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Edge-based personal computing services: fall detection as a pilot study

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Abstract

Current developments in information and electronic technologies have pushed a tremendous amount of applications to meet the demands of personal computing services. Various kinds of smart devices have been launched and applied in our daily lives to provide services for individuals; however, the existing computing frameworks including local silo-based and cloud-based architectures, are not quite fit for personal computing services. Meanwhile, personal computing applications exhibit special features, they are latency-sensitive, energy efficient, highly reliable, mobile, etc, which further indicates that a new computing architecture is urgently needed to support such services. Thanks to the emerging edge computing paradigm, we were inspired to apply the distributed cooperative computing idea at the data source, which perfectly solves issues occurring among existing computing paradigms while meeting the requirements of personal computing services. Therefore, we explore personal computing services utilizing the edge computing paradigm, discuss the overall edge-based system architecture for personal computing services, and design the conceptual framework for an edge-based personal computing system. We analyze the functionalities in detail. To validate the feasibility of the proposed architecture, a fall detection application is simulated in our preliminary evaluation as an example service in which three Support Vector Machine based fall detection algorithms with different kernel functions are implemented. Experimental results show edge computing architecture can improve the performance of the system in terms of total latency, with about 22.75% reduction on average in the case of applying 4G at the second hop even when the data and computing stream of the application is small.

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References

  1. Islam SMR, Kwak D, Kabir MH, Hossain M, Kwak KS (2015) The Internet of Things for health care: a comprehensive survey. IEEE Access 3:678–708

    Article  Google Scholar 

  2. Sahni Y, Cao J, Zhang S, Yang L (2017) Edge mesh: a new paradigm to enable distributed intelligence in Internet of Things. IEEE Access 5:16441–16458

    Article  Google Scholar 

  3. Frost Sullivan (2015) Power management in Internet of Things (IoT) and connected devices. https://www.frost.com/sublib/display-market-insight.do?id=294383010. Accessed 22 Jan 2018 (Online)

  4. Meola A (2016) Internet of things in healthcare: information technology in health. http://www.businessinsider.com/internet-of-things-in-healthcare-2016-8. Accessed 22 Jan 2018 (Online)

  5. Akmandor AO, Jha NK (2017) Smart health care: an edge-side computing perspective. IEEE Consum Electron Mag 7(1):29–37

    Article  Google Scholar 

  6. Lin H, Shih Y, Pang A, Lou Y (2016) A virtual local-hub solution with function module sharing for wearable devices. In: MSWiM ’16 proceedings of the 19th ACM international conference on modeling, analysis and simulation of wireless and mobile system, NY, USA, pp 278–286

  7. Yi S, Hao Z, Zhang Q, Zhang Q, Shi W, Li Q (2017) LAVEA: latency-aware video analytics on edge computing platform, Sec17, CA, USA

  8. 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, pp 13–16

  9. Satyanarayanan M, Bahl P, Caceres R, Davies N (2009) The case for vm-based cloudlets in mobile computing. IEEE Pervasive Comput 8(4):14–23

    Article  Google Scholar 

  10. Patel M et al (2014) Mobile-edge computing introductory technical white paper. White Paper, mobile-edge computing (MEC) industry initiative

  11. CISCO (2015) White paper: fog computing and the Internet of Things: extend the cloud to where the things are. http://www.cisco.com/c/dam/enus/solutions/trends/iot/docs/computing-overview.pdf. Accessed 22 Jan 2018 (Online)

  12. Al-Fuqaha A, Guizani M, Mohammadi M, Aledhari M, Ayyash M (2015) Internet of things: a survey on enabling technologies, protoclos, and applications. IEEE Commun Surv Tutor 17(4):2347–2376

    Article  Google Scholar 

  13. Zhanikeev M (2015) A cloud visistation platform to facilitate cloud federation and fog computing. Computer 48(5):80–83

    Article  Google Scholar 

  14. Shi W, Cao J, Zhang Q, Li Y, Xu L (2016) Edge computing: vision and challenges. IEEE Internet Things J 3(5):637–646

    Article  Google Scholar 

  15. Zhang Q, Zhang X, Shi W, Zhang Q, Zhong H (2016) Firework: big data sharing and processing in collaborative edge environment. In: Proceedings of 4th IEEE workshop on hot topics in web systems and technologies (HotWeb), Washington DC, October, pp 24–25

