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A unified mobility model for analysis and simulation of mobile wireless networks

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Abstract

We propose a novel mobility model, named Semi-Markov Smooth (SMS) model, to characterize the smooth movement of mobile users in accordance with the physical law of motion in order to eliminate sharp turns, abrupt speed change and sudden stops exhibited by existing models. We formulate the smooth mobility model by a semi-Markov process to analyze the steady state properties of this model because the transition time between consecutive phases (states) has a discrete uniform distribution, instead of an exponential distribution. Through stochastic analysis, we prove that this model unifies many good features for analysis and simulations of mobile networks. First, it is smooth and steady because there is no speed decay problem for arbitrary starting speed, while maintaining uniform spatial node distribution regardless of node placement. Second, it can be easily and flexibly applied for simulating node mobility in wireless networks. It can also adapt to different network environments such as group mobility and geographic constraints. To demonstrate the impact of this model, we evaluate the effect of this model on distribution of relative speed, link lifetime between neighboring nodes, and average node degree by ns-2 simulations.

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References

  1. Bai, F., Sadagopan, N., Krishnamachari, B., & Helmy, A. (2003). “Important: A framework to systematically an-alyze the impact of mobility on performance of routing protocols for ad hoc networks,” in Proc. of IEEE INFOCOM, Vol. 2, San Francisco, CA, USA, 825–835

  2. Sadagopan, N., Bai, F., Krishnamachari, B., & Helmy, A. (2003). “Paths: Analysis of path duration statistics and their impact on reactive manet routing protocols,” in Proc. of ACM MobiHoc, Annapolis, MD, USA, 245–256

  3. Jain, R., Lelescu, D., & Balakrishnan, M. (2005). “Model T: An empirical model for user registration patterns in a campus wireless LAN,” in Proc. of ACM MobiCom, Cologne, Germany, 170–184

  4. Kyriakakos, M., Frangiadakis, N., Merakos, L., & Hadjiefthymiades, S. (2003). “Enhanced path prediction for network resource management in wireless LANs”, IEEE Wireless Communications, Vol. 10, 62–69

    Article  Google Scholar 

  5. Iraqi, Y., Ghaderi, M., & Boutaba, R. (2004). “Enabling real-time All-IP wireless networks,” in Proc. of IEEE Wireless Communications and Networking Conference (WCNC), Vol. 3, Atlanta, Georgia, USA, 1500–1505

  6. Gupta, V., & Dixit, A. (1996). “The design and deployment of a mobility supporting network,” in Proc. of international Symposium on Parallel Architectures, Algorithms, and Networks, 1996., Beijing, China, 228–234, IEEE Computer Society

  7. Cheng, M., Cardei, M., Sun, J., Cheng, X., Wang, L., Xu. Y., & Du, D. -Z. (2004). “Topology control of ad hoc wireless networks for energy efficiency”, IEEE Transactions on Computers, Vol.␣53, 1629–1635

    Article  Google Scholar 

  8. Borkar, V., & Manjunath, D. (2005). “Distributed topology control of wireless networks,” in Proc. of 3rd IEEE International Symposium on Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks (WIOPT), Garda, Trentino, Italy, 155–163

  9. Grossglauser, M., & Tse, D. (2001). “Increases the capacity of ad-hoc wireless networks,” in Proc. of IEEE INFO-COM, Vol. 3, Anchorage, Alaska, USA, 1360–1369

  10. Bettstetter, C. (2004). “On the connectivity of ad hoc networks,” Computer Journal, Special Issue on Mobile and Pervasive Computing, 4, 432–447

    Google Scholar 

  11. Yuen, W.H., & Sung, C.W. (2003). “On energy efficiency and network connectivity of mobile ad hoc networks,” in Proc. of 23rd IEEE International Conference on Distributed Computing Systems, Providence, RI, USA, 38–45

  12. Bettstetter, C. (2001). “Smooth is better than sharp: A random mobility model for simulation of wireless networks,” in Proc. of ACM International Symposium on MSWiM. (Rome, Italy) 19–27

