Skip to main content
SpringerLink
Log in

Deep learning-based route reconfigurability for intelligent vehicle networks to improve power-constrained using energy-efficient geographic routing protocol

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

Abstract

Transferring data in the mobile ad hoc network can be enabled to analyze data transferring and the network that manages the data and route them into the VPN-based routing. Here is the process of maintaining the gateway for the analysis. The main problem here is the routing of the data packets, and the analysis of the nodes in the form of packages is the main issue in this study. To fix this, troubleshooting problems can be enabled for the packets which reach the destinations and the echo response. The primary technique used in this study is energy efficient geographic routing protocol and reward-based intelligent Ad hoc routing is used for the analysis. The energy-efficient geographic routing protocol enables the EGRPM method to reduce the sensor nodes and the WSN. This allows gathering the data and the nodes to maintain the geographic way. Reward-based intelligent Ad hoc routing is used in automatic decision-making, and the analysis of the system to produce the selection action for the research is reinforcement learning. This results from the study of the configuration and the analysis of the data in the ad hoc network. This enables the formation of learning about the routing protocol and facilitates the current data transfer to the research done in the ad hoc networks. This data analysis in the mobile network helps analyze the system and the entire data management.

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
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Data Availability

All data generated or analysed during this study are included in the manuscript.

References

  1. Bala, P. C., Eisenreich, B. R., Yoo, S. B. M., et al. (2020). Automated markerless pose estimation in freely moving macaques with OpenMonkeyStudio. Nature Communications, 11, 4560. https://doi.org/10.1038/s41467-020-18441-5

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  2. Xu, L., Yan, W., & Ji, J. (2023). The research of a novel WOG-YOLO algorithm for autonomous driving object detection. Science and Reports, 13, 3699. https://doi.org/10.1038/s41598-023-30409-1

    Article  ADS  CAS  Google Scholar 

  3. Ilyas, N., Ahmad, Z., Lee, B., et al. (2022). An effective modular approach for crowd counting in an image using convolutional neural networks. Science and Reports, 12, 5795. https://doi.org/10.1038/s41598-022-09685-w

    Article  ADS  CAS  Google Scholar 

  4. Al-Ezaly, E., El-Bakry, H. M., Abo-Elfetoh, A., et al. (2023). An innovative traffic light recognition method using vehicular ad-hoc networks. Sci Rep, 13, 4009. https://doi.org/10.1038/s41598-023-31107-8

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  5. Choi, C., Kim, H., Kang, J. H., et al. (2022). Reconfigurable heterogeneous integration using stackable chips with embedded artificial intelligence. Nat Electron, 5, 386–393. https://doi.org/10.1038/s41928-022-00778-y

    Article  Google Scholar 

  6. Ramdhany, R., Grace, P., Coulson, G., et al. (2010). Dynamic deployment and reconfiguration of ad-hoc routing protocols. J Internet Serv Appl, 1, 135–152. https://doi.org/10.1007/s13174-010-0010-y

    Article  Google Scholar 

  7. Chen, G. G., Branch, J. W., & Szymanski, B. K. (2006). A self-selection technique for flooding and routing in wireless ad-hoc networks. Journal of Network and Systems Management, 14, 359–380. https://doi.org/10.1007/s10922-006-9036-7

    Article  CAS  Google Scholar 

  8. Choudhury, R. R., Paul, K., & Bandyopadhyay, S. (2002). An agent-based connection management protocol for ad hoc wireless networks. Journal of Network and Systems Management, 10, 483–504. https://doi.org/10.1023/A:1021164222319

    Article  Google Scholar 

  9. Chang, J. M., Lai, C. F., Chao, H. C., et al. (2014). An energy-efficient geographic routing protocol design in vehicular ad-hoc network. Computing, 96, 119–131. https://doi.org/10.1007/s00607-012-0235-7

    Article  Google Scholar 

  10. Zhang, D., Liu, X., Cui, Y., et al. (2019). A kind of novel RSAR protocol for mobile vehicular ad hoc network. CCF Transactions on Networking, 2, 111–125. https://doi.org/10.1007/s42045-019-00019-5

