dc.contributor.author | Ahmed, Usman | |
dc.contributor.author | Lin, Jerry Chun-Wei | |
dc.contributor.author | Srivastava, Gautam | |
dc.date.accessioned | 2022-04-11T13:46:31Z | |
dc.date.available | 2022-04-11T13:46:31Z | |
dc.date.created | 2021-12-24T21:43:38Z | |
dc.date.issued | 2021 | |
dc.identifier.citation | Ahmed, U., Lin, J. C.-W., & Srivastava, G. (2021). Privacy-preserving deep reinforcement learning in vehicle adhoc networks. IEEE Consumer Electronics Magazine | en_US |
dc.identifier.issn | 2162-2248 | |
dc.identifier.uri | https://hdl.handle.net/11250/2990974 | |
dc.description | © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | en_US |
dc.description.abstract | The increasing number of road vehicles results in more fatalities and accidents. Thus, the manufacturing industry is working on driver safety to secure and safe transportation in Vehicle Adhoc networks. In addition, the mobile vehicles run in the geographical zone and communicate roadside units over the wireless medium with a certain radius. The Internet of Vehicles has become a new network type where vehicles communicate with the application over public networks. This results in an increase in data exploration and threats related to network security. We propose the deep reinforcement learning method to sensitize the private information for a given vehicle connect over Vehicle Adhoc networks, maintaining a balance between security and privacy through any sanitization process. Furthermore, we provide a set of recommendations and potential applications for the Vehicle Adhoc networks as use cases. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | IEEE | en_US |
dc.subject | privacy | en_US |
dc.subject | security | en_US |
dc.subject | wireless communication | en_US |
dc.subject | internet | en_US |
dc.subject | data privacy | en_US |
dc.subject | wireless sensor networks | en_US |
dc.subject | safety | en_US |
dc.title | Privacy-Preserving Deep Reinforcement Learning in Vehicle AdHoc Networks | en_US |
dc.type | Peer reviewed | en_US |
dc.type | Journal article | en_US |
dc.description.version | acceptedVersion | en_US |
dc.rights.holder | © 2021 IEEE | en_US |
dc.source.journal | IEEE Consumer Electronics Magazine | en_US |
dc.identifier.doi | 10.1109/MCE.2021.3088408 | |
dc.identifier.cristin | 1971947 | |
cristin.ispublished | true | |
cristin.fulltext | preprint | |
cristin.qualitycode | 1 | |