Abstract
Ad hoc network consists of a set of identical nodes that move freely and independently and communicate among themselves via wireless links. The most interesting feature of this network is that they do not require any existing infrastructure of central administration and hence is very suitable for temporary communication links in an emergency situation. This flexibility, however, is achieved at a price of communication uncertainty induced due to frequent topology changes. In this article, we have tried to identify the system dynamics using the proven concepts of time series modeling. Here, we have analyzed variation of path length between a particular source destination pair nodes over a fixed area for different mobility patterns under different routing algorithm. We have considered four different mobility models—(i) Gauss-Markov mobility model, (ii) Manhattan Grid mobility model and (iii) Random Way Point mobility model and (iv) Reference Point Group mobility model. The routing protocols under which, we carried out our experiments are (i) Ad hoc On demand Distance Vector routing (AODV), (ii) Destination Sequenced Distance Vector routing (DSDV) and (iii) Dynamic Source Routing (DSR). The path length between two particular nodes behaves as a random variable for all mobility models for all routing algorithms. The pattern of path length for every combination of mobility model and for every routing protocol can be well modeled as an autoregressive model of order p i.e. AR(p). The order p is estimated and it is found that most of them are of order unity only. We also calculate the average path length for all mobility models and for all routing algorithms.
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References
M. Gerla, and J.T.C. Tsai, Multicluster, mobile, multimedia radio network. IEEE Journal of Wireless Networks, Vol. 1, No. 3, pp. 255–265, 1995.
R. Hekmat, Ad-hoc Networks: Fundamental Properties and Network Topologies. Springer, Dordrecht, 2006.
E. Hyytia, P. Lassila, and J. Virtamo. A markovian waypoint mobility model with application to hotspot modeling. In: IEEE International Conference on Communications, Vol. 3, pp. 979–986, 2006.
B. Liang, and Z. Haas. Predictive distance-based mobility management for pcs networks. In: IEEE International Conference on Computer Communications, Vol. 3, pp. 1377–1384, 1999.
C. Bettstetter, Topology properties of ad hoc networks with random waypoint mobility, ACM SIGMOBILE Mobile Computing and Communication Review, Vol. 7, No. 3, pp. 50–52, 2003.
P. Lassila, E. Hyyti, and H. Koskinen, Connectivity properties of random waypoint mobility model for ad hoc networks. In: MedHoc-Net, le de Porquerolles, pp. 159–168, 2005.
X. Hong, M. Gerla, G. Pei, and C.-C. Chiang, A Group Mobility Model for Ad Hoc Wireless Networks. In: Workshop on Modelling and Simulation of Wireless and Mobile Systems (MSWiM), pp. 53–60, 1999.
T. Camp, J. Boleng, and V. Davies, A survey of mobility models for ad hoc network research, Wireless Communications and Mobile Computing (WCMC): Special issue on Mobile Ad Hoc Networking: Research, Trends and Applications, Vol. 2, No. 5, pp. 483–502, 2002.
D. B. Johnson, and D. A. Maltz, Dynamic source routing in ad hoc wireless networks. In: Mobile Computing, Vol. 353, pp. 153–181. Kluwer Academic Publishers, 1996.
C. E. Perkins, and P. Bhagwat, Highly dynamic destination-sequenced distance-vector routing (dsdv) for mobile computers. In: ACM SIGCOMM Computer Communication Review, Vol. 24, pp. 234–244, 1994.
C. E. Perkins, Ad-hoc on-demand distance vector routing. In: 2nd IEEE Workshop on Mobile Computing Systems and Applications, pp. 90–100, 1999.
F. Bai, N. Sadagopan, and A. Helmy, Important: a framework to systematically analyze the impact of mobility on performance of routing protocols for adhoc networks. In: IEEE Infocom, pp. 825–835, 2003.
F. Bai, G. Bhaskara, and A. Helmy, Brics: a building-block approach for analyzing routing protocols in ad hoc networks-a case study of reactive routing protocols, SIGCOMM Computer Communication Review, Vol. 34, No. 3, pp. 57–70, 2004.
G. Bhaskara, A. Helmy, and S. Gupta, Micro-mobility protocol design and evaluation: a parameterized building block approach. In: Vehicular Technology Conference VTC-2003, Vol .3, pp. 2019–2024, 2003.
J. Hamilton, Time Series Analysis. Princeton University Press, Princeton, 1994.
P. J. Brockwell, and R. A. Davis, Time Series: Theory and Methods. Springer, New York, 1987.
S. Basu, A. Mukherjee, and S. Klivansky, Time series models for internet traffic. In INFOCOM, Vol. 2, pp. 611–620, 1996.
J. P. Singh, and P. Dutta, Temporal behavior analysis of mobile ad hoc network with different mobility patterns. In: ICAC3 ’09: International Conference on Advances in Computing, Communication and Control, pp .696–702. ACM, 2009.
J. P. Singh, and P. Dutta, Temporal modeling of node mobility in mobile ad hoc network, Journal of Computing and Information Technology, Vol. 18, No. 1, pp. 19–29, 2010.
University of Bonn. Bonnmotion—a mobility scenario generation and analysis tool. http://www.cs.uni-bonn.de/IV/bomonet/BonnMotion.htm.
J. Yoon, M. Liu, and B. Noble, Random waypoint considered harmful, In: INFOCOM 2003, pp. 1312–1321, 2003.
S. McCanne, and S. Floyd, ns Network Simulator. http://www.isi.edu/nsnam/ns.
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Singh, J.P., Dutta, P. The Temporal Effect of Mobility on Path Length in MANET. Int J Wireless Inf Networks 19, 38–48 (2012). https://doi.org/10.1007/s10776-011-0163-z
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DOI: https://doi.org/10.1007/s10776-011-0163-z