering Scheme for Participatory Sensing

Cheny, Jianwei; Zhang, Dong

doi:10.1109/msn.2016.037

Cited by 1 publication

(3 citation statements)

References 26 publications

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“…The use of a trusted set of participants was efficient against corruption, collusion, and on-off attacks in reputation systems [183]. Chen and Zhao [202] proposed a task allocation schema resilient against collusion attacks. A Blockchain-based mechanism was used to prevent impersonation attacks on incentivebased schemas [196].…”

Section: Discussionmentioning

confidence: 99%

“…Gong and Shroff [63] incentivize strategic users to truthfully reveal their private qualities and truthfully make efforts as desired by the requester. Jin et al [64] present [190], Erfani et al [191], Vergara-Laurenset al [192], Kazemi and Shahabi [193], Xu et al [194], Mousa et al [195], Wang et al [196], Miao et al [197], Cai et al [198], Jin et al [199], Zheng et al [200], Alswailim et al [201], Chen and Zhao [202], Wu et al [203], and Alsheikh et al [204] a double auction-based incentive mechanism which involves auctions among not only the collectors, but also the data requesters, and can incentivize the participation of both data requesters and collectors. Anawar et al [70] introduced a design guideline for non-monetary incentive mechanism by mapping a set of behavioral incentive constructs into incentive features for mobile health participatory sensing.…”

Section: A Rq1: What Is the Strategy Used To Ensure The Data Credibimentioning

confidence: 99%

“…They proposed a blockchain-based solution in which node cooperation based privacy protection method was used not only to protect the privacies of sensing data and identity information but also prevent the impersonation attacks. Chen and Zhao [202] distributed a receiver filtering policy (RFP) along with tasks to guarantee that only those participants whose data attributes satisfy the RFP, transfer the sensing data. In the authors' schema, the participants encrypt the sensing data under a set of attributes such that only the receiver who holds these attributes obtain these data.…”

Section: A Rq1: What Is the Strategy Used To Ensure The Data Credibimentioning

confidence: 99%

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Credibility on Crowdsensing Data Acquisition

Neto

Gomes

Soares

2019

JCIS

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This paper focuses on the credibility of crowd sensed data. The ubiquity of crowdsensing platforms has enabled the capture of sensed information useful for several applications domains. However, one concern with crowdsensing is the information credibility and, over the last few years, we have seen a variety of approaches to leverage credibility on the crowdsensing platforms. Here, we carried out a systematic mapping study on 177 credibility-related articles about crowdsensing. The results show that the absence of standard models in the data capture process and the human factors such as individualism, inattention, and the possibility of errors (whether they are intentional or not), are the primary harmful to the credibility on these platforms. Additionally, we propose a three-level taxonomy that classifies crowdsensing credibility into two broad branches: direct and indirect approaches. To the best of our knowledge, we believe this is the first systematic mapping to address both credibility approaches and the factors that negatively impact the data credibility on crowdsensing platforms.

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Section: Discussionmentioning

confidence: 99%