  16. Constant N, Borthakur D, Abtahi M, Dubey H, Mankodiya K (2017) Fog-assisted wIoT: a smart fog gateway for end-to-end analytics in wearable Internet of Things. In: 23rd IEEE symposium on high performance computer architecture HPCA 2017. Texas, USA

  17. Dubey H, Monteiro A, Constant N, Abtahi M, Borthakur D, Mahler L, Sun Y, Yang Q, Akbar U, Mankodiya K (2017) Fog computing in medical internet-ofthings: architecture, implementation, and applications. In: Khan SU, Zomaya AY (eds) Handbook of large-scale distributed computing in smart healthcare. Springer, Berlin

    Google Scholar 

  18. Cao Y, Hou P, Chen S (2015) Distributed analytics and edge intelligence: pervasive health monitoring at the era of fog computing, Mobidata 15, Hangzhou, China, pp 43–48

  19. Barik RK, Dubey H, Samaddar AB, Gupta RD, Ray PK (2016) Foggis: fog computing for geospatial big data analytics. In: 3rd IEEE Uttar Pradesh section international conference on electrical, computer and electronics engineering, Varanasi, India

  20. Cao J, Xu L, Abdallah R, Shi W (2017) EdgeOS\_H: a home operating system for internet of everything. In: Proceedings of the 37th IEEE international conference on distributed computing systems (ICDCS), Vision/Blue Sky Track, Atlanta, USA

  21. Bylund M, Waern A (2001) Personal service environment-openness and user control in user-service interaction, SICS research report

  22. Ren L, Zhang Q, Shi W (2012) Low-power fall detection in home-based environments, MobileHealth12, South Carolina, USA

  23. Mell P, Grance T (2011) The NIST definition of cloud computing. National Institute of Standards and Technology, U.S. Department of Commerce, Gaithersburg, MD, USA, technical report, pp 50-50

  24. TIME.COM (2017) This new computer wants to be the ultimate AI assistant for the home. https://www.tuicool.com/articles/ZraMjuz. Accessed 22 Jan 2018 (Online)

  25. Lee N (2017) Simplehuman made a trashcan you can open with your voice. https://www.engadget.com/2017/01/05/simplehuman-made-a-trashcan-you-can-open-with-your-voice/. Accessed 22 Jan 2018 (Online)

  26. Ha K, Chen Z, Hu W, Richter W, Pillai P, Satyanarayanan M (2014) Towards wearable cognitive assistance. In: International conference on mobile systems, NY, USA, pp 68–88

  27. Satyanarayanan M (2017) The emergence of edge computing. Computer 50(1):30–39

    Article  Google Scholar 

  28. Ren L, Shi W, Yu Z, Liu Z (2016) Real-time energy-efficient fall detection based on SSR enery efficiency strategy. Int J Sens Netw 20(4):243–251

    Article  Google Scholar 

  29. Solaz M, Bourke A, Conway R, Nelson J, OLaighin G, (2010) Real-time low-energy fall detection algorithm with a programmable truncated MAC. In: 32nd annual international conference of the IEEE EMBS. Buenos Aires, Argentina, pp 2423–2426

  30. Ganz F, Barnaghi P, Carrez F (2013) Information abstraction for heterogeneous real world internet data. IEEE Sens J 13(10):3793–3805

    Article  Google Scholar 

  31. Gia TN, Thanigaivelan NK, Rahmani AM, Westerlund T, Liljeberg P, Tenhunen H (2014) Customizing 6LoWPAN networks towards Internet-of-Things based ubiquitous healthcare systems. In: Proceeding of NORCHIP, Tampere, Finland, pp 1–6

  32. Bai Y, Hao P, Zhang Y (2018) A case for web service bandwidth reduction on mobile devices with edge-hosted personal services. In: IEEE INFOCOM 2018, Honolulu, USA

  33. Traub J, Breß RT, Katsifodimos A, Markl V (2017) Optimized on-demand data streaming from sensor nodes. In: SoCC’17, Santa Clara, USA

