Incentive mechanism and protocol design for crowdsourcing systems

Xie, Hong; Lui, John C. S.; Jiang, Joe Wenjie; Chen, Wei

doi:10.1109/allerton.2014.7028448

Cited by 54 publications

(25 citation statements)

References 16 publications

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“…Zhang et al [9] propose a reputation-based incentive protocol for crowdsourcing applications and use repeated giftgiving game to model the interaction between task requesters and workers. In [10,11], the authors integrate the effort and skill levels of workers into their mechanisms. Another series of work [12][13][14] utilize online auctions to design incentive mechanisms in crowdsourcing systems where users arrive sequentially.…”

Section: Related Workmentioning

confidence: 99%

“…Furthermore, [7,8] design social cost minimizing incentive mechanisms. [9][10][11] study crowdsourcing systems with multiple task requesters and workers. Zhang et al [9] propose a reputation-based incentive protocol for crowdsourcing applications and use repeated giftgiving game to model the interaction between task requesters and workers.…”

Section: Related Workmentioning

confidence: 99%

“…For any user i that is neither a winner nor a loser in the current iteration, the Algorithm gives her an option to choose whether she will enlarge her current bidding bundle Γi to any bundle Γ ′ i that contains Γi (line 5). If after the bundle enlarging αqi|Γ ′ i | − bi ≥ ǫ holds, this user is included in the winner set (line [11][12]. Otherwise, she is given the following two options to choose from.…”

Section: Mechanism Designmentioning

confidence: 99%

“…Aware of the paramount importance of stimulating user participation, the research community has recently developed some gametheoretic incentive mechanisms for MCS systems [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18]. However, most of the existing mechanisms fail to incorporate one important aspect, that is users' quality of information (QoI), into their designs.…”

Section: Introductionmentioning

confidence: 99%

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Quality of Information Aware Incentive Mechanisms for Mobile Crowd Sensing Systems

Jin

Chen

et al. 2015

Proceedings of the 16th ACM International Symposium on Mobile Ad Hoc Networking and Computing

237

112

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Recent years have witnessed the emergence of mobile crowd sensing (MCS) systems, which leverage the public crowd equipped with various mobile devices for large scale sensing tasks. In this paper, we study a critical problem in MCS systems, namely, incentivizing user participation. Different from existing work, we incorporate a crucial metric, called users' quality of information (QoI), into our incentive mechanisms for MCS systems. Due to various factors (e.g., sensor quality, noise, etc.) the quality of the sensory data contributed by individual users varies significantly. Obtaining high quality data with little expense is always the ideal of MCS platforms. Technically, we design incentive mechanisms based on reverse combinatorial auctions. We investigate both the singleminded and multi-minded combinatorial auction models. For the former, we design a truthful, individual rational and computationally efficient mechanism that approximately maximizes the social welfare with a guaranteed approximation ratio. For the latter, we design an iterative descending mechanism that achieves close-tooptimal social welfare while satisfying individual rationality and computational efficiency. Through extensive simulations, we validate our theoretical analysis about the close-to-optimal social welfare and fast running time of our mechanisms.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Mechanism Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Quality of Information Aware Incentive Mechanisms for Mobile Crowd Sensing Systems

Jin

Chen

et al. 2015

Proceedings of the 16th ACM International Symposium on Mobile Ad Hoc Networking and Computing

237

112

View full text Add to dashboard Cite

show abstract

“…Xie et al [20] propose an incentive mechanism composed of a rating system and a reward-based scheme. To allocate rewards between all users, a reputation protocol is employed to investigate users' history and eliminate users with poor reputation.…”

Section: Background and Motivationmentioning

confidence: 99%

Game-Theoretic Recruitment of Sensing Service Providers for Trustworthy Cloud-Centric Internet-of-Things (IoT) Applications

Pouryazdan

Fiandrino

Kantarcı

et al. 2016

2016 IEEE Globecom Workshops (GC Wkshps)

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Abstract-Widespread use of connected smart devices that are equipped with various built-in sensors has introduced the mobile crowdsensing concept to the IoT-driven information and communication applications. Mobile crowdsensing requires implicit collaboration between the crowdsourcer/recruiter platforms and users. Additionally, users need to be incentivized by the crowdsensing platform because each party aims to maximize their utility. Due to the participatory nature of data collection, trustworthiness and truthfulness pose a grand challenge in crowdsensing systems in the presence of malicious users, who either aim to manipulate sensed data or collaborate unfaithfully with the motivation of maximizing their income. In this paper, we propose a game-theoretic approach for trustworthiness-driven user recruitment in mobile crowdsensing systems that consists of three phases: i) user recruitment, ii) collaborative decision making on trust scores, and iii) badge rewarding. Our proposed framework incentivizes the users through a sub-game perfect equilibrium (SPE) and gamification techniques. Through simulations, we show that the platform and user utilities, defined in terms of costs and revenues, can be improved respectively by up to the order of 50% and of at least 15% when compared to fully-distributed and user-centric trustworthy crowdsensing.

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Cost and Quality Trade-Offs in Crowdsourcing

Chen¹

2018

Encyclopedia of Database Systems

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Incentive mechanism and protocol design for crowdsourcing systems

Cited by 54 publications

References 16 publications

Quality of Information Aware Incentive Mechanisms for Mobile Crowd Sensing Systems

Quality of Information Aware Incentive Mechanisms for Mobile Crowd Sensing Systems

Game-Theoretic Recruitment of Sensing Service Providers for Trustworthy Cloud-Centric Internet-of-Things (IoT) Applications

Cost and Quality Trade-Offs in Crowdsourcing

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