  13. Camp, T., Boleng, J., & Davies, V. (2002). “A survey of mobility models for ad hoc networks research,” Wireless Communication and Mobile Computing (WCMC): Special issue on Mobile Ad Hoc␣Networking: Research, Trends and Applications, Vol. 2 (5), 483–502

    Google Scholar 

  14. Song, L., Kotz, D., Jain, R., & He, X. (2004). “Evaluating location predictors with extensive Wi-Fi mobility data,” in Proc. of IEEE INFOCOM, Vol. 2, Hong Kong, China, 1414–1424

  15. Chinchilla, F., Lindsey, M., & Papadopouli, M. (2004). “Analysis of Wireless information locality and association patterns in a campus,” in Proc. of IEEE INFOCOM, Vol. 2, Hong Kong, China, 906–917

  16. Johnson, D.B., & Maltz, D.A. (1996). “Dynamic source routing in ad hoc wireless networks,” in Mobile Computing (Imielinski and␣Korth, eds.), Vol. 353, Ch. 5, 153–181, Kluwer Academic Publishers

  17. Yoon, J., Liu, M., & Noble, B. (2003). “Random waypoint considered harmful,” in Proc. of IEEE INFOCOM, Vol. 2, San Francisco, CA, USA, 1312–1321

  18. Bettstetter, C., Resta, G., & Santi, P. (2003). “The node distribution of the random waypoint mobility model for wireless ad hoc networks,” IEEE Transactions on Mobile Computing, Vol. 2, 257–269

    Article  Google Scholar 

  19. Blough, D. M., Resta, G., & Santi, P. (2004). “A Statistical analysis of the long-run node spatial distribution in mobile ad hoc networks,” ACM Wireless Networks, Vol. 10, 543–554

    Article  Google Scholar 

  20. Royer, E., Melliar-Smith, P., & Moser, L. (2001). “An analysis of the optimum node density for ad hoc mobile networks,” in Proc. of IEEE International Conference on Communications (ICC), Vol. 3, Helsinki, Finland, 857–861

  21. Nain, P., Towsley, D., Liu, B., & Liu, Z. (2005). “Properties of random direction models,” in Proc. of IEEE INFOCOM, Vol. 3, Miami, FL, USA, 1897–1907

  22. Boudec, J.-Y.L., & Vojnovic, M. (2005). “Perfect simulation and stationarity of a class of mobility models,” in Proc. of IEEE INFOCOM, Vol. 4, Miami, FL, USA, 2743–2754

  23. Liang, B., & Haas, Z. (1999). “Predictive distance-based mobility management for pcs networks,” in Proc. of IEEE INFOCOM, Vol. 3, (New York, NY, USA), 1377–1384

  24. Zhao, M., & Wang, W. (2006). “A Novel Semi-Markov Smooth Mobility Model for Mobile Ad Hoc Networks,” in Proc. of IEEE GLOBECOM, Best Student Paper Award-Communication Networks

  25. Yoon, J., Liu, M., & Noble, B. (2003). “Sound mobility models,” in Proc. of ACM MobiCom, San Francisco, CA, USA, 205–216

  26. Fang, Y., Chlamtac, I., & Lin, Y. -B. (2000). “Portable movement modeling for PCS networks,” IEEE Transactions on Vehicular Technology, Vol. 46, 1356–1363

    Article  Google Scholar 

  27. Wang, G., Cao, G., & Porta, T.L. (2004). “Movement-assisted sensor deployment,” in Proc. of IEEE INFOCOM, Vol. 4, Hong Kong, China, 2469–2479

  28. Bansal, N., & Liu, Z. (2003). “Capacity, delay and mobility an wireless ad-hoc networks,” in Proc. of IEEE INFOCOM, San Francisco, CA, USA, 1553–1563

  29. Samar, P., & Wicker, S.B. (2004). “On the behavior of communication links of node in a multi-hop mobile environment,” in Proc. of ACM MobiHoc, Tokyo, Japan, 145–156

  30. McGuire, M. (2005). “Stationary distribution of random walk mobility models for wireless ad hoc networks,” in Proc. of ACM MobiHoc, Urbana-Champaign, IL, USA, 90–98

  31. Feller, W. (1971). An Introduction to Probability Theory and Its Applications, Volume II. Wiley Series in Probability and Mathematical Statistics, 2 ed.

