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Abstract
The methodology, formulating a reasonable task assignment to find the most suitable workers for a task and achieving the desired objectives, is the most fundamental challenge in spatial crowdsourcing. Many task assignment approaches have been proposed to improve the quality of crowdsourcing results and the number of task assignment and to limit the budget and the travel cost. However, these approaches have two shortcomings: (1) these approaches are commonly based on the attributes influencing the result of task assignment. However, different tasks may have different preferences for individual attributes; (2) the performance and efficiency of these approaches are expected to be improved further. To address the above issues, we proposed a task assignment approach in spatial crowdsourcing based on multiattribute decision-making (TASC-MADM), with the dual objectives of improving the performance as well as the efficiency. Specifically, the proposed approach jointly considers the attributes on the quality of the worker and the distance between the worker and the task, as well as the influence differences caused by the task’s attribute preference. Furthermore, it can be extended flexibly to scenarios with more attributes. We tested the proposed approach in a real-world dataset and a synthetic dataset. The proposed TASC-MADM approach was compared with the RB-TPSC and the Budget-TASC algorithm using the real dataset and the synthetic dataset; the TASC-MADM approach yields better performance than the other two algorithms in the task assignment rate and the CPU cost.
Highlights
Spatial crowdsourcing, first introduced by Kazemi and Shahabiin [1], refers to an economic and efficient solution to participation in completing tasks, such as sensing tasks [2, 3]. e popularity of mobile devices and advanced Internet technologies have made it a popular trend in performing spatial tasks [4, 5]
To address the two problems, we propose a flexible and efficient task assignment approach in spatial crowdsourcing based on multiattribute decision-making (TASC-MADM), which takes into account the distance attribute and the reputation attribute simultaneously, as well as tasks’ different preferences of attributes
We compared TASC-MADM and RB-TPSC under different β values. e results are shown in Figure 1, where the extra allowance per kilometer β varies from 0 ∼ 20 monetary units in 1-unit increments, c 0.5, and Bj is obtained from the dataset. e horizontal axis represents the different settings of β, while the vertical axis represents the different values of the first six metrics
Summary
First introduced by Kazemi and Shahabiin [1], refers to an economic and efficient solution to participation in completing tasks, such as sensing tasks [2, 3]. e popularity of mobile devices and advanced Internet technologies have made it a popular trend in performing spatial tasks [4, 5]. E popularity of mobile devices and advanced Internet technologies have made it a popular trend in performing spatial tasks [4, 5]. Spatial crowdsourcing requires a worker to travel to a given location to perform a given task [6]. Spatial crowdsourcing is becoming a compelling paradigm for recruiting workers to perform the tasks. Due to the openness of crowdsourcing, there are some core issues: (1) how to guarantee the quality of crowdsourcing results and the number of tasks completed; (2) how to control the cost, such as the budget used and the travel cost; and (3) how to ensure the efficiency of task completed. All three core issues in spatial crowdsourcing are involved in task assignment. Us, the task assignment is considered as the most fundamental challenge in spatial crowdsourcing [11] All three core issues in spatial crowdsourcing are involved in task assignment. us, the task assignment is considered as the most fundamental challenge in spatial crowdsourcing [11]
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