EEDOS: an energy-efficient and delay-aware offlloading scheme based on device to device collaboration in mobile edge computing

  • Ramtin Ranji
  • Ali Mohammed MansoorEmail author
  • Asmiza Abdul Sani


Device to device (D2D) communication and mobile edge computing (MEC) are two promising technologies in fifth generation (5G) cellular mobile communication. Besides MEC, a new task offloading technique attracts the attention as D2D collaboration. However, there is lack of integrated D2D and MEC framework to address the energy and delay costs in a joint approach. This work, proposes an energy efficient and delay-aware offloading scheme (EEDOS) based on D2D collaboration in MEC. In EEDOS, mobile devices can offload their task to the MEC or an idle mobile device in their proximity. The task execution and offloading to the MEC or an idle nearby device is formulated, and the optimization problem is defined. The whole process of allocating proper offloading destination is designed in the edge server. EEDOS, classifies offloading requests according to the deadline and energy constraint of requesting device. Then, it finds the proper offloading destination by utilising the maximum matching with minimum cost graph algorithm. Through simulation, we show that EEDOS achieves 95 percent of energy efficiency in comparison of no-offloading task execution and outperforms existing studies in term of energy efficiency with an improved delay in task execution. Moreover, EEDOS is capable of performing more successful task offloading and requires less edge server resources.


Task offloading Device to device communication Mobile edge computing Energy efficiency 



The study is supported by Project No.: BK067-2015 from University of Malaya, and the Fundamental Research Grant Scheme (FRGS), Project: FP007-2016 from Ministry of Higher Education, Malaysia.

Compliance with ethical standards

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.


