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Crowd+Cloud Machines

  • Seng W. Loke
Chapter
Part of the SpringerBriefs in Computer Science book series (BRIEFSCOMPUTER)

Abstract

This chapter reviews several examples of how (machine and human) resources of a (mobile) crowd of people with separately owned devices can be pooled together and combined with a cloud computing mediating platform to form a type of crowd-powered system, or what we roughly call a crowd+cloud machine, to emphasise this combination between the two.

Keywords

Mobile Device Cloud Computing Activity Recognition Mobile Cloud Crowd Behaviour 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Ahmad A. Alzahrani, Seng W. Loke, and Hongen Lu. An advanced location-aware physical annotation system: From models to implementation. J. Ambient Intell. Smart Environ., 6(1):71–91, January 2014.Google Scholar
  2. 2.
    S. Arif, S. Olariu, J. Wang, G. Yan, W. Yang, and I. Khalil. Datacenter at the airport: Reasoning about time-dependent parking lot occupancy. IEEE Transactions on Parallel and Distributed Systems, 23(11):2067–2080, Nov 2012.Google Scholar
  3. 3.
    Amin Bakhshandehabkenar, Seng W. Loke, and J. Wenny Rahayu. A framework for continuous group activity recognition using mobile devices: Concept and experimentation. In IEEE 15th International Conference on Mobile Data Management, MDM 2014, Brisbane, Australia, July 14-18, 2014 - Volume 2, pages 23–26, 2014.Google Scholar
  4. 4.
    Erin Brady and Jeffrey P. Bigham. Crowdsourcing accessibility: Human-powered access technologies. Found. Trends Hum.-Comput. Interact., 8(4):273–372, November 2015.Google Scholar
  5. 5.
    R. Chandra, S. Hodges, A. Badam, and J. Huang. Offloading to improve the battery life of mobile devices. IEEE Pervasive Computing, 15(4):5–9, Oct 2016.Google Scholar
  6. 6.
    Matt Duckham. Decentralized Spatial Computing: Foundations of Geosensor Networks. Springer Publishing Company, Incorporated, 2012.Google Scholar
  7. 7.
    N. Fernando, Seng W. Loke, and W. Rahayu. Computing with nearby mobile devices: a work sharing algorithm for mobile edge-clouds. IEEE Transactions on Cloud Computing, PP(99):1–1, 2016.Google Scholar
  8. 8.
    Niroshinie Fernando, Seng W. Loke, and Wenny Rahayu. Honeybee: A Programming Framework for Mobile Crowd Computing. In Proc. of the 9th Int. Conf. on Mobile and Ubiquitous Systems: Comp., Netw. and Serv. (MobiQuitous), pages 224–236, 2012.Google Scholar
  9. 9.
    Frank H.P. Fitzek and Marcos D. Katz. Mobile Clouds: Exploiting Distributed Resources in Wireless, Mobile and Social Networks. Wiley Publishing, 1st edition, 2014.Google Scholar
  10. 10.
    Tobias Franke, Paul Lukowicz, Martin Wirz, and Eve Mitleton-Kelly. Participatory sensing and crowd management in public spaces. In Proceeding of the 11th Annual International Conference on Mobile Systems, Applications, and Services, MobiSys ’13, pages 485–486, New York, NY, USA, 2013. ACM.Google Scholar
  11. 11.
    M. Gerla, E. K. Lee, G. Pau, and U. Lee. Internet of vehicles: From intelligent grid to autonomous cars and vehicular clouds. In Internet of Things (WF-IoT), 2014 IEEE World Forum on, pages 241–246, March 2014.Google Scholar
  12. 12.
    Dawud Gordon. Group Activity Recognition Using Wearable Sensing Devices. PhD thesis, Karlsruhe Institute of Technology, 2014.Google Scholar
  13. 13.
