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Efficient Resource Management Scheme for Storage Processing in Cloud Infrastructure with Internet of Things

Abstract

Recently, research on cloud-integrated Internet of Things where an Internet of Things (IoT) is converged with a cloud environment has been actively pursued. An IoT operates through interaction among many composition elements, such as actuators and sensors. At present, IoTs are used in diverse areas (for example, traffic control and safety, energy savings, process control, communications systems, distributed robots, and other important applications). In daily life, IoTs should provide services of high reliability corresponding with various physical elements. In order to guarantee highly reliable IoT services, optimized modeling, simulation, and resource management technologies integrating physical elements and computing elements are required. For such reasons, many systems are being developed where autonomic computing technologies are applied that sense any internal errors or external environmental changes occurring during system operation and where systems adapt or evolve themselves. In an IoT environment composed of large-scale nodes, autonomic computing requires a high processing amount and efficient storage processing of computing in order to process sensing data efficiently. In addition, due to the heterogeneous composition of IoT environments, separate middleware is required to share collected information. Accordingly, this paper proposed an efficient resource management scheme (ERMS) that efficiently manages IoT resources using cloud infrastructure satisfying the high availability, expansion, and high processing amount requirements. ERMS provides a XML-based standard sensing data storage scheme in order to store and process heterogeneous IoT sensing data in the cloud infrastructure. In addition, ERMS provides classification techniques to efficiently store and process distributed IoT data.

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References

  1. 1.

    Haque, S. A., Aziz, S. M., & Rahman, M. (2014). Review of cyber-physical system in healthcare. International Journal of Distributed Sensor Networks, 2014, 1–21.

  2. 2.

    Jeong, Y. S., Han, Y. H., Park, J., & Lee, S. Y. (2012). MSNS: Mobile sensor network simulator for area coverage and obstacle avoidance based on GML. EURASIP Journal on Wireless Communications and Networking, 95(1), 1–15.

  3. 3.

    Han, Y. H., Kim, Y. H., Kim, W. T., & Jeong, Y. S. (2011). An energy-efficient self-deployment with the centroid-directed virtual force in mobile sensor network. Simulation, 88(10), 1152–1165.

  4. 4.

    Song, Y. J., & Pang, Y. (2014). Leveraged BMIS model for cloud risk control. Journal of Information Processing Systems, 10(2), 240–255.

  5. 5.

    Nhat, V. V. M., & Quoc, N. H. (2014). A model of adaptive grouping scheduling in OBS core nodes. Journal of Convergence, 5(1), 9–13.

  6. 6.

    Jeong, Y. S., Han, W. H., Song, E. H., & Yeo, S. S. (2010). Performance evaluation with DEVS formalism and implementation of active emergency call system for realtime location and monitoring. Simulation Modelling Practice and Theory, 18(4), 416–430.

  7. 7.

    Binh, H. T. T. (2014). Multi-objective genetic algorithm for solving the multilayer survivable optical network design problem. Journal of Convergence, 5(1), 20–25.

  8. 8.

    Park, J. H., Kim, H. W., & Jeong, Y. S. (2014). Efficiency sustainability resource visual simulator for clustered desktop virtualization based on cloud infrastructure. Sustainability, 6(11), 8079–8091.

  9. 9.

    Sinha, A., & Lobiyal, D. K. (2013). Performance evaluation of data aggregation for cluster-based wireless sensor network. Human-centric Computing and Information Sciences, 3(13), 1–17.

  10. 10.

    Jeong, Y. S., Song, E. H., Chae, G. B., Hong, M., & Park, D. S. (2010). Large-scale middleware for ubiquitous sensor networks. IEEE Intelligent Systems, 25(2), 48–59.

  11. 11.

    Kang, A. N., Kim, H. W., Barolli, L., & Jeong, Y. S. (2013). An efficient WSN simulator for GPU-based node performance. International Journal of Distributed Sensor Networks, 2013, 1–7.

  12. 12.

    Misra, S., Krishna, P. V., Saritha, V., Agarwal, H., Shu, L., & Obaidat, M. S. (2013). Efficient medium access control for cyber-physical systems with heterogeneous networks. IEEE Systems Journal, 99, 1–9.

  13. 13.

    Wan, J., Zhang, D., Zhao, S., Yang, L. T., & Lloret, J. (2014). Context-aware vehicular cyber-physical systems with cloud support: Architecture, challenges, and solutions. IEEE Communications Magazine, 52(8), 106–113.

  14. 14.

    Dong, B., Zheng, Q., Tian, F., Chao, K., Ma, R., & Anane, R. (2012). An optimized approach for storing and accessing small files on cloud storage. Journal of Network and Computer Applications, 35(6), 1847–1862.

  15. 15.

    Tang, B., & Wang, Y. (2012). Design of large-scale sensory data processing system based on cloud computing. Research Journal of Applied Sciences, Engineering and Technology, 4(8), 1004–1009.

  16. 16.

    Shvachko, K., Kuang, H., Radia, S., & Chansler, R. (2010). The hadoop distributed file system. In Proceedings of MSST, Incline Village, NV, 2010, pp. 1–10.

  17. 17.

    Sharma, A. B., Ivančić, F., Niculescu-Mizil, A., Chen, H., & Jiang, G. (2014). Modeling and analytics for cyber-physical system in the age of big data. ACM SIGMETRICS Performance Evaluation Review, 41(4), 74–77.

  18. 18.

    Jara, A. J., Genoud, D., Bocchi, Y. (2014) Big data for cyber physical systems an analysis of challenges, solutions and opportunities. In Proceedings of IMIS , Birmingham, UK, 2014, pp. 376–380.

  19. 19.

    Jha, S. K. (2014). Medical cyber physical system. International Journal of Emerging Technology and Advanced Engineering, 4(5), 819–823.

  20. 20.

    Ning, H., & Sha, H. (2012). Technology classification, industry, and education for future internet of things. International Journal of Communication System, 25(9), 1230–1241.

  21. 21.

    Kang, Y., & Zhongyi, Z. (2012). Summarize on internet of things and exploration into technical system framework. In Proceedings of 2012 IEEE symposium on robotics and applications ( ISRA 2012), IEEE, Kuala Lumpur, 2012. pp. 653-656.

  22. 22.

    Riggins, F. J., & Wamba, S. F. (2015) Research direction on the adoption, usage and impact of the internet of things through the use of big data analytics. In Proceedings of 48th Hawaii international conference on system scie nces (HICSS 2015), IEEE, Kauai, HI. pp. 1531–1540.

  23. 23.

    Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M. (2013). Internet of Things (IoT): A vision, architectural elements, and future directions. Future Generation Computer System, 29(7), 1645–1660.

  24. 24.

    Wei, Y., Sha, F., & Yan, W. (2014). The construction of information management system based on cloud computing and the internet of things. Applied Mechanics and Materials, 543–547, 2981–2983.

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Acknowledgments

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2014R1A1A2053564).

Author information

Correspondence to Jong Hyuk Park or Young-Sik Jeong.

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Kim, H., Park, J.H. & Jeong, Y. Efficient Resource Management Scheme for Storage Processing in Cloud Infrastructure with Internet of Things. Wireless Pers Commun 91, 1635–1651 (2016). https://doi.org/10.1007/s11277-015-3093-8

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Keywords

  • Resource management
  • Internet of Things (IoT)
  • Cloud computing
  • Resource cassification
  • QoS