Skip to main content
Log in

A performance evaluation of routing protocols for vehicular ad hoc networks with swarm intelligence

  • Original Article
  • Published:
International Journal of System Assurance Engineering and Management Aims and scope Submit manuscript

Abstract

Swarm intelligence work on artificial intelligence which is defined as collective behavior of self organized but centralized system. Movie effect, swarm robotics and network routing are applications of swarm intelligence. Vehicular ad-hoc network (VANET) is also self controlled, high dynamic network system. Road safety and traffic management are the main applications of VANETs. If we integrate swarm intelligence with VANETs, it will show outstanding results in terms of latency, throughput, data delivery cost and ratio. There are some issues in VANETs like data aging, heavy cost and message prioritization which can be solved with the help of swarm intelligence. In this paper, we will come across some good results in VANET when we apply some algorithm of swarm intelligence. There are some algorithms of swarm intelligence that can be applied in VANETs like artificial bee colony and AntNet. Swarm intelligence can be implemented in multicasting and data center routing. Moreover, we will also analysis AODV, DSR routing protocol with the smarm intelligence routing protocol in the network.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  • Ahmed H, Glasgow J (2012) Swarm intelligence: concept, model and its application. Queens University Technical Report

  • Baras JS, Mehta H (2003) A probabilistic emergent routing algorithm for mobile ad hoc networks. In: WiOpt’03: modeling and optimization in mobile, ad hoc and wireless networks, p 10

  • 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(10):953–967

    Article  Google Scholar 

  • Camara D, Loureiro AA (2000) A gps/ant-like routing algorithm for ad hoc networks. In: Wireless communications and networking conference, 2000, WCNC, 2000 IEEE, vol 3, pp 1232–1236

  • Di Caro G, Dorigo M (1997) AntNet: a mobile agents approach to adaptive routing. Technical report IRIDIA/97-12, IRIDIA, Universite´ Libre de Bruxelles, Belgium

  • Di Caro G, Ducatelle F, Gambardella LM (2005) Swarm intelligence for routing in mobile ad hoc networks. In SIS, pp 76–83

  • Doolan R, Muntean GM (2014) Time-ants: an innovative temporal and spatial ant-based vehicular routing mechanism. In: Intelligent vehicles symposium proceedings, 2014 IEEE, pp 951–956

  • Farooq M, Di Caro GA (2008) Routing protocols for next-generation networks inspired by collective behaviors of insect societies: an overview. In: Swarm intelligence. Springer, Berlin, pp 101–160

  • Garnier S, Gautrais J, Theraulaz G (2007) The biological principles of swarm intelligence. Swarm Intell 1(1):3–31

    Article  Google Scholar 

  • Gunes M, Sorges U, Bouazizi I (2002) ARA-the ant-colony based routing algorithm for MANETs. In: Parallel processing workshops, 2002. Proceedings. International conference on. IEEE, pp 79–85

  • Heissenbüttel M, Braun T (2003). Ants-based routing in large scale mobile ad-hoc networks. In: KiVS Kurzbeiträge, pp 91–99)

  • Karaboga D, Gorkemli B, Ozturk C, Karaboga N (2014) A comprehensive survey: artificial bee colony (ABC) algorithm and applications. Artif Intell Rev 42(1):21–57

    Article  Google Scholar 

  • Kassabalidis I, El-Sharkawi MA, Marks RJ, Arabshahi P, Gray AA (2001). Swarm intelligence for routing in communication networks. In: Global telecommunications conference, 2001. GLOBECOM’01. IEEE, vol 6, pp 3613–3617

  • Kshirsagar NS, Sutar DU (2015) Review on intelligent traffic management system based on VANET. Int J Innov Res Comput Commun Eng (An ISO 3297: 2007 Certified Organization), 3:2001–2004

  • Kulkarni U, Sachin D, Swapnil G (2013) Survey of swarm intelligence inspired routing algorithms and mobile ad-hoc network routing protocols. Int J Adv Res Technol 2(9):146–152

    Google Scholar 

  • Pathak A, Pandey MS (2013) Ant colony optimization techniques in MANET for efficient routing: a survey. Int J Eng Res Appl 3(6):1026–1032

    Google Scholar 

  • Prasad S, Singh YP, Rai CS (2009) Swarm based intelligent routing for MANETs. Int J Recent Trends Eng 1(1):153–158

    Google Scholar 

  • Rondinone M, Maneros J, Krajzewicz D, Bauza R, Cataldi P, Hrizi F, Lazaro O (2013) iTETRIS: a modular simulation platform for the large scale evaluation of cooperative ITS applications. Simul Model Pract Theory 34:99–125

    Article  Google Scholar 

  • Sim KM, Sun WH (2003) Ant colony optimization for routing and load-balancing: survey and new directions. IEEE Trans Syst Man Cybern-Part A Syst Hum 33(5):560–572

    Article  Google Scholar 

  • Stu¨tzle T, Hoos HH (2000) MAX–MIN ant system. Future Gener Comput Syst 16(8):889–914

    Article  MATH  Google Scholar 

  • Wang SS, Lin YS (2013) PassCAR: a passive clustering aided routing protocol for vehicular ad hoc networks. Comput Commun 36(2):170–179

    Article  MathSciNet  Google Scholar 

  • Wedde HF, Farooq M, Pannenbaecker T, Vogel B, Mueller C, Meth J, Jeruschkat R (2005) BeeAdHoc: an energy efficient routing algorithm for mobile ad hoc networks inspired by bee behavior. In: Proceedings of the 7th annual conference on genetic and evolutionary computation. ACM, pp 153–160

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ramesh C. Poonia.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Poonia, R.C. A performance evaluation of routing protocols for vehicular ad hoc networks with swarm intelligence. Int J Syst Assur Eng Manag 9, 830–835 (2018). https://doi.org/10.1007/s13198-017-0661-1

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13198-017-0661-1

Keywords

Navigation