Intelligent Cubic-Designed Piezoelectric Node (iCUPE) with Simultaneous Sensing and Energy Harvesting Ability toward Self-Sustained Artificial Intelligence of Things (AIoT)
Abstract
The evolution of artificial intelligence of things (AIoT) drastically facilitates the development of a smart city via comprehensive perception and seamless communication. As a foundation, various AIoT nodes are experiencing low integration and poor sustainability issues. Herein, a cubic-designed intelligent piezoelectric AIoT node iCUPE is presented, which integrates a high-performance energy harvesting and self-powered sensing module via a micromachined lead zirconate titanate (PZT) thick-film-based high-frequency (HF)-piezoelectric generator (PEG) and poly(vinylidene fluoride- co -trifluoroethylene) (P(VDF-TrFE)) nanofiber thin-film-based low-frequency (LF)-PEGs, respectively. The LF-PEG and HF-PEG with specific frequency up-conversion (FUC) mechanism ensures continuous power supply over a wide range of 10–46 Hz, with a record high power density of 17 mW/cm 3 at 1 g acceleration. The cubic design allows for orthogonal placement of the three FUC-PEGs to ensure a wide range of response to vibrational energy sources from different directions. The self-powered triaxial piezoelectric sensor (TPS) combined with machine learning (ML) assisted three orthogonal piezoelectric sensing units by using three LF-PEGs to achieve high-precision multifunctional vibration recognition with resolutions of 0.01 g, 0.01 Hz, and 2° for acceleration, frequency, and tilting angle, respectively, providing a high recognition accuracy of 98%–100%. This work proves the feasibility of developing a ML-based intelligent sensor for accelerometer and gyroscope functions at resonant frequencies. The proposed sustainable iCUPE is highly scalable to explore multifunctional sensing and energy harvesting capabilities under diverse environments, which is essential for AIoT implementation.
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Huang et al. developed a self-powered piezoelectric AIoT node named iCUPE.
“Moreover, Huang et al. a self-powered piezoelectric AIoT node, named iCUPE (Fig. 11c) (Huang et al. 2023).”
From Artificial intelligence enhanced sensors - enabling technologies to next-generation healthcare and biomedical platform (2023) · cites this paper as Huang et al. 2023
The iCUPE is built around a three-dimensional hexahedral modular arrangement and embodies a self-sustainable system with AIoT capabilities.
“Built around a three-dimensional (3D) hexahedral modular arrangement, the iCUPE embodies a self-sustainable system with AIoT capabilities.”
From Artificial intelligence enhanced sensors - enabling technologies to next-generation healthcare and biomedical platform (2023) · cites this paper as Huang et al. 2023
A self-powered triaxial piezoelectric sensor (TPS) combined with machine learning enables high-precision, multifunctional vibration recognition with resolutions of 0.01g for acceleration, 0.01 Hz for frequency, and 2° for tilting angle.
“This integration enables high-precision, multifunctional vibration recognition with resolutions of 0.01g for acceleration, 0.01 Hz for frequency, and 2° for tilting angle.”
From Artificial intelligence enhanced sensors - enabling technologies to next-generation healthcare and biomedical platform (2023) · cites this paper as Huang et al. 2023
The iCUPE, without requiring an external power supply, can capture vibrational signals in its environment, yielding preliminary data points such as frequency and acceleration.
“Remarkably, the iCUPE, without requiring an external power supply, is competent at capturing vibrational signals in its environment, thereby yielding preliminary data points such as frequency and acceleration.”
From Artificial intelligence enhanced sensors - enabling technologies to next-generation healthcare and biomedical platform (2023) · cites this paper as Huang et al. 2023
The TPS can achieve high recognition accuracy ranging from 98% to 100%.
“Consequently, the TPS can achieve high recognition accuracy, ranging from 98% to 100%.”
From Artificial intelligence enhanced sensors - enabling technologies to next-generation healthcare and biomedical platform (2023) · cites this paper as Huang et al. 2023
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Self-citation
- Authors
- Huang, M., Zhu, M., Feng, X., Zhang, Z., Tang, T., Guo, X., Chen, T., Liu, H., Sun, L., & Lee, C.
- Affiliations
- Soochow University (CN)
- National University of Singapore (SG)
- Corresponding author
- Liu, Huicong
- Sun, Lining
- Lee, Chengkuo
- ORCID
- Venue
- ACS Nano
- Publisher
- American Chemical Society (ACS)
- Published
- 2023-03-20
- Volume
- 17
- Issue
- 7
- Pages
- 6435-6451
- ISSN
- 1936-0851 (ISSN-L)
- 1936-086X
- OpenAlex
- W4327900284
- Cited by
- 90
- References
- 92
- Citations by year
- 2026: 18 · 2025: 21 · 2024: 30 · 2023: 21
- FWCI
- 8.86
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- ACS Nano (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Ministry of Science and Technology of the People's Republic of China (2019YFB2004800)
- Natural Science Foundation of Jiangsu Province (BK20220056)
- National Research Foundation Singapore (A18A4b0055)
- Topics
- Physical Sciences › Engineering › Mechanical Engineering › Innovative Energy Harvesting Technologies
- Physical Sciences › Engineering › Biomedical Engineering › Advanced Sensor and Energy Harvesting Materials
- Physical Sciences › Materials Science › Materials Chemistry › Ferroelectric and Piezoelectric Materials
- Keywords
- AIoT, piezoelectric energy harvesting, self-powered sensing, vibration sensing, frequency up-conversion
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