Abstract
This paper propounds a novel Biogeography Inspired Group Mobility model for Mobile Ad Hoc Networks (MANETs) based on the Biogeography Based Optimization (BBO) algorithm. BBO describes the migration behavior of species between the islands and how they become extinct and new species arise. Many mobility models present in the literature failed to realistically represent the movement of nodes within the group and migration of nodes from one group to another group. To address these issues, each group of nodes in the proposed mobility model follows the bird flocking rules; inspired from the movement of flock of birds. These nodes then migrate from one group to another group based on BBO approach, showing group mobility behavior among the group of nodes in MANETs which exhibit frequent group motion and network topology changes. The experimental results obtained through ns-2 simulator is compared with a Random Waypoint Mobility model and Reference Point Group Mobility model and evaluated their network performance under different routing protocols.
Similar content being viewed by others
References
C. S. Murthy and B. S. Manoj, Ad hoc wireless networks: architectures and protocols, Pearson EducationNew Delhi, 2004.
T. Camp, J. Boleng and V. Davies, A survey of mobility models for ad hoc network research, Wireless Communications and Mobile Computing, Vol. 2, No. 5, pp. 483–502, 2002.
M. L. Sichitiu, Mobility models for ad hoc networks. In S. Misra, I. Woungang and S. Chandra Misra, editors. Guide to Wireless Ad Hoc Networks, Springer, London, 2009, pp. 237–254.
D. B. Johnson and D. A. Maltz, Dynamic source routing in ad hoc wireless networks. In T. Imelinsky and H. Korth, editors. Mobile Computing, Kluwer Academic publishersNorwell, 1996. pp. 153–181.
X. Hong, M. Gerla, G. Pei and C. C. Chiang, A group mobility model for ad hoc wireless networks, In Proceedings of the 2nd ACM international workshop on Modeling, analysis and simulation of wireless and mobile systems, pp. 53–60, 1999.
D. Simon, Biogeography-based optimization, IEEE Transactions on Evolutionary Computation, Vol. 12, No. 6, pp. 702–713, 2008.
R. K. Sharma and D. Ghose, Collision avoidance between UAV clusters using swarm intelligence techniques, International Journal of Systems Science, Vol. 40, No. 5, pp. 521–538, 2009.
J. Verma and N. Kesswani, A review on bio-inspired migration optimization techniques, International Journal of Business Data Communications and Networking (IJBDCN), Vol. 11, No. 1, pp. 24–35, 2015.
J. Kennedy, J. F. Kennedy, R. C. Eberhart and Y. Shi, Swarm intelligence, Morgan KaufmannSan Francisco, 2001.
S. J. Abdullah, A. Shaf, and A. A. Minhas, Location prediction for improvement of communication protocols in wireless communications: considerations and future directions, In Proceedings of the World Congress on Engineering and Computer Science, pp. 19–21, 2011.
I. Kassabalidis, M. A. El-Sharkawi, R. J. Marks, P. Arabshahi and A. A. Gray, Swarm intelligence for routing in communication networks, In Global Telecommunications Conference, GLOBECOM’01, IEEE, pp. 3613–3617, 2001.
B. Zhou, K. Xu, and M. Gerla, Group and swarm mobility models for ad hoc network scenarios using virtual tracks, In Military Communications Conference, MILCOM, IEEE, pp. 289–294, 2004.
J. Huo, B. Deng, S. Wu, J. Yuan and I. You, A topographic-awareness and situational-perception based mobility model with artificial bee colony algorithm for tactical MANET, Computer Science and Information Systems, Vol. 10, No. 2, pp. 46–725, 2013.
S. Misra and P. Agarwal, Bio-inspired group mobility model for mobile ad hoc networks based on bird-flocking behavior, Soft Computing, Vol. 16, No. 3, pp. 437–450, 2012.
F. Bai and A. Helmy, A survey of mobility models, In Wireless Ad hoc Networks, University of Southern California, USA, pp. 206–147, 2004.