    Bin Guo, Zhu Wang, Zhiwen Yu, Yu Wang, Neil Y. Yen, Runhe Huang, and Xingshe Zhou. Mobile crowd sensing and computing: The review of an emerging human-powered sensing paradigm. ACM Comput. Surv., 48(1):7:1–7:31, August 2015.Google Scholar
  14. 14.
    Shaohan Hu, Lu Su, Hengchang Liu, Hongyan Wang, and Tarek F. Abdelzaher. Smartroad: Smartphone-based crowd sensing for traffic regulator detection and identification. ACM Trans. Sen. Netw., 11(4):55:1–55:27, July 2015.Google Scholar
  15. 15.
    Azhar Mohd Ibrahim, Ibrahim Venkat, K. G. Subramanian, Ahamad Tajudin Khader, and Philippe De Wilde. Intelligent evacuation management systems: A review. ACM Trans. Intell. Syst. Technol., 7(3):36:1–36:27, February 2016.Google Scholar
  16. 16.
    Jakob Eg Larsen, Piotr Sapiezynski, Arkadiusz Stopczynski, Morten Mørup, and Rasmus Theodorsen. Crowds, bluetooth, and rock’n’roll: Understanding music festival participant behavior. In Proceedings of the 1st ACM International Workshop on Personal Data Meets Distributed Multimedia, PDM ’13, pages 11–18, New York, NY, USA, 2013. ACM.Google Scholar
  17. 17.
    Walter S. Lasecki, Phyo Thiha, Yu Zhong, Erin Brady, and Jeffrey P. Bigham. Answering visual questions with conversational crowd assistants. In Proceedings of the 15th International ACM SIGACCESS Conference on Computers and Accessibility, ASSETS ’13, pages 18:1–18:8, New York, NY, USA, 2013. ACM.Google Scholar
  18. 18.
    E. Lee, E. K. Lee, M. Gerla, and S. Y. Oh. Vehicular cloud networking: architecture and design principles. IEEE Communications Magazine, 52(2):148–155, February 2014.Google Scholar
  19. 19.
    Yazhi Liu, Jianwei Niu, and Xiting Liu. Comprehensive tempo-spatial data collection in crowd sensing using a heterogeneous sensing vehicle selection method. Personal Ubiquitous Comput., 20(3):397–411, June 2016.Google Scholar
  20. 20.
    Seng W. Loke. Heuristics for spatial finding using iterative mobile crowdsourcing. Hum.-centric Comput. Inf. Sci., 6(1):61:1–61:31, December 2016.Google Scholar
  21. 21.
    Seng W. Loke, Keegan Napier, Abdulaziz Alali, Niroshinie Fernando, and Wenny Rahayu. Mobile computations with surrounding devices: Proximity sensing and multilayered work stealing. ACM Trans. Embed. Comput. Syst., 14(2):22:1–22:25, February 2015.Google Scholar
  22. 22.
    Seng W. Loke and Batni Prabhanjan. Guidemate: a crowd-powered system to assist the disabled. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2015 ACM International Symposium on Wearable Computers, UbiComp/ISWC Adjunct 2015, Osaka, Japan, September 7-11, 2015, pages 1379–1384, 2015.Google Scholar
  23. 23.
    Peter Lucas, Joe Ballay, and Mickey McManus. Trillions: Thriving in the Emerging Information Ecology. Wiley Publishing, 1st edition, 2012.Google Scholar
  24. 24.
    Sabrina Merkel. Building Evacuation with Mobile Devices. KIT Scientific Publishing, 2014. Available at http://www.ksp.kit.edu/9783731502074.
  25. 25.
    J. Phuttharak and S. W. Loke. Towards declarative programming for mobile crowdsourcing: P2p aspects. In 2014 IEEE 15th International Conference on Mobile Data Management, volume 2, pages 61–66, July 2014.Google Scholar
  26. 26.
    Layla Pournajaf, Daniel A. Garcia-Ulloa, Li Xiong, and Vaidy Sunderam. Participant privacy in mobile crowd sensing task management: A survey of methods and challenges. SIGMOD Rec., 44(4):23–34, May 2016.Google Scholar
  27. 27.
    Eduardo Quintana and Jesus Favela. Augmented reality annotations to assist persons with alzheimers and their caregivers. Personal and Ubiquitous Computing, 17(6):1105–1116, 2013.CrossRefGoogle Scholar
