Wireless Personal Communications

, Volume 77, Issue 3, pp 1859–1884 | Cite as

OANTALG: An Orientation Based Ant Colony Algorithm for Mobile Ad Hoc Networks



Mobile ad hoc (MANET) network is collection of nodes, which establish communication among moving nodes in a decentralized way without the use of any fixed infrastructure. Due to unpredictable network topological changes, routing in MANET is a challenging task as it requires a specialized approach to handle these changes due to the random movement of nodes. The routing protocol designed for MANETs should be able to detect and maintain route(s) between the source and the destination nodes in an efficient manner to handle the above defined issues. In this direction, ant colony algorithm is an important category of meta-heuristics techniques, which can provide an efficient solution to many engineering problems. But most of the existing ant colony algorithms explore the search space without initial directions, which lead to the risk of having local optima. To address this issue, in the present paper, we have been motivated and inspired by our previous work (Kumar et al. in Simul Model Pract Theory 19(9):1933–1945, 2011) in which the orientation factor was not considered, and the ant algorithm was applied for service selection in wireless mesh networks (WMNs). But in the current proposal, we have considered the orientation factor and applied the same in MANETs. Hence keeping this point in view, we propose an orientation based ant algorithm (OANTALG) for Routing in MANETs in which the selection of destination nodes and the exchange of ants (agents) between the source and the destination is based upon the orientation factor. During the movement of ants, the pheromone tables and the data structures are created that record the ants trip time between the nodes through which ants make a move. An efficient algorithm for orientation based routing has also been designed in the proposed scheme. The results obtained show that the proposed algorithm performs better than the other state of art algorithms, which are traditional and other ant based algorithms such as AODV, DSR, and HOPNET with respect to various performance metrics such as number of data packets send, throughput, jitter and path length. Simulation results show that OANTALG can send 1.02, 1.44, 1.61 times more number of data packets than AODV, DSR, and HOPNET, respectively. The throughput in OANTALG is 1.79, 30.69, and 48 % more than AODV, DSR and HOPNET, respectively. Packet drop ratio has also been reduced in the proposed OANTALG algorithm as compared to AODV and DSR. Average Jitter is also reduced by 42, 256 and 26.3 % from AODV, DSR and HOPNET, respectively. Average path length of OANTALG is 1.021 and 1.62 times less than AODV and DSR, respectively.


MANETs Ant colony optimization Routing Orientation factor 



We would like to thank the handling editor and anonymous reviewers for their constructive suggestions and comments which have greatly helped us to improve the content, quality, and presentation of this paper.


