Online Pricing for Mobile Crowdsourcing with Multi-Minded Users

Han, Kai; He, Yuntian; Tan, Haisheng; Tang, Shaojie; Huang, He; Luo, Jun

doi:10.1145/3084041.3084058

Cited by 23 publications

(5 citation statements)

References 36 publications

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“…Han et al. (2017) develop an online pricing scheme to incentivize users who arrive sequentially. The pricing scheme dynamically adjusts the posted prices for a heterogeneous dataset by learning from data buyers’ behaviors.…”

Section: Related Workmentioning

confidence: 99%

“…Strong et al (2015) propose a way to quantify the expected value of sample information to enhance model prediction performance. Han et al (2017) develop an online pricing scheme to incentivize users who arrive sequentially. The pricing scheme dynamically adjusts the posted prices for a heterogeneous dataset by learning from data buyers' behaviors.…”

Section: Related Workmentioning

confidence: 99%

“…The supplier then faces an online decision requiring it to select a price L T and screen out the best analyst in each period T. The supplier would like to maximize the expected reward subject to the budget constraint. In this study, we implement the classic online learning algorithm, LIME (Han et al 2017), to compute the pricing policy.…”

Section: Indirect Data Sharingmentioning

confidence: 99%

“…In this study, we implement the classic online learning algorithm, LIME (Han et al. 2017), to compute the pricing policy.…”

Section: Usage‐based Data Sharingmentioning

confidence: 99%

See 3 more Smart Citations

Blockchain‐Enabled Data Sharing in Supply Chains: Model, Operationalization, and Tutorial

Wang

Zheng

Jiang

et al. 2021

Production and Operations Management

Self Cite

126

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Data sharing between upstream and downstream entities is vital for the success of a supply chain. However, distrust, privacy concerns, data misuse, and the asymmetric valuation of shared data between entities often hinder data sharing. This problem calls for a secure, efficient, fair, and trustworthy data‐sharing mechanism. The key to such a successful system hinges on how to trace the data usage, determine the value of the seller’s data to the buyer and then compensate the seller accordingly. To this end, we design and implement a blockchain‐enabled data‐sharing marketplace for a stylized supply chain. We demonstrate how a blockchain can be used to overcome these impediments in supply‐chain data sharing and provide a detailed tutorial with a step‐by‐step implementation for how to set up such a data exchange prototype using Hashgraph.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Indirect Data Sharingmentioning

confidence: 99%

“…In this study, we implement the classic online learning algorithm, LIME (Han et al. 2017), to compute the pricing policy.…”

Section: Usage‐based Data Sharingmentioning

confidence: 99%

See 2 more Smart Citations

Blockchain‐Enabled Data Sharing in Supply Chains: Model, Operationalization, and Tutorial

Wang

Zheng

Jiang

et al. 2021

Production and Operations Management

Self Cite

126

View full text Add to dashboard Cite

show abstract

“…The incentivization problem in Crowesensing/Crowdsourcing has aroused great interests in the literature [5]- [7], [15], [17]- [19], [32]- [36]. However, most of the work in this line has neglected the quality issue with only a few exceptions such as [9] and [11].…”

Section: A Mobile Crowdsensing/crowdsourcingmentioning

confidence: 99%

Quality-Aware Pricing for Mobile Crowdsensing

Han

Huang

Luo

2018

IEEE/ACM Trans. Networking

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Mobile crowdsensing has been considered as a promising approach for large scale urban data collection, but has also posed new challenging problems, such as incentivization and quality control. Among the other incentivization approaches, posted pricing has been widely adopted by commercial systems due to the reason that it naturally achieves truthfulness and fairness and is easy to be implemented. However, the fundamental problem of how to set the "right" posted prices in crowdsensing systems remains largely open. In this paper, we study a qualityaware pricing problem for mobile crowdsensing, and our goal is to choose an appropriate posted price to recruit a group of participants with reasonable sensing qualities for robust crowdsensing, while the total expected payment is minimized. We show that our problem is NP-hard and has close ties with the well-known Poisson binomial distributions (PBDs). To tackle our problem, we first discover some non-trivial submodular properties of PBD, which have not been reported before, and then propose a novel "ironing method" that transforms our problem from a non-submodular optimization problem into a submodular one by leveraging the newly discovered properties of PBD. Finally, with the ironing method, several approximation algorithms with provable performance ratios are provided, and we also conduct extensive numerical experiments to demonstrate the effectiveness of our approach.

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Spatial and Temporal Pricing Approach for Tasks in Spatial Crowdsourcing

Qian

Liu

2020

Web Information Systems Engineering – WISE 2020

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Online Pricing for Mobile Crowdsourcing with Multi-Minded Users

Cited by 23 publications

References 36 publications

Blockchain‐Enabled Data Sharing in Supply Chains: Model, Operationalization, and Tutorial

Blockchain‐Enabled Data Sharing in Supply Chains: Model, Operationalization, and Tutorial

Quality-Aware Pricing for Mobile Crowdsensing

Spatial and Temporal Pricing Approach for Tasks in Spatial Crowdsourcing

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