Skip to main content
SpringerLink
Log in

Improved rider for vehicular adhoc NETwork routing via neural network

  • Special Issue
  • Published:
Evolutionary Intelligence Aims and scope Submit manuscript

Abstract

Nowadays, it is necessary to know about the traffic information in a city for traffic regulation. The gathering of effectual traffic information helps travelers to make better decisions regarding the traveling paths. Queries like “how do we get traffic information?” and “which path is the shortest distance between two vertices?” are widely addressed, while queries of the type “how do we get traffic information efficiently and economically?” and “which path is the shortest travel time between two vertices?” need further analysis. Therefore, this paper introduces a new shortest path selection model, which is based on the shortest travel time required to travel (source to destination). For this prediction, an optimized Neural Network model is introduced, where the historical data is insisted under the training process. The factors considered for predicting the average travel time to reach the destination are congestion level, distance and time interval, respectively. Moreover, the training is carried out using a new Rider with an Updated Overtaker evaluation (RU-OE). Finally, the betterment of the presented approach is validated over the existing models in terms of certain measures.

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

Similar content being viewed by others

Abbreviations

ACO:

Ant colony optimization

AHP:

Analytical hierarchical process

AMGRP:

AHP-dependent multimetric geographical routing protocol

AODV:

Ad hoc on-demand distance vector

DSR:

Dynamic source routing

e2e:

End-to-end

FDR:

False discovery rate

FNR:

False negative rate

FPR:

False positive rate

MANETs:

Mobile ad hoc networks

MBO:

Monarch butterfly optimization

NN:

Neural network

OLSR:

Optimized version of cross-layer weighted position based routing

HMR:

Hadoop map reduce

PDR:

Packet delivery rate

PSO:

Particle swarm optimization

RU-OE:

Rider with updated overtaker evaluation

RSS:

Received signal strength

SES:

Sampling-based estimation scheme

TROPHY:

Trustworthy VANET routing with group authentication keys

VANET:

Vehicular ad hoc network

References

  1. Lakshmanaprabu SK, Shankar K, Rani SS, Abdulhay E, Arunkumar N, Ramirez G, Uthayakumar J (2019) An effect of big data technology with ant colony optimization based routing in vehicular ad hoc networks: towards smart cities. J Cleaner Product 217:584–593

    Article  Google Scholar 

  2. Mohaisen LF, Joiner LL (2017) Interference aware bandwidth estimation for load balancing in EMHR-energy based with mobility concerns hybrid routing protocol for VANET-WSN communication. Ad Hoc Netw 66:1–15

    Article  Google Scholar 

  3. SkanderDaas M, Chikhi S (2018) “Optimizing geographic routing protocols for urban VANETs using stigmergy, social behavior and adaptive C-n-F mechanisms: an optimized CLWPR.” Vehicular Commun 14:97–108

    Article  Google Scholar 

  4. Song C, Jie Wu, Liu M, Zheng H (2017) Efficient routing through discretization of overlapped road segments in VANETs. J Parallel Distrib Comput 102:57–70

    Article  Google Scholar 

  5. Wang X, Wang D, Sun Qi (2018) Reliable routing in IP-based VANET with network gaps. Comput Standards Interfaces 55:80–94

    Article  Google Scholar 

  6. Dharani Kumari NV, Shylaja BS (2019) AMGRP: AHP-based Multimetric Geographical Routing Protocol for Urban environment of VANETs. J King Saud Univ Comput Inform Sci 31(1):72–81

    Google Scholar 

  7. Oubbati Omar Sami, Lakas Abderrahmane, Zhou Fen, Güneş Mesut, Yagoubi Mohamed Bachir (2017) Intelligent UAV-assisted routing protocol for urban VANETs. Comput Commun 107:93–111

    Article  Google Scholar 

  8. Cirne Pedro, Zúquete André, Sargento Susana (2018) TROPHY: trustworthy VANET routing with group authentication keys. Ad Hoc Netw 71:45–67

    Article  Google Scholar 

  9. Wu C, Ohzahata S, Kato T (2013) Flexible, portable, and practicable solution for routing in vanets: a fuzzy constraint q-learning approach. IEEE Trans Veh Technol 62(9):4251–4263

    Article  Google Scholar 

  10. Nzouonta J, Rajgure N, Wang G, Borcea C (2009) VANET routing on city roads using real-time vehicular traffic information. IEEE Trans Veh Technol 58(7):3609–3626

    Article  Google Scholar 

  11. Huang J (2015) Accurate probability distribution of rehealing delay in sparse VANETs. IEEE Commun Lett 19(7):1193–1196

    Article  Google Scholar 

  12. Shahidi R, Ahmed MH (2014) Probability distribution of end-to-end delay in a highway VANET. IEEE Commun Lett 18(3):443–446

    Article  Google Scholar 

  13. Huang C, Ku H, Kung H (2009) Efficient power-consumption-based load-sharing topology control protocol for harsh environments in wireless sensor networks. IET Commun 3(5):859–870

    Article  Google Scholar 

  14. Ludovico Guidoni D, Sumika Hojo Souza F, Ueyama J, Aparecido Villas L (2014) RouT: a routing protocol based on topologies for heterogeneous wireless sensor networks. IEEE Latin Am Trans. https://doi.org/10.1109/TLA.2014.6868887

    Article  Google Scholar 

  15. YanSHI X-yeJIN (2011) Shan-zhiCHEN," AGP: an anchor-geography based routing protocol with mobility prediction for VANET in city scenarios". J China Univ Posts Telecommun 18(1):112–117

    Google Scholar 

  16. YuDING Y-zhiLIU (2014) Xiang-yangGONG, Wen-dongWANG," Road traffic and geography topology based opportunistic routing for VANETs". J China Univ Posts Telecommun 21(4):32–39

    Article  Google Scholar 

  17. Akshat Gaurav, Awadhesh Kumar Singh," Light weight approach for secure backbone construction for MANETs", Journal of King Saud University - Computer and Information Sciences, Available online 6 June 2018.