  1. 1.
    Camp, T., Boleng, J., & Davies, V. (2002). A survey of mobility models for ad hoc networks research. Journal of Wireless communication & Mobile Computing (WCMC), 2(5), 483–502.CrossRefGoogle Scholar
  2. 2.
    Cauvery, N. K., & Viswanatha, K. V. (2008). Enhanced ant colony based algorithm for routing in mobile ad hoc network. Engineering and Technology: World Academy of Science, 46, 30–35.Google Scholar
  3. 3.
    Dressler, F., & Akan, O. B. (2010). A survey on bio-inspired networking. Computer Networks, 54(6), 881–900.CrossRefMATHGoogle Scholar
  4. 4.
    Ducatelle, F., Caro, G. D., & Gambardella, L. M. (2005). Ant agents for hybrid multipath routing in mobile ad hoc networks. In Proceedings of second annual conference on wireless on-demand network systems and services, 2005. (WONS 2005), 19–21 January 2009, Manno-Lugano, Switzerland (pp. 44–53).Google Scholar
  5. 5.
    Singh, R., Singh, D. K., & Kumar, L. (2010). Swarm intelligence based approach for routing in mobile ad hoc networks. International Journal of Science and Technology Education Research, 1(7), 147–153.Google Scholar
  6. 6.
    Marwaha, S., & Portmann, J. I. M. (2009). Biologically Inspired ant-based routing in mobile ad hoc networks (MANET): A survey. Symposia and workshops on ubiquitous, autonomic and trusted computing, 7–9 July 2009 (pp. 12–15). Brisbane, QLD: Queensland Res. Lab. (QRL), Univ. of Queensland.Google Scholar
  7. 7.
    Kumar, G. V., Reddyr, Y. V., & Nagendra, M. (2010). Current research work on routing protocols for MANET: A literature survey. International Journal on Computer Science and Engineering (IJCSE), 02(03), 706–713.Google Scholar
  8. 8.
    Deepalakshmi, P., & Radhakrishnan, S. (2009). QOS routing algorithm for mobile ad hoc networks using ACO. In International conference on control, automation, communication and energy conservation, Perundurai, Tamilnadu, 4–6 June 2009 (pp. 1–6).Google Scholar
  9. 9.
    Abolhasan, M., Wysocki, T., & Dutkiewicz, E. (2004). A review of routing protocols for mobile ad hoc networks. Adhoc Networks, 2(1), 1–22.CrossRefGoogle Scholar
  10. 10.
    Kumar, A., & Singh, R. (2011). Mobile ad hoc networks routing optimization techniques using swarm intelligence. International Journal of Research in IT & Management, 1(4), 2231–4334.Google Scholar
  11. 11.
    Gunes, M., Sorges, U., & Bouazzi, I. (2002). ARA: The ant-colony based routing algorithm for MANETs. In Proceedings of international conference parallel processing workshops, 10 December 2002 (pp. 79–85).Google Scholar
  12. 12.
    Gunes, M., & Spaniol, O. (2003). Ant-routing-algorithm for mobile multi-hop ad-hoc networks. In D. Gaïti, G. Pujolle, A. Al-Naamany, H. Bourdoucen, L. Khriji (Eds.), Network control and engineering for Qos, security and mobility II (Vol. 1, pp. 120–138). Norwell, MA: Kluwer Academic Publishers.Google Scholar
  13. 13.
    Baras, J. S., & 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, March 3–5, 2003 (pp. 68–73).Google Scholar
  14. 14.
    Marwaha, S., Tham, C. K., Srinivasan, D. (2002). Mobile agents based routing protocol for mobile ad hoc networks. In Proceedings of the IEEE global communications conference (GlobeCom 02), 17–21 November 2002, Taipei, Taiwan (pp. 198–209).Google Scholar
  15. 15.
    Hussein, O., & Saadawi, T. (2003). Ant routing algorithm for mobile ad-hoc networks (ARAMA). In Proceedings IEEE international conference on performance, computing, and communications conference, 9–11 April 2003 (pp. 281–290).Google Scholar
  16. 16.
    Caro, G. D., Ducatelle, F., & Gambardella, L. M. (2004). AntHocNet: An ant-based hybrid routing algorithm for mobile ad hoc networks. In Proceedings of parallel problem solving from nature (PPSN VIII), Vol. 3242 of LNCS (pp. 461–470). Springer, Berlin.Google Scholar
  17. 17.
    Yuan, Z.Y., & Xiang, H.Y. (2005). Ant routing algorithm for mobile ad-hoc networks based on adaptive improvement. In Proceedings of international conference on wireless communications, networking and mobile computing, 23–25 September 2005 (Vol. 2, pp. 678–681).Google Scholar
  18. 18.
    Wang, H., Shi, Z., & Li, S. (2009). Multicast routing for delay variation bound using a modified ant colony algorithm. Journal of Network and Computer Applications, 32(1), 258–272.CrossRefMathSciNetGoogle Scholar
  19. 19.
    Yang, J. X., Li, L., & Cheng, C. (2006). Application research based ant colony optimization for MANET. In Proceedings of IEEE international conference on wireless communications, networking and mobile computing 2006 (WiCOM 2006), Wuhan, 22–24 September 2006 (pp. 1–4).Google Scholar
