Related Works of RSSI-based Sensing
Taxonomy
- RSSI-based Features:
- Time related features (e.g., period of slope)
- Statistical features (e.g, peak, mean, variance)
- Frequency related features (e.g. low-pass filter, high-pass filter, spectral energy, entropy)
- Edge features (e.g., rising edge, falling edge)
- Histogram (i.e., the signal distribution)
- Correlation (e.g., spearman, Pearson)
- Distance (e.g., manhattan, euclidean)
- Sensing Objects: Localization, Presence Detection (Coarse-grained), Activity Recognition (Fine-grained)
- Methods: Filtering (with threshold), ML (e.g. KNN, SVM, Bayesian Network), Wavelet Transform
- Another taxonomy can be “Device-based” and “Device-free”
Comments
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Tracking Human Queues[MobiSys14] has a very detailed description of what are derived from RSSI raw data and used as feature for following sensing.
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Although [MobiCom15-Understanding] is a CSI based work, there is a good survey on the RSSI-based human activity recognition topic. But the survey here is to citing some RSSI-based works and show their performance defects compared with (author’s) CSI-based work.
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There is also a survey on RSSI-based sensing in [IMWUT17-Detecting] and [MobiCom15-Keystroke].
Special Topics
- Challenges, MobiCom07
- RSSI-based features: Statistical features (Moving average, Moving variance)
- Sensing Object: Localization
- Method: Filtering (with threshold)
- IODetector, SenSys12
- RSSI-based features: Statistical features (RSSI variation)
- Sensing Object: Localization
- Leveraging RF-channel fluctuation for activity recognition Active, MoMM13
- RSSI-based features: Statistical features , Frequency related features
- Sensing Object: Activity Recognition
- Method: KNN
- Predicting Length of Stay at WiFi Hotspots, INFOCOM13
- RSSI-based features: Statistical features (e.g, peak, mean, variance)
- Sensing Object: Presence Detection
- Method: ML (SVM)
- Tracking Human Queues, MobiSys14
- RSSI-based features: Time related features, Statistical features
- Sensing Objects: Presence Detection
- Method: Filtering, ML (Bayesian Network)
- Three features are extracted from raw RSSI data: (1) Longest period of negative slopes in the trace; (2) Stable RSS & largest RSS value during service; (3) Largest change of RSS in the trace.
(Figure from the Paper)
- The Telepathic Phone, PerCom14
- RSSI-based features: Statistical features (peak value, mean, variance)
- Methods: ML (KNN)
- RF-Sensing of Activities, TMC14
- RSSI-based features: Statistical features (peak value, mean, variance), Frequency related features (energy, entropy)
- Sensing Objects: Activity Recognition
- Methods: ML (KNN)
- UbiBreathe, MobiHoc15
- RSSI-based features: Frequency related features (e.g. low-pass filter, high-pass filter)
- Sensing Objects: Activity Recognition
- Method: Wavelet Transform
- WiGest: INFOCOM15
- RSSI-based features: Edge features (e.g., rising edge, falling edge)
- Sensing Objects: Activity Recognition
- Methods: Wavelet Transform
- Inferring Person-to-person Proximity Using WiFi Signals, IMWUT17
- RSSI-based features: Correlation (spearman and pearson correlation of RSSI from two APs), Distance (manhattan and euclidean RSSI from two APs).
- Sensing Objects: Presence Detection
Ref.
[MobiCom07] Youssef, M., Mah, M., & Agrawala, A. (2007, September). Challenges: device-free passive localization for wireless environments. In MobiCom(pp. 222-229). ACM.
[SenSys12] Zhou, P., Zheng, Y., Li, Z., Li, M., & Shen, G. (2012, November). IODetector: A generic service for indoor outdoor detection. In SenSys (pp. 113-126). ACM.
[MoMM13] Sigg, S., Shi, S., Buesching, F., Ji, Y., & Wolf, L. (2013, December). Leveraging RF-channel fluctuation for activity recognition: Active and passive systems, continuous and RSSI-based signal features. In MoMM(p. 43). ACM.
[INFOCOM13] Manweiler, J., Santhapuri, N., Choudhury, R. R., & Nelakuditi, S. (2013, April). Predicting length of stay at wifi hotspots. In IEEE INFOCOM (pp. 3102-3110). IEEE.
[MobiSys14] Wang, Y., Yang, J., Chen, Y., Liu, H., Gruteser, M., & Martin, R. P. (2014, June). Tracking human queues using single-point signal monitoring. In MobiSys (pp. 42-54). ACM.
[PerCom14] Sigg, S., Blanke, U., & Tröster, G. (2014, March). The telepathic phone: Frictionless activity recognition from wifi-rssi. In PerCom (pp. 148-155). IEEE.
[TMC14] Sigg, S., Scholz, M., Shi, S., Ji, Y., & Beigl, M. (2014). RF-sensing of activities from non-cooperative subjects in device-free recognition systems using ambient and local signals. IEEE TMC, 13(4), 907-920.
[MobiCom15-Keystroke] Ali, K., Liu, A. X., Wang, W., & Shahzad, M. (2015, September). Keystroke recognition using wifi signals. In MobiCom (pp. 90-102). ACM.
[MobiCom15-Understanding] Wang, W., Liu, A. X., Shahzad, M., Ling, K., & Lu, S. (2015, September). Understanding and modeling of wifi signal based human activity recognition. In MobiCom(pp. 65-76). ACM.
[INFOCOM15] Abdelnasser, H., Youssef, M., & Harras, K. A. (2015, April). Wigest: A ubiquitous wifi-based gesture recognition system. In INFOCOM (pp. 1472-1480). IEEE.
[MobiHoc15] Abdelnasser, H., Harras, K. A., & Youssef, M. (2015, June). UbiBreathe: A ubiquitous non-invasive WiFi-based breathing estimator. In MobiHoc(pp. 277-286). ACM.
[IMWUT17-Detecting] Ohara, K., Maekawa, T., & Matsushita, Y. (2017). Detecting state changes of indoor everyday objects using Wi-Fi channel state information. IMWUT, 1(3), 88.
[IMWUT17-Inferring] Sapiezynski, P., Stopczynski, A., Wind, D. K., Leskovec, J., & Lehmann, S. (2017). Inferring person-to-person proximity using WiFi signals. IMWUT, 1(2), 24.