    Article  CAS  Google Scholar 

  11. Marwah, G. P. K., & Jain, A. (2022). A hybrid optimization with ensemble learning to ensure VANET network stability based on performance analysis. Science and Reports, 12, 10287. https://doi.org/10.1038/s41598-022-14255-1

    Article  ADS  CAS  Google Scholar 

  12. Mathis, A., Mamidanna, P., Cury, K. M., et al. (2018). DeepLabCut: Markerless pose estimation of user-defined body parts with deep learning. Nature Neuroscience, 21, 1281–1289. https://doi.org/10.1038/s41593-018-0209-y

    Article  CAS  PubMed  Google Scholar 

  13. Álvarez-Aparicio, C., Guerrero-Higueras, Á. M., González-Santamarta, M. Á., et al. (2022). Biometric recognition through gait analysis. Science and Reports, 12, 14530. https://doi.org/10.1038/s41598-022-18806-4

    Article  ADS  CAS  Google Scholar 

  14. Jiang, X., Hu, H., Qin, Y., et al. (2022). A real-time rural domestic garbage detection algorithm with an improved YOLOv5s network model. Science and Reports, 12, 16802. https://doi.org/10.1038/s41598-022-20983-1

    Article  ADS  CAS  Google Scholar 

  15. Pereira, T. D., Tabris, N., Matsliah, A., et al. (2022). SLEAP: A deep learning system for multi-animal pose tracking. Nature Methods, 19, 486–495. https://doi.org/10.1038/s41592-022-01426-1

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Che, J., He, Y., & Wu, J. (2023). Pedestrian multiple-object tracking based on FairMOT and circle loss. Science and Reports, 13, 4525. https://doi.org/10.1038/s41598-023-31806-2

    Article  ADS  CAS  Google Scholar 

  17. Zhao, Z., Yang, X., Zhou, Y., et al. (2021). Real-time detection of particleboard surface defects based on improved YOLOV5 target detection. Science and Reports, 11, 21777. https://doi.org/10.1038/s41598-021-01084-x

    Article  ADS  CAS  Google Scholar 

  18. Zhong, X., Qin, J., Guo, M., et al. (2022). Offset-decoupled deformable convolution for efficient crowd counting. Science and Reports, 12, 12229. https://doi.org/10.1038/s41598-022-16415-9

    Article  ADS  CAS  Google Scholar 

  19. Mon, E. E., Ochiai, H., Komolkiti, P., et al. (2022). Real-world sensor dataset for city inbound-outbound critical intersection analysis. Sci Data, 9, 357. https://doi.org/10.1038/s41597-022-01448-6

    Article  PubMed Central  Google Scholar 

  20. Kim, M., Kim, H., Sung, J., et al. (2023). High-resolution processing and sigmoid fusion modules for efficient detection of small objects in an embedded system. Science and Reports, 13, 244. https://doi.org/10.1038/s41598-022-27189-5

    Article  ADS  CAS  Google Scholar 

  21. Pastore, V. P., Moro, M., & Odone, F. (2022). A semi-automatic toolbox for markerless effective semantic feature extraction. Science and Reports, 12, 11899. https://doi.org/10.1038/s41598-022-16014-8

    Article  ADS  CAS  Google Scholar 

  22. Cheng, L., Ji, Y., Li, C., et al. (2022). Improved SSD network for fast concealed object detection and recognition in passive terahertz security images. Science and Reports, 12, 12082. https://doi.org/10.1038/s41598-022-16208-0

    Article  ADS  CAS  Google Scholar 

  23. Graczyk, K. M., Pawłowski, J., Majchrowska, S., et al. (2022). Self-normalized density map (SNDM) for counting microbiological objects. Science and Reports, 12, 10583. https://doi.org/10.1038/s41598-022-14879-3

    Article  ADS  CAS  Google Scholar 

  24. Lauer, J., Zhou, M., Ye, S., et al. (2022). Multi-animal pose estimation, identification and tracking with DeepLabCut. Nature Methods, 19, 496–504. https://doi.org/10.1038/s41592-022-01443-0

