Khandaker Foysal Haque

selected publications

1. WoWMoM

   SiMWiSense: Simultaneous Multi-Subject Activity Classification Through Wi-Fi Signals

   Haque, Khandaker Foysal, Zhang, Milin, and Restuccia, Francesco

   In 2023 IEEE 24th International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM) 2023

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   Recent advances in Wi-Fi sensing have ushered in a plethora of pervasive applications in home surveillance, remote healthcare, road safety, and home entertainment, among others. Most of the existing works are limited to the activity classification of a single human subject at a given time. Conversely, a more realistic scenario is to achieve simultaneous, multi-subject activity classification. The first key challenge in that context is that the number of classes grows exponentially with the number of subjects and activities. Moreover, it is known that Wi-Fi sensing systems struggle to adapt to new environments and subjects. To address both issues, we propose SiMWiSense, the first framework for simultaneous multi-subject activity classification based on Wi-Fi that generalizes to multiple environments and subjects. We address the scalability issue by using the Channel State Information (CSI) computed from the device positioned closest to the subject. We experimentally prove this intuition by confirming that the best accuracy is experienced when the CSI computed by the transceiver positioned closest to the subject is used for classification. To address the generalization issue, we develop a brand-new few-shot learning algorithm named Feature Reusable Embedding Learning (FREL). Through an extensive data collection campaign in 3 different environments and 3 subjects performing 20 different activities simultaneously, we demonstrate that SiMWiSense achieves classification accuracy of up to 97%, while FREL improves the accuracy by 85% in comparison to a traditional Convolutional Neural Network (CNN) and up to 20% when compared to the state-of-the-art few-shot embedding learning (FSEL), by using only 15 seconds of additional data for each class. For reproducibility purposes, we share our 1 TB dataset and code repository: https://github.com/kfoysalhaque/SiMWiSense

   @inproceedings{10195411,
     author = {Haque, Khandaker Foysal and Zhang, Milin and Restuccia, Francesco},
     abbr = {WoWMoM},
     booktitle = {2023 IEEE 24th International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM)},
     title = {SiMWiSense: Simultaneous Multi-Subject Activity Classification Through Wi-Fi Signals},
     year = {2023},
     volume = {},
     number = {},
     html = {https://ieeexplore.ieee.org/document/10195411},
     pages = {46-55},
     pdf = {SiMWiSense.pdf},
     bibtex_show = {true},
     selected = {true},
     doi = {10.1109/WoWMoM57956.2023.00019}
   }

2. WiNTECH

   Wi-BFI: Extracting the IEEE 802.11 Beamforming Feedback Information from Commercial Wi-Fi Devices

   Haque, Khandaker Foysal, Meneghello, Francesca, and Restuccia, Francesco

   In Proceedings of the 17th ACM Workshop on Wireless Network Testbeds, Experimental Evaluation & Characterization 2023

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   Recently, researchers have shown that the beamforming feedback angles (BFAs) used for Wi-Fi multiple-input multiple-output (MIMO) operations can be effectively leveraged as a proxy of the channel frequency response (CFR) for different purposes. Examples are passive human activity recognition and device fingerprinting. However, even though the BFAs report frames are sent in clear text, there is not yet a unified open-source tool to extract and decode the BFAs from the frames. To fill this gap, we developed Wi-BFI, the first tool that allows retrieving Wi-Fi BFAs and reconstructing the beamforming feedback information (BFI) – a compressed representation of the CFR – from the BFAs frames captured over the air. The tool supports BFAs extraction within both IEEE 802.11ac and 802.11ax networks operating on radio channels with 160/80/40/20 MHz bandwidth. Both multi-user and single-user MIMO feedback can be decoded through Wi-BFI. The tool supports real-time and offline extraction and storage of BFAs and BFI. The real-time mode also includes a visual representation of the channel state that continuously updates based on the collected data. Wi-BFI code is open source and the tool is also available as a pip package1.

   @inproceedings{10.1145/3615453.3616514,
     author = {Haque, Khandaker Foysal and Meneghello, Francesca and Restuccia, Francesco},
     title = {Wi-BFI: Extracting the IEEE 802.11 Beamforming Feedback Information from Commercial Wi-Fi Devices},
     abbr = {WiNTECH},
     year = {2023},
     isbn = {9798400703409},
     publisher = {Association for Computing Machinery},
     address = {New York, NY, USA},
     url = {https://doi.org/10.1145/3615453.3616514},
     doi = {10.1145/3615453.3616514},
     booktitle = {Proceedings of the 17th ACM Workshop on Wireless Network Testbeds, Experimental Evaluation \& Characterization},
     pages = {104–111},
     numpages = {8},
     pdf = {Wi-BFI.pdf},
     bibtex_show = {true},
     selected = {true},
     html = {https://dl.acm.org/doi/10.1145/3615453.3616514},
     keywords = {beamforming, Wi-Fi, compressed beamforming feedback, multiple-input multiple-output (MIMO)},
     location = {Madrid, Spain},
     series = {WiNTECH '23}
   }

3. arXiv

   BeamSense: Rethinking Wireless Sensing with MU-MIMO Wi-Fi Beamforming Feedback

   Haque, Khandaker Foysal, Zhang, Milin, Meneghello, Francesca, and Restuccia, Francesco

   arXiv preprint arXiv:2303.09687 2023

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   In this paper, we propose BeamSense– a completely novel approach to implement standard compliant Wi-Fi sensing applications. Wi-Fi sensing enables game-changing applications in remote healthcare, home entertainment, and home surveillance, among others. However, existing work leverages the manual extraction of channel state information (CSI) from Wi-Fi chips to classify activities, which is not supported by the Wi-Fi standard and hence requires the usage of specialized equipment. On the contrary, BeamSense leverages the standard-compliant beamforming feedback information (BFI) to characterize the propagation environment. Conversely from CSI, the BFI (i) can be easily recorded without any firmware modification, and (ii) captures the multiple channels between the access point and the stations, thus providing much better sensitivity. BeamSense includes a novel cross-domain few-shot learning (FSL) algorithm to handle unseen environments and subjects with few additional data points. We evaluate BeamSense through an extensive data collection campaign with three subjects performing twenty different activities in three different environments. We show that our BFI-based approach achieves about 10% more accuracy when compared to CSI-based prior work, while our FSL strategy improves accuracy by up to 30% and 80% when compared with state-of-the-art cross-domain algorithms.

   @article{haque2023beamsense,
     title = {BeamSense: Rethinking Wireless Sensing with MU-MIMO Wi-Fi Beamforming Feedback},
     author = {Haque, Khandaker Foysal and Zhang, Milin and Meneghello, Francesca and Restuccia, Francesco},
     journal = {arXiv preprint arXiv:2303.09687},
     abbr = {arXiv},
     pdf = {BeamSense.pdf},
     bibtex_show = {true},
     selected = {true},
     html = {https://arxiv.org/abs/2303.09687},
     year = {2023}
   }