Skip to main content

Advertisement

SpringerLink
Log in

An evolutionary trajectory planning algorithm for multi-UAV-assisted MEC system

  • Focus
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

This paper presents a multi-unmanned aerial vehicle (UAV)-assisted mobile edge computing system, where multiple UAVs are used to serve mobile users. We aim to minimize the overall energy consumption of the system by planning the trajectories of UAVs. To plan the trajectories of UAVs, we need to consider the deployment of hovering points (HPs) of UAVs, their association with UAVs, and their order for each UAV. Therefore, the problem is very complicated, as it is non-convex, nonlinear, NP-hard, and mixed-integer. To solve the problem, this paper proposed an evolutionary trajectory planning algorithm (ETPA), which comprises four phases. In the first phase, a variable-length GA is adopted to update the deployments of HPs for UAVs. Accordingly, redundant HPs are removed by the remove operator. Subsequently, a differential evolution clustering algorithm is adopted to cluster HPs into different clusters without knowing the number of HPs in advance. Finally, a GA is proposed to construct the order of HPs for UAVs. The experimental results on a set of eight instances show that the proposed ETPA outperforms other compared algorithms in terms of the energy consumption of the system.

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

Access this article

Log in via an institution

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

Similar content being viewed by others

References

  • Asim M, Mashwani WK, Jan MA (2017a) Hybrid genetic firefly algorithm for global optimization problems. Sindh Univ Res J Sci Ser 49(004):899–906

    Google Scholar 

  • Asim M, Mashwani WK, Jan MA, Iqbal J (2017b) Derivative based hybrid genetic algorithm: a preliminary experimental results. Punjab Univ J Math 49(2):89–99

    MathSciNet  Google Scholar 

  • Asim M, Khan W, Yeniay O, Jan MA, Tairan N, Hussian H, Wang G-G (2018) Hybrid genetic algorithms for global optimization problems. Hacettepe J Math Stat 47(3):539–551

    MathSciNet  MATH  Google Scholar 

  • Asim M, Wang Y, Wang K, Huang PQ (2020) A review on computational intelligence techniques in cloud and edge computing. IEEE Trans Emerg Top Comput Intell 4(6):742–763

    Article  Google Scholar 

  • Asimand M, Mashwani WK, Belhaouari SB, Hassan S (2021) A novel genetic trajectory planning algorithm with variable population size for multi-UAV-assisted mobile edge computing system. IEEE Access 9:125–569

    Google Scholar 

  • Bandyopadhyay S, Maulik U (2002) Genetic clustering for automatic evolution of clusters and application to image classification. Pattern Recognit 35(6):1197–1208

    Article  Google Scholar 

  • Davies DL, Bouldin DW (1979) A cluster separation measure. IEEE Trans Pattern Anal Mach Intell PAM–1(2):224–227

    Article  Google Scholar 

  • Deb K, Agrawal R (1995) Simulated binary crossover for continuous search space. Complex Syst 9(99):115–148

    MathSciNet  MATH  Google Scholar 

  • Deb K, Georg Beyer H (1995) Real-coded genetic algorithms with simulated binary crossover: studies on multi-modal and multi-objective problems. Complex Syst, pp 431–454

  • Deb K, Sindhya K, Okabe T (2007) Self-adaptive simulated binary crossover for real-parameter optimization. In: Proceedings of the 9th annual conference on genetic and evolutionary computation, ser. GECCO ’07. New York, NY, USA: Association for Computing Machinery, pp 1187–1194

  • Diao X, Zheng J, Cai Y, Wu Y, Anpalagan A (2019) Fair data allocation and trajectory optimization for UAV-assisted mobile edge computing. IEEE Commun Lett 23(12):2357–2361

    Article  Google Scholar 

  • Falkenauer E (1998) Genetic algorithms and grouping problems. John Wiley & Sons Inc, New York

    MATH  Google Scholar 

  • Garg S, Singh A, Batra S, Kumar N, Yang LT (2018) UAV-empowered edge computing environment for cyber-threat detection in smart vehicles. IEEE Network 32(3):42–51

    Article  Google Scholar 

  • Goldberg DE, Deb K (1991) A comparative analysis of selection schemes used in genetic algorithms. In: Foundations of genetic algorithms. Morgan Kaufmann, pp 69–93

  • Gomez K, Hourani A, Goratti L, Riggio R, Kandeepan S, Bucaille I (2015) Capacity evaluation of aerial lte base-stations for public safety communications. In: European conference on networks and communications (EuCNC), pp 133–138

