Dynamic Delayed-Decision Task Assignment Under Spatial-Temporal Constraints in Mobile Crowdsensing

Yu, Deshui; Zhang, Lichen; Guo, Longjiang

doi:10.1109/tnse.2022.3163925

Cited by 12 publications

(3 citation statements)

References 42 publications

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“…In addition, task assignment strategies based on chance patterns usually require prediction of workers' trajectories, and, although different trajectory prediction algorithms [40,41] have been proposed and proven to be effective to some extent, the accuracy cannot be theoretically guaranteed due to complex and unpredictable realistic conditions, which have a significant impact on the final sensing quality of task assignment. Ding et al [42] proposed a dynamic delayed decision user recruitment problem, and designed three algorithms, among which, semi-Markov prediction algorithm and prediction algorithm for meta-paths were used for user selection to maximise user utility, and delayed decision-based task assignment algorithm determined the time point of task assignment and the set of assigned tasks with high task completion rate, budget utilisation and user diversity. Guo et al [43] investigated the multi-task MCS environment by considering two scenarios of whether workers change their movement trajectory to actively engage in perception and proposed the ActiveCrowd task assignment framework, which, for time-sensitive tasks, requires workers to intentionally move to the task location to minimise the total movement distance.…”

Section: Task Allocation Of Mcsmentioning

confidence: 99%

A Dynamic Task Allocation Framework in Mobile Crowd Sensing with D3QN

Shen

Tang

2023

Sensors

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With the coverage of sensor-rich smart devices (smartphones, iPads, etc.), combined with the need to collect large amounts of data, mobile crowd sensing (MCS) has gradually attracted the attention of academics in recent years. MCS is a new and promising model for mass perception and computational data collection. The main function is to recruit a large group of participants with mobile devices to perform sensing tasks in a given area. Task assignment is an important research topic in MCS systems, which aims to efficiently assign sensing tasks to recruited workers. Previous studies have focused on greedy or heuristic approaches, whereas the MCS task allocation problem is usually an NP-hard optimisation problem due to various resource and quality constraints, and traditional greedy or heuristic approaches usually suffer from performance loss to some extent. In addition, the platform-centric task allocation model usually considers the interests of the platform and ignores the feelings of other participants, to the detriment of the platform’s development. Therefore, in this paper, deep reinforcement learning methods are used to find more efficient task assignment solutions, and a weighted approach is adopted to optimise multiple objectives. Specifically, we use a double deep Q network (D3QN) based on the dueling architecture to solve the task allocation problem. Since the maximum travel distance of the workers, the reward value, and the random arrival and time sensitivity of the sensing tasks are considered, this is a dynamic task allocation problem under multiple constraints. For dynamic problems, traditional heuristics (eg, pso, genetics) are often difficult to solve from a modeling and practical perspective. Reinforcement learning can obtain sub-optimal or optimal solutions in a limited time by means of sequential decision-making. Finally, we compare the proposed D3QN-based solution with the standard baseline solution, and experiments show that it outperforms the baseline solution in terms of platform profit, task completion rate, etc., the utility and attractiveness of the platform are enhanced.

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Section: Task Allocation Of Mcsmentioning

confidence: 99%

A Dynamic Task Allocation Framework in Mobile Crowd Sensing with D3QN

Shen

Tang

2023

Sensors

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“…A common challenge of PCS is to achieve optimal task assignment and path planning. Considerable efforts have been devoted to task assignment and path planning mechanisms that rationally assign tasks to users, based on various metrics such as profit [ 12 , 13 , 14 ], space and time [ 15 , 16 , 17 ], and task coverage [ 18 , 19 ]. Despite the considerable body of research devoted to optimizing PCS systems, notable lacunae persist in the domains of task assignment and path planning.…”

Section: Introductionmentioning

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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“…Whether a mobile crowdsourcing task can be successfully completed by a worker depends mainly on the worker's spatiotemporal behavior and interest preferences. In addition, worker mobility predictions have a significant impact on task completion rates [21]. In this paper, we propose a mobile crowdsourcing task recommendation method based on analyzing users' historical trajectories and task execution records.…”

Section: Introductionmentioning

confidence: 99%

A Task Recommendation Model in Mobile Crowdsourcing

Qiu

et al. 2022

Wireless Communications and Mobile Computing

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With the development of the Internet of Things and the popularity of smart terminal devices, mobile crowdsourcing systems are receiving more and more attention. However, the information overload of crowdsourcing platforms makes workers face difficulties in task selection. This paper proposes a task recommendation model based on the prediction of workers’ mobile trajectories. A recurrent neural network is used to obtain the movement pattern of workers and predict the next destination. In addition, an attention mechanism is added to the task recommendation model in order to capture records that are similar to candidate tasks and to obtain task selection preferences. Finally, we conduct experiments on two real datasets, Foursquare and AMT (Amazon Mechanical Turk), to verify the effectiveness of the proposed recommendation model.

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Dynamic Delayed-Decision Task Assignment Under Spatial-Temporal Constraints in Mobile Crowdsensing

Cited by 12 publications

References 42 publications

A Dynamic Task Allocation Framework in Mobile Crowd Sensing with D3QN

A Dynamic Task Allocation Framework in Mobile Crowd Sensing with D3QN

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

A Task Recommendation Model in Mobile Crowdsourcing

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