Skip to main content

Advertisement

Log in

Optimal task partitioning, Bit allocation and trajectory for D2D-assisted UAV-MEC systems

  • Published:
Peer-to-Peer Networking and Applications Aims and scope Submit manuscript

Abstract

Unmanned aerial vehicle is a promising technology in upcoming wireless networks due to its potential aerial feature and line-of-sight capability. Mobile edge computing systems that rely on unmanned aerial vehicles consider offloading computationally intensive tasks to unmanned aerial vehicle to be executed via a powerful edge server. In this paper, we exploit the power of device-to-device communications in terms of low latency and low-power transmission as an additional option to edge offloading. Specifically, any smart device that have a nearby device partner can offload part of its task to be executed by his partner. Additionally, we aim to minimize the total energy consumption during the offloading and local computing procedures at smart devices via jointly optimizing the trajectory of the unmanned aerial vehicle, number of bits allocated for both unmanned aerial vehicle and the partner device, and finally task partitioning. We divide the non-convex major problem into two sub-problems which are efficiently solved via alternative optimization. Simulation results reveal the superiority of device-to-device-assisted mobile edge computing systems that rely on unmanned aerial vehicles over counterparts with no device-to-device assistance.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Valavanis K, Vachtsevanos G (2014) Handbook of unmanned aerial vehicles. Springer, Berlin

    MATH  Google Scholar 

  2. Federal Aviation Administration Reports. https://www.faa.gov/about/plans_reports/, 2018, [Online; accessed 05-November-2018]

  3. Zeng Y, Zhang R, Lim TJ (2016) Wireless communications with unmanned aerial vehicles: opportunities and challenges. IEEE Commun Mag 54(5):36–42

    Article  Google Scholar 

  4. Asadpour M, Van den Bergh B, Giustiniano D, Hummel KA, Pollin S, Plattner B (2014) Micro aerial vehicle networks: an experimental analysis of challenges and opportunities. IEEE Commun Mag 52(7):141–149

    Article  Google Scholar 

  5. Rihan M, Selim MM, Xu C, Huang L (2019) D2D Communication underlaying UAV on multiple bands in disaster area: stochastic geometry analysis. IEEE Access 7:156646–58

    Article  Google Scholar 

  6. Mao Y, You C, Zhang J, Huang K, Letaief KB (2017) A survey on mobile edge computing: the communication perspective. IEEE Commun Surv Tutor 19(4):2322–58. Fourthquarter

    Article  Google Scholar 

  7. Abbas N, Zhang Y, Taherkordi A, Skeie T (2018) Mobile edge computing: a survey. IEEE Internet Things J 5(1):450–465

    Article  Google Scholar 

  8. Fan L, Yan W, Chen X, Chen Z, Shi Q (2019) An energy efficient design for UAV communication with mobile edge computing. China Commun 16(1):26–36

    Google Scholar 

  9. Xiong J, Guo H, Liu J (2019) Task offloading in UAV-aided edge computing: bit allocation and trajectory optimization. IEEE Commun Lett 23(3):538–541

    Article  Google Scholar 

  10. Zhang J, Zhou L, Tang Q, Ngai EC, Hu X, Zhao H, Wei J (2019) Stochastic Computation Offloading and Trajectory Scheduling for UAV-assisted Mobile Edge Computing. IEEE Internet Things J 6(2):3688–3699

    Article  Google Scholar 

  11. Hu Q, Cai Y, Yu G, Qin Z, Zhao M, Li GY (2019) Joint offloading and trajectory design for UAV-enabled mobile edge computing systems. IEEE Internet Things J 6(2):1879–1892

    Article  Google Scholar 

  12. Zhou F, Wu Y, Hu RQ, Qian Y (2018) Computation rate maximization in UAV-enabled wireless-powered mobile-edge computing systems. IEEE J Sel Areas Commun 36(9):1927–1941

    Article  Google Scholar 

  13. Liu J, Kato N, Ma J, Kadowaki N (2015) Device-to-device Communication in LTE-advanced networks: a survey. IEEE Commun Surv Tutor 17(4):1923–40. Fourthquarter

    Article  Google Scholar 

  14. Doppler K, Rinne M, Wijting C, Ribeiro CB, Hugl K (2009) Device-to-device communication as an underlay to LTE-advanced networks. IEEE Commun Mag 47(12):42–49

    Article  Google Scholar 

  15. Wang Y, Sheng M, Wang X, Wang L, Li J (2016) Mobile-Edge Computing: partial computation offloading using dynamic voltage scaling. IEEE Trans Commun 64(10):4268–4282

    Google Scholar 

  16. Boyd S, Vandenberghe L (2004) Convex optimization, Cambridge University Press, New York

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yatao Yang.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Selim, M.M., Rihan, M., Yang, Y. et al. Optimal task partitioning, Bit allocation and trajectory for D2D-assisted UAV-MEC systems. Peer-to-Peer Netw. Appl. 14, 215–224 (2021). https://doi.org/10.1007/s12083-020-00955-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12083-020-00955-w

Keywords

Navigation