  32. Heyman, D.P., & Sobel, M.J. (1982). Stochastic Models in Operations Research, Volume I. McGraw-Hill, 2 ed.

  33. Shiryaev, A. (1995) Probability. Springer, 2 ed.

  34. Papoulis, A. (1991). Probability, Random Variables, and Stochastic Process. McGraw-Hill

  35. Camp, T., Boleng, J., Williams, B., Wilcox, L., & Navidi, W. (2002). “Performance comparison of two location based routing protocols for ad hoc networks,” in Proc. of IEEE INFOCOM, New York, NY, USA, 1678–1687

  36. Ko, Y.-B., & Vaidya, N.H. (1998). “Location-aided routing (LAR) in mobile ad hoc networks,” in Proc. of ACM MobiCom, Dallas, Texas, USA, 66–75

  37. Boleng, J., & Camp, T. (2003). “Adaptive location aided mobile ad hoc network routing,” Technical Report MCS-03-09, The Colorado School of Mines

  38. Li, J., & Mohapatra, P. (2003). “Laker: Location aided knowledge extraction routing for mobile ad hoc networks,” in Proc. IEEE WCNC, Vol. 2, New Orleans, LA, USA, 1180–1184

  39. Li, J., Jannotti, J., DeCouto, D., Krager, D., & Morris, R. (2000). “A scalable location service for geographic ad-hoc routing,” in Proc. of ACM Mobicom, Boston, MA, USA, 120–130

  40. “Psi function. http://mathworld.Wolfram.com/digammafunction. html”

  41. “The network simulator ns-2. http://www.isi.edu/nsnam/ns/,”2003

  42. Lin, G., Noubir, G., & Rajaraman, R. (2004). “Mobility models for ad hoc network simulation,” in Proc. of IEEE INFOCOM, Vol. 1, Hong Kong, China. 454–463

  43. Navidi, W., & Camp, T. (2004). “Stationary distributions for the␣random way point mobility model,” IEEE Transactions on Mobile Computing, Vol. 3, 99–108

    Article  Google Scholar 

  44. Aoyama, H., Himoto, A., Fuchiwaki, O., Misaki, D., & Sumrall, T. (2005). “Micro hopping robot with IR sensor for disaster survivor detection,”in Proc. of IEEE International workshop on Safety, Security and Rescue Robotics, kobe, Japan, 189–194

  45. Li, Q., Rosa, M.D., & Rus, D. (2003). “Distributed algorithms for guiding navigation across a sensor network,” in Proc. of ACM Mobicom, San Francisco, CA, USA, 313–325

  46. Agarwal, P. K., Guibas, L. J., & etal., H. E. (2002). “Algorithmic issues in modeling motion,” ACM Computing Surveys (CSUR), Vol. 34 (4), 550–572

    Article  Google Scholar 

  47. Hong, X., Gerla, M., Pei, G., & Chiang, C. (1999). “A group mobility model for ad hoc wireless networks,” in Proc. of ACM International Symposium on MSWiM, Seattle, Washington, USA, 53–60

  48. Jardosh, A., Belding-Royer, E. M., Almeroth, K. C., & Suri, S. (2003). “Towards realistic mobility models for mobile ad hoc networks,” in Proc. of ACM Mobicom, San Francisco, CA, USA, 217–229

  49. McDonald, A. B., & Znati, T. F. (1999). “A mobility-based framework for adaptive clustering in wireless ad hoc networks,” IEEE Journal on Selected Areas in Communications, Vol. 17, 1466–1487

    Article  Google Scholar 

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Correspondence to Wenye Wang.

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This work was supported by National Science Foundation under award CNS-0546289.

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Zhao, M., Wang, W. A unified mobility model for analysis and simulation of mobile wireless networks. Wireless Netw 15, 365–389 (2009). https://doi.org/10.1007/s11276-007-0055-4

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