  • Gupta R, Shukla A, Mehta P, Bhattacharya P, Tanwar S, Tyagi S, Kumar N (2020) Vahak: a blockchain-based outdoor delivery scheme using UAV for healthcare 4.0 services. In: IEEE INFOCOM 2020 - IEEE conference on computer communications workshops (INFOCOM WKSHPS), pp 255–260

  • Hruschka ER, Campello RJGB, Freitas AA, Ponce AC, de Carvalho LF (2009) A survey of evolutionary algorithms for clustering. IEEE Trans Syst Man Cybern Part C Appl Rev 39(2):133–155

    Article  Google Scholar 

  • Hu X, Wong K, Yang K, Zheng Z (2019) UAV-assisted relaying and edge computing: scheduling and trajectory optimization. IEEE Trans Wirel Commun 18(10):4738–4752

    Article  Google Scholar 

  • Huang P, Wang Y, Wang K (2020) Energy-efficient trajectory planning for a multi-UAV-assisted mobile edge computing system. Front Inform Technol Electron Eng 21(12):1713–1725

    Article  Google Scholar 

  • Huang P, Wang Y, Wang K, Yang K (2020) Differential evolution with a variable population size for deployment optimization in a UAV-assisted IoT data collection system. IEEE Trans Emerg Top Comput Intell 4(3):324–335

    Article  Google Scholar 

  • Jain AK (2010) Data clustering: 50 years beyond k-means. Pattern Recognit Lett 31(8):651–666

    Article  Google Scholar 

  • Jeong S, Simeone O, Kang J (2018) Mobile edge computing via a UAV-mounted cloudlet: optimization of bit allocation and path planning. IEEE Trans Veh Technol 67(3):2049–2063

    Article  Google Scholar 

  • Ji J, Zhu K, Yi C, Wang R, Niyato D (2020) Joint resource allocation and trajectory design for UAV-assisted mobile edge computing systems. In: GLOBECOM 2020 - 2020 IEEE global communications conference, pp 1–6

  • Kalyanmoy D, Hans-georg B (1996) A combined genetic adaptive search (geneas) for engineering design. Comput Sci Inf 26(4):30–45

    Google Scholar 

  • Larrañaga P, Kuijpers CMH, Murga RH, Inza I, Dizdarevic S (1999) Genetic algorithms for the travelling salesman problem: a review of representations and operators. Artif Intell Rev 13(2):129–170. https://doi.org/10.1023/A:1006529012972

    Article  Google Scholar 

  • Liao T, Socha K, Montes de Oca MA, Stutzle T, Dorigo M (2014) Ant colony optimization for mixed-variable optimization problems. IEEE Trans Evol Comput 18(4):503–518

    Article  Google Scholar 

  • Low JE, Win LTS, Shaiful DSB, Tan CH, Soh GS, Foong S (2017) Design and dynamic analysis of a transformable hovering rotorcraft (thor). In: IEEE international conference on robotics and automation (ICRA), pp 6389–6396

  • Mashwani WK, Salhi A (2012) A decomposition-based hybrid multiobjective evolutionary algorithm with dynamic resource allocation. Appl Soft Comput 12(9):2765–2780

    Article  Google Scholar 

  • Mashwani WK, Khan A, Goktas A, Unvan YA, Yaniay O, Hamdi A (2021) Hybrid differential evolutionary strawberry algorithm for real-parameter optimization problems. Commun Stat Theory Methods 50(7):1685–1698

    Article  MathSciNet  Google Scholar 

  • Merwaday A, Guvenc I (2015) UAV assisted heterogeneous networks for public safety communications. In: IEEE wireless communications and networking conference workshops (WCNCW), pp 329–334

  • Mostapha HK (2015) Evolutionary data clustering in matlab. https://yarpiz.com/64/ypml101-evolutionary-clustering

  • Mozaffari M, Saad W, Bennis M, Nam Y, Debbah M (2019) A tutorial on UAVs for wireless networks: applications, challenges, and open problems. IEEE Commun Surv Tutor 21(3):2334–2360

    Article  Google Scholar 

  • Olsson P, Kvarnström J, Doherty P, Burdakov O, Holmberg K (2010) Generating UAV communication networks for monitoring and surveillance. In: 2010 11th international conference on control automation robotics vision, pp 1070–1077

