Attack-Resistant, Energy-Adaptive Monitoring for Smart Farms: Uncertainty-Aware Deep Reinforcement Learning Approach-Reference-Cited by-同舟云学术

Attack-Resistant, Energy-Adaptive Monitoring for Smart Farms: Uncertainty-Aware Deep Reinforcement Learning Approach

Published:2023-08-15 Issue:16 Volume:10 Page:14254-14268
ISSN:2327-4662
Container-title:IEEE Internet of Things Journal
language:
Short-container-title:IEEE Internet Things J.

Author:

Zhang Qisheng¹^ORCID,Chen Dian¹^ORCID,Mahajan Yash¹,Chen Ing-Ray¹^ORCID,Ha Dong Sam²,Cho Jin-Hee¹^ORCID

Affiliation:

1. Department of Computer Science, Virginia Tech, Falls Church, VA, USA

2. Bradley Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, VA, USA

Funder

National Science Foundation

Virginia Tech’s Institute for Critical Technology and Applied Science (ICTAS) The Engineering Faculty Organization (EFO) Opportunity Seed Investment Grant

Commonwealth Cyber Initiative (CCI) Southwest Virginia Node Research, Innovation, & Workforce Development Funding

Korea Agency for Infrastructure Technology Advancement (KAIA) Grant funded by the Korea Government

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Computer Science Applications,Hardware and Architecture,Information Systems,Signal Processing

Link

https://ieeexplore.ieee.org/ielam/6488907/10210448/10153977-aam.pdf

Reference59 articles.

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3. Context-Awareness for Multi-sensor Data Fusion in Smart Environments

4. An Attack-Resilient and Energy-Adaptive Monitoring System for Smart Farms

5. Deep Reinforcement Learning

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