Skip to main content
SpringerLink
Log in

A novel simulated annealing trajectory optimization algorithm in an autonomous UAVs-empowered MFC system for medical internet of things devices

  • Original Paper
  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

This article investigates a new autonomous mobile fog computing (MFC) system empowered by multiple unmanned aerial vehicles (UAVs) in order to serve medical Internet of Things devices (MIoTDs) efficiently. The aim of this article is to reduce the energy consumption of the UAVs-empowered MFC system by designing UAVs’ trajectories. To construct the trajectories of UAVs, we need to consider not only the order of SPs but also the association among UAVs, SPs, and MIoTDs. The above-mentioned problem is very complicated and is difficult to be handled via applying traditional techniques, as it is NP-hard, nonlinear, non-convex, and mixed-integer. To handle this problem, we propose a novel simulated annealing trajectory optimization algorithm (SATOA), which handles the problem in three phases. First, the deployment (i.e., number and locations) of stop points (SPs) is updated and produced randomly using variable population sizes. Accordingly, MIoTDs are associated with SPs and extra SPs are removed. Finally, a novel simulated annealing algorithm is proposed to optimize UAVs’ association with SPs as well as their trajectories. The performance of SATOA is demonstrated by performing various experiments on nine instances with 40 to 200 MIoTDs. The simulation results show that the proposed SATOA outperforms other compared state-of-the-art algorithms in terms of saving energy consumption.

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.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Data Availability

The data used to support the findings of this study are available from the authors upon request.

Abbreviations

UAV:

Unmanned Aerial Vehicle

QoS:

Quality of Service

ACO:

Ant Colony Optimization

MFC:

Mobile Fog Computing

TSP:

Travelling Salesman Problem

MIoTD:

Medical Internet of Things Devices

SATOA:

SA Trajectory Optimization Algorithm

EC:

Energy Consumption

MEC:

Mobile Edge Computing

IoT:

Internet of Things

SA:

Simulated Annealing

ISA:

Improved SA

DEC:

Differential Evolution Clustering

SATOA-W:

SATOA without Remove Operator

TS:

Tabu Search

GA:

Genetic Algorithm

References

  1. Panayides, A. S., Amini, A., Filipovic, N. D., Sharma, A., Tsaftaris, S. A., Young, A., Foran, D., Do, N., Golemati, S., Kurc, T., Huang, K., Nikita, K. S., Veasey, B. P., Zervakis, M., Saltz, J. H., & Pattichis, C. S. (2020). Ai in medical imaging informatics: Current challenges and future directions. IEEE Journal of Biomedical and Health Informatics, 24(7), 1837–1857. https://doi.org/10.1109/JBHI.2020.2991043

    Article  Google Scholar 

  2. Rajoria, S., & Mishra, K. (2022). A brief survey on 6g communications. Wireless Networks. https://doi.org/10.1007/s11276-022-03007-8

    Article  Google Scholar 

  3. Asim, M., Mashwani, W. K., Belhaouari, S. B., & Hassan, S. (2021). A novel genetic trajectory planning algorithm with variable population size for multi-uav-assisted mobile edge computing system. IEEE Access, 9, 125569–125579. https://doi.org/10.1109/ACCESS.2021.3111318

    Article  Google Scholar 

  4. Asim, M., Wang, Y., Wang, K., & Huang, P. Q. (2020). A review on computational intelligence techniques in cloud and edge computing. IEEE Transactions on Emerging Topics in Computational Intelligence, 4(6), 742–763. https://doi.org/10.1109/TETCI.2020.3007905

    Article  Google Scholar 

  5. Kumar, M., Mothku, S. K., & Anusha, K. (2023). Deep reinforcement learning mechanism for deadline-aware cache placement in device-to-device mobile edge networks. Wireless Networks. https://doi.org/10.1007/s11276-022-03135-1

    Article  Google Scholar 

  6. Mohajer, A., Sorouri, F., Mirzaei, A., Ziaeddini, A., Rad, K. J., & Bavaghar, M. (2022). Energy-aware hierarchical resource management and backhaul traffic optimization in heterogeneous cellular networks. IEEE Systems Journal, 16, 5188. https://doi.org/10.1109/JSYST.2022.3154162

