Abstract
The last decade has witnessed a progressive interest shown by the community on inferring the presence of people from changes in the signals exchanged by deployed wireless devices. This non-invasive approach finds its rationale in manifold applications where the provision of counting devices to the people expected to traverse the scenario at hand is not affordable nor viable in the practical sense, such as intrusion detection in critical infrastructures. A trend in the literature has focused on modeling this paradigm as a supervised learning problem: a dataset with WiFi traces and their associated number of people is assumed to be available a priori, which permits to learn the pattern between traces and the number of people by a supervised learning algorithm. This paper advances over the state of the art by proposing a novel convolutional neural network that infers such a pattern over space (frequency) and time by rearranging the received I/Q information as a three-dimensional tensor. The proposed layered architecture incorporates further processing elements for a better generalization capability of the overall model. Results are obtained over real WiFi traces and compared to those recently reported over the same dataset for shallow learning models. The superior performance shown by the model proposed in this work paves the way towards exploring the applicability of the latest advances in Deep Learning to this specific case study.
Keywords
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Abdelnasser, H., Harras, K.A., Youssef, M.: Ubibreathe: a ubiquitous non-invasive WiFi-based breathing estimator. In: 16th ACM International Symposium on Mobile Ad Hoc Networking and Computing, pp. 277–286. ACM (2015)
Altman, D.G.: Practical Statistics for Medical Research. Chapman and Hall/CRC, London (1990)
Balakrishnama, S., Ganapathiraju, A.: Linear discriminant analysis-a brief tutorial. Institute for Signal and information Processing 18, 1–8 (1998)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
Cianca, E., De Sanctis, M., Di Domenico, S.: Radios as sensors. IEEE Internet Things J. 4(2), 363–373 (2017)
Cohen, J.: A coefficient of agreement for nominal scales. Educ. Psychol. Meas. 20(1), 37–46 (1960)
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)
David, O.E., Greental, I.: Genetic algorithms for evolving deep neural networks. In: Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation, pp. 1451–1452. ACM (2014)
Floreano, D., Dürr, P., Mattiussi, C.: Neuroevolution: from architectures to learning. Evol. Intell. 1(1), 47–62 (2008)
Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 29, 1189–1232 (2001)
Friedman, J.H.: Stochastic gradient boosting. Comput. Stat. Data Anal. 38(4), 367–378 (2002)
Joshi, K.R., Bharadia, D., Kotaru, M., Katti, S.: Wideo: fine-grained device-free motion tracing using RF backscatter. In: NSDI, pp 189–204 (2015)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:14126980, published at ICLR 2015 (2014)
Liu, J., Wang, Y., Chen, Y., Yang, J., Chen, X., Cheng, J.: Tracking vital signs during sleep leveraging off-the-shelf WiFi. In: 16th ACM International Symposium on Mobile Ad Hoc Networking and Computing, pp. 267–276. ACM (2015)
Lv, J., Yang, W., Gong, L., Man, D., Du, X.: Robust WLAN-based indoor fine-grained intrusion detection. In: 2016 IEEE Global Communications Conference (GLOBECOM), pp 1–6 (2016)
Miikkulainen, R., Liang, J., Meyerson, E., Rawal, A., Fink, D., Francon, O., Raju, B., Shahrzad, H., Navruzyan, A., Duffy, N., et al.: Evolving deep neural networks. arXiv preprint arXiv:170300548 (2017)
Ohara, K., Maekawa, T., Matsushita, Y.: Detecting state changes of indoor everyday objects using Wi-Fi channel state information. Proc. ACM Interact Mob Wearable Ubiquitous Technol. 1(3), 88:1–88:28 (2017). https://doi.org/10.1145/3131898
