Recruitment Framework for Participatory Sensing Data Collections

By Sasank Reddy, Deborah Estrin, Mani B. Srivastava. International Conference on Pervasive Computing, 2010

439 citations recorded by the source corpus.

Subfields: Computer Science Applications, Computer Vision and Pattern Recognition, Transportation

View Paper: https://www.semanticscholar.org/paper/6baaf5cfdfc532f598b2d67549826172a4a372c5

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In Computer Science Applications

In Computer Vision and Pattern Recognition

In Transportation

Citations in the corpus

Self-constructive high-rate system energy modeling for battery-powered mobile systems (2011, methodology): “We report the implementation and evaluation of Sesame on a laptop and a smartphone.”
Opportunities in mobile crowd sensing (2014, background): “Reddy et al. considered the participatory sensing paradigm and developed a recruitment framework to enable organizers to identify well-suited participants for data collections based on geographic and temporal availability as well as participation habits [12].”
A Survey on Mobile Crowdsensing Systems: Challenges, Solutions, and Opportunities (2019, neutral): “To illustrate with few examples, in [272]”
A Survey on Security, Privacy, and Trust in Mobile Crowdsourcing (2018, methodology): “Different from the work in [81], the authors considered the influence of data correlation and tackled the problem by clustering the sensors into disjoint independent groups based on their relationships.”
On truth discovery in social sensing: A maximum likelihood estimation approach (2012, background): “Examples include documenting the quality of roads [25], the level of pollution in a city [20], or reporting garbage cans on campus [24].”
GreenGPS: a participatory sensing fuel-efficient maps application (2010, background): “ground is to provide the right incentives to the individuals (who are part of the system) [32].”
Incentive Mechanisms for Crowdsensing: Crowdsourcing With Smartphones (2016, background): “In [34], Reddy et al. developed recruitment frameworks to enable the crowdsourcer to identify well-suited participants for sensing services.”
Crowdsourcing to smartphones: incentive mechanism design for mobile phone sensing (2012, background): “In [21], Reddy et al. developed recruitment frameworks to enable the platform to identify well-suited participants for sensing services.”
From participatory sensing to Mobile Crowd Sensing (2014, background): “In [19], Reddy et al. developed a recruitment framework to enable organizers to identify well-suited participants for data collections based on geographic and temporal availability as well as participation habits.”
Mobile Crowd Sensing and Computing (2015, background): “Reddy et al. [2010] proposed a recruitment framework to identify well-suited participants for data collections based on spatial-temporal availability and participation habits.”