AI based energy harvesting security methods: A surveyopen access
- Authors
- Mohammadi, Masoumeh; Sohn, Insoo
- Issue Date
- Dec-2023
- Publisher
- 한국통신학회
- Keywords
- Artificial Intelligence(AI); Deep learning; Energy harvesting; Privacy; Security
- Citation
- ICT Express, v.9, no.6, pp 1198 - 1208
- Pages
- 11
- Indexed
- SCIE
SCOPUS
KCI
- Journal Title
- ICT Express
- Volume
- 9
- Number
- 6
- Start Page
- 1198
- End Page
- 1208
- URI
- https://scholarworks.dongguk.edu/handle/sw.dongguk/20980
- DOI
- 10.1016/j.icte.2023.06.002
- ISSN
- 2405-9595
2405-9595
- Abstract
- Energy Harvesting (EH) as a power source plays a critical role in the advent of new technologies such as the Internet of Things (IoT). But, by providing power within the networks, it may be susceptible to attacks such as eavesdropping, data manipulation, or denial of service, leading to issues like leakage of confidential, sensitive information, and energy scarcity. Therefore, it is important to implement appropriate security measures to protect the data and devices that use energy harvested from ambient sources. In this paper, we present a comprehensive overview of the current and future developments of security for EH systems that used artificial intelligence(AI) approaches. Furthermore, we highlight the application of AI approaches such as machine learning (ML) and federated learning (FL) in the security of EH systems. Then, we discuss the security techniques that are used in the EH literature, including cryptography techniques, physical-layer security schemes, blockchain, and FL. Finally, we outline research challenges and prospects for developing and applying AI algorithms in the security of EH. © 2023 The Author(s)
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Collections - College of Engineering > Department of Electronics and Electrical Engineering > 1. Journal Articles

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