  18. Antesar M.Shabut, M. ShamimKaiser, Keshav P.Dahal, WenbingChen, "A multidimensional trust evaluation model for MANETs", Journal of Network and Computer Applications, Available online 20 July 2018.

  19. Md GG (2016) Nawaz Ali, Peter Han JooChong, Syeda Khairunnesa Samantha, EdwardChan," Efficient data dissemination in cooperative multi-RSU vehicular ad hoc networks (VANETs)". J Syst Softw 117:508–527

    Article  Google Scholar 

  20. Azzuhri Saaidal Razalli, Ahmad Harith, Portmann Marius, Ahmedy Ismail, Pathak Ranjana (2016) An efficient hybrid manet-dtn routing scheme for OLSR". Wirel Pers Commun 89(4):1335–1354

    Article  Google Scholar 

  21. Darwish Tasneem, Bakar Kamalrulnizam Abu (2016) Traffic aware routing in vehicular ad hoc networks: characteristics and challenges. Telecommun Syst 61(3):489–513

    Article  Google Scholar 

  22. Moridi E (2017) Hamid Barati," RMRPTS: a reliable multi-level routing protocol with tabu search in VANET". Telecommun Syst 65(1):127–137

    Article  Google Scholar 

  23. Sun Y, Jiang Q, Singhal M (2012) A hill-area-restricted geographic routing protocol for mobile ad hoc and sensor networks. Comput J 55(8):932–949

    Article  Google Scholar 

  24. Zeadally S, Hunt R, Chen Y-S, Irwin A, Hassan A (2012) Vehicular ad hoc networks (VANETs): status, results, and challenges. Telecommun Syst 50(4):217–241

    Article  Google Scholar 

  25. D. Binu and B. S. Kariyappa, "RideNN: A new rider optimization algorithm-based neural network for fault diagnosis in analog circuits," IEEE Transactions on Instrumentation and Measurement.

  26. Yogeswaran Mohan, Sia Seng Chee, Donica Kan Pei Xin and Lee Poh Foong, "Artificial Neural Network for Classification of Depressive and Normal in EEG", 2016 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES), 2016.

  27. Zhang Junhao, Xia Pinqi (2017) An improved PSO algorithm for parameter identification of nonlinear dynamic hysteretic models. J Sound Vib 389:153–167

    Article  Google Scholar 

  28. Wang Gai-Ge, Deb Suash, Cui Zhihua (2015) Monarch butterfly optimization. Neural Comput Appl 31:1–20

    Google Scholar 

  29. Kelvin Santos AmorimGustavo Sousa Pavani, "Ant Colony Optimization-based distributed multilayer routing and restoration in IP/MPLS over optical networks", Computer Networks,Volume 185 (Cover date: 11 February 2021), Article 107747, 22 December 2020

  30. G.J. Klir, U.St. Clair, and Y. Bo, "Fuzzy Set Theory: Foundations and Applications," Englewood Cliffs, NJ, USA: Prentice-Hall, 1997

  31. Liang W, Li Z, Zhang H, Wang S, Bie R (2015) Vehicular ad hoc networks: architectures, research issues, methodologies, challenges, and trends. Int J Distrib Sens Netw 11(8):745303

    Article  Google Scholar 

  32. Campi, A., Guinea, S. and Spoletini, P., 2014. An operational semantics for XML fuzzy queries. eval (q, Ti), 1, p.1.

  33. Di Mauro M, Liotta A (2019) Statistical assessment of ip multimedia subsystem in a softwarized environment: a queueing networks approach. IEEE Trans Netw Serv Manage 16(4):1493–1506. https://doi.org/10.1109/TNSM.2019.2943776

    Article  Google Scholar 

  34. Botnet Identification in Multi-clustered DDoS Attacks. In proc. of Eusipco2017, Kos Island (Greece), pp. 2171–2175, August 2017.

  35. Veeraiah N, Krishna BT (2018) Intrusion detection based on piecewise fuzzy c-means clustering and fuzzy naive bayes rule. Multimed Res 1(1):27–32

    Google Scholar 

  36. Bhasha Shaik J, Ganesh V (2020) “Deep neural network and social ski-driver optimization algorithm for power system restoration with vsc - hvdc technology.” J Comput Mech Power Syst Control 3(1):1–9

    Article  Google Scholar 

  37. NavnathDattatraya K, RaghavaRao K (2020) “Hybrid fruitfly optimization algorithm and wavelet neural network for energy efficiency in WSN.” J Netw Commun Syst 3(1):41–49

    Google Scholar 

  38. Aloysius George and B. R. Rajakumar, "APOGA: An Adaptive Population Pool Size based Genetic Algorithm", AASRI Procedia - 2013 AASRI Conference on Intelligent Systems and Control (ISC 2013), 4: 288–296, 2013, https://doi.org/10.1016/j.aasri.2013.10.043

Download references

Author information

Authors and Affiliations

  1. Department of CSE, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, India

    N. Gomathi & Mukund B. Wagh

Authors
  1. N. Gomathi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mukund B. Wagh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mukund B. Wagh.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gomathi, N., Wagh, M.B. Improved rider for vehicular adhoc NETwork routing via neural network. Evol. Intel. 15, 1517–1530 (2022). https://doi.org/10.1007/s12065-021-00602-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12065-021-00602-0

Keywords

Search

Navigation