Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

A Survey of Mobile Cloud Computing Applications: Perspectives and Challenges

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

As mobile computing has been developed for decades, a new model for mobile computing, namely, mobile cloud computing, emerges resulting from the marriage of powerful yet affordable mobile devices and cloud computing. In this paper we survey existing mobile cloud computing applications, as well as speculate future generation mobile cloud computing applications. We provide insights for the enabling technologies and challenges that lie ahead for us to move forward from mobile computing to mobile cloud computing for building the next generation mobile cloud applications. For each of the challenges, we provide a survey of existing solutions, identify research gaps, and suggest future research areas.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Sanaei, Z., Abolfazli, S., Gani, A., & Buyya, R. (2014). Heterogeneity in mobile cloud computing: Taxonomy and open challenges. IEEE Communications Surveys and Tutorials, 16(1), 369–392.

    Article  Google Scholar 

  2. Fernando, N., Loke, S. W., & Rahayu, W. (2013). Mobile cloud computing: A survey. Future Generation Computer Systems, 29(1), 84–106.

    Article  Google Scholar 

  3. Dinh, H. T., Lee, C., Niyato, D., & Wang, P. (2013). A survey of mobile cloud computing: Architecture, applications, and approaches. Wireless Communications and Mobile Computing, 13(18), 1587–1611.

  4. Buyya, R. (2014). Introduction to the IEEE transactions on cloud computing. IEEE Transactions on Cloud Computing, 1(1), 3–9.

    Article  Google Scholar 

  5. Mell, P., & Grance, T. (2011). The NIST definition of cloud computing. National Institute of Standards and Technology Std. 800–145, September 2011 [Online]. http://csrc.nist.gov/publications/nistpubs/800-145/SP800-145.

  6. D’Angelo, G., Ferretti, S., Ghini, V., & Panzieri, F. (2011). Mobile computing in digital ecosystems: Design issues and challenges. In 7th IEEE wireless communications and mobile computing conference, pp. 2127–2132.

  7. Li, Y., & Chen, I. R. (2011). Design and performance analysis of mobility management schemes based on pointer forwarding for wireless mesh networks. IEEE Transactions on Mobile Computing, 10(3), 349–361.

    Article  Google Scholar 

  8. Hu, H., Wen, Y., Chua, T. S., & Li, X. (2014). Toward scalable systems for big data analytics: A technology tutorial. IEEE Access, 2, 652–687.

    Article  Google Scholar 

  9. Khan, A. U. R., Othman, M., Madani, S. A., & Khan, S. U. (2014). A survey of mobile cloud computing application models. IEEE Communications Surveys and Tutorials, 16(1), 393–413.

    Article  Google Scholar 

  10. Vaquero, L. M., Rodero-Merino, L., Caceres, J., & Lindner, M. (2008). A break in the clouds: Towards a cloud definition. ACM SIGCOMM Computer Communication Review, 39(1), 50–55.

    Article  Google Scholar 

  11. Zhang, Q., Cheng, L., & Boutaba, R. (2010). Cloud computing: State-of-the-art and research challenges. Journal of Internet Services and Applications, 1, 7–18.

    Article  Google Scholar 

  12. Foster, I., Zhao, Y., Raicu, I., & Lu, S. (2008). Cloud computing and grid computing 360-degree compared. In Grid computing environments workshop, November 2008, pp. 1–10.

  13. Grobauer, B., Walloschek, T., & Stocker, E. (2011). Understanding cloud computing vulnerabilities. IEEE Security Privacy, 9(2), 50–57.

    Article  Google Scholar 

  14. Armbrust, M., Fox, A., Griffith, R., Joseph, A. D., Katz, R., Konwinski, A., et al. (2010). A view of cloud computing. ACM Communications, 53(4), 50–58.

    Article  Google Scholar 

  15. Yang, L., Cao, J., Tang, S., Li, T., & Chan, A. (2012) A framework for partitioning and execution of data stream applications in mobile cloud computing. In IEEE 5th international conference on cloud computing, June 2012, pp. 794–802.

