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FDFA: A fog computing assisted distributed analytics and detecting system for family activities

  • Fei Gu
  • Jianwei NiuEmail author
  • Xin Jin
  • Shui Yu
Article
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Abstract

Researches have shown that taking parting in family activities could establish good relationships with family members. Fine-grained family activities detection is proven effective for increasing self-awareness and motivating people to modify their life styles for improved well being. Mobile health provides the possibility to solve this problem. However, with the increase of such applications, the requirements for computation, communication, and storage capability are becoming higher and higher. Fog computing, a new computing paradigm, utilizes a collaborative multitude of end-user clients or near-user edge devices to conduct a substantial amount of computing, communication, storage, and so on. In this paper, we propose FDFA, the first fog computing assisted distributed analytics and detecting system for family activities using smartphones and smart watches. Specifically, FDFA firstly uses the built-in sensors to obtain sensing data, such as the striding frequency and heart rate of the users, the sound of environment, and so forth. Then, a fog computing assisted resolution framework is proposed to efficiently detect family activities in an unobtrusive manner based on sensed data. Finally, considering the characteristics of different people, FDFA sets a personal plan for family members in doing some exercise and making continuous progress in the process of communicating. We have fully implemented FDFA on the Android platform and the extensive experimental results demonstrate that FDFA is easy to use, accurate, and appropriate for family activities with the accuracy of 79.1% and the user satisfaction degree of 82.4%. Moreover, the system can achieve more than 90% bandwidth efficiency and offer low-latency real time response with fog computing.

Keywords

Mobile sensing Fog computing Family activities Latency User satisfaction degree 

Notes

Acknowledgments

This work was supported by the National Natural Science Foundation of China (61572060, 61772060, 61728201), the Academic Excellence Foundation of BUAA for PhD Students, and CERNET Innovation Project (NGII20160316, NGII20170315).

