Cargando…
Robot location privacy protection based on Q-learning particle swarm optimization algorithm in mobile crowdsensing
In the recent years, with the rapid development of science and technology, robot location-based service (RLBS) has become the main application service on mobile intelligent devices. When people use location services, it generates a large amount of location data with real location information. If a m...
Autores principales: | Ma, Dandan, Kong, Dequan, Chen, Xiaowei, Zhang, Lingyu, Yuan, Mingrun |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9561907/ https://www.ncbi.nlm.nih.gov/pubmed/36247361 http://dx.doi.org/10.3389/fnbot.2022.981390 |
Ejemplares similares
-
PARS: Privacy-Aware Reward System for Mobile Crowdsensing Systems
por: Zhang, Zhong, et al.
Publicado: (2021) -
Incentivizing Verifiable Privacy-Protection Mechanisms for Offline Crowdsensing Applications
por: Sun, Jiajun, et al.
Publicado: (2017) -
When compressive sensing meets mobile crowdsensing
por: Kong, Linghe, et al.
Publicado: (2019) -
Probabilistic Coverage Constraint Task Assignment with Privacy Protection in Vehicular Crowdsensing
por: Li, Zhe, et al.
Publicado: (2023) -
Particle Swarm Algorithm Path-Planning Method for Mobile Robots Based on Artificial Potential Fields
por: Zheng, Li, et al.
Publicado: (2023)