  16. Klein, A., Mannweiler, C., Schneider, J., & Schotten, H. D. (2010). Access schemes for mobile cloud computing. In 2010 Eleventh international conference on mobile data management, May 2010, pp. 387–392.

  17. Bahl, P., Han, R. Y., Li, L. E., & Satyanarayanan, M. (2012). Advancing the state of mobile cloud computing. In ACM workshop on Mobile cloud computing and services, New York, NY, USA, pp. 21–28.

  18. Miluzzo, E., Cáceres, R., & Chen, Y.-F. (2012). Vision: mclouds-computing on clouds of mobile devices. In ACM workshop on mobile cloud computing and services, pp. 9–14.

  19. Campbell, A., Eisenman, S., Lane, N., Miluzzo, E., Peterson, R., Lu, H., et al. (2008). The rise of people-centric sensing. IEEE Internet Computing, 12(4), 12–21.

    Article  Google Scholar 

  20. Satyanarayanan, M. (2010). Mobile computing: The next decade. In ACM workshop on mobile cloud computing services: Social networks and beyond, pp. 1–6.

  21. Yang, D., Xue, G., Fang, X., & Tang, J. (2012). Crowdsourcing to smartphones: Incentive mechanism design for mobile phone sensing. In ACM MobiCom.

  22. Lane, N., Miluzzo, E., Lu, H., Peebles, D., Choudhury, T., & Campbell, A. (2010). A survey of mobile phone sensing. IEEE Communications Magazine, 48(9), 140–150.

    Article  Google Scholar 

  23. Cheng, H., Sun, F., Buthpitiya, S., & Griss, M. (2012). Sensorchestra: Collaborative sensing for symbolic location recognition. In Mobile computing, applications, and services, pp. 195–210.

  24. Lu, H., Pan, W., Lane, N., Choudhury, T., & Campbell, A. (2009). Soundsense: Scalable sound sensing for people-centric applications on mobile phones. In International conference on mobile systems, applications, and services, pp. 165–178.

  25. “Sensorly”. [Online]. http://www.sensorly.com/.

  26. Thiagarajan, A., Ravindranath, L., LaCurts, K., Madden, S., Balakrishnan, H., Toledo, S., et al. (2009). Vtrack: Accurate, energy-aware road traffic delay estimation using mobile phones. In ACM conference on embedded networked sensor systems, pp. 85–98.

  27. Herring, R., Hofleitner, A., Amin, S., Nasr, T., Khalek, A., Abbeel, P., et al. (2009). Using mobile phones to forecast arterial traffic through statistical learning. In Transportation research board.

  28. Hunter, T., Moldovan, T., Zaharia, M., Merzgui, S., Ma, J., Franklin, M., et al. (2011). Scaling the mobile millennium system in the cloud. In ACM symposium on cloud computing, p. 28.

  29. Mun, M., Reddy, S., Shilton, K., Yau, N., Burke, J., Estrin, D., et al. (2009). Peir, the personal environmental impact report, as a platform for participatory sensing systems research. In ACM international conference on Mobile systems, applications, and services, pp. 55–68.

  30. Khalid, O., Khan, M., Khan, S., & Zomaya, A. (2014). OmniSuggest: A ubiquitous cloud based context aware recommendation system for mobile social networks. IEEE Transactions on Services Computing. doi:10.1109/TSC.2013.53

  31. Wang, Y., Wu, J., & Yang, W. S. (2013). Cloud-based multicasting with feedback in mobile social networks. IEEE Transactions on Wireless Communications, 12(12), 6043–6053.

    Article  Google Scholar 

  32. Miluzzo, E., Lane, N., Fodor, K., Peterson, R., Lu, H., Musolesi, M., et al. (2008). Sensing meets mobile social networks: The design, implementation and evaluation of the cenceme application. In ACM conference on embedded network sensor systems, pp. 337–350.

  33. Cimler, R., Matyska, J., & Sobeslav, V. (2014). Cloud based solution for mobile healthcare application. ACM 18th international database engineering and applications symposium, July 2014.