  • Qin AK, Huang VL, Suganthan PN (2009) Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans Evol Comput 13(2):398–417

    Article  Google Scholar 

  • Sinaga KP, Yang M (2020) Unsupervised k-means clustering algorithm. IEEE Access 8:80–716

    Article  Google Scholar 

  • Ting C-K, Lee C-N, Chang H-C, Wu J-S (2009) Wireless heterogeneous transmitter placement using multiobjective variable-length genetic algorithm. IEEE Trans Syst Man Cybern Part B (Cybern) 39(4):945-958

    Article  Google Scholar 

  • Tun Y, Yan K, Park YM, Tran HN, Saad W, Pandey RS, Hong SC (2021) Energy-efficient resource management in UAV-assisted mobile edge computing. IEEE Commun Lett 25(1):249–253

    Article  Google Scholar 

  • Wang L, Wang K, Pan C, Xu W, Aslam N, Hanzo L (2020) Multi-agent deep reinforcement learning based trajectory planning for multi-UAV assisted mobile edge computing. IEEE Trans Cognit Commun Netw 7:73–84

    Article  Google Scholar 

  • Wang Y, Ru ZY, Wang K, Huang PQ (2020) Joint deployment and task scheduling optimization for large-scale mobile users in multi-UAV-enabled mobile edge computing. IEEE Trans Cybern 50(9):3984–3997

    Article  Google Scholar 

  • Wu Q, Zhang R (2018) Common throughput maximization in UAV-enabled OFDMA systems with delay consideration. IEEE Trans Commun 66(12):6614–6627

    Article  Google Scholar 

  • Xu Y, Zhang T, Loo J, Yang D, Xiao L (2021)Completion time minimization for UAV-assisted mobile-edge computing systems. IEEE Trans Veh Technol

  • Yang Z, Pan C, Wang K, Shikh-Bahaei M (2019) Energy efficient resource allocation in UAV-enabled mobile edge computing networks. IEEE Trans Wirel Commun 18(9):4576–4589

    Article  Google Scholar 

  • Yuan C, Ghamry KA, Liu Z, Zhang Y, Unmanned aerial vehicle based forest fire monitoring and detection using image processing technique. In: IEEE Chinese guidance, navigation and control conference (CGNCC), pp 1870–1875

  • Zaini A, Xie L (2019) Distributed drone traffic coordination using triggered communication. Unmanned Syst 08:07

    Google Scholar 

  • 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 

  • Zeng Y, Xu J, Zhang R (2019) Energy minimization for wireless communication with rotary-wing UAV. IEEE Trans Wirel Commun 18(4):2329–2345

    Article  Google Scholar 

  • Zhang B, Zhang G, Ma S, Yang K, Wang K (2020) Efficient multitask scheduling for completion time minimization in UAV-assisted mobile edge computing. Mob Inform Syst 2020:1–11

    Google Scholar 

Download references

Acknowledgements

The authors are thankful to the Deanship of Scientific Research at King Khalid University for awarding project ID: RGP.2/190/42 and titled Advanced Computational Methods for Solving Complex Computer Science and Mathematical Engineering Problems.

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, Central South University, Changsha, 410083, China

    Muhammad Asim

  2. Institute of Numerical Sciences, Kohat University of Science and Technology, Kohat, Pakistan

    Wali Khan Mashwani

  3. College of Computer Science, King Khalid University, Abha, Saudi Arabia

    Habib Shah

  4. College of Science and Engineering, Hamad Bin Khalifa University, Ar-Rayyan, Qatar

    Samir Brahim Belhaouari

Authors
  1. Muhammad Asim
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wali Khan Mashwani
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Habib Shah
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Samir Brahim Belhaouari
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

MA conceived the idea of this study. WKM guided the research and refined the idea. MA performed the research and drafted the manuscript. SBB discussed the results. MA and WKM and HS revised and finalized the paper.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest.

Informed consent

All authors have read this manuscript and are willing to process it for publication.

Ethical approval

There is no need for ethical approval while conducting the study in this manuscript.

Additional information

Communicated by Jia-Bao Liu.

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

Asim, M., Mashwani, W.K., Shah, H. et al. An evolutionary trajectory planning algorithm for multi-UAV-assisted MEC system. Soft Comput 26, 7479–7492 (2022). https://doi.org/10.1007/s00500-021-06465-y

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-021-06465-y

Keywords

Search

Navigation