  28. 28.
    Moo-Ryong Ra, Bin Liu, Tom F. La Porta, and Ramesh Govindan. Medusa: A programming framework for crowd-sensing applications. In Proceedings of the 10th International Conference on Mobile Systems, Applications, and Services, MobiSys ’12, pages 337–350, New York, NY, USA, 2012. ACM.Google Scholar
  29. 29.
    Sergio Rajsbaum and Jorge Urrutia. Some problems in distributed computational geometry. Theoretical Computer Science, 412(41):5760–5770, 2011.MathSciNetCrossRefzbMATHGoogle Scholar
  30. 30.
    Haggai Roitman, Jonathan Mamou, Sameep Mehta, Aharon Satt, and L.V. Subramaniam. Harnessing the crowds for smart city sensing. In Proceedings of the 1st International Workshop on Multimodal Crowd Sensing, CrowdSens ’12, pages 17–18, New York, NY, USA, 2012. ACM.Google Scholar
  31. 31.
    Adam Sadilek, John Krumm, and Eric Horvitz. Crowdphysics: Planned and opportunistic crowdsourcing for physical tasks. In Proceedings of the Seventh International Conference on Weblogs and Social Media, ICWSM 2013, Cambridge, Massachusetts, USA, July 8-11, 2013., 2013.Google Scholar
  32. 32.
    M. Satyanarayanan. The emergence of edge computing. Computer, 50(1):30–39, Jan 2017.Google Scholar
  33. 33.
    M. Satyanarayanan, P. Bahl, R. Caceres, and N. Davies. The case for vm-based cloudlets in mobile computing. IEEE Pervasive Computing, 8(4):14–23, Oct 2009.Google Scholar
  34. 34.
    M. Satyanarayanan, P. Simoens, Y. Xiao, P. Pillai, Z. Chen, K. Ha, W. Hu, and B. Amos. Edge analytics in the internet of things. IEEE Pervasive Computing, 14(2):24–31, Apr 2015.Google Scholar
  35. 35.
    W. Sherchan, P. P. Jayaraman, S. Krishnaswamy, A. Zaslavsky, S. Loke, and A. Sinha. Using on-the-move mining for mobile crowdsensing. In 2012 IEEE 13th International Conference on Mobile Data Management, pages 115–124, July 2012.Google Scholar
  36. 36.
    Lijun Sun, Kay W. Axhausen, Der-Horng Lee, and Xianfeng Huang. Understanding metropolitan patterns of daily encounters. Proceedings of the National Academy of Sciences, 110(34):13774–13779, 2013.Google Scholar
  37. 37.
    Jameson L. Toole, Yves-Alexandre de Montjoye, Marta C. González, and Alex (Sandy) Pentland. Modeling and Understanding Intrinsic Characteristics of Human Mobility, pages 15–35. Springer International Publishing, Cham, 2015.Google Scholar
  38. 38.
    Matteo Venanzi, Alex Rogers, and Nicholas R. Jennings. Crowdsourcing spatial phenomena using trust-based heteroskedastic gaussian processes. In Proceedings of the First AAAI Conference on Human Computation and Crowdsourcing, HCOMP 2013, November 7-9, 2013, Palm Springs, CA, USA, 2013.Google Scholar
  39. 39.
    Fusang Zhang, Beihong Jin, Tingjian Ge, Qiang Ji, and Yanling Cui. Who are my familiar strangers? revealing hidden friend relations and common interests from smart card data. In Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, CIKM ’16, pages 619–628, New York, NY, USA, 2016. ACM.Google Scholar
  40. 40.
    Wangsheng Zhang, Guande Qi, Gang Pan, Hua Lu, Shijian Li, and Zhaohui Wu. City-scale social event detection and evaluation with taxi traces. ACM Trans. Intell. Syst. Technol., 6(3):40:1–40:20, May 2015.Google Scholar

Copyright information

© The Author(s) 2017

Authors and Affiliations

  • Seng W. Loke
    • 1
  1. 1.School of Information TechnologyDeakin UniversityBurwoodAustralia

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