A Reputation-Based Collaborative User Recruitment Algorithm in Edge-Aided Mobile Crowdsensing
Mobile CrowdSensing (MCS) has become a convenient method for many Internet of Things (IoT) applications in urban scenarios due to the full utilization of the mobility of people and the powerful capabilities of their intelligent devices. Nowadays, edge computing has been introduced into MCS to reduce...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/10/6040 |
| _version_ | 1797601235796230144 |
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| author | Yang Liu Yong Li Wei Cheng Weiguang Wang Junhua Yang |
| author_facet | Yang Liu Yong Li Wei Cheng Weiguang Wang Junhua Yang |
| author_sort | Yang Liu |
| collection | DOAJ |
| description | Mobile CrowdSensing (MCS) has become a convenient method for many Internet of Things (IoT) applications in urban scenarios due to the full utilization of the mobility of people and the powerful capabilities of their intelligent devices. Nowadays, edge computing has been introduced into MCS to reduce the time delays and computational complexity in cloud platforms. To improve task completion and coverage rates, how to design a reasonable user recruitment algorithm to find suitable users and take full advantage of edge nodes has raised huge challenges for Mobile CrowdSensing. In this study, we propose a Reputation-based Collaborative User Recruitment algorithm (RCUR) under a certain budget in an edge-aided Mobile CrowdSensing system. We first introduce edge computing into MCS and build an edge-aided MCS system in urban scenarios. Moreover, we analyze the influence of user reputation on user recruitment. Then we establish a user reputation module to deduce the user reputation equation by combining the user’s past reputation score with an instantaneous reputation score. Finally, we utilize the sensing ability of edge nodes and design a collaborative sensing method. We use the greedy method to help choose the appropriate users for the tasks. Simulation results compared with the other three algorithms prove that our RCUR approach can significantly achieve better performance in task completion rate and task coverage rate. |
| first_indexed | 2024-03-11T03:58:18Z |
| format | Article |
| id | doaj.art-11197bdf42b94824a9f0b6e8f42cb903 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T03:58:18Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-11197bdf42b94824a9f0b6e8f42cb9032023-11-18T00:19:40ZengMDPI AGApplied Sciences2076-34172023-05-011310604010.3390/app13106040A Reputation-Based Collaborative User Recruitment Algorithm in Edge-Aided Mobile CrowdsensingYang Liu0Yong Li1Wei Cheng2Weiguang Wang3Junhua Yang4School of Electronics and Information, Northwestern Polytechnical University, Xi’an 710129, ChinaSchool of Electronics and Information, Northwestern Polytechnical University, Xi’an 710129, ChinaSchool of Electronics and Information, Northwestern Polytechnical University, Xi’an 710129, ChinaSchool of Electronics and Information, Northwestern Polytechnical University, Xi’an 710129, ChinaSchool of Electronic Engineering, Xi’an University of Posts and Telecommunications, Xi’an 710121, ChinaMobile CrowdSensing (MCS) has become a convenient method for many Internet of Things (IoT) applications in urban scenarios due to the full utilization of the mobility of people and the powerful capabilities of their intelligent devices. Nowadays, edge computing has been introduced into MCS to reduce the time delays and computational complexity in cloud platforms. To improve task completion and coverage rates, how to design a reasonable user recruitment algorithm to find suitable users and take full advantage of edge nodes has raised huge challenges for Mobile CrowdSensing. In this study, we propose a Reputation-based Collaborative User Recruitment algorithm (RCUR) under a certain budget in an edge-aided Mobile CrowdSensing system. We first introduce edge computing into MCS and build an edge-aided MCS system in urban scenarios. Moreover, we analyze the influence of user reputation on user recruitment. Then we establish a user reputation module to deduce the user reputation equation by combining the user’s past reputation score with an instantaneous reputation score. Finally, we utilize the sensing ability of edge nodes and design a collaborative sensing method. We use the greedy method to help choose the appropriate users for the tasks. Simulation results compared with the other three algorithms prove that our RCUR approach can significantly achieve better performance in task completion rate and task coverage rate.https://www.mdpi.com/2076-3417/13/10/6040mobile crowdsensingedge computinguser reputationcollaborative sensinguser recruitment algorithm |
| spellingShingle | Yang Liu Yong Li Wei Cheng Weiguang Wang Junhua Yang A Reputation-Based Collaborative User Recruitment Algorithm in Edge-Aided Mobile Crowdsensing Applied Sciences mobile crowdsensing edge computing user reputation collaborative sensing user recruitment algorithm |
| title | A Reputation-Based Collaborative User Recruitment Algorithm in Edge-Aided Mobile Crowdsensing |
| title_full | A Reputation-Based Collaborative User Recruitment Algorithm in Edge-Aided Mobile Crowdsensing |
| title_fullStr | A Reputation-Based Collaborative User Recruitment Algorithm in Edge-Aided Mobile Crowdsensing |
| title_full_unstemmed | A Reputation-Based Collaborative User Recruitment Algorithm in Edge-Aided Mobile Crowdsensing |
| title_short | A Reputation-Based Collaborative User Recruitment Algorithm in Edge-Aided Mobile Crowdsensing |
| title_sort | reputation based collaborative user recruitment algorithm in edge aided mobile crowdsensing |
| topic | mobile crowdsensing edge computing user reputation collaborative sensing user recruitment algorithm |
| url | https://www.mdpi.com/2076-3417/13/10/6040 |
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