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Lee, S. M. W., Shaw, A., Simpson, J. L., et al. (2021). Differential cell counts using center-point networks achieves human-level accuracy and efficiency over segmentation. Science and Reports, 11, 16917. https://doi.org/10.1038/s41598-021-96067-3

    Article  ADS  CAS  Google Scholar 

  26. Gao, H., Zhao, W., Zhang, D., et al. (2023). Application of improved transformer based on weakly supervised in crowd localization and crowd counting. Science and Reports, 13, 1144. https://doi.org/10.1038/s41598-022-27299-0

    Article  ADS  CAS  Google Scholar 

  27. Chiriboga, M., Green, C. M., Hastman, D. A., et al. (2022). Rapid DNA origami nanostructure detection and classification using the YOLOv5 deep convolutional neural network. Science and Reports, 12, 3871. https://doi.org/10.1038/s41598-022-07759-3

    Article  ADS  CAS  Google Scholar 

  28. Dehghani, M., Trojovská, E., & Trojovský, P. (2022). A new human-based metaheuristic algorithm for solving optimization problems on the base of simulation of driving training process. Science and Reports, 12, 9924. https://doi.org/10.1038/s41598-022-14225-7

    Article  ADS  CAS  Google Scholar 

  29. Nath, T., Mathis, A., Chen, A. C., et al. (2019). Using DeepLabCut for 3D markerless pose estimation across species and behaviors. Nature Protocols, 14, 2152–2176. https://doi.org/10.1038/s41596-019-0176-0

    Article  CAS  PubMed  Google Scholar 

  30. Korecki, M. (2022). Adaptability and sustainability of machine learning approaches to traffic signal control. Science and Reports, 12, 16681. https://doi.org/10.1038/s41598-022-21125-3

    Article  ADS  CAS  Google Scholar 

  31. Huang, H., Tang, X., Wen, F., et al. (2022). Small object detection method with shallow feature fusion network for chip surface defect detection. Science and Reports, 12, 3914. https://doi.org/10.1038/s41598-022-07654-x

    Article  ADS  CAS  Google Scholar 

  32. Li, W., Liu, K., Zhang, L., et al. (2020). Object detection based on an adaptive attention mechanism. Science and Reports, 10, 11307. https://doi.org/10.1038/s41598-020-67529-x

    Article  CAS  Google Scholar 

Download references

Funding

This research is funded by Ministry of Education and Training under project number B2021.DNA.09.

Author information

Authors and Affiliations

  1. Department of Computer Science, College of Computer Science and Engineering, Taibah University, Madinah, Saudi Arabia

    Liyakathunisa Syed

  2. School of Computing, SASTRA Deemed to be University, Thanjavur, India

    P. Sathyaprakash

  3. Department of Data Science and Business Systems, School of Computing, SRM Institute of Science and Technology, Chennai, India

    A. Shobanadevi

  4. The University of Danang, Da Nang, Vietnam

    Ha Huy Cuong Nguyen

  5. Department of Information Security, Faculty of Information Technology, University of Petra, Amman, 11196, Jordan

    Mohammad Alauthman

  6. Department of Computer Science and Engineering, R.M.D. Engineering College, Chennai, India

    M. Vedaraj

  7. IFET College of Engineering, Gangarampalaiyam, India

    R. Premalatha

Authors
  1. Liyakathunisa Syed
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. P. Sathyaprakash
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. A. Shobanadevi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ha Huy Cuong Nguyen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mohammad Alauthman
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. M. Vedaraj
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. R. Premalatha
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All author is contributed to the design and methodology of this study, the assessment of the outcomes and the writing of the manuscript.

Corresponding author

Correspondence to Ha Huy Cuong Nguyen.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

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

Syed, L., Sathyaprakash, P., Shobanadevi, A. et al. Deep learning-based route reconfigurability for intelligent vehicle networks to improve power-constrained using energy-efficient geographic routing protocol. Wireless Netw 30, 939–960 (2024). https://doi.org/10.1007/s11276-023-03525-z

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-023-03525-z

Keywords

Search

Navigation