  34. Wu, H., Wang, Q., & Wolter, K. (2013). Mobile healthcare systems with multi-cloud offloading. In IEEE 14th international conference on mobile data management, pp. 188–193.

  35. Consolvo, S., McDonald, D., Toscos, T., Chen, M., Froehlich, J., Harrison, B., et al. (2008). Activity sensing in the wild: A field trial of ubifit garden. In ACM SIGCHI conference on Human factors in computing systems, pp. 1797–1806.

  36. Tamai, K., & Shinagawa, A. (2011). Platform for location-based services. Fujitsu Scientific & Technical Journal, 47(4), 426–433.

    Google Scholar 

  37. La H., & Kim, S. (2010). A conceptual framework for provisioning context-aware mobile cloud services. In IEEE 3rd international conference on cloud computing, pp. 466–473.

  38. Kangas K., & Röning, J. (1999). Using code mobility to create ubiquitous and active augmented reality in mobile computing. In ACM/IEEE international conference on mobile computing and networking, pp. 48–58.

  39. Luo, X. (2009). From augmented reality to augmented computing: A look at cloud-mobile convergence. In International symposium on ubiquitous virtual reality, July 2009, pp. 29–32.

  40. Barbera, M. V., Kosta, S., Mei, A., & Stefa, J. (2013). To offload or not to offload? The bandwidth and energy costs of mobile cloud computing. In IEEE INFOCOM 2013. Turin, Italy.

  41. Hunt, G., Scott, M., et al. (1998). The coign automatic distributed partitioning system. ACM Operating Systems Review, 33, 187–200.

    Google Scholar 

  42. Giurgiu, I., Riva, O., Juric, D., Krivulev, I., & Alonso, G. (2009). Calling the cloud: Enabling mobile phones as interfaces to cloud applications. In Middleware 2009, pp. 83–102.

  43. Cooper, E., Lindley, S., Wadler, P., & Yallop, J. (2007). Links: Web programming without tiers. In Formal methods for components and objects (pp. 266–296). Berlin: Springer.

  44. Yang, F., Shanmugasundaram, J., Riedewald, M., & Gehrke, J. (2006). Hilda: A high-level language for data-drivenweb applications. In IEEE international conference on data engineering, pp. 32–32.

  45. Chun, B.-G., Ihm, S., Maniatis, P., Naik, M., & Patti, A. (2011). CloneCloud: Elastic execution between mobile device and cloud. In ACM conference on computer systems, pp. 301–314.

  46. Cuervo, E., Balasubramanian, A., Cho, D.-K., Wolman, A., Saroiu, S., Chandra, R., et al. (2010). Maui: Making smartphones last longer with ccode offload. In AMC international conference on Mobile systems, applications, and services, New York, NY, USA, pp. 49–62

  47. Kemp, R., Palmer, N., Kielmann, T., & Bal, H. (2012). Cuckoo: A computation offloading framework for smartphones. In Mobile computing, applications, and services, ser. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering (Vol. 76, pp. 59–79). Berlin: Springer.

  48. Kosta, S., Aucinas, A., Hui, P., Mortier, R., & Zhang, X. (March 2012). ThinkAir: Dynamic resource allocation and parallel execution in the cloud for mobile code offloading. In IEEE INFOCOM, pp. 945–953.

  49. Rudenko, A., Reiher, P., Popek, G. J., & Kuenning, G. H. (1999). The remote processing framework for portable computer power saving. In ACM symposium on Applied computing, pp. 365–372.

  50. Newhouse T., & Pasquale, J. (2004). Resource-controlled remote execution to enhance wireless network applications. In Workshop on applications and services in wireless networks, August 2004, pp. 30–38.

  51. Cloud computing drives mobile data growth, October 2009. [Online]. http://spectrum.ieee.org/telecom/wireless/cloud-computing-drives-mobile-data-growth.

  52. Guo, T., Yan, Z., & Aberer, K. (2012). An adaptive approach for online segmentation of multi-dimensional mobile data. In ACM international workshop on data engineering for wireless and mobile access, pp. 7–14.

