PhyMask: An Adaptive Masking Paradigm for Efficient Self-Supervised Learning in IoT
Authors: 
Denizhan Kara, Tomoyoshi Kimura, Yatong Chen, Jinyang Li, Ruijie Wang, Yizhuo Chen, Tianshi Wang, Shengzhong Liu, Tarek AbdelzaherAuthors Info & Claims
Denizhan Kara, Tomoyoshi Kimura, Yatong Chen, Jinyang Li, Ruijie Wang, Yizhuo Chen, Tianshi Wang, Shengzhong Liu, Tarek AbdelzaherAuthors Info & ClaimsPages 97 - 111
Abstract
This paper introduces PhyMask, an adaptive masking paradigm designed to enhance the efficiency and interpretability of Masked Autoencoders (MAEs) in analyzing IoT sensing signals. Different from all mainstream MAEs, which rely on random masking techniques, PhyMask employs an adaptive masking strategy that aligns with critical signal information. Its main contributions are threefold. First, PhyMask leverages the energy significance of frequency components to prioritize information-rich time-frequency regions, improving the reconstruction of original signals. Second, it includes a coherence-based masking component to identify and preserve essential temporal dynamics within the data. Finally, PhyMask integrates these components into an adaptive masking paradigm tailored to optimize the sensing context awareness within the masking configuration, focusing on the most informative parts of the data. This allows PhyMask to mask up to 96% of the input, reducing memory requirements by 14% and accelerating pre-training. Evaluations across two sensing applications, four datasets, and two real-world deployments demonstrate PhyMask's superior performance. PhyMask improves MAE accuracy by 7%, reduces pre-training data requirements by up to 75%, and enhances robustness to domain shifts and signal quality variations, making it of great value to robust and efficient intelligent IoT deployments.
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Index Terms
- PhyMask: An Adaptive Masking Paradigm for Efficient Self-Supervised Learning in IoT
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November 2024
950 pages
ISBN:9798400706974
DOI:10.1145/3666025
- Chair:
- Jie Liu,
- Co-chairs:
- Yuanchao Shu,
- Jiming Chen,
- Program Chair:
- Yuan He,
- Program Co-chair:
- Rui Tan
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Published: 04 November 2024
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SenSys '24: 22nd ACM Conference on Embedded Networked Sensor Systems
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