References

  1. 1.
    Basu S (2003) A linked-hmm model for robust voicing and speech detection. In: 2003 IEEE International conference on acoustics, speech, and signal processing, 2003. Proceedings.(ICASSP’03), vol 1. IEEE, pp I–IGoogle Scholar
  2. 2.
    Benesty J, Chen J, Huang Y, Cohen I (2009) Pearson correlation coefficient. In: Noise reduction in speech processing. Springer, pp 1–4Google Scholar
  3. 3.
    Bi C, Xing G, Hao T, Huh J, Peng W, Ma M (2017) Familylog: a mobile system for monitoring family mealtime activities. In: 2017 IEEE International conference on pervasive computing and communications (PerCom). IEEE, pp 21–30Google Scholar
  4. 4.
    Bodybugg. http://www.bodybugg.com/ (2016)
  5. 5.
    Bonomi F, Milito R, Natarajan P, Zhu J (2014) Fog computing: a platform for internet of things and analytics. In: Big data and internet of things: a roadmap for smart environments. Springer, pp 169–186Google Scholar
  6. 6.
    Bonomi F, Milito R, Zhu J, Addepalli S (2012) Fog computing and its role in the internet of things. In: Proceedings of the first edition of the MCC workshop on mobile cloud computing. ACM, pp 13–16Google Scholar
  7. 7.
    Consolvo S, McDonald DW, Toscos T, Chen MY, Froehlich J, Harrison B, Klasnja P, LaMarca A, LeGrand L, Libby R et al (2008) Activity sensing in the wild: a field trial of ubifit garden. In: Proceedings of the SIGCHI conference on human factors in computing systems. ACM, pp 1797–1806Google Scholar
  8. 8.
    Crouter SE, Schneider PL, Karabulut M, Bassett DR (2003) Validity of ten electronic pedometers for measuring steps, distance, and kcals. Med Sci Sports Exer 35(5):S283CrossRefGoogle Scholar
  9. 9.
    Freund Y, Schapire RE (1995) A desicion-theoretic generalization of on-line learning and an application to boosting. In: European conference on computational learning theory. Springer, pp 23–37Google Scholar
  10. 10.
    Ganti RK, Jayachandran P, Abdelzaher TF, Stankovic JA (2006) Satire: a software architecture for smart attire. In: Proceedings of the 4th international conference on mobile systems, applications and services. ACM, pp 110–123Google Scholar
  11. 11.
    Ginsburg KR, et al. (2007) The importance of play in promoting healthy child development and maintaining strong parent-child bonds. Pediatrics 119(1):182–191CrossRefGoogle Scholar
  12. 12.
    Gu F, Niu J, Das SK, He Z (2016) Runnerpal: a runner monitoring and advisory system based on smart devices. IEEE Transactions on Services ComputingGoogle Scholar
  13. 13.
    Gu F, Niu J, Duan L (2017) Waipo: a fusion-based collaborative indoor localization system on smartphones. IEEE/ACM Transactions on NetworkingGoogle Scholar
  14. 14.
    Gu F, Niu J, He Z, Jin X (2017) Familypal: an effective system for detecting family activities based on smartphones. In: Proceedings of the 15th international conference on industrial informatics (INDIN 2017). IEEEGoogle Scholar
  15. 15.
    Gu F, Niu J, He Z, Jin X, Rodrigues JJPC (2017) Smartbuddy: an integrated mobile sensing and detecting system for family activities. In: Proceedings of the 60th IEEE global communications conference (GLOBECOM 2017). IEEEGoogle Scholar
  16. 16.
    Inoue N, Saito T, Shinoda K, Furui S (2010) High-level feature extraction using sift gmms and audio models. In: 2010 20th International conference on pattern recognition (ICPR). IEEE, pp 3220–3223Google Scholar
  17. 17.
    Keally M, Zhou G, Xing G, Wu J, Pyles A (2011) Pbn: towards practical activity recognition using smartphone-based body sensor networks. In: Proceedings of the 9th ACM conference on embedded networked sensor systems. ACM, pp 246–259Google Scholar
  18. 18.
    Laine TH, Lee C, Suk H (2014) Mobile gateway for ubiquitous health care system using zigbee and bluetooth. In: 2014 Eighth international conference on innovative mobile and internet services in ubiquitous computing (IMIS). IEEE, pp 139–145Google Scholar
  19. 19.
    Lin JJ, Mamykina L, Lindtner S, Delajoux G, Strub HB (2006) Fishnsteps: encouraging physical activity with an interactive computer game. In: International conference on ubiquitous computing. Springer, pp 261–278Google Scholar
  20. 20.
    Lu H, Pan W, Lane ND, Choudhury T, Campbell AT (2009) Soundsense: scalable sound sensing for people-centric applications on mobile phones. In: Proceedings of the 7th international conference on mobile systems, applications, and services. ACM, pp 165–178Google Scholar
  21. 21.
    Lu H, Yang J, Liu Z, Lane ND, Choudhury T, Campbell AT (2010) The jigsaw continuous sensing engine for mobile phone applications. In: Proceedings of the 8th ACM conference on embedded networked sensor systems. ACM, pp 71–84Google Scholar
  22. 22.
    Miluzzo E, Cornelius CT, Ramaswamy A, Choudhury T, Liu Z, Campbell AT (2010) Darwin phones: the evolution of sensing and inference on mobile phones. In: Proceedings of the 8th international conference on mobile systems, applications, and services. ACM, pp 5–20Google Scholar
  23. 23.
    Miluzzo E, Lane ND, Fodor K, Peterson R, Lu H, Musolesi M, Eisenman SB, Zheng X, Campbell AT (2008) Sensing meets mobile social networks: the design, implementation and evaluation of the cenceme application. In: Proceedings of the 6th ACM conference on embedded network sensor systems. ACM, pp 337–350Google Scholar
  24. 24.
    Moosavi SR, Gia TN, Rahmani A-M, Nigussie E, Virtanen S, Isoaho J, Tenhunen H (2015) Sea: a secure and efficient authentication and authorization architecture for iot-based healthcare using smart gateways. Procedia Comput Sci 52:452–459CrossRefGoogle Scholar
  25. 25.
  26. 26.
    Pang H, Tan K-L (2004) Authenticating query results in edge computing. In: 20th International conference on data engineering, 2004. Proceedings. IEEE, pp 560–571Google Scholar
  27. 27.
    Peebles D, Lu H, Lane ND, Choudhury T, Campbell AT (2010) Community-guided learning: exploiting mobile sensor users to model human behavior. In: AAAI, vol 10, pp 1600–1606Google Scholar
  28. 28.
    Scheirer E, Slaney M (1997) Construction and evaluation of a robust multifeature speech/music discriminator. In: 1997 IEEE International conference on acoustics, speech, and signal processing, 1997. ICASSP-97, vol 2. IEEE, pp 1331–1334Google Scholar
  29. 29.
    Schneider PL, Crouter SE, Bassett DR, et al. (2004) Pedometer measures of free-living physical activity: comparison of 13 models. Med Sci Sports Exer 36(2):331–335CrossRefGoogle Scholar
  30. 30.
    Stantchev V, Barnawi A, Ghulam S, Schubert J, Tamm G (2015) Smart items, fog and cloud computing as enablers of servitization in healthcare. Sensors Transducers 185(2):121Google Scholar
  31. 31.
    Stojmenovic I, Wen S (2014) The fog computing paradigm: scenarios and security issues. In: 2014 Federated conference on computer science and information systems (FedCSIS). IEEE, pp 1–8Google Scholar
  32. 32.
    Tian Y, Lu S, Yang C (2013) Macro-pico amplitude-space sharing with optimized han-kobayashi coding. IEEE Trans Commun 61(10):4404–4415CrossRefGoogle Scholar
  33. 33.
    Vaquero LM, Rodero-Merino L (2014) Finding your way in the fog: towards a comprehensive definition of fog computing. ACM SIGCOMM Comput Commun Rev 44(5):27–32CrossRefGoogle Scholar
  34. 34.
    Xu J (2011) Web-based billing system exploits mature and emerging technology. IT Prof 13(2):49–55CrossRefGoogle Scholar
  35. 35.
    Yu D, Yao K, Su H, Li G, Seide F (2013) Kl-divergence regularized deep neural network adaptation for improved large vocabulary speech recognition. In: 2013 IEEE International conference on acoustics, speech and signal processing (ICASSP). IEEE, pp 7893–7897Google Scholar
  36. 36.
    Zhan A, Chang M, Chen Y, Terzis A (2012) Accurate caloric expenditure of bicyclists using cellphones. In: Proceedings of the 10th ACM conference on embedded network sensor systems. ACM, pp 71–84Google Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.State Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science and EngineeringBeihang UniversityBeijingChina
  2. 2.Hangzhou Innovation Institute of Beihang UniversityHangzhouChina
  3. 3.Beijing Advanced Innovation Center for Big Data and Brain Computing (BDBC)Beihang UniversityBeijingChina
  4. 4.School of Information TechnologyDeakin UniversityMelbourneAustralia

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