  34. Trihinas D, Pallis G, Dikaiakos MD (2018) Low-cost adaptive monitoring techniques for the Internet of Things. IEEE Trans Serv Comput. https://doi.org/10.1109/TSC.2018.2808956

  35. Trihinas D, Pallis G, Dikaiakos MD (2017) ADMin: adaptive monitoring dissemination for the Internet of Things. In: IEEE INFOCOM 2017, Atlanta, GA, USA

  36. Murtha J (2018) How edge computing can advance healthcre. http://www.hcanews.com/news/how-edge-computing-can-advance-healthcare. Accessed 22 Jan 2018 (Online)

  37. Rahmani AM, Gia TN, Negash B, Anzanpour A, Azimi I, Jiang M, Liljeberg P (2017) Exploiting smart e-health gateways at the edge of healthcare Internet-of Things: a fog computing approach. Future Gener Comput Syst 78(2018):641–658

    Google Scholar 

  38. Apexcz (2018) Fall activity detection. https://github.com/apexcz/FallActivityDetection. Accessed 12 June 2018 (Online)

  39. Barik RK, Dubey H, Mankodiya K (2017) SOA-FOG: secure service-oriented edge computing architecture for smart health big data analytics. In: 5th IEEE global conference on signal and information processing, GlobalSIP 2017, Montreal, Canada

  40. Wu X, Dunne R, Zhang Q, Shi W (2017) Edge computing enabled smart firefighting: opportunities and challenges. In: HotWeb17, CA, USA

  41. Zhang Q, Zhang Q, Zhong H (2017) Poster: enhancing AMBER alert using collaborative edges. In: SEC17, CA, USA

  42. Nelson P (2016) Just one autonomous car will use 4000 gb of data/day. http://www.networkworld.com/article/3147892/internet/one-autonomous-car-will-use-4000-gb-of-dataday.html. Accessed Mar 2018 (Online)

  43. Zhang Q, Wang Y, Zhang X, Liu L, Wu X, Shi W, Zhong H (2018) OpenVDAP: an open vehicular data analytics platform for CAVs. In: Proceedings of the 38th IEEE international conference on distributed computing systems (ICDCS), Vision/Blue Sky Track, Vienna, Austria

  44. Sundar S, Liang B (2018) Offloading dependent tasks with communication delay and deadline constraint. In: IEEE INFOCOM 2018, Honolulu, USA

  45. Wang H, Gong J, Zhuang Y, Shen H, Lach J (2017) HealthEdge: task scheduling for edge computing with health emergency and human behavior consideration in smart homes. In: International conference on networking, architecture, and storage (NAS), Shenzhen, China, pp 1–2

  46. Roman R, Zhou J, Lopez J (2013) On the features and challenges of security and privacy in distributed Internet of Things. Comput Netw 57(10):2266–2279

    Article  Google Scholar 

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Acknowledgements

The authors would like to thank for Qiangyang Zhang for his help in experiments. The author also would like to thanks the anonymous reviewers for their valuable comments and suggestions. This research was supported by Natural Science Foundation of Shandong Province(ZR2018BF014), Scientific Research Foundation of Shandong University of Science and Technology for Recruited Talents(2017RCJJ042), and Key Laboratory for wisdom mine information technology of Shandong Province, Shandong University of Science and Technology.

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Authors and Affiliations

  1. Shandong University of Science and Technology, Qingdao, Shandong, China

    Lingmei Ren

  2. Anhui University, Hefei, Anhui, China

    Qingyang Zhang

  3. Wayne State University, Detroit, MI, USA

    Weisong Shi

  4. Key Laboratory for Wisdom Mine Information Technology of Shandong Province, Shandong University of Science and Technology, Qingdao, Shandong, China

    Yanjun Peng

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  1. Lingmei Ren
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  2. Qingyang Zhang
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Correspondence to Lingmei Ren.

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Ren, L., Zhang, Q., Shi, W. et al. Edge-based personal computing services: fall detection as a pilot study. Computing 101, 1199–1223 (2019). https://doi.org/10.1007/s00607-018-00697-x

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