Towards Personalized Task-Oriented Worker Recruitment in Mobile Crowdsensing

Wang, Zhen; Zhao, Jing; Hu, Jiahui; Zhu, Tianqing; Wang, Qian; Ren, Ju; Li, Chao

doi:10.1109/tmc.2020.2973990

Cited by 55 publications

(18 citation statements)

References 35 publications

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“…Authors in [38] proposed a task allocation method that recommended a task to participants based on their preferences and reliability levels. Wang et al [39] argued that tasks of MCS were usually time-sensitive and location-dependent. Therefore, they proposed a task allocation task method that took task information, such as time and location, into consideration.…”

Section: Task Allocation Methods 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: Task Allocation Methods 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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“…To overcome the limitations of the content-based method and the collaborative method, a hybrid recommendation approach is proposed in [13] to predict the probability of participants choosing each task. Wang et al [14] exploited the content information to accu rately model participants' preference on tasks and utilized the Logit model to integrate the heterogeneous factors into a single framework to predict the matching probability of each task-participant pair. Although these studies can achieve task recommendations, they need to predefine the factors affecting participant preferences, this is not practical as the influential factors are quite complex, and a full map of participant profiles needs to be preexisted.…”

Section: A Task Recommendation In Mobile Crowdsensingmentioning

confidence: 99%

“…Most previous studies have used the logistic regression model [12]- [14] to learn participants' preferences to realize task recommendations. However, in these studies, the factors affecting participant preferences need to be predefined, which is not practical.…”

mentioning

confidence: 99%

Profile-Free and Real-Time Task Recommendation in Mobile Crowdsensing

Yang

et al. 2021

IEEE Trans. Comput. Soc. Syst.

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This document is the author's post-print version, incorporating any revisions agreed during the peer-review process. Some differences between the published version and this version may remain and you are advised to consult the published version if you wish to cite from it.This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination.

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“…Then, the authors proposed two greedy-based approaches to select an assistant group according to the circumstance of principal group. Wang et al [23] combined the content information with context information of tasks and unified all the factors together to measure the preference score of a user to a task. Then, the user with the maximum matching probability is selected to complete tasks.…”

Section: Related Workmentioning

confidence: 99%

Social-Aware Task Allocation in Mobile Crowd Sensing

Zhu

Guo

2020

Wireless Communications and Mobile Computing

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Task allocation is a significant issue in crowd sensing, which trades off the data quality and sensing cost. Existing task allocation works are based on the assumption that there is plenty of users available in the candidate pool. However, for some specific applications, there may be only a few candidate users, resulting in the poor completion of tasks. To tackle this problem, in this paper, we investigate the task allocation problem with the assistance of social networks. We select a subset of users; if a user can not complete the task, he can propagate the task information to his friends. The object of this problem is to maximize the expected number of completed tasks. We prove that the task allocation problem is an NP-hard and submodular problem and then propose a native greedy selection (NGS) algorithm, which selects the user with maximum margin gain in each round. To improve the efficiency of the NGS algorithm, we further propose a fast greedy selection algorithm (FGS), which selects the user who can actually complete the maximum number of tasks. Experimental results show that although FGS gets slightly worse results in terms of the expected number of completed tasks, it can greatly reduce the running time of seed selection.

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Towards Personalized Task-Oriented Worker Recruitment in Mobile Crowdsensing

Cited by 55 publications

References 35 publications

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

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

Profile-Free and Real-Time Task Recommendation in Mobile Crowdsensing

Social-Aware Task Allocation in Mobile Crowd Sensing

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