GP-selector: a generic participant selection framework for mobile crowdsourcing systems

Wang, Jiangtao; Wang, Yasha; Wang, Leye; He, Yuanduo

doi:10.1007/s11280-017-0480-y

Cited by 9 publications

(26 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A number of works [54], [55], [56], [57], [58] leveraged Expectation Maximization (EM) based algorithms to estimate the reliability of participants or mobile devices, which will be used as the weight to infer the ground truth of sensing data. Some other works [59], [60] have adopted unsupervised learning approaches, in which they employ an additional optimization objective to improve the EM-based Yes [23] Yes Yes Yes Yes [24] Yes Yes [28] Yes Yes [29], [30], [31], [32], [33] Yes [34] Yes Yes [35] Yes [36], [37], [38], [39] Yes method. Recently, truth discovery concerning the privacypreserving issue has been studied [61], which infers the missing data using matrix factorization techniques.…”

Section: Sensing Results Aggregationmentioning

confidence: 99%

“…These results indicate that low-SES areas are currently less able to take advantage of the benefits of MCS. In a mobile crowdsourcing framework named GP-Selector [23], the authors developed a multi-classifier based approach to infer if a participant will accept an MCS task or not, where the influencing features are the incentive reward, domain interest, task workload, and privacy concern. In the focused scenario of [24], the authors assume that the participants decide whether to accept the task based on the incentive reward and movement distance.…”

Section: Participant-oriented Learningmentioning

confidence: 99%

“…Then, we can use the simulated data generated by the agents to perform the evaluation. A significant number of studies adopt the simulation-based approach to evaluate their learning-based MCS optimization approaches [23], [25], [27], [29], [31], [32]. We also note that several papers published in top venues choose to conduct an evaluation of both the real-world and simulated data.…”

Section: How To Conduct Evaluationmentioning

confidence: 99%

“…With the rapid technical progress in learning-based artificial intelligence, we notice that it is now an emerging trend to integrate the learning techniques into the research problem of MCS optimization. On the one hand, a group of studies, such as [21], [22], [23], [25], [26], [27], focus on how to understand participation behavior, and then exploit the obtained knowledge to future optimize the MCS process (such as participant selection and task assignment). On the other hand, another category of works, such as [42], [43], [44], [48], [49], [50], leverage the correlation among sensing data (such as spatial-temporal correlation) or data inference techniques to optimize MCS in several aspects, such as reducing the cost in sensing data sampling and discovering truth through sensing results aggregation.…”

mentioning

confidence: 99%

See 3 more Smart Citations

Learning-Assisted Optimization in Mobile Crowd Sensing: A Survey

Wang¹,

Wang

Zhang³

et al. 2019

IEEE Trans. Ind. Inf.

Self Cite

View full text Add to dashboard Cite

Mobile Crowd Sensing (MCS) is a relatively new paradigm for collecting real-time and location-dependent urban sensing data. Given its applications, it is crucial to optimize the MCS process with the objective of maximizing the sensing quality and minimizing the sensing cost. While earlier studies mainly tackle this issue by designing different combinatorial optimization algorithms, there is a new trend to further optimize MCS by integrating learning techniques to extract knowledge, such as participants' behavioral patterns or sensing data correlation. In this article, we perform an extensive literature review of learning-assisted optimization approaches in MCS. Specifically, from the perspective of the participant and the task, we organize the existing work into a conceptual framework, present different learning and optimization methods, and describe their evaluation. Furthermore, we discuss how different techniques can be combined to form a complete solution. In the end, we point out existing limitations which can inform and guide future research directions.

show abstract

Section: Sensing Results Aggregationmentioning

confidence: 99%

Section: Participant-oriented Learningmentioning

confidence: 99%

Section: How To Conduct Evaluationmentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

Learning-Assisted Optimization in Mobile Crowd Sensing: A Survey

Wang¹,

Wang

Zhang³

et al. 2019

IEEE Trans. Ind. Inf.

Self Cite

View full text Add to dashboard Cite

show abstract

“…As predicted by Sarasua et al in 2015, firstly, 24 papers report on benefiting from the knowledge representation capabilities of Semantic Web technologies (SW4HC-Know.Repr.). For example, ontologies of tasks allow improved participant selection in mobile crowdsourcing settings [47] and semantic descriptions of workflows facilitate the crowdsourcing of a constitution [27]. Another line of work focuses on describing the workers, their CVs and skills [6,28,41].…”

Section: Semantic Web For Human Computationmentioning

confidence: 99%

Semantic Web and Human Computation: The status of an emerging field

Sabou

Aroyo

Bontcheva

et al. 2018

View full text Add to dashboard Cite

This editorial paper introduces a special issue that solicited papers at the intersection of Semantic Web and Human Computation research. Research in that inter-disciplinary space dates back a decade, and has been acknowledged as a research line of its own by a seminal research manifesto published in 2015. But where do we stand in 2018? How did this research line evolve during the last decade? How do the papers in this special issue align with the main lines of work of the community? In this editorial we inspect and reflect on the evolution of research at the intersection of Semantic Web and Human Computation. We use a methodology based on Systematic Mapping Studies to collect quantitative bibliographic data which we analyze through the lens of research topics envisioned by the research manifesto to characterize the evolution of research in this area, thus providing a context for introducing the papers of this special issue. We found evidences of a thriving research field; while steadily maturing, the field offers a number of open research opportunities for work where Semantic Web best practices and techniques are applied to support and improve the state-of-the-art in Human Computation, but also for work that exploits the strength of both areas to address scientifically and societally relevant issues.

show abstract