Abstract
With the rapid popularization and application of smart sensing devices, mobile crowd sensing (MCS) has made rapid development. MCS mobilizes personnel with various sensing devices to collect data. Task distribution as the key point and difficulty in the field of MCS has attracted wide attention from scholars. However, the current research on participant selection methods whose main goal is data quality is not deep enough. Different from most of these previous studies, this paper studies the participant selection scheme on the multitask condition in MCS. According to the tasks completed by the participants in the past, the accumulated reputation and willingness of participants are used to construct a quality of service model (QoS). On the basis of maximizing QoS, two heuristic greedy algorithms are used to solve participation; two options are proposed: task‐centric and user‐centric. The distance constraint factor, integrity constraint factor, and reputation constraint factor are introduced into our algorithms. The purpose is to select the most suitable set of participants on the premise of ensuring the QoS, as far as possible to improve the platform’s final revenue and the benefits of participants. We used a real data set and generated a simulation data set to evaluate the feasibility and effectiveness of the two algorithms. Detailedly compared our algorithms with the existing algorithms in terms of the number of participants selected, moving distance, and data quality. During the experiment, we established a step data pricing model to quantitatively compare the quality of data uploaded by participants. Experimental results show that two algorithms proposed in this paper have achieved better results in task quality than existing algorithms.
Highlights
The rapid development of smart sensing technology and the widespread popularity of mobile smart devices have made it possible for the holder of each mobile device to become a sensing unit [1] which has led to the rapid development of mobile crowd sensing(MCS) [2, 3]
When pursuing higher quality of service model (QoS), the time required for the participant to complete the task will be higher, which increases the cost of the platform
This paper studies the participant selection method in the multitask situation in MCS
Summary
The rapid development of smart sensing technology and the widespread popularity of mobile smart devices have made it possible for the holder of each mobile device to become a sensing unit [1] which has led to the rapid development of mobile crowd sensing(MCS) [2, 3]. The use of mobile devices to build an interactive and participatory sensor network allows ordinary users to participate in the data collection process, which makes the data collection technology under the big data environment highly developed. Compared with traditional static sensing technology, MCS utilizes existing sensing equipment and communication infrastructures, saving the expense of building additional sensing equipment [4]. Compared with traditional wireless perception technology, MCS pays more attention to and emphasizes the participation process of participants in the collection process and the decisive role of perception data [10]
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