Rational Task Assignment and Path Planning Based on Location and Task Characteristics in Mobile Crowdsensing

Yin, Bo; Li, Jiaqi; Wei, Xuetao

doi:10.1109/tcss.2021.3095946

Cited by 23 publications

(2 citation statements)

References 47 publications

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“…Location and task-characteristics-based task allocation (LTCTA): In [ 39 ], LTCTA is employed to allocate a set of tasks to a group of users and generate a sequence of paths list. LTCTA assesses the rationality of the allocation by considering the geographical information and task features, specifically weighing route distance, task similarity, and task priority.…”

Section: Discussionmentioning

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: Discussionmentioning

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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show abstract

“…Task assignment stands as a critical research aspect in the realm of vehicular crowdsensing [ 11 , 12 , 13 , 14 , 15 ]. The success of vehicular crowdsensing largely hinges on the design of effective task assignment schemes.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Coverage Constraint Task Assignment with Privacy Protection in Vehicular Crowdsensing

Li,

Liu,

Huang

et al. 2023

Sensors

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The increasing popularity of portable smart devices has led to the emergence of vehicular crowdsensing as a novel approach for real-time sensing and environmental data collection, garnering significant attention across various domains. Within vehicular crowdsensing, task assignment stands as a fundamental research challenge. As the number of vehicle users and perceived tasks grows, the design of efficient task assignment schemes becomes crucial. However, existing research solely focuses on task deadlines, neglecting the importance of task duration. Additionally, the majority of privacy protection mechanisms in the current task assignment process emphasize safeguarding user location information but overlook the protection of user-perceived duration. This lack of protection exposes users to potential time-aware inference attacks, enabling attackers to deduce user schedules and device information. To address these issues in opportunistic task assignment for vehicular crowdsensing, this paper presents the minimum number of participants required under the constraint of probability coverage and proposes the User-Based Task Assignment (UBTA) mechanism, which selects the smallest set of participants to minimize the payment cost while measuring the probability of accomplishing perceived tasks by user combinations. To ensure privacy protection during opportunistic task assignment, a privacy protection method based on differential privacy is introduced. This method fuzzifies the sensing duration of vehicle users and calculates the probability of vehicle users completing sensing tasks, thus avoiding the exposure of users’ sensitive data while effectively assigning tasks. The efficacy of the proposed algorithm is demonstrated through theoretical analysis and a comprehensive set of simulation experiments.

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DE-DQN: A Dual-Embedding Based Deep Q-Network for Task Assignment Problem in Spatial Crowdsourcing

Gao

Dejun

Dai

et al. 2023

Lecture Notes in Computer Science

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Rational Task Assignment and Path Planning Based on Location and Task Characteristics in Mobile Crowdsensing

Cited by 23 publications

References 47 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

Probabilistic Coverage Constraint Task Assignment with Privacy Protection in Vehicular Crowdsensing

DE-DQN: A Dual-Embedding Based Deep Q-Network for Task Assignment Problem in Spatial Crowdsourcing

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