A routing protocol for vehicular ad hoc networks using simulated annealing algorithm and neural networks | The Journal of Supercomputing Skip to main content
Log in

A routing protocol for vehicular ad hoc networks using simulated annealing algorithm and neural networks

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

Vehicular ad hoc network (VANET) is special type of mobile ad hoc networks which establish communications between adjacent vehicles and also between vehicles and roadside units. Thanks to their dynamic and fast topology changes, inter-vehicular ad hoc networks are like dynamic networks without organizations. Hence, developing a reliable routing algorithm is regarded as a notable challenge in these networks. In this paper, a clustering-based reliable routing algorithm was proposed for VANETs with reliable applications. In this way, simulated annealing was used for appropriate clustering of nodes and the parameters of node degree, coverage and ability were considered in the proposed method. For selecting cluster head, radial basis function neural network was used and a suitable fitness function with velocity and free buffer size parameters was used. Each cluster has two gateway nodes which are used as the communication interface for transmitting data from one cluster to another cluster. The simulation results indicated the efficiency of the proposed method in terms of route discovery rate and packet delivery rate.

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

Access this article

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

References

  1. Ayaida M, Barhoumi M, Fouchal H, Ghamri-Doudane Y, Afilal L (2014) Joint routing and location-based service in VANETs. J Parallel Distrib Comput 74:2077–2087

    Article  Google Scholar 

  2. Ghaffari A (2017) Real-time routing algorithm for mobile ad hoc networks using reinforcement learning and heuristic algorithms. Wirel Netw 23:703–714

    Article  Google Scholar 

  3. Ghasemnezhad S, Ghaffari A (2018) Fuzzy logic based reliable and real-time routing protocol for mobile ad hoc networks. Wireless Pers Commun 98(1):593–611

    Article  Google Scholar 

  4. Bernsen J, Manivannan D (2008) Greedy routing protocols for vehicular ad hoc networks. In: Wireless Communications and Mobile Computing Conference, 2008, IWCMC’08, International, pp 632–637

  5. Bitam S, Mellouk A, Zeadally S (2013) HyBR: a hybrid bio-inspired bee swarm routing protocol for safety applications in vehicular ad hoc networks (VANETs). J Syst Archit 59:953–967

    Article  Google Scholar 

  6. Al-Sultan S, Al-Doori MM, Al-Bayatti AH, Zedan H (2014) A comprehensive survey on vehicular ad hoc network. J Netw Comput Appl 37:380–392

    Article  Google Scholar 

  7. Dua A, Kumar N, Bawa S (2014) A systematic review on routing protocols for vehicular ad hoc networks. Veh Commun 1:33–52

    Article  Google Scholar 

  8. Cunha F, Villas L, Boukerche A, Maia G, Viana A, Mini RA et al (2016) Data communication in VANETs: protocols, applications and challenges. Ad Hoc Netw 44:90–103

    Article  Google Scholar 

  9. Kumar N, Dave M (2016) BIIR: a beacon information independent VANET routing algorithm with low broadcast overhead. Wirel Pers Commun 87:869–895

    Article  Google Scholar 

  10. Mohammed Nasr MM, Abdelgader AMS, Wang Z-G, Shen L-F (2016) VANET clustering based routing protocol suitable for deserts. Sensors 16:478

    Article  Google Scholar 

  11. Daeinabi A, Rahbar AGP, Khademzadeh A (2011) VWCA: an efficient clustering algorithm in vehicular ad hoc networks. J Netw Comput Appl 34:207–222

    Article  Google Scholar 

  12. Cordeschi N, Polli V, Baccarelli E (2013) Interference management for multiple multicasts with joint distributed source/channel/network coding. IEEE Trans Commun 61:5176–5183

    Article  Google Scholar 

  13. Baccarelli E, Cordeschi N, Polli V (2013) Optimal self-adaptive QoS resource management in interference-affected multicast wireless networks. IEEE/ACM Trans Netw (TON) 21:1750–1759

    Article  Google Scholar 

  14. Cordeschi N, Amendola D, Baccarelli E (2015) Reliable adaptive resource management for cognitive cloud vehicular networks. IEEE Trans Veh Technol 64:2528–2537

    Article  Google Scholar 

  15. Baccarelli E, Biagi M, Pelizzoni C, Cordeschi N (2007) Optimized power allocation for multiantenna systems impaired by multiple access interference and imperfect channel estimation. IEEE Trans Veh Technol 56:3089–3105

    Article  Google Scholar 

  16. Baccarelli E, Biagi M (2003) Optimized power allocation and signal shaping for interference-limited multi-antenna “ad hoc” networks. In: IFIP International Conference on Personal Wireless Communications, pp 138–152

