ISIATasker: Task Allocation for Instant-SensingߝInstant-Actuation Mobile Crowdsensing

Yin, Houchun; Yu, Zhiwen; Wang, Liang; Wang, Jiangtao; Han, Lei; Guo, Bin

doi:10.1109/jiot.2021.3095160

Cited by 16 publications

(1 citation statement)

References 31 publications

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“…Sensitivity to the duration of each task and the ability of participants are considered to increase the number of tasks completed, as found in [ 27 ]. In order to assign tasks to participants, a two-stage task allocation framework is proposed by considering the time interval of task detection and execution given in [ 28 ]. The authors of [ 29 ] propose a multiple time-constrained task allocation problem in terms of semi-opportunistic mobile crowdsensing (SO-MTTA), with the goal of maximizing the sensing value obtained by the platform.…”

Section: Related Workmentioning

confidence: 99%

Task Assignment and Path Planning Mechanism Based on Grade-Matching Degree and Task Similarity in Participatory Crowdsensing

He,

Wang,

Zhao

et al. 2024

Sensors

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Participatory crowdsensing (PCS) is an innovative data sensing paradigm that leverages the sensors carried in mobile devices to collect large-scale environmental information and personal behavioral data with the user’s participation. In PCS, task assignment and path planning pose complex challenges. Previous studies have only focused on the assignment of individual tasks, neglecting or overlooking the associations between tasks. In practice, users often tend to execute similar tasks when choosing assignments. Additionally, users frequently engage in tasks that do not match their abilities, leading to poor task quality or resource wastage. This paper introduces a multi-task assignment and path-planning problem (MTAPP), which defines utility as the ratio of a user’s profit to the time spent on task execution. The optimization goal of MATPP is to maximize the utility of all users in the context of task assignment, allocate a set of task locations to a group of workers, and generate execution paths. To solve the MATPP, this study proposes a grade-matching degree and similarity-based mechanism (GSBM) in which the grade-matching degree determines the user’s income. It also establishes a mathematical model, based on similarity, to investigate the impact of task similarity on user task completion. Finally, an improved ant colony optimization (IACO) algorithm, combining the ant colony and greedy algorithms, is employed to maximize total utility. The simulation results demonstrate its superior performance in terms of task coverage, average task completion rate, user profits, and task assignment rationality compared to other algorithms.

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Section: Related Workmentioning

confidence: 99%

Task Assignment and Path Planning Mechanism Based on Grade-Matching Degree and Task Similarity in Participatory Crowdsensing

He,

Wang,

Zhao

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

Task reduction using regression-based missing data imputation in sparse mobile crowdsensing

2022

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Systematic survey on artificial intelligence based mobile crowd sensing and sourcing solutions: Applications and security challenges

Nasser,

Mizouni,

Singh

et al. 2024

Ad Hoc Networks

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ISIATasker: Task Allocation for Instant-SensingߝInstant-Actuation Mobile Crowdsensing

Abstract: 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.

Cited by 16 publications

References 31 publications

Task Assignment and Path Planning Mechanism Based on Grade-Matching Degree and Task Similarity in Participatory Crowdsensing

Task Assignment and Path Planning Mechanism Based on Grade-Matching Degree and Task Similarity in Participatory Crowdsensing

Task reduction using regression-based missing data imputation in sparse mobile crowdsensing

Systematic survey on artificial intelligence based mobile crowd sensing and sourcing solutions: Applications and security challenges

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