Wi-Fi Sensing Recommended Reading List

Survey/Tutorial

2023
J. Yang et al., “SenseFi: A library and benchmark on deep-learning-empowered WiFi human sensing,” Patterns, vol. 4, no. 3, p. 100703, 2023, doi: 10.1016/j.patter.2023.100703.

Why this paper? Provides an open-source library and benchmark for comparing deep-learning approaches to Wi-Fi human sensing. It is useful for studying model architectures and learning strategies across sensing tasks and hardware platforms.

DOI arXiv Code
```
@article{yang2023sensefi,
  author = {Yang, Jianfei and Chen, Xinyan and Zou, Han and Lu, Chris Xiaoxuan and Wang, Dazhuo and Sun, Sumei and Xie, Lihua},
  title = {{SenseFi}: A library and benchmark on deep-learning-empowered {WiFi} human sensing},
  journal = {Patterns},
  year = {2023},
  volume = {4},
  number = {3},
  pages = {100703},
  doi = {10.1016/j.patter.2023.100703},
  arxiv = {2207.07859}
}
```
2023
F. Meneghello, C. Chen, C. Cordeiro, and F. Restuccia, “Toward Integrated Sensing and Communications in IEEE 802.11bf Wi-Fi Networks,” IEEE Communications Magazine, vol. 61, no. 7, pp. 128–133, 2023, doi: 10.1109/MCOM.001.2200806.

Why this paper? Connects Wi-Fi sensing algorithms with the physical and medium-access-control mechanisms considered for IEEE 802.11bf. It discusses how communication parameters affect sensing; the authors provide an IEEE 802.11ax implementation.

DOI arXiv Code
```
@article{meneghello2023toward,
  author = {Meneghello, Francesca and Chen, Cheng and Cordeiro, Carlos and Restuccia, Francesco},
  title = {Toward Integrated Sensing and Communications in {IEEE} 802.11bf {Wi-Fi} Networks},
  journal = {IEEE Communications Magazine},
  year = {2023},
  volume = {61},
  number = {7},
  pages = {128--133},
  doi = {10.1109/MCOM.001.2200806},
  arxiv = {2212.13930}
}
```
2022
Z. Yang, Y. Zhang, G. Chi, and G. Zhang, “Hands-on Wireless Sensing with Wi-Fi: A Tutorial.” arXiv preprint arXiv:2206.09532, 2022. doi: 10.48550/arXiv.2206.09532.

Why this paper? A practical introduction covering data collection, signal processing, feature extraction, and model design. The companion tutorial includes downloadable code and data for working through the sensing pipeline.

DOI arXiv Code
```
@misc{yang2022handson,
  author = {Yang, Zheng and Zhang, Yi and Chi, Guoxuan and Zhang, Guidong},
  title = {Hands-on Wireless Sensing with {Wi-Fi}: A Tutorial},
  year = {2022},
  howpublished = {arXiv preprint arXiv:2206.09532},
  doi = {10.48550/arXiv.2206.09532},
  arxiv = {2206.09532}
}
```
2019
Y. Ma, G. Zhou, and S. Wang, “WiFi Sensing with Channel State Information: A Survey,” ACM Computing Surveys, vol. 52, no. 3, pp. 46:1–46:36, 2019, doi: 10.1145/3310194.

Why this paper? A broad introduction to CSI-based Wi-Fi sensing, organised around detection, recognition, and estimation. It also discusses robustness, privacy, and the coexistence of sensing and networking.

DOI
```
@article{ma2019wifisurvey,
  author = {Ma, Yongsen and Zhou, Gang and Wang, Shuangquan},
  title = {{WiFi} Sensing with Channel State Information: A Survey},
  journal = {ACM Computing Surveys},
  year = {2019},
  volume = {52},
  number = {3},
  pages = {46:1--46:36},
  doi = {10.1145/3310194}
}
```
2017
S. Yousefi, H. Narui, S. Dayal, S. Ermon, and S. Valaee, “A Survey on Behavior Recognition Using WiFi Channel State Information,” IEEE Communications Magazine, vol. 55, no. 10, pp. 98–104, 2017, doi: 10.1109/MCOM.2017.1700082.

