MAIM: A Novel Incentive Mechanism Based on Multi-Attribute User Selection in Mobile Crowdsensing

Xiong, Jinbo; Chen, Xiuhua; Tian, Youliang; Ma, Rong; Chen, Lei; Yao, Zhiqiang

doi:10.1109/access.2018.2878761

Cited by 14 publications

(14 citation statements)

References 21 publications

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“…Existing studies mostly employ single-attribute incentive mechanisms. Although some hybrid incentive mechanisms have been proposed for crowdsensing [51,52], there are still bottleneck problems in usability due to the difficulty of hybrid data management and the need to adjust weightings under a hybrid incentive mechanism. Different from existing incentive mechanisms, our hybrid mechanism is based on consortium blockchain, which has better openness and flexibility for requesters and workers.…”

Section: The Incentive Mechanism Of Crowdsensingmentioning

confidence: 99%

A Blockchain-Based Hybrid Incentive Model for Crowdsensing

Wei

Long

2020

Electronics

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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.

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Section: The Incentive Mechanism Of Crowdsensingmentioning

confidence: 99%

A Blockchain-Based Hybrid Incentive Model for Crowdsensing

Wei

Long

2020

Electronics

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“…Liu et al [11] designed the participant selection framework TaskMe for multi-task scenarios. Xiong et al [12] selected participants with the aim to maximize the coverage quality of the…”

Section: Task Allocation In the Mcsmentioning

confidence: 99%

“…Liu et al [11] designed the participant selection framework TaskMe for multi-task scenarios. Xiong et al [12] selected participants with the aim to maximize the coverage quality of the sensing task while satisfying the incentive budget constraint, but their work is not about multi-task allocation. Reddy et al [13] studied a recruitment framework to identify appropriate participants for data collection based on geographic and temporal availability.…”

Section: Task Allocation In the Mcsmentioning

confidence: 99%

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A Real-World-Oriented Multi-Task Allocation Approach Based on Multi-Agent Reinforcement Learning in Mobile Crowd Sensing

Han

Zhang

2020

Information

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Mobile crowd sensing is an innovative and promising paradigm in the construction and perception of smart cities. However, multi-task allocation in real-world scenarios is a huge challenge. There are many unexpected factors in the execution of mobile crowd sensing tasks, such as traffic jams or accidents, that make participants unable to reach the target area. In addition, participants may quit halfway due to equipment failure, network paralysis, dishonest behavior, etc. Previous task allocation approaches mainly ignored some of the heterogeneity of participants and tasks in the real-world scenarios. This paper proposes a real-world-oriented multi-task allocation approach based on multi-agent reinforcement learning. Firstly, under the premise of fully considering the heterogeneity of participants and tasks, the approach enables participants as agents to learn multiple solutions independently, based on modified soft Q-learning. Secondly, two cooperation mechanisms are proposed for obtaining the stable joint action, which can minimize the total sensing time while meeting the sensing quality constraint, which optimizes the sensing quality of mobile crowd sensing (MCS) tasks. Experiments verify that the approach can effectively reduce the impact of emergencies on the efficiency of large-scale MCS platform and outperform baselines based on a real-world dataset under different experiment settings.

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“…A number of the analyzed publications adopts a long-term approach, using the history of contributions in the decision making of the incentive mechanisms. The primary studies that adopt reputation-scores include that of Sun and Ma [34] [72], and Xiong et al [74]. They used reputation-scores into the incentive mechanism design to quantify the credibility and reward the best collectors.…”

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

confidence: 99%

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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MAIM: A Novel Incentive Mechanism Based on Multi-Attribute User Selection in Mobile Crowdsensing

Cited by 14 publications

References 21 publications

A Blockchain-Based Hybrid Incentive Model for Crowdsensing

A Blockchain-Based Hybrid Incentive Model for Crowdsensing

A Real-World-Oriented Multi-Task Allocation Approach Based on Multi-Agent Reinforcement Learning in Mobile Crowd Sensing

Credibility on Crowdsensing Data Acquisition

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