    Article  Google Scholar 

  7. Mohajer, A., Daliri, M. S., Mirzaei, A., Ziaeddini, A., Nabipour, M., & Bavaghar, M. (2022). Heterogeneous computational resource allocation for noma: Toward green mobile edge-computing systems. IEEE Transactions on Services Computing, 29, 1859. https://doi.org/10.1109/TSC.2022.3186099

    Article  Google Scholar 

  8. Nikjoo, F., Mirzaei, A., & Mohajer, A. (2018). A novel approach to efficient resource allocation in noma heterogeneous networks: Multi-criteria green resource management. Applied Artificial Intelligence, 32(7–8), 583–612. https://doi.org/10.1080/08839514.2018.1486132

    Article  Google Scholar 

  9. Zaini, A., & Xie, L. (2020). Distributed drone traffic coordination using triggered communication. Unmanned Systems, 08, 1–20. https://doi.org/10.1142/S2301385020500016

    Article  Google Scholar 

  10. Mozaffari, M., Saad, W., Bennis, M., Nam, Y., & Debbah, M. (2019). A tutorial on UAVs for wireless networks: Applications, challenges, and open problems. IEEE Communications Surveys Tutorials, 21(3), 2334–2360.

    Article  Google Scholar 

  11. Xiao, Z., Chen, Y., Jiang, H., Hu, Z., Lui, J. C., Min, G., & Dustdar, S. (2022). Resource management in uav-assisted mec: state-of-the-art and open challenges. Wireless Networks, 28, 1–18.

    Article  Google Scholar 

  12. Low, J. E., Win, L. T. S., Shaiful, D. S. B., Tan, C. H., Soh, G. S., & Foong, S. (2017) Design and dynamic analysis of a transformable hovering rotorcraft (thor), in: 2017 IEEE International Conference on Robotics and Automation (ICRA), pp. 6389–6396.

  13. Zeng, Y., Zhang, R., & Lim, T. J. (2016). Wireless communications with unmanned aerial vehicles: opportunities and challenges. IEEE Communications Magazine, 54(5), 36–42. https://doi.org/10.1109/MCOM.2016.7470933

    Article  Google Scholar 

  14. 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. https://doi.org/10.1109/INFOCOMWKSHPS50562.2020.9162738.

  15. 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: 2015 European Conference on Networks and Communications (EuCNC), pp. 133–138. https://doi.org/10.1109/EuCNC.2015.7194055.

  16. Merwaday, A., & Guvenc, I. (2015). UAV assisted heterogeneous networks for public safety communications, in: 2015 IEEE Wireless Communications and Networking Conference Workshops (WCNCW), pp. 329–334. https://doi.org/10.1109/WCNCW.2015.7122576.

  17. Zhang, B., Zhang, G., Ma, S., Yang, K., & Wang, K. (2020). Efficient multitask scheduling for completion time minimization in UAV-assisted mobile edge computing. Mobile Information Systems, 2020, 1–11.

    Article  Google Scholar 

  18. Chen, Z., Nan, X., & Han, D. (2020). A multilevel mobile fog computing offloading model based on uav-assisted and heterogeneous network. Wireless Communications and Mobile Computing, 2020, 11.

    Article  Google Scholar 

  19. Liu, P., He, H., Fu, T., Lu, H., Alelaiwi, A., & Wasi, M. W. I. (2021). Task offloading optimization of cruising uav with fixed trajectory. Computer Networks, 199, 108397. https://doi.org/10.1016/j.comnet.2021.108397. https://www.sciencedirect.com/science/article/pii/S1389128621003741

  20. Wang, H., Ke, H., & Sun, W. (2020). Unmanned-aerial-vehicle-assisted computation offloading for mobile edge computing based on deep reinforcement learning. IEEE Access, 8, 180784–180798. https://doi.org/10.1109/ACCESS.2020.3028553

    Article  Google Scholar 

  21. Lu, W., Ding, Y., Gao, Y., Hu, S., Wu, Y., Zhao, N., & Gong, Y. (2021). Resource and trajectory optimization for secure communications in dual-uav-mec systems. IEEE Transactions on Industrial Informatics. https://doi.org/10.1109/TII.2021.3087726

    Article  Google Scholar 

  22. Han, D., & Shi, T. (2020). Secrecy capacity maximization for a uav-assisted mec system. China Communications, 17(10), 64–81. https://doi.org/10.23919/JCC.2020.10.005