Why this paper? Reviews CSI-based human behaviour recognition and explores recurrent neural networks for activity classification. The accompanying repository provides code for the paper’s learning-based example.

DOI Code
```
@article{yousefi2017survey,
  author = {Yousefi, Siamak and Narui, Hirokazu and Dayal, Sankalp and Ermon, Stefano and Valaee, Shahrokh},
  title = {A Survey on Behavior Recognition Using {WiFi} Channel State Information},
  journal = {IEEE Communications Magazine},
  year = {2017},
  volume = {55},
  number = {10},
  pages = {98--104},
  doi = {10.1109/MCOM.2017.1700082}
}
```

Technical Papers

2024
G. Yin, J. Zhang, G. Shen, and Y. Chen, “FewSense, Towards a Scalable and Cross-Domain Wi-Fi Sensing System Using Few-Shot Learning,” IEEE Transactions on Mobile Computing, vol. 23, no. 1, pp. 453–468, 2024, doi: 10.1109/tmc.2022.3221902.

Why this paper? Uses few-shot learning to recognise new activities in unseen sensing domains with only a small number of labelled examples. The authors provide source code for studying this approach to cross-domain Wi-Fi sensing.

DOI arXiv Code
```
@article{yin2022fewsense,
  title = {{FewSense}, Towards a Scalable and Cross-Domain {Wi-Fi} Sensing System Using Few-Shot Learning},
  author = {Yin, Guolin and Zhang, Junqing and Shen, Guanxiong and Chen, Yingying},
  journal = {IEEE Transactions on Mobile Computing},
  volume = {23},
  number = {1},
  pages = {453 - 468},
  year = {2024},
  doi = {10.1109/tmc.2022.3221902},
  arxiv = {2203.02014},
  keywords = {wifi-sensing, data_code}
}
```
2023
F. Meneghello, D. Garlisi, N. Dal Fabbro, I. Tinnirello, and M. Rossi, “SHARP: Environment and Person Independent Activity Recognition With Commodity IEEE 802.11 Access Points,” IEEE Transactions on Mobile Computing, vol. 22, no. 10, pp. 6160–6175, 2023, doi: 10.1109/TMC.2022.3185681.

Why this paper? Uses Wi-Fi channel phase processing to extract Doppler information for activity recognition across people and environments. The public implementation supports studying the signal-processing pipeline as well as the classifier.

DOI arXiv Code
```
@article{meneghello2023sharp,
  author = {Meneghello, Francesca and Garlisi, Domenico and {Dal Fabbro}, Nicol\`o and Tinnirello, Ilenia and Rossi, Michele},
  title = {{SHARP}: Environment and Person Independent Activity Recognition With Commodity {IEEE} 802.11 Access Points},
  journal = {IEEE Transactions on Mobile Computing},
  year = {2023},
  volume = {22},
  number = {10},
  pages = {6160--6175},
  doi = {10.1109/TMC.2022.3185681},
  arxiv = {2103.09924}
}
```
2018
J. Zhang, Z. Tang, M. Li, D. Fang, P. Nurmi, and Z. Wang, “CrossSense: Towards Cross-Site and Large-Scale WiFi Sensing,” in Proceedings of the 24th Annual International Conference on Mobile Computing and Networking, 2018, pp. 305–320. doi: 10.1145/3241539.3241570.

Why this paper? Introduces synthetic training samples and a mixture-of-experts approach for extending Wi-Fi sensing to new environments and larger recognition problems. The authors provide code and dataset instructions.

DOI Code
```
@inproceedings{zhang2018crosssense,
  author = {Zhang, Jie and Tang, Zhanyong and Li, Meng and Fang, Dingyi and Nurmi, Petteri and Wang, Zheng},
  title = {{CrossSense}: Towards Cross-Site and Large-Scale {WiFi} Sensing},
  booktitle = {Proceedings of the 24th Annual International Conference on Mobile Computing and Networking},
  year = {2018},
  pages = {305--320},
  doi = {10.1145/3241539.3241570}
}
```
2017
S. Palipana, D. Rojas, P. Agrawal, and D. Pesch, “FallDeFi: Ubiquitous Fall Detection using Commodity Wi-Fi Devices,” Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, vol. 1, no. 4, pp. 155:1–155:25, 2017, doi: 10.1145/3161183.