  53. Hao, F., Lakshman, T., Mukherjee, S., & Song, H. (2009). Enhancing dynamic cloud-based services using network virtualization. In ACM workshop on Virtualized infrastructure systems and architectures, pp. 37–44.

  54. Zhu, W., Luo, C., Wang, J., & Li, S. (2011). Multimedia cloud computing. IEEE Signal Processing Magazine, 28(3), 59–69.

    Article  Google Scholar 

  55. Ferretti, S., Ghini, V., Panzieri, F., & Turrini, E. (July 2010). Seamless support of multimedia distributed applications through a cloud. In IEEE 3rd international conference on cloud computing, pp. 548–549.

  56. Nan, X., He, Y., & Guan, L. (2011). Optimal resource allocation for multimedia cloud based on queuing model. In IEEE 13th international workshop on multimedia signal processing, October 2011, pp. 1–6.

  57. Altamimi, M., Palit, R., Naik, K., & Nayak, A. (June 2012). Energy-as-a-service (EaaS): On the efficacy of multimedia cloud computing to save smartphone energy. In IEEE 5th international conference on cloud computing, pp. 764–771.

  58. Int. cloud services platform beta location-based services, September 2012. [Online]. http://software.intel.com/en-us/articles/cloud-services-location-based-api-overview.

  59. AT&T to launch cloud-based lbs mobility data offering, January 2011. [Online]. http://www.mobilecommercedaily.com/att-to-launch-cloud-based-lbs-mobility-data-offering.

  60. Tan, Z., Chu, D., & Zhong, L. (2014). Vision: Cloud and crowd assistance for GPS urban canyons. ACM Mobisys, pp. 23–27.

  61. Cho, E., Myers, S. A., & Leskovec, J. (2011). Friendship and mobility: user movement in location-based social networks. In ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1082–1090.

  62. Stuedi, P., Mohomed, I., & Terry, D. (2010). Wherestore: Location-based data storage for mobile devices interacting with the cloud. In ACM workshop on mobile cloud computing and services: Social networks and beyond, pp. 1–8.

  63. Ananthanarayanan, G., Douglas, C., Ramakrishnan, R., Rao, S., & Stoica, I. (2012). True elasticity in multi-tenant data-intensive compute clusters. In ACM symposium on cloud computing, pp. 1–7.

  64. Rai, A., Bhagwan, R., & Guha, S. Generalized resource allocation for the cloud. In ACM symposium on cloud computing, pp. 15:1–15:12.

  65. He, Y., Elnikety, S., Larus, J., & Yan, C. (2012). Zeta: Scheduling interactive services with partial execution. In ACM symposium on cloud computing, pp. 1–14.

  66. Tumanov, A., Cipar, J., Ganger, G. R., & Kozuch, M. A. (2012). alsched: Algebraic scheduling of mixed workloads in heterogeneous clouds. In ACM Symposium on Cloud Computing (pp. 1–7).

  67. Shen, Z., Subbiah, S., Gu, X., & Wilkes, J. (2011). CloudScale: Elastic resource scaling for multi-tenant cloud systems. In ACM symposium on cloud computing, pp. 1–14.

  68. Harbitter A., & Menascé, D. A. (2001). The performance of public key-enabled kerberos authentication in mobile computing applications. In ACM conference on computer and communications security, pp. 78–85.

  69. Al-Muhtadi, J., Ranganathan, A., Campbell, R., & Mickunas, M. (2002). A flexible, privacy-preserving authentication framework for ubiquitous computing environments. In International conference on distributed computing systems workshops, pp. 771–776.

  70. Bonneau, J., Herley, C., van Oorschot, P., & Stajano, F. (2012). The quest to replace passwords: A framework for comparative evaluation of web authentication schemes. In IEEE symposium on security and privacy, pp. 553–567.

  71. Recordon D., & Reed, D. (2006). Openid 2.0: A platform for user-centric identity management. In ACM workshop on digital identity management, pp. 11–16.

