2021 Fiscal Year Research-status Report
Experimental and theoretical study on physical layer authentication for IoT systems
| Project/Area Number |
20K19801
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| Research Institution | Toyo University |
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Principal Investigator |
朱 金暁 東洋大学, 情報連携学部, 助教 (30754329)
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| Project Period (FY) |
2020-04-01 – 2023-03-31
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| Keywords | physical layer / radiometric feature / visibility graph / identification / authentication |
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| Outline of Annual Research Achievements |
This project focuses on studying physical layer authentication schemes for device authentication/identification in wireless networks. In particular, we focused on analyzing radiometric features of wireless devices from both experimental and theoretical aspects.
In this year, we have expanded the experiment scale and proposed a new radiometric feature. Firstly, in our experiment platform, the number of wireless devices has been increased from 20 to 50 and those devices include four types, namely, USB network interface card, mobile phone, laptop, and wireless router. Secondly, after observing that the existing multi-feature based identification schemes explore features from signal's time, frequency, or phase domain, we are motivated to use visibility graph as a nonlinear characteristic analysis tool to mine intrinsic device characteristics from graph domain. Correspondingly, we proposed a new radiometric feature called normalized horizontal visibility graph Shannon entropy (HVGE) and illustrated that HVGE is independent of other existing features and that it improves the performance of identifying devices in the above experiment platform. Our related result has been published as a journal paper in (Elsevier) Ad Hoc Networks recently.
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| Current Status of Research Progress |
Current Status of Research Progress
2: Research has progressed on the whole more than it was originally planned.
Reason
Since I took four months maternity leave this academic year, I have extended the research period and rescheduled the research plan. According to the new plan, experiment scale will be expanded to improve the accuracy in performance measurement. Correspondingly, the number of wireless devices in our experiment platform has been increased from 20 to 50. Also we have proposed a new radiometric feature to identify wireless devices in the experiment platform, which meets the research schedule in my new research plan.
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| Strategy for Future Research Activity |
By now, the major part of research works on physical layer identification focus on proposing new radiometric features together with designing multi-feature based identification schemes. Our previous research effort also falls in such direction. Note that calculating features from the received frames requires a careful design of feature extraction algorithm, and the number of available features effective for device identification is largely limited due to the lack of understanding of the underlying mechanisms of device-specific and channel-specific features.
Meanwhile, some research works try to apply deep learning-based methods, including fully connected neural network (FNN), convolutional neural network (CNN) to extract hidden features from wireless frames without using explicit feature calculation algorithms. Therefore, in the future, I plan to apply machine-learning methods to identify wireless devices based on raw data collected from the experiment platform.
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| Causes of Carryover |
Firstly, the research period has been extended due to that I took a maternity leave in the last academic year. Hence, the research schedule including the budget has been changed accordingly.
For the remaining amount of the grant, I will mainly use it in the following aspects. Firstly, purchase more devices so as to expand the scale of the experiment platform. Secondly, attend domestic and international conferences to present papers and communicate in the research community. Thirdly, pay publication fees for corresponding papers.
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