Oppermann, F.J., Boano, C.A., Römer, K.: A decade of wireless sensing applications: survey and taxonomy. In: The Art of Wireless Sensor Networks, vol 1: Fundamentals, pp. 11–50. Springer, New York, NY, USA (2014)
Pu, Q., Gupta, S., Gollakota, S., Patel, S.: Whole-home gesture recognition using wireless signals. In: 19th Annual International Conference on Mobile Computing & Networking, pp. 27–38. ACM (2013)
Qian, K., Wu, C., Yang, Z., Liu, Y., Zhou, Z.: PADS: passive detection of moving targets with dynamic speed using PHY layer information. In: 2014 20th IEEE International Conference on Parallel and Distributed Systems (ICPADS), pp 1–8 (2014)
Raja, M., Sigg, S.: Applicability of RF-based methods for emotion recognition: a survey. In: 2016 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops), pp. 1–6. IEEE (2016)
Schölkopf, B., Bartlett, P.L., Smola, A.J., Williamson, R.C.: Shrinking the tube: a new support vector regression algorithm. In: Advances in Neural Information Processing Systems, pp 330–336 (1999)
Seifeldin, M., Saeed, A., Kosba, A.E., El-Keyi, A., Youssef, M.: Nuzzer: a large-scale device-free passive localization system for wireless environments. IEEE Trans. Mob. Comput. 12(7), 1321–1334 (2013)
Sobron, I., Del Ser, J., Eizmendi, I., Velez, M.: EHUCOUNT dataset (2017). www.ehu.eus/tsr_radio/index.php/research-areas/data-analytics-in-wireless-networks. Accessed 30th Nov 2017
Sobron, I., Del Ser, J., Eizmendi, I., Velez, M.: Device-free people counting in IoT environments: new insights, results and open challenges. IEEE Internet Things J. ,1 (2018). https://doi.org/10.1109/JIOT.2018.2806990, early Access
Such, F.P., Madhavan, V., Conti, E., Lehman, J., Stanley, K.O., Clune, J.: Deep neuroevolution: genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning. arXiv preprint arXiv:171206567 (2017)
Wang, X., Gao, L., Mao, S., Pandey, S.: Deepfi: deep learning for indoor fingerprinting using channel state information. In: IEEE Wireless Communications and Networking Conference (WCNC), pp 1666–1671 (2015). https://doi.org/10.1109/WCNC.2015.7127718
Wang, X., Gao, L., Mao, S.: CSI phase fingerprinting for indoor localization with a deep learning approach. IEEE Internet Things J. 3(6), 1113–1123 (2016). https://doi.org/10.1109/JIOT.2016.2558659
Wang, Y., Wu, K., Ni, L.M.: Wifall: device-free fall detection by wireless networks. IEEE Trans. Mob. Comput. 16(2), 581–594 (2017)
Wu, C., Yang, Z., Zhou, Z., Liu, X., Liu, Y., Cao, J.: Non-invasive detection of moving and stationary human with WiFi. IEEE J. Sel. Areas Commun. 33(11), 2329–2342 (2015)
Zeng, Y., Pathak, P.H., Mohapatra, P.: Analyzing shopper’s behavior through WiFi signals. In: Proceedings of the 2nd Workshop on Workshop on Physical Analytics, WPA 2015, New York, NY, USA, pp. 13–18. ACM (2015)
Zhang, C., Li, H., Wang, X., Yang, X.: Cross-scene crowd counting via deep convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 833–841 (2015)
Zhang, Y., Zhou, D., Chen, S., Gao, S., Ma, Y.: Single-image crowd counting via multi-column convolutional neural network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 589–597 (2016)
Acknowledgements
This work was supported in part by the Spanish Ministry of Economy and Competitiveness under project 5GnewBROS (TEC2015-66153-P MINECO/FEDER, EU) and by the Basque Government (IT683-13 and the EMAITEK program).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Sobron, I., Del Ser, J., Eizmendi, I., Velez, M. (2018). A Deep Learning Approach to Device-Free People Counting from WiFi Signals. In: Del Ser, J., Osaba, E., Bilbao, M., Sanchez-Medina, J., Vecchio, M., Yang, XS. (eds) Intelligent Distributed Computing XII. IDC 2018. Studies in Computational Intelligence, vol 798. Springer, Cham. https://doi.org/10.1007/978-3-319-99626-4_24
Download citation
DOI: https://doi.org/10.1007/978-3-319-99626-4_24
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-99625-7
Online ISBN: 978-3-319-99626-4
eBook Packages: EngineeringEngineering (R0)