  72. Parno, B., Kuo, C., & Perrig, A. (2006). Phoolproof phishing prevention. In Financial cryptography and data security (pp. 1–19). International Financial Cryptography Association.

  73. Oauth2.0. [Online]. http://oauth.net/2/.

  74. Sun, S.-T., & Beznosov, K. (2012). The devil is in the (implementation) details: An empirical analysis of oauth sso systems. In ACM conference on Computer and communications security, pp. 378–390.

  75. Chari, S., Jutla, C., & Roy, A. (2011). Universally composable security analysis of oauth v2. 0. 0. Cryptology ePrint Archive, Report 2011/526, Tech. Rep.

  76. Pai, S., Sharma, Y., Kumar, S., Pai, R., & Singh, S. (2011). Formal verification of oauth 2.0 using alloy framework. In IEEE international conference on communication systems and network technologies, pp. 655–659.

  77. Oauth 2.0 (without signatures) is bad for the web. [Online]. http://hueniverse.com/2010/09/oauth-2-0-without-signatures-is-bad-for-the-web/.

  78. Miculan, M., & Urban, C. (2011). Formal analysis of facebook connect single sign-on authentication protocol. In SOFSEM, Vol. 11, pp. 22–28.

  79. Under the covers of oauth 2.0 at facebook. [Online]. http://www.sociallipstick.com/?p=239.

  80. Compromising twitter’s oauth security system. [Online]. http://www.immagic.com/eLibrary/ARCHIVES/GENERAL/GENPRESS/A090903P.

  81. van Dijk, M., Juels, A., Oprea, A., Rivest, R. L., Stefanov, E. & Triandopoulos, N. (2012). Hourglass schemes: How to prove that cloud files are encrypted. In ACM conference on computer and communications security, pp. 265–280.

  82. Chen, I., & Wang, Y. (2012). Reliability analysis of wireless sensor networks with distributed code attestation. IEEE Communications Letters, 16(10), 1640–1643.

    Article  Google Scholar 

  83. Zaman, S., & Grosu, D. (2011). Combinatorial auction-based dynamic vm provisioning and allocation in clouds. In IEEE third international conference on cloud computing technology and science, pp. 107–114.

  84. Wang, W., Li, B., & Liang, B. (2012). Towards optimal capacity segmentation with hybrid cloud pricing. In IEEE 32nd international conference on distributed computing systems, pp. 425–434.

  85. Lampe, U., Siebenhaar, M., Papageorgiou, A., Schuller, D., & Steinmetz, R. (2012). Maximizing cloud provider profit from equilibrium price auctions. In IEEE 5th international conference on cloud computing, pp. 83–90.

  86. Zafer, M., Song, Y., & Lee, K. (2012). Optimal bids for spot vms in a cloud for deadline constrained jobs. In IEEE 5th international conference on cloud computing, pp. 75–82.

  87. Tang, S., Yuan, J., & Li, X. (2012). Towards optimal bidding strategy for amazon ec2 cloud spot instance. In IEEE 5th international conference on cloud computing, pp. 91–98.

  88. Li, H., & Li, H. (2011). A research of resource provider-oriented pricing mechanism based on game theory in cloud bank model. In IEEE international conference on cloud and service computing, pp. 126–130.

  89. Mokbel, M., Ghanem, T., & Aref, W. (2003). Spatio-temporal access methods. IEEE Data Engineering Bulletin, 26(2), 40–49.

    Google Scholar 

  90. Jensen, C., Lin, D., & Ooi, B. (2004). Query and update efficient b+-tree based indexing of moving objects. In International conference on very large data bases-Volume 30, pp. 768–779.

  91. Kwon, D., Lee, S., & Lee, S. (2002). Indexing the current positions of moving objects using the lazy update r-tree. In IEEE international conference on mobile data management, pp. 113–120.

  92. Lee, M., Hsu, W., Jensen, C., Cui, B., & Teo, K. (2003). Supporting frequent updates in r-trees: A bottom-up approach. In International conference on very large databases, pp. 608–619.

  93. Lin, B., & Su, J. (2004). On bulk loading TPR-tree. In IEEE international conference on mobile data management, pp. 114–124.

