CENTURION: Incentivizing multi-requester mobile crowd sensing

Jin, Haiming; Lu, Shan; Nahrstedt, Klara

doi:10.1109/infocom.2017.8057111

Cited by 93 publications

(33 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although there have been many studies on the incentive mechanism design of crowdsourcing systems, a lot of them (e.g., [9]- [14]) do not consider quality requirements, so they are not applicable to the multimedia crowdsourcing applications.…”

Section: Literature Reviewmentioning

confidence: 99%

“…As a result, any worker who selects a task will achieve a zero payoff, regardless of his quality capability and the task that he selects (including the choice of not to participate). 14 the rewards in (8a). We can replace the quality requirement value with Q m in (8a) with (9a).…”

Section: Propositionmentioning

confidence: 99%

“…Based on the above discussions, we reformulate Problem 3 as follows: 14 This is under the assumption that the requester has the complete information about the workers' quality levels and costs. In reality, the requester can set a reward which covers the workers' costs but provides an extremely small payoff to motivate the workers.…”

Section: Propositionmentioning

confidence: 99%

See 2 more Smart Citations

Multimedia Crowdsourcing With Bounded Rationality: A Cognitive Hierarchy Perspective

Shao

Cheung

Huang

2019

IEEE J. Select. Areas Commun.

View full text Add to dashboard Cite

In multimedia crowdsourcing, the requester's quality requirements and reward decisions will affect the workers' task selection strategies and the quality of their multimedia contributions. In this paper, we present a first study on how the workers' bounded cognitive rationality interacts with and affects the decisions and performance of a multimedia crowdsourcing system. Specifically, we consider a two-stage model, where a requester first determines the reward and the quality requirement for each task, and the workers select the tasks to accomplish accordingly. First, we consider the benchmark case where users are fully rational, and derive the requester's optimal rewards and quality requirements for the tasks. Next, we focus on the more practical bounded rational case by modeling the workers' task selection behaviors using the cognitive hierarchy theory. Comparing with the fully rational benchmark, we show that the requester can increase her profit by taking advantage of the workers' bounded cognitive rationality, especially when the workers' population is large or the workers' average cognitive level is low. When the workers' average cognitive level is very high, however, the equilibrium under the practical bounded rational model converges to that under the benchmark fully rational model. It is because workers at different levels make decisions sequentially and high cognitive level workers can accurately predict other users' strategies. Under both the fully and bounded rational models, we show that if workers are Part of this paper was presented in GLOBECOM'18 [1]. The major differences between the journal and conference versions are: a) Multimedia crowdsourcing background: In [1], we considered a general crowdsourcing platform and studied only the rewards for the tasks. In this journal submission, we further consider the quality requirement together with the reward for each task, which is important for multimedia applications. b) A more general system model: We extend our crowdsourcing model from two tasks to multiple tasks, which involves solving a challenging mixed integer non-linear programming problem. c) Performance evaluation: In this journal submission, we have some new simulation results. We demonstrate the impacts of additional parameters that were not considered in [1]. To summarize, the overall difference between the journal and conference versions exceeds 60%.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Section: Propositionmentioning

confidence: 99%

See 1 more Smart Citation

Multimedia Crowdsourcing With Bounded Rationality: A Cognitive Hierarchy Perspective

Shao

Cheung

Huang

2019

IEEE J. Select. Areas Commun.

View full text Add to dashboard Cite

show abstract

“…Jin et al [35] propose an integrated framework for multirequester mobile crowdsourcing systems, called CENTURION, consisting of a truth discovery mechanism and an incentive mechanism. The truth discovery mechanism takes workers' reliability into consideration, and calculates highly accurate aggregated results for the requesters.…”

Section: A Truth Discovery In Crowdsourcingmentioning

confidence: 99%

Incentivizing the Workers for Truth Discovery in Crowdsourcing with Copiers

Jiang

Niu

et al. 2019

2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS)

