TaskMe

Liu, Yan; Guo, Bin; Wang, Yang; Wu, Wenle; Yu, Zhiwen; Zhang, Daqing

doi:10.1145/2971648.2971709

Cited by 152 publications

(13 citation statements)

References 26 publications

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“…In terms of different classification standards, the problem of task assignment in MCS can have different classification 41 . For instance, according to the number of objectives in the problem, we can classify the task assignment problem as single‐objective‐oriented 32,42 and multiobjective‐oriented 43,44 . We can also classify the task assignment problem as offline 40,42 and online 45 based on the time when the task assignment plan is formulated.…”

Section: Related Workmentioning

confidence: 99%

Task bundling in worker‐centric mobile crowdsensing

Zhao

Yang

Wang

et al. 2021

Int J of Intelligent Sys

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Most existing research about task allocation in mobile crowdsensing mainly focus on requester‐centric mobile crowdsensing (RCMCS), where the requester assigns tasks to workers to maximize his/her benefits. A worker in RCMCS might suffer benefit damage because the tasks assigned to him/her may not maximize his/her benefit. Contrarily, worker‐centric mobile crowdsensing (WCMCS), where workers autonomously select tasks to accomplish to maximize their benefits, does not receive enough attention. The workers in WCMCS can maximize their benefits, but the requester in WCMCS will suffer benefit damage (cannot maximize the number of expected completed tasks). It is hard to maximize the number of expected completed tasks in WCMCS, because some tasks may be selected by no workers, while others may be selected by many workers. In this paper, we apply task bundling to address this issue, and we formulate a novel task bundling problem in WCMCS with the objective of maximizing the number of expected completed tasks. To solve this problem, we design an algorithm named LocTrajBundling which bundles tasks based on the location of tasks and the trajectories of workers. Experimental results show that, compared with other algorithms, our algorithm can achieve a better performance in maximizing the number of expected completed tasks.

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Section: Related Workmentioning

confidence: 99%

Task bundling in worker‐centric mobile crowdsensing

Zhao

Yang

Wang

et al. 2021

Int J of Intelligent Sys

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“…Different task assignment schemes have different assignment criteria, such as travel distance, sensing data quality, total budget, and energy consumption. Among these criteria, travel distance is considered by many schemes 33,46,47 . These schemes are all designed to reduce the distance that workers travel to perform their assigned tasks.…”

Section: Related Workmentioning

confidence: 99%

Repot: Real‐time and privacy‐preserving online task assignment for mobile crowdsensing

Xia

Zhao

Tang

et al. 2020

Trans Emerging Tel Tech

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Recently, the development of Internet of Things and mobile sensor technology has facilitated mobile crowdsensing (MCS) as a popular data collection paradigm. In MCS, requesters publish tasks to the sensing platform, and then the sensing platform assigns tasks to workers who carry mobile sensing devices. In general, the sensing platform matches tasks and workers according to the location and time attributes of tasks and workers. However, most current studies rarely consider location and time attributes, simultaneously. In previous work, it is difficult to achieve real‐time task assignment while protecting worker's location privacy. To tackle the above problem, in this article, we propose a real‐time and privacy‐preserving online task assignment scheme, named Repot. In Repot, the location privacy of workers is protected by using geoindistinguishability. And we design a probabilistic method to quantify the reachability between the obfuscated worker and the task. In addition, the worker‐based distance comparison mechanism and the task‐based distance comparison mechanism are designed to reduce the overall distance of workers in Repot, respectively. The experimental evaluation is performed on a real‐world data set and the results show the feasibility and effectiveness of Repot.

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“…A number of frameworks aimed to recruit participants to maximize the coverage of the sensing area have been recently proposed [25,27,43,47,50,51]. In Reference [25], the authors propose a framework to ensure coverage of the collected data, localization of the participating smartphones, and overall energy efficiency of the data collection process.…”

Section: Related Workmentioning

confidence: 99%

First

Restuccia

Ferraro

Sanders³

et al. 2018

ACM Trans. Sen. Netw.

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Thanks to the collective action of participating smartphone users, mobile crowdsensing allows data collection at a scale and pace that was once impossible. The biggest challenge to overcome in mobile crowdsensing is that participants may exhibit malicious or unreliable behavior, thus compromising the accuracy of the data collection process. Therefore, it becomes imperative to design algorithms to accurately classify between reliable and unreliable sensing reports. To address this crucial issue, we propose a novel Framework for optimizing Information Reliability in Smartphone-based participaTory sensing (FIRST) that leverages mobile trusted participants (MTPs) to securely assess the reliability of sensing reports. FIRST models and solves the challenging problem of determining before deployment the minimum number of MTPs to be used to achieve desired classification accuracy. After a rigorous mathematical study of its performance, we extensively evaluate FIRST through an implementation in iOS and Android of a room occupancy monitoring system and through simulations with real-world mobility traces. Experimental results demonstrate that FIRST reduces significantly the impact of three security attacks (i.e., corruption, on/off, and collusion) by achieving a classification accuracy of almost 80% in the considered scenarios. Finally, we discuss our ongoing research efforts to test the performance of FIRST as part of the National Map Corps project.

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TaskMe

Cited by 152 publications

References 26 publications

Task bundling in worker‐centric mobile crowdsensing

Task bundling in worker‐centric mobile crowdsensing

Repot: Real‐time and privacy‐preserving online task assignment for mobile crowdsensing

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