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

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• 作者：Gurpreet Singh (1)
  Neeraj Kumar (1)
  Anil Kumar Verma (1)
  
• 关键词：MANETs ; Ant colony optimization ; Routing ; Orientation factor
• 刊名：Wireless Personal Communications
• 出版年：2014
• 出版时间：August 2014
• 年：2014
• 卷：77
• 期：3
• 页码：1859-1884
• 全文大小：1,539 KB
• 参考文献：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), 483-02. CrossRef
  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, 30-5.
  3. Dressler, F., & Akan, O. B. (2010). A survey on bio-inspired networking. / Computer Networks, / 54(6), 881-00. CrossRef
  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), 19-1 January 2009, Manno-Lugano, Switzerland (pp. 44-3).
  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), 147-53.
  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- July 2009 (pp. 12-5). Brisbane, QLD: Queensland Res. Lab. (QRL), Univ. of Queensland.
  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), 706-13.
  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- June 2009 (pp. 1-).
  9. Abolhasan, M., Wysocki, T., & Dutkiewicz, E. (2004). A review of routing protocols for mobile ad hoc networks. / Adhoc Networks, / 2(1), 1-2. CrossRef
  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), 2231-334.
  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. 79-5).
  12. Gunes, M., & Spaniol, O. (2003). Ant-routing-algorithm for mobile multi-hop ad-hoc networks. In D. Ga?ti, G. Pujolle, A. Al-Naamany, H. Bourdoucen, L. Khriji (Eds.), / Network control and engineering for Qos, security and mobility II (Vol. 1, pp. 120-38). Norwell, MA: Kluwer Academic Publishers.
  13. Baras, J. S., & Mehta, H. (2003). A probabilistic emergent routing algorithm for mobile ad hoc networks. In / WiOpt-3: Modeling and optimization in mobile, ad hoc and wireless networks, March 3-, 2003 (pp. 68-3).
  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), 17-1 November 2002, Taipei, Taiwan (pp. 198-09).
  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, 9-1 April 2003 (pp. 281-90).
  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. 461-70). Springer, Berlin.
  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, 23-5 September 2005 (Vol. 2, pp. 678-81).
  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), 258-72. CrossRef
  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, 22-4 September 2006 (pp. 1-).
  20. Rosati, L., Berioli, M., & Reali, G. (2008). On ant routing algorithms in ad hoc networks with critical connectivity. / Adhoc Networks, / 6(6), 827-59. CrossRef
  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), 529-36.
  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, 25-8 March 2008 (pp. 204-11).
  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. 673-82). London: Springer
  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), 31-1.
  25. Correia, F., & Vazao, T. (2010). Simple ant routing algorithm strategies for a (multipurpose) manet model. / Adhoc Network, / 8(8), 810-23.
  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), 690-05. CrossRef
  27. Gupta, R. (2012). RSAR: Ring search based ant routing for MANETs. / International Journal of Computer Applications, / 38(11), 22-6. CrossRef
  28. Prasad, S. P., Singh, Y. P., & Rai, C. S. (2009). PAR: Probabilistic ant routing. / International Journal on Recent Trends Engineering, / 1(1), 153-58.
  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), 123-37.
  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), 40-5. CrossRef
  31. Singh, G., Kumar, N., & Verma, A. K. (2012). ant colony algorithms in MANETs: A review. / Journal of Network and Computer Applications, / 35(6), 1964-972. CrossRef
  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: 2319-598, / 1(3), 8-2.
  33. Karthikeyan, D., & Dharmalingam, M. (2013). Ant based intelligent routing protocol for MANET. In / Proceedings of pattern recognition, informatics and medical, engineering (PRIME-2013), 21-2 February 2013 (pp. 11-6).
  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. 873-78). India: Springer
  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. 314-22). Berlin: Springer
  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. 278-87). Berlin: Springer
  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), 1933-945.
  38. The Network Simulator NS-2. http://www.isi.edu/nsnam/ns/
  
• 作者单位：Gurpreet Singh (1)
  Neeraj Kumar (1)
  Anil Kumar Verma (1)
  
  1. Department of Computer Science and Engineering, Thapar University, Patiala, Punjab, India
  
• ISSN：1572-834X

文摘

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):1933-945, 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.