Smart City Gnosys
Smart city article details
| Title | Recruitment Algorithm In Edge-Cloud Servers Based On Mobile Crowd-Sensing In Smart Cities |
|---|---|
| ID_Doc | 44699 |
| Authors | Wildan M.A.; Widyaningrum M.E.; Padmapriya T.; Sah B.; Pani N.K. |
| Year | 2023 |
| Published | International Journal of Interactive Mobile Technologies, 17, 16 |
| DOI | http://dx.doi.org/10.3991/ijim.v17i16.42685 |
| Abstract | As more and more mobile devices rely on cloud services since the introduction of cloud computing, data privacy has emerged as one of the most pressing security concerns. Users typically encrypt their important data before uploading it to cloud servers to safeguard data privacy, which makes data usage challenging. On the other side, this also increases the possibility of brand-new issues in cities. A clever, effective and efficient urban monitoring system is required to address possible challenges that may arise in urban settings. In the smart city concept, which makes use of sensors, one strategy that might be used in IoT and cloud computing is to monitor and gather data on problems that develop in cities in real-time. However, it will take a while and be rather expensive to install IoT and sensors throughout the city. The Mobile Crowd-Sensing (MCS) method is proposed to be used in this study to retrieve and gather data on issues that arise in metropolitan areas from citizen reports made using mobile devices. And we suggest a budget-constrained, reputation-based collaborative user recruitment (RCUR) procedure for a MCS system. To construct an edge-assisted MCS system in urban situations, we first integrate edge computing into MCS. We also examine how user reputation affects user recruitment. Finally, we create a collaborative sensing approach using the edge nodes’ sensing capabilities. © 2023 by the authors of this article. Published under CC-BY. |
| Author Keywords | collaborative sensing; edge cloud servers (ECSs); mobile crowd sensing (MCS); smart city; user recruiting algorithm |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
570  | 0.902 | Belli D.; Chessa S.; Kantarci B.; Foschini L. | A Capacity-Aware User Recruitment Framework For Fog-Based Mobile Crowd-Sensing Platforms | Proceedings - IEEE Symposium on Computers and Communications, 2019-June (2019) |
| 37261 | 0.885 | Chowdhury C.; Roy S. | Mobile Crowd-Sensing For Smart Cities | Smart Cities: Foundations, Principles, and Applications (2017) |
| 18258 | 0.882 | De Bock Y.; Braem B.; Subotic D.; Weyn M.; Marquez-Barja J.M. | Demo Abstract: Crowd Analysis With Infrared Sensor Arrays On The Smart City Edge | INFOCOM 2019 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2019 (2019) |
| 31040 | 0.881 | Du Y.; Issarny V.; Sailhan F. | In-Network Collaborative Mobile Crowdsensing | 2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020 (2020) |
| 32021 | 0.878 | Zhu H.; Chau S.C.-K.; Guarddin G.; Liang W. | Integrating Iot-Sensing And Crowdsensing With Privacy: Privacy-Preserving Hybrid Sensing For Smart Cities | ACM Transactions on Internet of Things, 3, 4 (2022) |
| 27660 | 0.874 | Dasari V.S.; Kantarci B.; Pouryazdan M.; Foschini L.; Girolami M. | Game Theory In Mobile Crowdsensing: A Comprehensive Survey | Sensors (Switzerland), 20, 7 (2020) |
| 5177 | 0.873 | Ray A.; Chowdhury C.; Bhattacharya S.; Roy S. | A Survey Of Mobile Crowdsensing And Crowdsourcing Strategies For Smart Mobile Device Users | CCF Transactions on Pervasive Computing and Interaction, 5, 1 (2023) |
| 4063 | 0.87 | El Khatib R.F.; Zorba N.; Hassanein H.S. | A Reputation-Aware Mobile Crowd Sensing Scheme For Emergency Detection | Proceedings - IEEE Symposium on Computers and Communications, 2019-June (2019) |
| 21768 | 0.87 | Rajagopal S.; Tripathi P.K.; Deshmukh M.; Choudari S.; Kumar A.; Long C.S. | Edge Computing- Smart Cities: Optimizing Data Processing & Resource Management In Urban Environments | Journal of Information Systems Engineering and Management, 10 (2025) |
| 35131 | 0.869 | Liu W.; Gao X. | Leveraging Social Networks To Enhance Effective Coverage For Mobile Crowdsensing | Proceedings - 2020 IEEE 13th International Conference on Web Services, ICWS 2020 (2020) |