View full text Add to dashboard Cite

Crowdsourcing has become an efficient paradigm for performing large scale tasks. Truth discovery and incentive mechanism are fundamentally important for the crowdsourcing system. Many truth discovery methods and incentive mechanisms for crowdsourcing have been proposed. However, most of them cannot be applied to deal with the crowdsourcing with copiers. To address the issue, we formulate the problem of maximizing the social welfare such that all tasks can be completed with the least confidence for truth discovery. We design an incentive mechanism consisting truth discovery stage and reverse auction stage. In truth discovery stage, we estimate the truth for each task based on both the dependence and accuracy of workers. In reverse auction stage, we design a greedy algorithm to select the winners and determine the payment. Through both rigorous theoretical analysis and extensive simulations, we demonstrate that the proposed mechanisms achieve computational efficiency, individual rationality, truthfulness, and guaranteed approximation. Moreover, our truth discovery method shows prominent advantage in terms of precision when there are copiers in the crowdsourcing systems.

show abstract

“…For this reason, a number of incentive mechanisms have been proposed to increase not only the collected amount of data, but also its quality. Furthermore, a number of frameworks aimed to recruit participants in order to maximize the coverage of the sensing area have been recently proposed [30,61,85,86,104,182,183,208], including detailed reviews of the related literature [58,81,147]. Therefore, in this paper we focus on the QoI Estimation and Enforcement steps of the QoI loop, which has received relatively less attention.…”

Section: A Comprehensive Framework For Qoi In Mobile Crowdsensingmentioning

confidence: 99%

Quality of Information in Mobile Crowdsensing

Restuccia

Ghosh

Bhattacharjee

et al. 2017

ACM Trans. Sen. Netw.

153

View full text Add to dashboard Cite

Smartphones have become the most pervasive devices in people's lives, and are clearly transforming the way we live and perceive technology. Today's smartphones benefit from almost ubiquitous Internet connectivity and come equipped with a plethora of inexpensive yet powerful embedded sensors, such as accelerometer, gyroscope, microphone, and camera. This unique combination has enabled revolutionary applications based on the mobile crowdsensing paradigm, such as real-time road traffic monitoring, air and noise pollution, crime control, and wildlife monitoring, just to name a few. Differently from prior sensing paradigms, humans are now the primary actors of the sensing process, since they become fundamental in retrieving reliable and up-to-date information about the event being monitored. As humans may behave unreliably or maliciously, assessing and guaranteeing Quality of Information (QoI) becomes more important than ever. In this paper, we provide a new framework for defining and enforcing the QoI in mobile crowdsensing, and analyze in depth the current state-of-the-art on the topic. We also outline novel research challenges, along with possible directions of future work. F. Restuccia et al.Not only are today's smartphones ubiquitous devices, they are also equipped with a plethora of embedded multi-modal sensors; integrate wireless communication technologies such as 4G/WiFi Internet connectivity, and possess complex processing capabilities. For example, the cameras on smartphones can be used as video and image sensors [29], the microphone can be used as an acoustic sensor [39,154,206], and the embedded global positioning system (GPS) receiver can be used to gather accurate location information, while gyroscopes, accelerometers, and proximity sensors can be used to extract contextual information about the users, such as driving or walking states [75,122]. Further, additional sensors can be easily interfaced with smartphones via Bluetooth or wired connections, such as temperature, air quality, and humidity [69,168].These technological features, combined with the advanced sensing capability of humans, has spurred a significant amount of research from both academia and industry, which together have proposed over the last 10 years a myriad of applications based on the emerging mobile crowdsensing paradigm 1 . Mobile crowdsensing empowers ordinary citizens (or users 2 ) with the capability to actively monitor various phenomena pertaining to themselves (e.g., health, social connections) or their community (e.g., environment). This rich information or inference about themselves or the community may also be sent back to the participating or other concerned users to improve their life experiences, thus influencing their choices. Real-life applications, which can take advantage of both low-level sensor data and high-level user activities, range from real-time traffic monitoring applications, to environmental pollution monitoring, crime monitoring, and social networking, just to name a few. For a survey on mobile crowdsensin...

show abstract

CENTURION: Incentivizing multi-requester mobile crowd sensing

Cited by 93 publications

References 37 publications

Multimedia Crowdsourcing With Bounded Rationality: A Cognitive Hierarchy Perspective

Multimedia Crowdsourcing With Bounded Rationality: A Cognitive Hierarchy Perspective

Incentivizing the Workers for Truth Discovery in Crowdsourcing with Copiers

Quality of Information in Mobile Crowdsensing

Contact Info

Product

Resources

About