Online Organizing Large-Scale Heterogeneous Tasks and Multi-Skilled Participants in Mobile Crowdsensing

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

doi:10.1109/tmc.2021.3132616

Cited by 7 publications

(3 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In terms of sensing tasks and the time and location of mobile users, there are many research works on task assignment and path planning to encourage mobile users to complete sensing tasks of high quality in PCS [ 8 , 22 , 23 ]. These studies mainly concern the task deadline instead of the duration needed to complete the task.…”

Section: Related Workmentioning

confidence: 99%

“…Here, |G i | is the number of execution tasks that are set, G i . Equation (8) indicates that the time u i ∈ U to complete the execution tasks G i ⊆ T should not exceed the time budget tb i . Equation (9) ensures that each task is allocated to only one user.…”

Section: Definition 4 (Multi-task Assignment and Path-planning Problem)mentioning

confidence: 99%

“…Participatory crowdsensing (PCS) has the potential to bring observations of higher quality since the end-user is conscious of carrying out a sensing task. Furthermore, supporting incentive mechanisms allow assigning tasks to the most effective crowd [ 8 ], while complementary path-planning schemes allow for maximizing the number of tasks that each participant may achieve [ 9 ]. In recent years, PCS has permeated a broad range of applications, such as checking traffic conditions [ 10 ], building smart cities [ 10 ], detecting air quality [ 11 ], etc.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

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.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Definition 4 (Multi-task Assignment and Path-planning Problem)mentioning

confidence: 99%