Truthful online double auctions for dynamic mobile crowdsourcing

Wei, Yueming; Zhu, Yanmin; Zhu, Hongzi; Zhang, Qian; Xue, Guangtao

doi:10.1109/infocom.2015.7218592

Cited by 75 publications

(45 citation statements)

References 13 publications

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“…Our paper focuses on the Stackelberg game-based incentive mechanism design to reveal the iteration strategy of the participating clients by solving the local subproblems for building a high-quality centralized learning model. Interestingly, incentive mechanism has been studied for years in mobile crowdsourcing/crowdsensing systems, especially with auction mechanisms (e.g., [31], [32], [33]), contract and tournament models (e.g, [34], [35]) and Stackelberg game-based incentive mechanisms such as in [36] and [37]. However, the design goals were specific towards fair and truthful data trading of distributed sensing tasks.…”

Section: Related Workmentioning

confidence: 99%

A Crowdsourcing Framework for On-Device Federated Learning

Pandey

Tran

Bennis

et al. 2020

IEEE Trans. Wireless Commun.

242

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Federated learning (FL) rests on the notion of training a global model in a decentralized manner. Under this setting, mobile devices perform computations on their local data before uploading the required updates to improve the global model. However, when the participating clients implement an uncoordinated computation strategy, the difficulty is to handle the communication efficiency (i.e., the number of communications per iteration) while exchanging the model parameters during aggregation. Therefore, a key challenge in FL is how users participate to build a high-quality global model with communication efficiency. We tackle this issue by formulating a utility maximization problem, and propose a novel crowdsourcing framework to leverage FL that considers the communication efficiency during parameters exchange. First, we show an incentivebased interaction between the crowdsourcing platform and the participating client's independent strategies for training a global learning model, where each side maximizes its own benefit. We formulate a two-stage Stackelberg game to analyze such scenario and find the game's equilibria. Second, we formalize an admission control scheme for participating clients to ensure a level of local accuracy. Simulated results demonstrate the efficacy of our proposed solution with up to 22% gain in the offered reward.

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Section: Related Workmentioning

confidence: 99%

A Crowdsourcing Framework for On-Device Federated Learning

Pandey

Tran

Bennis

et al. 2020

IEEE Trans. Wireless Commun.

242

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“…Focusing on dynamic crowdsensing where participants arrive in an online manner, Zhao et al [13] presented two online incentive mechanisms using a multiple-stage sampling-accepting process. Similarly, Y. Wei et al [15] proposed an online incentive mechanism to maximize the number of matched pairs of participants and crowdsourcing service users when participants and service users are dynamically changing.…”

Section: Related Workmentioning

confidence: 99%

“…After the trimming process, the platform calculates temporary fee q j for each r j ∈ R s and temporary payment p i to each w i ∈ W s . For the calculation of q j and w i , the platform uses the ratio value v th /(α β th |Γ th |) of r th and the ratio value c th /λ β th of w th , which consequently make q j and p i the critical values to guarantee truthfulness of requesters and workers, respectively (line [14][15][16][17][18][19]. Note that we denote q j (p i ) temporary fee (temporary payment) because both q j and p i in the TA were initially calculated based on the assumption that all w i ∈ W s will meet the deadline and all r j ∈ R s will accordingly achieve their full valuation.…”

Section: Matching Stepmentioning

confidence: 99%

Small Profits and Quick Returns: An Incentive Mechanism Design for Crowdsourcing Under Continuous Platform Competition

Baek

Chen

Choi

2020

IEEE Internet Things J.

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Crowdsourcing can be applied to the Internet-of-Things (IoT) systems to provide more scalable and efficient services to support various tasks. As the driving force of crowdsourcing is the interaction among participants, various incentive mechanisms have been proposed to attract and retain a sufficient number of participants to provide a sustainable crowdsourcing service. However, there exist some gaps between the modeled entities or markets in the existing works and those in reality: 1) dichotomous task valuation and workers' punctuality, and 2) crowdsourcing service market monopolized by a platform. To bridge those gaps of such impractical assumption, we model workers' heterogeneous punctuality behavior and task depreciation over time. Based on those models, we propose an Expected Social Welfare Maximizing (ESWM) mechanism that aims to maximize the expected social welfare by attracting and retaining more participants in the long-term, i.e., multiple rounds of crowdsourcing. In the evaluation, we modeled the continuous competition between the ESWM and one of the existing works in both short-term and long-term scenarios. Simulation results show that the ESWM mechanism achieves higher expected social welfare and platform utility than the benchmark by attracting and retaining more participants. Moreover, we prove that the ESWM mechanism achieves the desirable economic properties: individual rationality, budget-balance, computational efficiency, and truthfulness.

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“…Aware of the paramount importance of attracting worker participation, the research community has recently developed various incentive mechanisms for MCS systems. Among them, game-theoretic incentive mechanisms , which utilize either auction [10][11][12][13][18][19][20][21][22][23][24][25][26][27][28] or other gametheoretic models [8,9,[14][15][16][17], have gained increasing popularity due to their ability to tackle workers' selfish and strategic behaviors. These mechanisms typically aim to maximize the platform's profit [14][15][16][17][18][19][20][21][22][23] or social welfare [9][10][11][12][13], and minimize the platform's payment [7,8,[24][25][26][27] or social cost [28].…”

Section: Related Workmentioning

confidence: 99%

CENTURION: Incentivizing multi-requester mobile crowd sensing

Jin

Nahrstedt

2017

IEEE INFOCOM 2017 - IEEE Conference on Computer Communications

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Abstract-The recent proliferation of increasingly capable mobile devices has given rise to mobile crowd sensing (MCS) systems that outsource the collection of sensory data to a crowd of participating workers that carry various mobile devices. Aware of the paramount importance of effectively incentivizing participation in such systems, the research community has proposed a wide variety of incentive mechanisms. However, different from most of these existing mechanisms which assume the existence of only one data requester, we consider MCS systems with multiple data requesters, which are actually more common in practice. Specifically, our incentive mechanism is based on double auction, and is able to stimulate the participation of both data requesters and workers. In real practice, the incentive mechanism is typically not an isolated module, but interacts with the data aggregation mechanism that aggregates workers' data. For this reason, we propose CENTURION, a novel integrated framework for multi-requester MCS systems, consisting of the aforementioned incentive and data aggregation mechanism. CENTURION's incentive mechanism satisfies truthfulness, individual rationality, computational efficiency, as well as guaranteeing non-negative social welfare, and its data aggregation mechanism generates highly accurate aggregated results. The desirable properties of CENTURION are validated through both theoretical analysis and extensive simulations.

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Truthful online double auctions for dynamic mobile crowdsourcing

Cited by 75 publications

References 13 publications

A Crowdsourcing Framework for On-Device Federated Learning

A Crowdsourcing Framework for On-Device Federated Learning

Small Profits and Quick Returns: An Incentive Mechanism Design for Crowdsourcing Under Continuous Platform Competition

CENTURION: Incentivizing multi-requester mobile crowd sensing

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