A Survey of Incentive Mechanisms for Participatory Sensing

Gao, Hui; Liu, Chi Harold; Wang, Wendong; Zhao, Jianxin; Song, Zheng; Su, Xin; Crowcroft, Jon; Leung, Kin K.

doi:10.1109/comst.2014.2387836

Cited by 176 publications

(84 citation statements)

References 79 publications

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“…If ground truth can be verified, human observation errors in mobile sensing can also be linked to incentives [27]. We want to direct interested readers to a recent survey paper on incentives [12]. Similar to the previous section, we evaluate the same eight existing mobile data collection frameworks against the issues discussed in this section.…”

Section: Incentivesmentioning

confidence: 95%

Mobile Data Collection Frameworks

Cao

Chen

et al. 2015

Proceedings of the 2015 Workshop on Mobile Big Data

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Mobile phones equipped with powerful sensors have become ubiquitous in recent years. Mobile sensing applications present an unprecedented opportunity to collect and analyze information from mobile devices. Much of the work in mobile sensing has been done on designing monolithic applications but inadequate attention has been paid to general mobile data collection frameworks. In this paper, we provide a survey on how to build a general purpose mobile data collection framework. We identify the basic requirements and present an architecture for such a framework. We survey existing works to summarize existing approaches to address the basic requirements. Eight major mobile data collection frameworks are compared with respect to the requirements as well as additional issues on privacy, energy and incentives.

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Section: Incentivesmentioning

confidence: 95%

Mobile Data Collection Frameworks

Cao

Chen

et al. 2015

Proceedings of the 2015 Workshop on Mobile Big Data

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“…Interested readers are directed to recent reviews by several authors (see e.g. Gao et al, 2015;Jaimes et al, 2015;Restuccia et al, 2015).…”

Section: Monetary Incentivesmentioning

confidence: 99%

“…Crowdsensing platforms, in turn, rely on the active commitment of users to provide data and observations. How well users are incentivised to do so is a key success factor for crowdsensing (Gao et al, 2015;Jaimes, Vergara-Laurens, & Raij, 2015;Restuccia, Das, & Payton, 2015).…”

Section: Background and Motivationmentioning

confidence: 99%

Crowdsensing-based transportation services — An analysis from business model and sustainability viewpoints

Heiskala

Jokinen

Tinnilä

2016

Research in Transportation Business & Management

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“…It was originally proposed recruiting ordinary citizens to collect and share massive amounts of sensory data using their portable smart devices [1]. Vehicular participatory sensing networks (VPSNs) is a typical participatory sensing architecture in vehicular sensor networks (VSN).…”

Section: Introductionmentioning

confidence: 99%

“…There are three categories of the main stakeholders in a general participatory sensing system: task publishers, trading platforms, and participants [1], which is showed in Figure 1. A task publisher is the provider of tasks.…”

Section: Introductionmentioning

confidence: 99%

Task and Participant Scheduling of Trading Platforms in Vehicular Participatory Sensing Networks

Shi

Song

Gao

et al. 2016

Sensors

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The vehicular participatory sensing network (VPSN) is now becoming more and more prevalent, and additionally has shown its great potential in various applications. A general VPSN consists of many tasks from task, publishers, trading platforms and a crowd of participants. Some literature treats publishers and the trading platform as a whole, which is impractical since they are two independent economic entities with respective purposes. For a trading platform in markets, its purpose is to maximize the profit by selecting tasks and recruiting participants who satisfy the requirements of accepted tasks, rather than to improve the quality of each task. This scheduling problem for a trading platform consists of two parts: which tasks should be selected and which participants to be recruited? In this paper, we investigate the scheduling problem in vehicular participatory sensing with the predictable mobility of each vehicle. A genetic-based trading scheduling algorithm (GTSA) is proposed to solve the scheduling problem. Experiments with a realistic dataset of taxi trajectories demonstrate that GTSA algorithm is efficient for trading platforms to gain considerable profit in VPSN.

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A Survey of Incentive Mechanisms for Participatory Sensing

Cited by 176 publications

References 79 publications

Mobile Data Collection Frameworks

Mobile Data Collection Frameworks

Crowdsensing-based transportation services — An analysis from business model and sustainability viewpoints

Task and Participant Scheduling of Trading Platforms in Vehicular Participatory Sensing Networks

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