Economics of Peer-to-Peer Mobile Crowdsensing

Jiang, Changkun; Lin, Gao; Duan, Lingjie; Huang, Jianwei

doi:10.1109/glocom.2014.7417152

Cited by 6 publications

(9 citation statements)

References 12 publications

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“…[125][126][127] Pricing mechanisms Discussing mathematical or economic approaches in evaluating, valuating, or pricing datasets (or data services) in data marketplaces. [47,[128][129][130][131][132][133][134][135][136][137][138][139][140][141][142][143][144][145] Articles in the finance domain are not equally distributed across categories because most discussions are centralized in pricing mechanisms. Unlike the computational pricing in the STOF technology domain that focuses on technical aspects like query-or machine learning-based pricing (see Section 3.2), the pricing mechanisms here emphasize more on mathematical or economic approaches in valuating or pricing data in data marketplaces.…”

Section: Economic Feasibilitymentioning

confidence: 99%

“…Other topics are auction-based pricing using the Bayesian mathematical model [140,141], a pricing mechanism negotiation based on a negotiation game theory based in the energy domain [142], and a generic pricing mechanism based on a non-cooperative game theory in Mobile Crowdsensing [143]. Finally, Stahl and Vossen [144] discuss data quality criteria (such as accuracy, completeness) that can be used to relatively price data, while Jang et al [145] propose a three-hierarchal model of data trading and create a pricing function to achieve Nash Equilibrium (NE).…”

Section: Economic Feasibilitymentioning

confidence: 99%

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Business Data Sharing through Data Marketplaces: A Systematic Literature Review

Abbas

Agahari

Ven

et al. 2021

JTAER

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Data marketplaces are expected to play a crucial role in tomorrow’s data economy, but such marketplaces are seldom commercially viable. Currently, there is no clear understanding of the knowledge gaps in data marketplace research, especially not of neglected research topics that may advance such marketplaces toward commercialization. This study provides an overview of the state-of-the-art of data marketplace research. We employ a Systematic Literature Review (SLR) approach to examine 133 academic articles and structure our analysis using the Service-Technology-Organization-Finance (STOF) model. We find that the extant data marketplace literature is primarily dominated by technical research, such as discussions about computational pricing and architecture. To move past the first stage of the platform’s lifecycle (i.e., platform design) to the second stage (i.e., platform adoption), we call for empirical research in non-technological areas, such as customer expected value and market segmentation.

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Section: Economic Feasibilitymentioning

confidence: 99%

Section: Economic Feasibilitymentioning

confidence: 99%

Business Data Sharing through Data Marketplaces: A Systematic Literature Review

Abbas

Agahari

Ven

et al. 2021

JTAER

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“…In [265], the authors investigate the economics of delay-sensitive MCS campaigns by presenting an Optimal Participation and Reporting Decisions (OPRD) algorithm that aims to maximize the service provider's profit. In [266], the authors study market behaviors and incentive mechanisms by proposing a non-cooperative game under a pricing scheme for a theoretical analysis of the social welfare equilibrium.…”

Section: E Mcs As a Business Paradigmmentioning

confidence: 99%

A Survey on Mobile Crowdsensing Systems: Challenges, Solutions, and Opportunities

Capponi

Fiandrino

Kantarcı

et al. 2019

IEEE Commun. Surv. Tutorials

427

176

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Mobile crowdsensing (MCS) has gained significant attention in recent years and has become an appealing paradigm for urban sensing. For data collection, MCS systems rely on contribution from mobile devices of a large number of participants or a crowd. Smartphones, tablets, and wearable devices are deployed widely and already equipped with a rich set of sensors, making them an excellent source of information. Mobility and intelligence of humans guarantee higher coverage and better context awareness if compared to traditional sensor networks. At the same time, individuals may be reluctant to share data for privacy concerns. For this reason, MCS frameworks are specifically designed to include incentive mechanisms and address privacy concerns. Despite the growing interest in the research community, MCS solutions need a deeper investigation and categorization on many aspects that span from sensing and communication to system management and data storage. In this paper, we take the research on MCS a step further by presenting a survey on existing works in the domain and propose a detailed taxonomy to shed light on the current landscape and classify applications, methodologies and architectures. Our objective is not only to analyze and consolidate past research but also to outline potential future research directions and synergies with other research areas.

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“…The impact of the reserve price on optimality can be shown as follows. With the reserve price, some task planners, i.e., those with a valuation lower than the minimum payment given in (17), will decide to not join the auction. Hence, the maximum social welfare may be reduced.…”

Section: Budget Balancementioning

confidence: 99%

A Double Auction Mechanism for Mobile Crowd Sensing with Data Reuse

Zhang

Gao

Cao

et al. 2017

GLOBECOM 2017 - 2017 IEEE Global Communications Conference

Self Cite

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Mobile Crowd Sensing (MCS) is a new paradigm of sensing, which can achieve a flexible and scalable sensing coverage with a low deployment cost, by employing mobile users/devices to perform sensing tasks. In this work, we propose a novel MCS framework with data reuse, where multiple tasks with common data requirement can share (reuse) the common data with each other through an MCS platform. We study the optimal assignment of mobile users and tasks (with data reuse) systematically, under both information symmetry and asymmetry, depending on whether the user cost and the task valuation are public information. In the former case, we formulate the assignment problem as a generalized Knapsack problem and solve the problem by using classic algorithms. In the latter case, we propose a truthful and optimal double auction mechanism, built upon the above Knapsack assignment problem, to elicit the private information of both users and tasks and meanwhile achieve the same optimal assignment as under information symmetry. Simulation results show by allowing data reuse among tasks, the social welfare can be increased up to 100% ∼ 380%, comparing with those without data reuse. We further show that the proposed double auction is not budget balance for the auctioneer, mainly due to the data reuse among tasks. To this end, we further introduce a reserve price into the double auction (for each data item) to achieve a desired tradeoff between the budget balance and the social efficiency.

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Economics of Peer-to-Peer Mobile Crowdsensing

Cited by 6 publications

References 12 publications

Business Data Sharing through Data Marketplaces: A Systematic Literature Review

Business Data Sharing through Data Marketplaces: A Systematic Literature Review

A Survey on Mobile Crowdsensing Systems: Challenges, Solutions, and Opportunities

A Double Auction Mechanism for Mobile Crowd Sensing with Data Reuse

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