J. M. Ng and Y. Zhang, Reference region group mobility model for ad hoc networks, In Second IFIP International Conference on Wireless and Optical Communications Networks, IEEE, pp. 290–294, 2005.
A. Einstein, Investigations on the Theory of the Brownian Movement, Courier CorporationMineola, 1956.
E. M. Royer, P. M. Melliar-Smith and L. E. Moser, An analysis of the optimum node density for ad hoc mobile networks, In IEEE International Conference on Communications, ICC 2001, pp. 857–861, 2001.
Z. Gong and M. Haenggi, Interference and outage in mobile random networks: expectation, distribution, and correlation, IEEE Transactions on Mobile Computing, Vol. 13, No. 2, pp. 337–349, 2014.
Y. Cong, X. Zhou and R. A. Kennedy, Interference prediction in mobile ad hoc networks with a general mobility model, IEEE Transactions on Wireless Communications, Vol. 14, No. 8, pp. 4277–4290, 2015.
M. Bergamo, R. R. Hain, K. Kasera, D. Li, R. Ramanathan and M. Steenstrup, System design specification for mobile multimedia wireless network (MMWN) (draft), DARPA project DAAB07-95-C-D156, 1996.
M. Sanchez, Mobility models. http://www.disca.upv.es/misan/mobmodel.htm. Accessed 15 March 2016.
M. Sánchez and P. Manzoni, ANEJOS: a java based simulator for ad hoc networks, Future Generation Computer Systems, Vol. 17, No. 5, pp. 573–583, 2001.
K. H. Wang and B. Li, Group mobility and partition prediction in wireless ad-hoc networks, In IEEE International Conference on Communications, 2002. ICC 2002, pp. 1017–1021, 2002.
K. Blakely and B. Lowekamp, A structured group mobility model for the simulation of mobile ad hoc networks, In Proceedings of the Second International Workshop on Mobility Management and Wireless Access Protocols, ACM, pp. 111–118, 2004.
M. Musolesi and C. Mascolo, A community based mobility model for ad hoc network research, In Proceedings of the 2nd International Workshop on Multi-hop Ad Hoc Networks: From Theory to Reality, ACM, pp. 31–38, 2006.
C. Zhao, M. L. Sichitiu and I. Rhee, N-body: a social mobility model with support for larger populations, Ad Hoc Networks, Vol. 25, pp. 185–196, 2015.
P. Venkateswaran, R. Ghosh, A. Das, S. K. Sanyal and R. Nandi, An obstacle based realistic ad-hoc mobility model for social networks, Journal of Networks, Vol. 1, No. 2, pp. 37–44, 2006.
F. Bai, N. Sadagopan and A. Helmy, IMPORTANT: A framework to systematically analyze the impact of mobility on performance of routing protocols for ad hoc networks, In INFOCOM 2003, Twenty-Second Annual Joint Conference of the IEEE Computer and Communications, IEEE Societies, IEEE, pp. 825–835, 2003.
C. W. Reynolds, Flocks, herds and schools: a distributed behavioral model, ACM SIGGRAPH Computer Graphics, Vol. 21, No. 4, pp. 25–34, 1987.
T. Back, Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms, Oxford University PressOxford, 1996.
The VINT Project, The Network Simulator—ns-2. http://www.isi.edu/nsnam/ns/. Accessed 6 January 2016.
Monarch Project, Rice Monarch Project Extension to ns-2. http://www.monarch.cs.rice.edu/emu-ns.html. Accessed 6 January 2016.
Acknowledgements
This research is supported by the NFO Fellowship under the University Grant Commission.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Verma, J., Kesswani, N. BIGM: A Biogeography Inspired Group Mobility Model for Mobile Ad Hoc Networks. Int J Wireless Inf Networks 25, 488–505 (2018). https://doi.org/10.1007/s10776-018-0410-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10776-018-0410-7