Credible and energy-aware participant selection with limited task budget for mobile crowd sensing

2016 ◽  
Vol 43 ◽  
pp. 56-70 ◽  
Author(s):  
Wendong Wang ◽  
Hui Gao ◽  
Chi Harold Liu ◽  
Kin K. Leung
Keyword(s):  
Mobile Crowd Sensing ◽  
Energy Aware ◽  
Crowd Sensing ◽  
Participant Selection ◽  
Mobile Crowd

Energy-Aware Participant Selection for Smartphone-Enabled Mobile Crowd Sensing

2017 ◽  
Vol 11 (3) ◽  
pp. 1435-1446 ◽  
Author(s):  
Chi Harold Liu ◽  
Bo Zhang ◽  
Xin Su ◽  
Jian Ma ◽  
Wendong Wang ◽  
...  
Keyword(s):  
Mobile Crowd Sensing ◽  
Energy Aware ◽  
Crowd Sensing ◽  
Participant Selection ◽  
Selection For ◽  
Mobile Crowd

Dynamic Participant Selection for Large-Scale Mobile Crowd Sensing

2019 ◽  
Vol 18 (12) ◽  
pp. 2842-2855 ◽  
Author(s):  
Hanshang Li ◽  
Ting Li ◽  
Weichao Wang ◽  
Yu Wang
Keyword(s):  
Large Scale ◽  
Mobile Crowd Sensing ◽  
Crowd Sensing ◽  
Participant Selection ◽  
Selection For ◽  
Mobile Crowd

scholarly journals Participant Recruitment Method Aiming at Service Quality in Mobile Crowd Sensing

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Weijin Jiang ◽  
Junpeng Chen ◽  
Xiaoliang Liu ◽  
Yuehua Liu ◽  
Sijian Lv
Keyword(s):  
Data Quality ◽  
Distance Constraint ◽  
Mobile Crowd Sensing ◽  
Constraint Factor ◽  
Data Set ◽  
Selection Scheme ◽  
Crowd Sensing ◽  
Participant Selection ◽  
Mobile Crowd

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.

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