Next Article in Journal
An Intelligent Smart Plug with Shared Knowledge Capabilities
Next Article in Special Issue
Participant Service Ability Aware Data Collecting Mechanism for Mobile Crowd Sensing
Previous Article in Journal
Structural Health Monitoring (SHM) and Determination of Surface Defects in Large Metallic Structures using Ultrasonic Guided Waves
Previous Article in Special Issue
Image Segmentation Based on Dynamic Particle Swarm Optimization for Crystal Growth
Open AccessArticle

User Characteristic Aware Participant Selection for Mobile Crowdsensing

by Dapeng Wu 1,2,3,*, Haopeng Li 1,2,3 and Ruyan Wang 1,2,3
School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
Key Laboratory of Optical Communication and Networks, Chongqing 400065, China
Key Laboratory of Ubiquitous Sensing and Networking, Chongqing 400065, China
Author to whom correspondence should be addressed.
Sensors 2018, 18(11), 3959;
Received: 10 October 2018 / Revised: 5 November 2018 / Accepted: 12 November 2018 / Published: 15 November 2018
Mobile crowdsensing (MCS) is a promising sensing paradigm that leverages diverse embedded sensors in massive mobile devices. One of its main challenges is to effectively select participants to perform multiple sensing tasks, so that sufficient and reliable data is collected to implement various MCS services. Participant selection should consider the limited budget, the different tasks locations, and deadlines. This selection becomes even more challenging when the MCS tries to efficiently accomplish tasks under different heat regions and collect high-credibility data. In this paper, we propose a user characteristics aware participant selection (UCPS) mechanism to improve the credibility of task data in the sparse user region acquired by the platform and to reduce the task failure rate. First, we estimate the regional heat according to the number of active users, average residence time of users and history of regional sensing tasks, and then we divide urban space into high-heat and low-heat regions. Second, the user state information and sensing task records are combined to calculate the willingness, reputation and activity of users. Finally, the above four factors are comprehensively considered to reasonably select the task participants for different heat regions. We also propose task queuing strategies and community assistance strategies to ensure task allocation rates and task completion rates. The evaluation results show that our mechanism can significantly improve the overall data quality and complete sensing tasks of low-heat regions in a timely and reliable manner. View Full-Text
Keywords: mobile crowdsensing; participant selection; regional heat; user characteristic mobile crowdsensing; participant selection; regional heat; user characteristic
Show Figures

Figure 1

MDPI and ACS Style

Wu, D.; Li, H.; Wang, R. User Characteristic Aware Participant Selection for Mobile Crowdsensing. Sensors 2018, 18, 3959.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Search more from Scilit
Back to TopTop