An Adaptive Offloading Framework for Improving Performance of Applications in IoT Devices Using Fuzzy Multi Criteria Decision Making

  • Authors

    • Waskitho Wibisono
    • Mahaputra Widhi Pande Putu
    • Tohari Ahmad
    • Radityo Anggoro
    2018-12-16
    https://doi.org/10.14419/ijet.v7i4.40.24031
  • Computation Offloading, Cloud Computing, Embedded Systems, Internet of Things, Fuzzy MCDM

  • The recent advances of Internet of Things (IoT) technologies have changed the requirements of IoT device to not only provide basic sensing and communication services but also of executing more complex applications with different goals. These challenges have highlighted the need to provide high computation capability in IoT devices. However, common limited resources in IoT devices bring challenges to support application requirements as well as to deal with limited computation resources. To address with this problem, computation offloading can be applied. In this approach heavy computational tasks can be transferred and executed in the cloud computing service to get the result. However, sending heavy computational jobs along with the data to the cloud server are not always efficient, especially where the mobile environments where network performances may changes unpredictably. This paper proposes a prototype of smart offloading framework designed to work in IoT devices using the Fuzzy Multi Criteria Decision Making as the decision tool. The decision whether the job execution will be done in the IoT device itself or being uploaded to the cloud computing server is done by considering internal and external factors such as current network conditions. The smart offloading framework prototype has been developed and tested in a real IoT device. The experiment results showed that the smart offloading approach can improve the performance of applications running in an IoT device by deciding location of job executions in dynamic situations with good results.

     

  • References

    1. [1] K. K. Patel and S. M. Patel, "Internet of Things: Definition, Characteristics, Architecture, Enabling Technologies, Application & Future Challenges," International Journal of Engineering Science and Computing,, vol. 6, no. 5, pp. 6122-6132, 2016.

      [2] A. Khairi, H. H. Ammar, and R. Bahgat, "Smartphone Energizer: Extending Smartphone's Battery Life with Smart Offloading," in The 9th IEEE Wireless Communications and Mobile Computing Conference (IWCMC), 2013, pp. 329-336,.

      [3] I. Giurgiu, O. Riva, D. Juric, I. Krivulev, and G. Alonso, "Calling the cloud: Enabling mobile phones as interfaces to cloud applications," in Middleware 2009, 2009, pp. 83–102.

      [4] R. Kemp et al., "EyeDentify: Multimedia Cyber Foraging from a Smartphone," presented at the 11th IEEE International Symposium on Multimedia, 2009.

      [5] K. Liu, J. Peng, H. Li, X. Zhang, and W. Liu, "Multi-device task offloading with time-constraints for energy efficiency in mobile cloud computing.," Future Generation Computer Systems, vol. 64, 2016.

      [6] R. Chandra and P. Bahl, "Maui: Making smartphones last longer with code offload.," in Proceedings of the 8th International Conference on Mobile Systems, Applications, and Services MobiSys ’10, 2010, pp. 49-62.

      [7] B. G. Chun, S. Ihm, P. Maniatis, M. Naik, and A. Patti, "Clonecloud: Elastic execution between mobile device and cloud," in Proceedings of the Sixth Conference on Computer Systems EuroSys ’11, 2011, pp. 301–314.

      [8] E. Cuervo et al., "Maui: Making smartphones last longer with code offload," in Mobysys 10, 2010.

      [9] A. Bhattacharyaa and P. Dec, "A Survey of Adaptation Techniques in Computation Offloading," Journal of Network and Computer Applications archive, vol. 78, no. C, pp. 97-115 2017.

      [10] M. AhmadKhan, "A survey of computation offloading strategies for performance improvement of applications running on mobile devices " Journal of Network and Computer Applications, vol. 56, pp. 28-40, 2015.

      [11] Atta ur Rehman Khan, O. Mazliza, A. N. Khan, J. Shuja, and S. Mustafa, "Computation Offloading Cost Estimation in Mobile Cloud Application Models," Wireless Personal Communications, vol. 97, no. 3, pp. 4897–4920, 2017.

      [12] J. Niu, W. Song, and M. Atiquzzaman, "Bandwidth-adaptive partitioning for distributed execution optimization of mobile applications," Journal Network Computing Application, vol. 37, pp. 334–347, 2014.

      [13] S. Wang and S. Dey, "Rendering adaptation to raddress communication and computation constraints in cloud mobile gaming," in Proceedings of the IEEE Global Telecommunications Conference (GLOBECOM 2010, 2010.

      [14] J. Li, Z. Peng, B. Xiao, and Y. Hua, "Make smartphones last a day: Pre-processing based computer vision application offloading," in Proceedings of the 12th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON), 2015, pp. 462–470.

      [15] Y. Chang, S. Hung, N. J. Wang, and B. Lin, "Csr: A cloud-assisted speech recognition service for personal mobile device," in Proceedings of the International Conference on Parallel Processing, 2011, pp. 305-314.

      [16] W. Zhang, Y. Wen, and Z. Zhang, "Towards virus scanning as a service in mobile cloud computing energy-efficient dispatching policy under n-version protection," IEEE Transactions on Emerging Topics in Computing vol. 6, no. 1, pp. 122-134, 2015.

      [17] D. Ho, G. S. Park, and H. Song, "Mobile Data Offloading System for Video Streaming Services over SDN-enabled Wireless Networks " in Proceedings of the 9th ACM Multimedia Systems Conference, 2018, pp. 174-185

      [18] J. Zhang et al., "Hybrid computation offloading for smart home automation in mobile cloud computing," Personal and Ubiquitous Computing, vol. 22, no. 1, 2018.

      [19] C. Carlsson and R. Fullér, "Fuzzy multiple criteria decision making: Recent developments," Fuzzy Sets and Systems, vol. 78, no. 2, pp. 139-153, 1996.

      [20] G. Bojadziev and M. Bojadziev, Fuzzy Logic for Bussiness, Finance and Management (Advances in Fuzzy Systems-Applications and Theory, Vol 12). World Scientific Publishing, 1998.

      [21] A. Nagar, "Development of Fuzzy Multi Criteria Decision Making Method for Selection of Optimum Maintenance Alternative," International Journal of Applied Research In Mechanical Engineering, vol. 1, no. 2, 2011.

      [22] A. G. Howard et al., "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Application," in CoRR, 2017.

      [23] A. Rosebrock, "Object detection with deep learning and OpenCV," in https://www.pyimagesearch.com/2017/09/11/object-detection-with-deep-learning-and-opencv/, Accessed May 2018

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  • How to Cite

    Wibisono, W., Widhi Pande Putu, M., Ahmad, T., & Anggoro, R. (2018). An Adaptive Offloading Framework for Improving Performance of Applications in IoT Devices Using Fuzzy Multi Criteria Decision Making. International Journal of Engineering & Technology, 7(4.40), 31-36. https://doi.org/10.14419/ijet.v7i4.40.24031