    Article  Google Scholar 

  23. Michailidis, E. T., Miridakis, N. I., Michalas, A., Skondras, E., Vergados, D. J., & Vergados, D. D. (2021). Energy optimization in massive MIMO UAV-aided MEC-enabled vehicular networks. IEEE Access, 9, 117388–117403. https://doi.org/10.1109/ACCESS.2021.3106495

    Article  Google Scholar 

  24. Xu, Y., Zhang, T., Yang, D., & Xiao, L. (2021). Uav-assisted relaying and mec networks: Resource allocation and 3d deployment, in: 2021 IEEE International Conference on Communications Workshops (ICC Workshops), pp. 1–6. https://doi.org/10.1109/ICCWorkshops50388.2021.9473550.

  25. Yang, L., Yao, H., Zhang, X., Wang, J., & Liu, Y. (2020). Multi-UAV deployment for MEC enhanced IoT networks, in: 2020 IEEE/CIC International Conference on Communications in China (ICCC), pp. 436–441. https://doi.org/10.1109/ICCC49849.2020.9238870.

  26. Huang, P., Wang, Y., & Wang, K. (2020). Energy-efficient trajectory planning for a multi-UAV-assisted mobile edge computing system. Frontiers of Information Technology & Electronic Engineering, 21(12), 1713–1725. https://doi.org/10.1631/FITEE.2000315

    Article  Google Scholar 

  27. Zeng, Y., Xu, J., & Zhang, R. (2019). Energy minimization for wireless communication with rotary-wing UAV. IEEE Transactions on Wireless Communications, 18(4), 2329–2345.

    Article  Google Scholar 

  28. Asim, M., Mashwani, W. K., & Abd El-Latif, A. A. (2022). Energy and task completion time minimization algorithm for uavs-empowered mec system. Sustainable Computing: Informatics and Systems, 35, 100698. https://doi.org/10.1016/j.suscom.2022.100698

    Article  Google Scholar 

  29. Li, W.-T., Zhao, M., Wu, Y.-H., Yu, J.-J., Bao, L.-Y., Yang, H., & Liu, D. (2021). Collaborative offloading for UAV-enabled time-sensitive MEC networks. EURASIP Journal on Wireless Communications and Networking, 2021(1), 1–17.

    Article  Google Scholar 

  30. Sun, C., Ni, W., & Wang, X. (2021). Joint computation offloading and trajectory planning for UAV-assisted edge computing. IEEE Transactions on Wireless Communications, 20(8), 5343–5358. https://doi.org/10.1109/TWC.2021.3067163

    Article  Google Scholar 

  31. Qin, Q., Liu, E., & Wang, R. (2020). Trajectory optimization for UAV assisted Fog-RAN network. In H. Gao, Z. Feng, J. Yu, & J. Wu (Eds.), Communications and Networking (pp. 344–355). Cham: Springer International Publishing.

    Chapter  Google Scholar 

  32. Zhang, S., Shi, S., Gu, S., & Gu, X. (2020). Power control and trajectory planning based interference management for uav-assisted wireless sensor networks. IEEE Access, 8, 3453–3464. https://doi.org/10.1109/ACCESS.2019.2962547

    Article  Google Scholar 

  33. Asim, M., Mashwani, W. K., Habib, S., & Belhaouari, S. B. (2022). An evolutionary trajectory planning algorithm for multi-UAV-assisted MEC system. Soft Computing, 26, 7479. https://doi.org/10.1007/s00500-021-06465-y

    Article  Google Scholar 

  34. Asim, M., & Abd El-Latif, A. A. (2021). Intelligent computational methods for multi-unmanned aerial vehicle-enabled autonomous mobile edge computing systems. ISA Transactions. https://doi.org/10.1016/j.isatra.2021.11.021

    Article  Google Scholar 

  35. András, K. Multiple traveling salesmen problem - genetic algorithm, using multi-chromosome representation, Retrieved from https://www.mathworks.com/matlabcentral/fileexchange/48133-multiple-traveling-salesmen-problem-genetic-algorithm-using-multi-chromosome-representation, MATLAB Central File Exchange.