  20. 20.
    Rosati, L., Berioli, M., & Reali, G. (2008). On ant routing algorithms in ad hoc networks with critical connectivity. Adhoc Networks, 6(6), 827–859.CrossRefGoogle Scholar
  21. 21.
    Sengottaiyan, N., Somasundaram, R., & Arumugam, S. (2009). A modified routing algorithm for reducing congestion in wireless sensor networks. European Journal of Scientific Research, 35(4), 529–536.Google Scholar
  22. 22.
    Osagie, E., Thulasiraman, P., & Thulasiram, R. K. (2008). PACONET: Improved ant colony optimization routing algorithm for mobile ad hoc networks. In 22nd international conference on advanced information networking and applications (AINA 2008), Okinawa, 25–28 March 2008 (pp. 204–211).Google Scholar
  23. 23.
    Caro, G. D., & Dorigo, M. (1998). Ant colonies for adaptive routing in packet-switched communications networks. In Proceedings 5th international conference of parallel problem solving from nature (pp. 673–682). London: SpringerGoogle Scholar
  24. 24.
    Kamali, S., & Opatrny, J. (2008). A position based ant colony routing algorithm for mobile ad-hoc networks. Journal Of Networks - Academy Publishers, 3(4), 31–41.Google Scholar
  25. 25.
    Correia, F., & Vazao, T. (2010). Simple ant routing algorithm strategies for a (multipurpose) manet model. Adhoc Network, 8(8), 810–823.Google Scholar
  26. 26.
    Wang, J., Osagie, E., Thulasiraman, P., & Thulasiram, R. K. (2009). HOPNET: A hybrid ant colony optimization routing algorithm for mobile ad hoc network. Ad Hoc Networks, 7(4), 690–705.CrossRefGoogle Scholar
  27. 27.
    Gupta, R. (2012). RSAR: Ring search based ant routing for MANETs. International Journal of Computer Applications, 38(11), 22–26.CrossRefGoogle Scholar
  28. 28.
    Prasad, S. P., Singh, Y. P., & Rai, C. S. (2009). PAR: Probabilistic ant routing. International Journal on Recent Trends Engineering, 1(1), 153–158.Google Scholar
  29. 29.
    Sharvani, G. S., Ananth, A. G., & Rangaswamy, T. M. (September 2012). Efficient stagnation avoidance for manets with local repair strategy using ant colony optimization. International Journal of Distributed and Parallel Systems (IJDPS), 3(5), 123–137.Google Scholar
  30. 30.
    Kaur, S., Sawhney, R. S., & Vohra, R. (2012). MANET link performance parameters using ant colony optimization approach. International Journal of Computer Applications, 47(8), 40–45.CrossRefGoogle Scholar
  31. 31.
    Singh, G., Kumar, N., & Verma, A. K. (2012). ant colony algorithms in MANETs: A review. Journal of Network and Computer Applications, 35(6), 1964–1972.CrossRefGoogle Scholar
  32. 32.
    Dhull, D., & Dhull, S. (2013). An improved ant colony optimization (IACO) based multicasting in MANET. International Journal of Inventive Engineering and Sciences (IJIES) ISSN: 2319–9598, 1(3), 8–12.Google Scholar
  33. 33.
    Karthikeyan, D., & Dharmalingam, M. (2013). Ant based intelligent routing protocol for MANET. In Proceedings of pattern recognition, informatics and medical, engineering (PRIME-2013), 21–22 February 2013 (pp. 11–16).Google Scholar
  34. 34.
    Baskaran, R., Paul, P. V., & Dhavachelvan, P. (2013). ant Colony Optimization for data cache technique in MANET. In Proceedings of international conference in advances in computing & advances in intelligent systems and computing (vol. 174, pp. 873–878). India: SpringerGoogle Scholar
  35. 35.
    Parsapoor, M., & Bilstrup, U. (2013). Ant colony optimization for channel assignment problem in a clustered mobile ad hoc network. In Advances in swarm intelligence. Lecture Notes in Computer Science (Vol. 7928, pp. 314–322). Berlin: SpringerGoogle Scholar
  36. 36.
    Wu, H., & Sun, K. (2013). Improved ant colony classification algorithm applied to membership classification. In Advances in swarm intelligence Lecture Notes in Computer Science (Vol. 7928, pp. 278–287). Berlin: SpringerGoogle Scholar
  37. 37.
    Kumar, N., Iqbal, R., Chilamkurti, N., & James, A. E. (2011). An ant based multi constraints QoS aware service selection algorithm in wireless mesh networks. Simulation Modelling: Practice and Theory, 19(9), 1933–1945.Google Scholar
  38. 38.
    The Network Simulator NS-2. http://www.isi.edu/nsnam/ns/

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Gurpreet Singh
    • 1
  • Neeraj Kumar
    • 1
  • Anil Kumar Verma
    • 1
  1. 1.Department of Computer Science and EngineeringThapar UniversityPatialaIndia

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