On Designing Strategy-Proof Budget Feasible Online Mechanisms for Mobile Crowdsensing With Time-Discounting Values

Zheng, Zhenzhe; Yang, Shuo; Xie, Jiapeng; Wu, Fan; Gao, Xiaofeng; Chen, Guihai

doi:10.1109/tmc.2020.3034499

Cited by 17 publications

(13 citation statements)

References 45 publications

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“…Zhang et al [50] formulated the incentive model with maximizing the reliability of collected sensing data and task coverage. Authors in [51] investigated the problem of online incentive mechanisms by considering time-sensitive tasks. They proposed a method to determine a time-dependent threshold to select participants and calculate payments.…”

Section: Incentive Mechanisms For Mcsmentioning

confidence: 99%

A UAV-Assisted Multi-Task Allocation Method for Mobile Crowd Sensing

Gao

Feng

Xiao

et al. 2023

IEEE Trans. on Mobile Comput.

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Mobile crowd sensing (MCS) with human participants has been proposed as an efficient way of collecting data for smart cities applications. However, there often exist situations where humans are not able or reluctant to reach the target areas, due to for example traffic jams or bad road conditions. One solution is to complement manual data collection with autonomous data collection using unmanned aerial vehicles (UAVs) equipped with various sensors. In this paper, we focus on the scenarios of UAV-assisted MCS and propose a task allocation method, called "UMA" (UAV-assisted Multi-task Allocation method) to optimize the sensing coverage and data quality. The method incentivizes human participants to contribute sensing data from nearby points of interest (PoIs), with a limited budget. Meanwhile, the method jointly considers the optimization of task assignment and trajectory scheduling. It schedules the trajectories of UAVs, considering the locations of human participants, other UAVs and PoIs which are rarely visited by human participants. In detail, UAVs take care of two tasks in our proposal. One is to calibrate the data collected by the human participants whom the UAVs come across along their trajectories. The other is to collect data from the PoIs which are not covered by other UAVs or human participants. We apply deep reinforcement learning to schedule UAVs moving trajectories and sensing activities in order to minimize the overall energy cost. We evaluate the proposed scheme via simulation using two real data sets. The results show that our proposal outperforms the compared methods, in terms of coverage completed ratio, calibrating ratio, energy efficiency, and task fairness.

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Section: Incentive Mechanisms For Mcsmentioning

confidence: 99%

A UAV-Assisted Multi-Task Allocation Method for Mobile Crowd Sensing

Gao

Feng

Xiao

et al. 2023

IEEE Trans. on Mobile Comput.

View full text Add to dashboard Cite

show abstract

“…The platform needs to predict the qualities of the workers arriving later. Thus, as each worker arrives, the platform must decide whether to pick up or drop off the worker [63] . If the worker is selected, the workers arriving later may have higher sensing qualities.…”

Section: Related Workmentioning

confidence: 99%

“…In contrast, if the worker is dropped, it is possible that the workers arriving later will have lower sensing qualities or no workers will arrive in a limited time. Moreover, the worker selection problem becomes more complex if the sensing qualities of the workers are added to the workers’ bids [63] . Because in this case the optimization objectives for worker selection change from 1-dimension to 2-dimension, and the complexity increases dramatically.…”

Section: Related Workmentioning

confidence: 99%

“…Because in this case the optimization objectives for worker selection change from 1-dimension to 2-dimension, and the complexity increases dramatically. For example, earlier workers may have higher sensing qualities and higher bids, while some have lower sensing qualities and lower bids [63] . Because it is still being determined whether there may be workers with high sensing qualities but low bids later, making the strategy design more difficult.…”

Section: Related Workmentioning

confidence: 99%

“…Because it is still being determined whether there may be workers with high sensing qualities but low bids later, making the strategy design more difficult. In addition, time-sensitive tasks further complicate the problem because they generate values that vary with time [63] . For example, for urgent abrupt events such as a communication jam, the earlier the data is reported, the higher the value will be obtained.…”

Section: Related Workmentioning

confidence: 99%

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