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2020 | OriginalPaper | Chapter

7. Fog-Enabled Smart Home and User Behavior Recognition

Authors : Yang Yang, Xiliang Luo, Xiaoli Chu, Ming-Tuo Zhou

Published in: Fog-Enabled Intelligent IoT Systems

Publisher: Springer International Publishing

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Abstract

One typical fog-enabled intelligent IoT system is the smart home, where each smart appliance/device is able to connect to the Internet and carry out some computing tasks. Each appliance/device can be viewed as an IoT node. These IoT nodes form a local network. To enable the home to better understand the humans and subsequently respond correctly, an efficient and secure human machine interact technology is necessary. Conventional remote controls are extremely inconvenient due to the larger number of appliances and the dependence on the hardware. A more efficient solution is to let the local network itself recognize the user behavior directly. Radio-based behavior recognition has advantages in smart home scenarios where comforts and privacy protection are of our major concern. Meanwhile, numerous wireless communications between the IoT nodes in the smart home also facilitate the implementation of these approaches. In this chapter, we will mainly focus on this type of behavior recognition. Besides, we can also take advantage of the acoustical signals to track the moving objects. Specifically, the speakers and microphones in cell phones can be employed to transmit and receive the sound signals. As accurate user behavior recognition becomes possible due to fog computing, our homes will surely become smarter and smarter.

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The sampled delay τ _u,l∕T _s can be regarded as an integer due to the high resolution of channel taps in wide-band mmWave systems. For example, T _s = 10 nm when the bandwidth is 100 MHz, which means each tap can be discriminated well in the delay domain.

Ma J, Wang H, Zhang D, Wang Y, Wang Y (2016) A survey on wi-fi based contactless activity recognition. In: 2016 International IEEE conferences on ubiquitous intelligence computing, advanced and trusted computing, scalable computing and communications, cloud and big data computing, internet of people, and smart world congress (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld). pp 1086–1091

Popoola OP, Wang K (2012) Video-based abnormal human behavior recognition—a review. IEEE Trans Syst Man Cybern Part C Appl Rev 42(6):865–878CrossRef

Gavrilova ML, Wang Y, Ahmed F, Polash Paul P (2018) Kinect sensor gesture and activity recognition: new applications for consumer cognitive systems. IEEE Consum Electron Mag 7(1):88–94CrossRef

Karantonis DM, Narayanan MR, Mathie M, Lovell NH, Celler BG (2006) Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring. IEEE Trans Inf Technol Biomed 10(1):156–167CrossRef

Lara OD, Labrador MA (2013) A survey on human activity recognition using wearable sensors. IEEE Commun Surv Tutorials 15(3):1192–1209CrossRef

Hegde N, Bries M, Swibas T, Melanson E, Sazonov E (2018) Automatic recognition of activities of daily living utilizing insole-based and wrist-worn wearable sensors. IEEE J Biomed Health Inform 22(4):979–988CrossRef

Wang Y, Liu J, Chen Y, Gruteser M, Yang J, Liu H (2014) E-eyes: device-free location-oriented activity identification using fine-grained wifi signatures. In: Proceedings of the 20th annual international conference on mobile computing and networking, MobiCom’14. ACM, New York, pp 617–628

Wang W, Liu AX, Shahzad M, Ling K, Lu S (2015) Understanding and modeling of WiFi signal based human activity recognition. In: Proceedings of the 21st annual international conference on mobile computing and networking, MobiCom’15. ACM, New York, pp 65–76CrossRef

Liu X, Cao J, Tang S, Wen J (2014) Wi-sleep: contactless sleep monitoring via WiFi signals. In: 2014 IEEE Real-time systems symposium, pp 346–355

10.

Liu X, Cao J, Tang S, Wen J, Guo P (2016) Contactless respiration monitoring via off-the-shelf WiFi devices. IEEE Trans Mob Comput 15(10):2466–2479CrossRef

11.

Liu J, Wang Y, Chen Y, Yang J, Chen X, Cheng J (2015) Tracking vital signs during sleep leveraging off-the-shelf WiFi. In: Proceedings of the 16th ACM international symposium on mobile Ad Hoc networking and computing, MobiHoc’15. ACM, New York, pp 267–276CrossRef

12.

Khan UM, Kabir Z, Hassan SA, Ahmed SH (2017) A deep learning framework using passive WiFi sensing for respiration monitoring. In: GLOBECOM 2017–2017 IEEE global communications conference, pp 1–6

13.

Wang H, Zhang D, Wang Y, Ma J, Wang Y, Li S (2017) Rt-fall: a real-time and contactless fall detection system with commodity WiFi devices. IEEE Trans Mob Comput 16(2):511–526CrossRef

14.

Wang Y, Wu K, Ni LM (2017) Wifall: device-free fall detection by wireless networks. IEEE Trans Mob Comput 16(2):581–594CrossRef

15.

Zheng X, Wang J, Shangguan L, Zhou Z, Liu Y (2016) Smokey: ubiquitous smoking detection with commercial WiFi infrastructures. In: IEEE INFOCOM 2016—the 35th annual IEEE international conference on computer communications, pp 1–9

16.