  36. Király, A., & Abonyi, J. (2015). Redesign of the supply of mobile mechanics based on a novel genetic optimization algorithm using google maps api. Engineering Applications of Artificial Intelligence, 38, 122–130.

    Article  Google Scholar 

  37. Asim, M., Khan, W., Yeniay, O., Jan, M. A., Tairan, N., Hussian, H., & Wang, G.-G. (2018). Hybrid genetic algorithms for global optimization problems. Hacettepe Journal of Mathematics and Statistics, 47(3), 539–551.

    MathSciNet  MATH  Google Scholar 

  38. Khan Mashwani, W., & Salhi, A. (2012). A decomposition-based hybrid multiobjective evolutionary algorithm with dynamic resource allocation. Applied Soft Computing, 12(9), 2765–2780. https://doi.org/10.1016/j.asoc.2012.03.067

    Article  Google Scholar 

  39. Kirkpatrick, S., Gelatt, C., & Vecchi, M. (1987). Optimization by simulated annealing, in: M. A. Fischler, O. Firschein (Eds.), Readings in Computer Vision, Morgan Kaufmann, San Francisco (CA), pp. 606–615. https://doi.org/10.1016/B978-0-08-051581-6.50059-3.

  40. Cerny, V. (1985). Thermodynamical approach to the traveling salesman problem: An efficient simulation algorithm. Journal of Optimization Theory and Applications, 45, 41–51. https://doi.org/10.1007/BF00940812

    Article  MathSciNet  MATH  Google Scholar 

  41. Király, A., & Abonyi, J. (2011). Optimization of Multiple Traveling Salesmen Problem by a Novel Representation Based Genetic Algorithm (pp. 241–269). Berlin Heidelberg, Berlin, Heidelberg: Springer.

    Google Scholar 

  42. Jain, A. K. (2010). Data clustering: 50 years beyond k-means. Pattern Recognition Letters, 31(8), 651–666.

    Article  Google Scholar 

  43. Mostapha, H. K. Evolutionary data clustering in matlab, https://yarpiz.com/64/ypml101-evolutionary-clustering, Yarpiz.

  44. Joseph, K. Traveling salesman problem - genetic algorithm, Retrieved from https://www.mathworks.com/matlabcentral/fileexchange/13680-traveling-salesman-problem-genetic-algorithm, MATLAB Central File Exchange.

Download references

Acknowledgements

The author would like to thank Prince Sultan University for their support. Also, the studies at St. Petersburg State University of Telecommunications. prof. M.A. Bonch-Bruevich were supported by the Ministry of Science and High Education of the Russian Federation by the grant 075-15-2022-1137.

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, 510006, China

    Muhammad Asim, Chen Junhong & Liu Wenyin

  2. EIAS Data Science Lab, College of Computer and Information Sciences, Prince Sultan University, Riyadh, 11586, Saudi Arabia

    Muhammad Asim & Ahmed A. Abd El-Latif

  3. Expertise Centre for Digital Media, Hasselt University, Hasselt, Belgium

    Chen Junhong

  4. Department of Telecommunication Networks and Data Transmission, The Bonch-Bruevich Saint-Petersburg State University of Telecommunications, 193232, Saint Petersburg, Russia

    Ammar Muthanna

  5. RUDN University, 6 Miklukho-Maklaya St, 117198, Moscow, Russian Federation

    Ammar Muthanna

  6. School of Software Engineering, South China University of Technology, Guangzhou, People’s Republic of China

    Siraj Khan

  7. Department of Mathematics and Computer Science, Faculty of Science, Menoufia University, Shebin El-Koom, 32511, Egypt

    Ahmed A. Abd El-Latif

Authors
  1. Muhammad Asim
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chen Junhong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ammar Muthanna
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Liu Wenyin
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Siraj Khan
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ahmed A. Abd El-Latif
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Chen Junhong or Ahmed A. Abd El-Latif.

Ethics declarations

Conflicts of Interest

The authors declare that there are no conflicts of interest regarding the publication of this paper.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Asim, M., Junhong, C., Muthanna, A. et al. A novel simulated annealing trajectory optimization algorithm in an autonomous UAVs-empowered MFC system for medical internet of things devices. Wireless Netw 29, 3163–3176 (2023). https://doi.org/10.1007/s11276-023-03370-0

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-023-03370-0

Keywords

Search

Navigation