Fog-Enabled Smart Home and User Behavior Recognition

  • Yang Yang
  • Xiliang Luo
  • Xiaoli Chu
  • Ming-Tuo Zhou


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.


Behavior recognition Fog computing Smart home MIDAR PAMT 


  1. 1.
    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 1086–1091Google Scholar
  2. 2.
    Popoola OP, Wang K (2012) Video-based abnormal human behavior recognition—a review. IEEE Trans Syst Man Cybern Part C Appl Rev 42(6):865–878CrossRefGoogle Scholar
  3. 3.
    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):88–94CrossRefGoogle Scholar
  4. 4.
    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):156–167CrossRefGoogle Scholar
  5. 5.
    Lara OD, Labrador MA (2013) A survey on human activity recognition using wearable sensors. IEEE Commun Surv Tutorials 15(3):1192–1209CrossRefGoogle Scholar
  6. 6.
    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):979–988CrossRefGoogle Scholar
  7. 7.
    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 617–628Google Scholar
  8. 8.
    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 65–76CrossRefGoogle Scholar
  9. 9.
    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 346–355Google Scholar
  10. 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):2466–2479CrossRefGoogle Scholar
  11. 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 267–276CrossRefGoogle Scholar
  12. 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 1–6Google Scholar
  13. 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):511–526CrossRefGoogle Scholar
  14. 14.
    Wang Y, Wu K, Ni LM (2017) Wifall: device-free fall detection by wireless networks. IEEE Trans Mob Comput 16(2):581–594CrossRefGoogle Scholar
  15. 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 1–9Google Scholar
  16. 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):3781–3793CrossRefGoogle Scholar
  17. 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 1472–1480Google Scholar
  18. 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 201–210CrossRefGoogle Scholar
  19. 19.
    Ali K, Liu AX, Wang W, Shahzad M (2017) Recognizing keystrokes using WiFi devices. IEEE J Sel Areas Commun 35(5):1175–1190CrossRefGoogle Scholar
  20. 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 1961–1972Google Scholar
  21. 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 6:1–6:10Google Scholar
  22. 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 350–361CrossRefGoogle Scholar
  23. 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):142:1–142:19Google Scholar
  24. 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 851–860CrossRefGoogle Scholar
  25. 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 117–129CrossRefGoogle Scholar
  26. 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 1515–1525Google Scholar
  27. 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 82–94CrossRefGoogle Scholar
  28. 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):53–53CrossRefGoogle Scholar
  29. 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. SecondquarterGoogle Scholar
  30. 30.
    Wei Z, Zhao Y, Liu X, Feng Z (2017) DoA-LF: a location fingerprint positioning algorithm with millimeter-wave. IEEE Access 5:22678–22688CrossRefGoogle Scholar
  31. 31.
    Lin Z, Lv T, Mathiopoulos PT (2018) 3-d indoor positioning for millimeter-wave massive MIMO systems. IEEE Trans Commun 66(6):2472–2486CrossRefGoogle Scholar
  32. 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):1822–1835CrossRefGoogle Scholar
  33. 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):4939–4954CrossRefGoogle Scholar
  34. 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):10–22Google Scholar
  35. 35.
    Keller JB (1962) Geometrical theory of diffraction. J Opt Soc Am 52(2):116–130MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Yang Yang
    • 1
  • Xiliang Luo
    • 1
  • Xiaoli Chu
    • 2
  • Ming-Tuo Zhou
    • 3
  1. 1.Shanghai Institute of Fog Computing Technology (SHIFT), School of Information Science and TechnologyShanghaiTech UniversityShanghaiChina
  2. 2.Department of Electronic & Electrical EngineeringUniversity of SheffieldSheffieldUK
  3. 3.Chinese Academy of Sciences, Shanghai Institute of Microsystem and Information TechnologyShanghaiChina

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