Zheng X, Wang J, Shangguan L, Zhou Z, Liu Y (2017) Design and implementation of a CSI-based ubiquitous smoking detection system. IEEE/ACM Trans Netw 25(6):3781–3793CrossRef

17.

Abdelnasser H, Youssef M, Harras KA (2015) Wigest: a ubiquitous WiFi-based gesture recognition system. In: 2015 IEEE conference on computer communications (INFOCOM), pp 1472–1480

18.

Tan S, Yang J (2016) Wifinger: leveraging commodity WiFi for fine-grained finger gesture recognition. In: Proceedings of the 17th ACM international symposium on mobile Ad Hoc networking and computing, MobiHoc’16. ACM, New York, pp 201–210CrossRef

19.

Ali K, Liu AX, Wang W, Shahzad M (2017) Recognizing keystrokes using WiFi devices. IEEE J Sel Areas Commun 35(5):1175–1190CrossRef

20.

Qian K, Wu C, Zhou Z, Zheng Y, Yang Z, Liu Y (2017) Inferring motion direction using commodity wi-fi for interactive exergames. In: Proceedings of the 2017 CHI conference on human factors in computing systems, CHI’17. ACM, New York, pp 1961–1972

21.

Qian K, Wu C, Yang Z, Liu Y, Jamieson K (2017) Widar: decimeter-level passive tracking via velocity monitoring with commodity wi-fi. In: Proceedings of the 18th ACM international symposium on mobile Ad Hoc networking and computing, Mobihoc’17. ACM, New York, pp 6:1–6:10

22.

Qian K, Wu C, Zhang Y, Zhang G, Yang Z, Liu Y (2018) Widar2.0: passive human tracking with a single wi-fi link. In: Proceedings of the 16th annual international conference on mobile systems, applications, and services, MobiSys’18. ACM, New York, pp 350–361CrossRef

23.

Lien J, Gillian N, Emre Karagozler M, Amihood P, Schwesig C, Olson E, Raja H, Poupyrev I (2016) Soli: ubiquitous gesture sensing with millimeter wave radar. ACM Trans Graph 35(4):142:1–142:19

24.

Wang S, Song J, Lien J, Poupyrev I, Hilliges O (2016) Interacting with soli: exploring fine-grained dynamic gesture recognition in the radio-frequency spectrum. In: Proceedings of the 29th annual symposium on user interface software and technology, UIST’16. ACM, New York, pp 851–860CrossRef

25.

Wei T, Zhang X (2015) mTrack: high-precision passive tracking using millimeter wave radios. In: Proceedings of the 21st annual international conference on mobile computing and networking, MobiCom’15. ACM, New York, pp 117–129CrossRef

26.

Nandakumar R, Iyer V, Tan D, Gollakota S (2016) Fingerio: using active sonar for fine-grained finger tracking. In: Proceedings of the 2016 CHI conference on human factors in computing systems, CHI’16. ACM, New York, pp 1515–1525

27.

Wang W, Liu AX, Sun K (2016) Device-free gesture tracking using acoustic signals. In: Proceedings of the 22Nd annual international conference on mobile computing and networking, MobiCom’16. ACM, New York, pp 82–94CrossRef

28.

Halperin D, Hu W, Sheth A, Wetherall D (2011) Tool release: gathering 802.11n traces with channel state information. SIGCOMM Comput Commun Rev 41(1):53–53CrossRef

29.

del Peral-Rosado JA, Raulefs R, López-Salcedo JA, Seco-Granados G (2018) Survey of cellular mobile radio localization methods: from 1G to 5G. IEEE Commun Surv Tutorials 20(2):1124–1148. Secondquarter

30.

Wei Z, Zhao Y, Liu X, Feng Z (2017) DoA-LF: a location fingerprint positioning algorithm with millimeter-wave. IEEE Access 5:22678–22688CrossRef

31.

Lin Z, Lv T, Mathiopoulos PT (2018) 3-d indoor positioning for millimeter-wave massive MIMO systems. IEEE Trans Commun 66(6):2472–2486CrossRef

32.

Shahmansoori A, Garcia GE, Destino G, Seco-Granados G, Wymeersch H (2018) Position and orientation estimation through millimeter-wave MIMO in 5G systems. IEEE Trans Wirel Commun 17(3):1822–1835CrossRef

33.

Abu-Shaban Z, Zhou X, Abhayapala T, Seco-Granados G, Wymeersch H (2018) Error bounds for uplink and downlink 3D localization in 5G millimeter wave systems. IEEE Trans Wirel Commun 17(8):4939–4954CrossRef

34.

Cyganek B, Krawczyk B, Wozniak M (2015) Multidimensional data classification with chordal distance based kernel and support vector machines. Eng Appl Artif Intell 46(PA):10–22

35.

Keller JB (1962) Geometrical theory of diffraction. J Opt Soc Am 52(2):116–130MathSciNetCrossRef

Title: Fog-Enabled Smart Home and User Behavior Recognition
Authors: Yang Yang
Xiliang Luo
Xiaoli Chu
Ming-Tuo Zhou
Publisher: Springer International Publishing
Book: Fog-Enabled Intelligent IoT Systems
Print ISBN: 978-3-030-23184-2

Electronic ISBN: 978-3-030-23185-9

Copyright Year: 2020
DOI: https://doi.org/10.1007/978-3-030-23185-9_7

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