Incentivizing for Truth Discovery in Edge-assisted Large-scale Mobile Crowdsensing

Xu, Jia; Yang, Shangshu; Lü, Weifeng; Xu, Lijie; Yang, Dejun

doi:10.3390/s20030805

Cited by 8 publications

(3 citation statements)

References 40 publications

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“…A few clear disadvantages of cloud-based MCS systems include their limited scalability for large-scale crowd-sensing due to computational and message load on cloud servers, their difficulty in identifying fake sensing data locations, and their high risk of information safety and user privacy contact. Lately, a MCS design based on edge computing has been proposed to address the aforementioned problems [3]. The following are the key advantages of an architecture based on edge computing for extensive mobile crowd-sensing:…”

Section: Fig 1 Principal Components and Activities Of A General Mcs S...mentioning

confidence: 99%

Recruitment Algorithm in Edge-Cloud Servers based on Mobile Crowd-Sensing in Smart Cities

Muhammad Alkirom Wildan,

Mahmudah Enny Widyaningrum,

T. Padmapriya

et al. 2023

Int. J. Interact. Mob. Technol.

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As more and more mobile devices rely on cloud services since the introduction of cloud computing, data privacy has emerged as one of the most pressing security concerns. Users typically encrypt their important data before uploading it to cloud servers to safeguard data privacy, which makes data usage challenging. On the other side, this also increases the possibility of brand-new issues in cities. A clever, effective and efficient urban monitoring system is required to address possible challenges that may arise in urban settings. In the smart city concept, which makes use of sensors, one strategy that might be used in IoT and cloud computing is to monitor and gather data on problems that develop in cities in real-time. However, it will take a while and be rather expensive to install IoT and sensors throughout the city. The Mobile Crowd-Sensing (MCS) method is proposed to be used in this study to retrieve and gather data on issues that arise in metropolitan areas from citizen reports made using mobile devices. And we suggest a budget-constrained, reputation-based collaborative user recruitment (RCUR) procedure for a MCS system. To construct an edge-assisted MCS system in urban situations, we first integrate edge computing into MCS. We also examine how user reputation affects user recruitment. Finally, we create a collaborative sensing approach using the edge nodes’ sensing capabilities.

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Section: Fig 1 Principal Components and Activities Of A General Mcs S...mentioning

confidence: 99%

Recruitment Algorithm in Edge-Cloud Servers based on Mobile Crowd-Sensing in Smart Cities

Muhammad Alkirom Wildan,

Mahmudah Enny Widyaningrum,

T. Padmapriya

et al. 2023

Int. J. Interact. Mob. Technol.

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“…Gao [21]. Xu et al [22] also proposed a mechanism with a strategyproof discovery phase and a budget-constrained purchasing stage.…”

Section: Related Workmentioning

confidence: 99%

Truthful Mechanism Design for Multiregion Mobile Crowdsensing

Qiao

Cheng

et al. 2020

Wireless Communications and Mobile Computing

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In the age of the development of artificial intelligence, we face the challenge on how to obtain high-quality data set for learning systems effectively and efficiently. Crowdsensing is a new powerful tool which will divide tasks between the data contributors to achieve an outcome cumulatively. However, it arouses several new challenges, such as incentivization. Incentive mechanisms are significant to the crowdsensing applications, since a good incentive mechanism will attract more workers to participate. However, existing mechanisms failed to consider situations where the crowdsourcer has to hire capacitated workers or workers from multiregions. We design two objectives for the proposed multiregion scenario, namely, weighted mean and maximin. The proposed mechanisms maximize the utility of services provided by a selected data contributor under both constraints approximately. Also, extensive simulations are conducted to verify the effectiveness of our proposed methods.

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“…However, the high coverage of crowdsensing is challenging to achieve. First, because the platform's budget is limited [5,6], the platform cannot recruit enough participants to participate in the task with a high reward, affecting the coverage. Second, the participants' number in crowdsensing is often limited.…”

Section: Introductionmentioning

confidence: 99%

A Location-Based Crowdsensing Incentive Mechanism Based on Ensemble Learning and Prospect Theory

Liu,

Xu,

Deng

et al. 2023

Mathematics

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Crowdsensing uses the participants’ smart devices to form a new perception network. The coverage of crowdsensing’s tasks determines the quality of services. Under the constraint of budget and the number of participants, the platform needs to increase the participation duration of participants through incentive mechanisms to increase the coverage of tasks. There are two problems with the existing incentive mechanisms: (1) many incentives ignore the participants’ characteristics, and using a single incentive mechanism for different participants will make the incentive effect not reach the expectation; (2) many incentives will affect the effectiveness because of the decision problem caused by asymmetric information. Inspired by ensemble learning and prospect theory, this paper proposes the Incentive Mechanism based on Ensemble Learning and Prospect Theory (IMELPT). First, we propose the Deep-Stacking-Generation algorithm based on Dropout (DSGD), to predict the participants and distinguish whether they are long-term or short-term participants. If the participants are short-term, we incentivize them through the Short-term Participant Incentive Mechanism based on Prospect Theory (SPIMPT). We increase the participation duration by transforming the change in reward into asymmetric information that aligns the participant’s goal with the platform. If the participants are long-term participants, we motivate them through the Long-term Participant Incentive Mechanism (LPIM), to maintain the participation rate of participants by maximizing their utility. Theoretical analysis and experiments on real datasets demonstrated that IMELPT can reliably improve the coverage of crowdsensing tasks.

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Incentivizing for Truth Discovery in Edge-assisted Large-scale Mobile Crowdsensing

Cited by 8 publications

References 40 publications

Recruitment Algorithm in Edge-Cloud Servers based on Mobile Crowd-Sensing in Smart Cities

Recruitment Algorithm in Edge-Cloud Servers based on Mobile Crowd-Sensing in Smart Cities

Truthful Mechanism Design for Multiregion Mobile Crowdsensing

A Location-Based Crowdsensing Incentive Mechanism Based on Ensemble Learning and Prospect Theory

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