Why this paper? An example of device-free fall detection using Wi-Fi CSI. It combines time-frequency analysis with feature selection to identify features that are less sensitive to environmental changes.

DOI Code
```
@article{palipana2017falldefi,
  author = {Palipana, Sameera and Rojas, David and Agrawal, Piyush and Pesch, Dirk},
  title = {{FallDeFi}: Ubiquitous Fall Detection using Commodity {Wi-Fi} Devices},
  journal = {Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies},
  year = {2017},
  volume = {1},
  number = {4},
  pages = {155:1--155:25},
  doi = {10.1145/3161183}
}
```
2018
Y. Ma, G. Zhou, S. Wang, H. Zhao, and W. Jung, “SignFi: Sign Language Recognition Using WiFi,” Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, vol. 2, no. 1, pp. 23:1–23:21, 2018, doi: 10.1145/3191755.

Why this paper? Demonstrates sign-language gesture recognition with Wi-Fi CSI and a convolutional neural network. The authors release a dataset and example training code, making this a useful practical starting point.

DOI Code
```
@article{ma2018signfi,
  author = {Ma, Yongsen and Zhou, Gang and Wang, Shuangquan and Zhao, Hongyang and Jung, Woosub},
  title = {{SignFi}: Sign Language Recognition Using {WiFi}},
  journal = {Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies},
  year = {2018},
  volume = {2},
  number = {1},
  pages = {23:1--23:21},
  doi = {10.1145/3191755}
}
```
2017
W. Wang, A. X. Liu, M. Shahzad, K. Ling, and S. Lu, “Device-Free Human Activity Recognition Using Commercial WiFi Devices,” IEEE Journal on Selected Areas in Communications, vol. 35, no. 5, pp. 1118–1131, 2017, doi: 10.1109/JSAC.2017.2679658.

Why this paper? A reference on device-free activity recognition using commercial Wi-Fi hardware, offering background for understanding how wireless channel measurements can support human activity recognition.

DOI
```
@article{wang2017devicefree,
  author = {Wang, Wei and Liu, Alex X. and Shahzad, Muhammad and Ling, Kang and Lu, Sanglu},
  title = {Device-Free Human Activity Recognition Using Commercial {WiFi} Devices},
  journal = {IEEE Journal on Selected Areas in Communications},
  year = {2017},
  volume = {35},
  number = {5},
  pages = {1118--1131},
  doi = {10.1109/JSAC.2017.2679658}
}
```
2016
H. Li, W. Yang, J. Wang, Y. Xu, and L. Huang, “WiFinger: Talk to Your Smart Devices with Finger-Grained Gesture,” in Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, 2016, pp. 250–261. doi: 10.1145/2971648.2971738.

Why this paper? Explores fine-grained finger gestures and continuous number input using CSI patterns from commodity Wi-Fi devices. It illustrates how small movements can be used for wireless human-computer interaction.

DOI
```
@inproceedings{li2016wifinger,
  author = {Li, Hong and Yang, Wei and Wang, Jianxin and Xu, Yang and Huang, Liusheng},
  title = {{WiFinger}: Talk to Your Smart Devices with Finger-Grained Gesture},
  booktitle = {Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing},
  year = {2016},
  pages = {250--261},
  doi = {10.1145/2971648.2971738}
}
```
2022
Y. Zhang et al., “Widar3.0: Zero-Effort Cross-Domain Gesture Recognition With Wi-Fi,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 11, pp. 8671–8688, 2022, doi: 10.1109/TPAMI.2021.3105387.

Why this paper? Introduces domain-independent motion features for cross-domain gesture recognition. The authors’ project page provides code and data, making this a useful reference for studying generalisation across sensing conditions.

DOI Code Data
```
@article{zhang2022widar3,
  author = {Zhang, Yi and Zheng, Yue and Qian, Kun and Zhang, Guidong and Liu, Yunhao and Wu, Chenshu and Yang, Zheng},
  title = {{Widar3.0}: Zero-Effort Cross-Domain Gesture Recognition With {Wi-Fi}},
  journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year = {2022},
  volume = {44},
  number = {11},
  pages = {8671--8688},
  doi = {10.1109/TPAMI.2021.3105387}
}
```