High quality participant recruitment in vehicle-based crowdsourcing using predictable mobility

He, Zongjian; Cao, Jiannong; Liu, Xuefeng

doi:10.1109/infocom.2015.7218644

Cited by 152 publications

(47 citation statements)

References 19 publications

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“…There are lots of incentive mechanisms which can be classified into nonmonetary incentives [18][19][20][21][22][23][24][25][26][27][28][29] and monetary incentives [30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45]. Paying for sensed data in crowd sensing tasks is the most intuitive incentive.…”

Section: Related Workmentioning

confidence: 99%

“…The crowdsourcer is a new role who assesses the posted contributions' quality. He et al [45] propose a recruitment strategy in vehicle-based crowdsourcing through taking full advantage of predictable mobility patterns of vehicles, which bring a new insight to improve the quality of crowd sensing system. However, the behaviors of human are affected by many factors.…”

Section: Related Workmentioning

confidence: 99%

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Quality-Aware Incentive Mechanism for Mobile Crowd Sensing

Jiang

et al. 2017

Journal of Sensors

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Mobile crowd sensing (MCS) is a novel sensing paradigm which can sense human-centered daily activities and the surrounding environment. The impact of mobility and selfishness of participants on the data reliability cannot be ignored in most mobile crowd sensing systems. To address this issue, we present a universal system model based on the reverse auction framework and formulate the problem as the Multiple Quality Multiple User Selection (MQMUS) problem. The quality-aware incentive mechanism (QAIM) is proposed to meet the quality requirement of data reliability. We demonstrate that the proposed incentive mechanism achieves the properties of computational efficiency, individual rationality, and truthfulness. And meanwhile, we evaluate the performance and validate the theoretical properties of our incentive mechanism through extensive simulation experiments.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Quality-Aware Incentive Mechanism for Mobile Crowd Sensing

Jiang

et al. 2017

Journal of Sensors

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“…Theorem 1 indicates that the SP will focus only on the system utility once the competitive ratio τ reaches the required maximum competitive ratio τ max , which is lower than or equal to k. Theorem 2 indicates that that the SP will focus only on the drop rate when the competitive ratio τ drops below than the required minimal competitive ratio τ min , which is greater than or equal to 1. Thus, the workload constraint in (7) can be calculated as follows:…”

Section: The Derivation Of the Workload Constraintmentioning

confidence: 99%

“…A common challenge for the system is to identify the participants (both the requesters and mobile workers) who can contribute more value to the system and motivate their participation in the system. Although many MC systems have been proposed, most of them (e.g., [1,2,7,8]) have simply assumed that the workers voluntarily participate in the system to perform sensing tasks. However, mobile users may not be willing to join an MC system unless they will receive adequate reward.…”

Section: Introduction and Related Workmentioning

confidence: 99%

CAPR: context‐aware participant recruitment mechanism in mobile crowdsourcing

Zhang

et al. 2016

Wireless Communications

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With the advances of sensing, wireless communication, and mobile computing, mobile crowdsourcing has become a new paradigm for data collection and retrieval that has attracted considerable attention. This paper addresses the fundamental research issue in mobile crowdsourcing: Which participants should be selected as winners in each time slot with the aim of maximizing the total utility of the service provider in the long term? First, a double-sided combinatorial auction model is introduced to describe the relationships between the mobile users and requesters from the perspective of supply and demand at a given time. Then, the coupling between the utility values of the system in different time slots is investigated. Based on the aforementioned analyses, this paper proposes a context-aware participant recruitment mechanism, in which the mobile crowdsourcing system dynamically adjusts the participant recruitment mechanism depending on the ratio between the numbers of mobile users and requesters. Context-aware participant recruitment consists of two main components: (1) a heuristic algorithm based on the greedy strategy to determine the winning participants and (2) a critical payment scheme, which guarantees the rationality of the proposed mechanism. Finally, extensive simulations demonstrate that the proposed mechanism achieves high system utility in the long term.

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“…We seek to maximize the profit of a trading platform, under the considerations of payment from publishers and costs from participants. In VPSN, vehicles have the distinct advantage of predictable mobility, which brings new insight into improving the crowdsourcing quality [5]. So predictable mobility of vehicles is adopted when designing a schedule of trading platforms to make profits.…”

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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High quality participant recruitment in vehicle-based crowdsourcing using predictable mobility

Cited by 152 publications

References 19 publications

Quality-Aware Incentive Mechanism for Mobile Crowd Sensing

Quality-Aware Incentive Mechanism for Mobile Crowd Sensing

CAPR: context‐aware participant recruitment mechanism in mobile crowdsourcing

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

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