  1. 1.
    Soyata, T., Muraleedharan, R., Funai, C., Kwon, M., & Heinzelman, W. (2012). Cloud-Vision: Real-time face recognition using a mobile-cloudlet-cloud acceleration architecture. In Proceedings—IEEE symposium on computers and communications (pp. 59–66).Google Scholar
  2. 2.
    Ali, F. A., Simoens, P., Verbelen, T., Demeester, P., & Dhoedt, B. (2016). Mobile device power models for energy efficient dynamic offloading at runtime. Journal of Systems and Software, 113, 173–187.CrossRefGoogle Scholar
  3. 3.
    Akpakwu, G., Silva, B., Hancke, G., & Abu-Mahfouz, a M. (2017). A survey on 5gnetworks for the internet of things: Communication technologies andchallenges. IEEE Access, 6, 3619–3647.CrossRefGoogle Scholar
  4. 4.
    Varma, A., Prabhakar, S., & Jayavel, K. (2017). Gas leakage detection and smart alerting and prediction using IoT. In 2nd international conference on computing and communication technologies (ICCCT) (pp. 327–333).Google Scholar
  5. 5.
    Hou, X., Li, Y., Chen, M., Wu, D., Jin, D., & Chen, S. (2016). Vehicular fog computing: A viewpoint of vehicles as the infrastructures. IEEE Transactions on Vehicular Technology, 65(6), 3860–3873.CrossRefGoogle Scholar
  6. 6.
    Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M. (2013). Internet of Things (IoT): A vision, architectural elements, and future directions. Future Generation Computer Systems, 29(7), 1645–1660.CrossRefGoogle Scholar
  7. 7.
    Vallati, C., Virdis, A., Mingozzi, E., & Stea, G. (2015). Exploiting LTE D2D communications in M2M Fog platforms: Deployment and practical issues. In IEEE world forum on internet of things, WF-IoT 2015 - proceedings (pp. 585–590).Google Scholar
  8. 8.
    Dolui, K., & Datta, S.K. (2017). Comparison of edge computing implementations: Fog computing, cloudlet and mobile edge computing. In GIoTS 2017—Global internet of things summit, proceedings.Google Scholar
  9. 9.
    Chávez-Santiago, R., Szydełko, M., Kliks, A., Foukalas, F., Haddad, Y., Nolan, K. E., et al. (2015). 5G: The convergence of wireless communications. Wireless Personal Communications, 83(3), 1617–1642.CrossRefGoogle Scholar
  10. 10.
    Reznik, A., Arora, R., Cannon, M., Cominardi, L., Featherstone, W., Frazao, R., Giust, F., Kekki, S., Li, A., Sabella, D., Turyagyenda, C., & Zheng, Z. (2017) ETSI white paper #20: Developing software for multi-access edge computing. In ETSI (20).Google Scholar
  11. 11.
    Jaffry, S., Hasan, S.F., Gui, X., & Kuo, Y.W. (2017). Distributed device discovery in ProSe environments. In IEEE region 10 annual international conference, Proceedings/TENCON 2017-Decem (pp. 614–618).Google Scholar
  12. 12.
    Feng, D., Lu, L., Yuan-Wu, Y., Li, G., & Li, S. (2014). Device-to-device communications in cellular networks. In IEEE communications magazine (pp 49–55), (April).Google Scholar
  13. 13.
    Alsahag, A. M., Ali, B. M., Noordin, N. K., et al. (2016). Maximum rate resource allocation algorithms with multiuser diversity and QoS support for downlink OFDMA based WiMAX system. Telecommunication Systems, 63(1), 1–14.CrossRefGoogle Scholar
  14. 14.
    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).Google Scholar
  15. 15.
    Zhang, K., Mao, Y., Leng, S., Zhao, Q., Li, L., Peng, X., et al. (2016). Energy-efficient offloading for mobile edge computing in 5G heterogeneous networks. IEEE Access, 4, 5896–5907.CrossRefGoogle Scholar
  16. 16.
    Lyu, X., Tian, Hui, Jiang, L., Vinel, A., Maharjan, S., Gjessing, S., et al. (2018). Selective offloading in mobile edge computing for the green internet of things. IEEE Network, 32(1), 54–60.CrossRefGoogle Scholar
  17. 17.
    Tran, T., & Pompili, D. (2019). Joint Task offloading and resource allocation for multi-server mobile-edge computing networks. IEEE Transactions On Vehicular Technology, 68(1), 856–868.CrossRefGoogle Scholar
  18. 18.
    Pu, L., Chen, X., Xu, J., & Fu, X. (2016). D2D fogging: An energy-efficient and incentive-aware task offloading framework via network-assisted D2D collaboration. IEEE Journal on Selected Areas in Communications, 34(12), 3887–39014.CrossRefGoogle Scholar
  19. 19.
    Chen, X., & Zhang, J. (2017). When D2D meets cloud: Hybrid mobile task offloadings in fog computing. In IEEE international conference on communications (pp. 0–5).Google Scholar
  20. 20.
    Chen, X., Pu, L., Gao, L., Wu, W., & Wu, D. (2017). Exploiting massive D2D collaboration for energy-efficient mobile edge computing. IEEE Wireless Communications, 24(4), 64–71.CrossRefGoogle Scholar
  21. 21.
    Orakzai, F., Iqbal, M., Naeem, M., & Ahmad, A. (2018). Energy efficient joint radio resource management in D2D assisted cellular communication. Telecommunication Systems, 69(4), 505–517.CrossRefGoogle Scholar
  22. 22.
    Ali, S., & Ahmad, A. (2017). Resource allocation, interference management, and mode selection in device-to-device communication: A survey. Transactions On Emerging Telecommunications Technologies, 28(7), e3148. Scholar
  23. 23.
    Diao, X., Zheng, J., Wu, Y., & Cai, Y. (2019). Joint computing resource, power, and channel allocations for D2D-assisted and NOMA-based mobile edge computing. IEEE Access, 7, 9243–9257.CrossRefGoogle Scholar
  24. 24.
    Xing, H., Liu, L., Xu, J., & Nallanathan, A. (2019). Joint task assignment and resource allocation for D2D-enabled mobile-edge computing. IEEE Transactions On Communications, 1–1.Google Scholar
  25. 25.
    Yan, H., Zhang, X., Chen, H., Zhou, Y., Bao, W., & Yang, L. (2019). DEED: Dynamic energy-efficient data offloading for IoT applications under unstable channel conditions. Future Generation Computer Systems, 96, 425–437.CrossRefGoogle Scholar
  26. 26.
    Salman, M. I., Mansoor, A. M., Jalab, H. A., Sabri, A. Q. M., & Ahmed, R. (2018). A joint evaluation of energy-efficient downlink scheduling and partial CQI feedback for LTE video transmission. Wireless Personal Communications, 98(1), 189–211.CrossRefGoogle Scholar
  27. 27.
    Edmonds, J. (1965). Maximum matching and a polyhedron with 0,1-vertices. Journal of Research of the National Bureau of Standards Section B, Mathematics and Mathematical Physics, 69B(1 and 2), 125.CrossRefGoogle Scholar
  28. 28.
    Kolmogorov, V. (2008). Blossom V : A new implementation of a minimum cost perfect matching algorithm 1 Introduction 2 Background: Edmonds ’ blossom algorithm. Mathematical Programming Computation, 1, 1–14.Google Scholar
  29. 29.
    Duan, R., Pettie, S., & Su, H. (2018). Scaling algorithms for weighted matching in general graphs. ACM Transactions On Algorithms, 14(1), 1–35.CrossRefGoogle Scholar
  30. 30.
    Virdis, A., Stea, G., & Nardini, G. (2015). Simulating LTE/LTE-advanced networks with simuLTE. Advances in Intelligent Systems and Computing, 402, 83–105.CrossRefGoogle Scholar
  31. 31.
    Kwak, J., Kim, Y., Lee, J., & Chong, S. (2015). DREAM: Dynamic resource and task allocation for energy minimization in mobile cloud systems. IEEE Journal on Selected Areas in Communications, 33(12), 2510–2523.CrossRefGoogle Scholar
  32. 32.
    Yi, C., Huang, S., & Cai, J. (2018). An incentive mechanism integrating joint power, channel and link management for social-aware D2D content sharing and proactive caching. IEEE Transactions on Mobile Computing, 17(4), 789–802.CrossRefGoogle Scholar
  33. 33.
    Huynh, D. T., Wang, X., Duong, T. Q., Vo, N. S., & Chen, M. (2018). Social-aware energy efficiency optimization for device-to-device communications in 5G networks. Computer Communications, 120, 102–111.CrossRefGoogle Scholar
  34. 34.
    Li, R., Song, T., Mei, B., Li, H., Cheng, X., & Sun, L. (2018). Blockchain for large-scale internet of things data storage and protection. IEEE Transactions on Services Computing.
  35. 35.
    Buttyan, L., & Hubaux, J.p. (2001). Nuglets: A virtual currency to stimulate cooperation in self-organized mobile Ad Hoc networks. Technical report DSC (pp. 1–15).Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Software Engineering, Faculty of Computer Science and ITUniversity MalayaKuala LumpurMalaysia

Personalised recommendations