  17. Campolo C, Sommer C, Dressler F, Molinaro A (2016) On the impact of adjacent channel interference in multi-channel VANETs. In: 2016 IEEE International Conference on Communications (ICC), pp 1–7

  18. Kwon J-H, Chang HS, Shon T, Jung J-J, Kim E-J (2016) Neighbor stability-based VANET clustering for urban vehicular environments. J Supercomput 72:161–176

    Article  Google Scholar 

  19. Arkian HR, Atani RE, Diyanat A, Pourkhalili A (2015) A cluster-based vehicular cloud architecture with learning-based resource management. J Supercomput 71:1401–1426

    Article  Google Scholar 

  20. Lin D, Kang J, Squicciarini A, Wu Y, Gurung S, Tonguz O (2017) MoZo: a moving zone based routing protocol using pure V2V communication in VANETs. IEEE Trans Mob Comput 16:1357–1370

    Article  Google Scholar 

  21. Van Laarhoven PJ, Aarts EH (1987) Simulated annealing: theory and applications. In: Mathematics and Its Applications, vol 37. Springer, p 187

  22. Yingwei L, Sundararajan N, Saratchandran P (1998) Performance evaluation of a sequential minimal radial basis function (RBF) neural network learning algorithm. IEEE Trans Neural Netw 9:308–318

    Article  Google Scholar 

  23. Ibrahim K, Weigle MC (2008) CASCADE: cluster-based accurate syntactic compression of aggregated data in VANETs. In: 2008 IEEE GLOBECOM Workshops, pp 1–10

  24. Wahab OA, Otrok H, Mourad A (2013) VANET QoS-OLSR: QoS-based clustering protocol for vehicular ad hoc networks. Comput Commun 36:1422–1435

    Article  Google Scholar 

  25. Yang Q, Lim A, Li S, Fang J, Agrawal P (2010) ACAR: adaptive connectivity aware routing for vehicular ad hoc networks in city scenarios. Mob Netw Appl 15:36–60

    Article  Google Scholar 

  26. Hassanabadi B, Shea C, Zhang L, Valaee S (2014) Clustering in vehicular ad hoc networks using affinity propagation. Ad Hoc Netw 13:535–548

    Article  Google Scholar 

  27. Rivoirard L, Wahl M, Sondi P, Berbineau M, Gruyer D (2018) Chain–Branch–Leaf: a clustering scheme for vehicular networks using only V2V communications. Ad Hoc Netw 68:70–84

    Article  Google Scholar 

  28. Wang S-S, Lin Y-S (2013) PassCAR: a passive clustering aided routing protocol for vehicular ad hoc networks. Comput Commun 36:170–179

    Article  Google Scholar 

  29. Bazzi A, Zanella A (2016) Position based routing in crowd sensing vehicular networks. Ad Hoc Netw 36:409–424

    Article  Google Scholar 

  30. Li G, Boukhatem L, Wu J (2017) Adaptive quality-of-service-based routing for vehicular ad hoc networks with ant colony optimization. IEEE Trans Veh Technol 66:3249–3264

    Article  Google Scholar 

  31. Zhang X, Zhang X, Gu C (2017) A micro-artificial bee colony based multicast routing in vehicular ad hoc networks. Ad Hoc Netw 58:213–221

    Article  Google Scholar 

  32. Qasem SN, Shamsuddin SM (2011) Radial basis function network based on time variant multi-objective particle swarm optimization for medical diseases diagnosis. Appl Soft Comput 11:1427–1438

    Article  Google Scholar 

  33. Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. Science 220:671–680

    Article  MathSciNet  MATH  Google Scholar 

  34. Metropolis N, Rosenbluth AW, Rosenbluth MN, Teller AH, Teller E (1953) Equation of state calculations by fast computing machines. J Chem Phys 21:1087–1092

    Article  Google Scholar 

  35. Gurung S, Chauhan S (2017) A novel approach for mitigating route request flooding attack in MANET. Wireless Netw. https://doi.org/10.1007/s11276-017-1515-0

    Google Scholar 

  36. Hagan MT, Menhaj MB (1994) Training feedforward networks with the Marquardt algorithm. IEEE Trans Neural Netw 5:989–993

    Article  Google Scholar 

  37. Aoki M, Fujii H (1996) Inter-vehicle communication: technical issues on vehicle control application. IEEE Commun Mag 34:90–93

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank the anonymous reviewers for their valuable comments which help us to improve the content and presentation of this paper.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ali Ghaffari.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bagherlou, H., Ghaffari, A. A routing protocol for vehicular ad hoc networks using simulated annealing algorithm and neural networks. J Supercomput 74, 2528–2552 (2018). https://doi.org/10.1007/s11227-018-2283-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-018-2283-z

Keywords

Navigation