  94. Saltenis, S., & Jensen, C. (2002). Indexing of moving objects for location-based services. In IEEE international conference on data engineering, pp. 463–472.

  95. Tao, Y., Papadias, D., & Sun, J. (2003). The TPR*-tree: an optimized spatio-temporal access method for predictive queries. In International conference on very large databases vol. 29, pp. 790–801.

  96. Mokbel, M., & Aref, W. (2005). Gpac: Generic and progressive processing of mobile queries over mobile data. In IEEE international conference on mobile data management, pp. 155–163.

  97. Badger, L., Grance, T., Patt-Corner, R., & Voas, J. (2011). Draft cloud computing synopsis and recommendations. NIST Special Publication, 800, 146.

    Google Scholar 

  98. Rimal, B., Choi, E., & Lumb, I. (2009). A taxonomy and survey of cloud computing systems. In IEEE international joint conference on INC, IMS and IDC, pp. 44–51.

  99. Reiss, C., Tumanov, A., Ganger, G. R., Katz, R. H., & Kozuch, M. A. (2012). Heterogeneity and dynamicity of clouds at scale: Google trace analysis. In ACM symposium on cloud computing, pp. 1–13.

  100. Amazon EC2. [Online]. http://aws.amazon.com/ec2/.

  101. Yilmaz, O., & Chen, I. R. (2009). Utilizing call admission control for pricing optimization of multiple service classes in wireless cellular networks. Computer Communications, 32(2), 317–323.

    Article  Google Scholar 

  102. Chen, I. R., Yilmaz, O., & Yen, I. L. (2006). Admission control algorithms for revenue optimization with QoS guarantees in mobile wireless networks. Wireless Personal Communications, 38(3), 357–376.

    Article  Google Scholar 

  103. Noor, T.H., Sheng, Q.Z., Zeadally, S., & Yu, J. (2013). Trust management of services in cloud environments: Obstacles and solutions. ACM Computing Survey, 46(1), article 12.

  104. Sherchan, W., Nepal, S., & Paris, C. (2013). A survey of trust in social networks. ACM Computing Survey, 45(4), article 47.

  105. Cho, J.H., Swami, A., & Chen, I. R. (2009). Modeling and analysis of trust management for cognitive mission-driven group communication systems in mobile ad hoc networks. In International Conference on Computational Science and Engineering, August 2009, pp. 641–650.

  106. Cho, J. H., Swami, A., & Chen, I. R. (2012). Modeling and analysis of trust management with trust chain optimization in mobile ad hoc networks. Journal of Network and Computer Applications, 35(3), 1001–1012.

    Article  Google Scholar 

  107. Bao, F., Chen, I. R., Chang, M. J., & Cho, J. H. Hierarchical trust management for wireless sensor networks and its applications to trust-based routing and intrusion detection. IEEE Transactions on Network and Service Management, 9(2), 169–183.

  108. Bao, F., Chen, I. R., Chang, M. J., & Cho, J. H. (2011). Trust-based intrusion detection in wireless sensor networks. IEEE international conference on communications (ICC), pp. 1–6.

  109. Chen, I. R., Bao, F., Chang, M., & Cho, J. H. (2014). Dynamic trust management for delay tolerant networks and its application to secure routing. IEEE Transactions on Parallel and Distributed Systems, 25(5), 1200–1210.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, Virginia Tech, 7054 Haycock Road, Falls Church, VA, 22043, USA

    Yating Wang & Ing-Ray Chen

  2. Department of Information Management, Southern Taiwan University of Science and Technology, Tainan, Taiwan

    Ding-Chau Wang

Authors
  1. Yating Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ing-Ray Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ding-Chau Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ing-Ray Chen.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, Y., Chen, IR. & Wang, DC. A Survey of Mobile Cloud Computing Applications: Perspectives and Challenges. Wireless Pers Commun 80, 1607–1623 (2015). https://doi.org/10.1007/s11277-014-2102-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-014-2102-7

Keywords

Search

Navigation