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A realistic mobility model with irregular obstacle constraints for mobile ad hoc networks

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Abstract

The nature of mobile ad hoc networks (MANETs) makes simulation research provide invaluable support for investigating mobile networking protocols, services and applications. Mobility is one of the main factors in simulation of MANETs, due to the fact that it has a strong impact on the design and performance of the networks. Mobility modeling has been an active field for the past decade, mostly focusing on matching a specific mobility or encounter metric with little focus on matching irregular obstacle constraints in realistic scenarios. Consequently, the existing mobility models (MMs) are almost unrealistic. On the other hand, the lack of systemic evaluation framework for MMs makes the mobility characteristics of MANETs be not properly evaluated. In this paper, a realistic mobility model based on Bezier curves (RMBC) is presented. The model operates in an irregular obstacle environment which restricts node movement and wireless transmission. In the RMBC model, a mobile node can calculate smooth pathways between the obstacles using the Bezier curve characterized by control points. Moreover, the flexible movement manners and the realistic application characteristics determined by RMBC are derived and analyzed, respectively. In order to effectively compare the proposed MM with several classical MMs, an integrated and systemic evaluation framework with a multi-dimensional mobility metric space is achieved. The simulation using NS2 tool is conducted. The results show that the proposed MM performs significantly better than the existing MMs in terms of mobility characteristics of MANETs in realistic scenarios.

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References

  1. Lauf, A. P., & Robinson, W. H. (2013). Resilient and efficient MANET aerial communications for search and rescue applications. In Proceeding of 2013 international conference on computing, networking and communications (ICNC), San Diego, USA.

  2. Gupta, V., Verma, A., Lala, A., & Chaurasia, A. (2013). Scenario based performance and comparative simulation analysis of routing protocols in MANET. International Journal of Computer Science and Network Security, 13(6), 98–104.

    Google Scholar 

  3. Zhang, L., Lakas, A., El-Sayed, H., & Barka, E. (2013). Mobility analysis in vehicular ad hoc network (VANET). Journal of Network and Computer Applications, 36(3), 1050–1056.

    Article  Google Scholar 

  4. Kim, K. (2013). An architecture to generate realistic mobility trace through verified flight simulator. International Journal of Software Engineering and Its Applications, 7(2), 287–293.

    MathSciNet  Google Scholar 

  5. Alshanyour, A., & Baroudi, U. (2010). A simulation study: The impact of random and realistic mobility models on the performance of bypass-AODV in ad hoc wireless networks. EURASIP Journal on Wireless Communications and Networking, 2010, 1–5.

    Article  Google Scholar 

  6. Ting, W., & Low, C. P. (2012). Evaluating inter-arrival time in general random waypoint mobility model. Ad Hoc Networks, 2012, 123–136.

    Google Scholar 

  7. Chowdhury, K. R., & Melodia, T. (2010). Platforms and testbeds for experimental evaluation of cognitive ad hoc networks. IEEE Communications Magazine, 48(9), 96–104.

    Article  Google Scholar 

  8. Bekmezci, I., Sahingoz, O. K., & Temel, S. (2013). Flying ad-hoc networks (FANETs): A survey. Ad Hoc Networks, 11(2013), 1254–1270.

    Article  Google Scholar 

  9. Khairnar, V. D., & Pradhan, S. N. (2010). Mobility models for vehicular ad hoc network simulation. International Journal of Computer Applications, 11(4), 8–12.

    Article  Google Scholar 

  10. Qian, C., & Liang, Z. (2012). The properties of T-Bezier curves and its applications. International Journal of Advance Computer Technology, 4(7), 53–60.

    Article  Google Scholar 

  11. Yang, G. J., & Byoung, W. C. (2013). Smooth trajectory planning along Bezier curve for mobile robots with velocity constraints. International Journal of Control and Automation, 6(2), 225–234.

    Google Scholar 

  12. Kumar-S, S., & Suman, C. S. B. (2011). Classification and evaluation of mobility metrics for mobility model movement patterns in mobile ad-hoc networks. International Journal on Applications of Graph Theory in Wireless Ad Hoc and Sensor Networks (GRAPH-HOC), 3(3), 25–38.

    Article  Google Scholar 

  13. Vasanthi, V., Romen, K. M., Ajith, S. N., & Hemalatha, M. (2011). A detailed study of mobility models in wireless sensor networks. Journal of Theoretical and Applied Information Technology, 33(1), 7–14.

    Google Scholar 

  14. Khider, I., Furong, W., WeiHua, Y., & Sacko. (2006). An overview of geographic restriction mobility models. Ubiquitous Computing and Communication Journal, 1(1), 1–9.

    Google Scholar 

  15. Younes, O., & Thomas, N. (2013). A path connection availability model for manets with random waypoint mobility. In Proceeding of the 9th european conference on computer performance engineering, Munich, Germany.

  16. Bettstetter, C. (2003). Opology properties of ad hoc networks with random waypoint mobility. ACM SIGMOBILE Mobile Computing and Communications Review, 7(2003), 50–52.

    Article  Google Scholar 

  17. Christian, B., Giovanni, R., & Paolo, S. (2003). The node distribution of the random waypoint mobility model for wireless ad hoc networks. IEEE Transactions on Mobile Computing, 2(2003), 257–269.

    Google Scholar 

  18. Royer, E.M., Melliar-Smith, P.M., & Moser, L.E. (2001). An analysis of the optimum node density for ad hoc mobile networks. In Proceedings of the IEEE international conference on communications (ICC’01), Helsinki, Finland.

  19. Sreenivasulu, K., & Srinivasulu, A. L. (2011). Improving routing efficiency based on random direction mobility model in manets. International Journal of Smart Sensors and Ad Hoc Networks, 1(1), 38–46.

    Google Scholar 

  20. Bettstetter, C., Hartenstein, H., & Pérez-Costa, X. (2004). Stochastic properties of the random waypoint mobility model. Wireless Networks, 10(5), 555–567.

    Article  Google Scholar 

  21. Liang, B., & Haas, Z. (1999). Predictive distance-based mobility management for PCS networks. In Proceeding of 18th Annual joint conference of the IEEE computer and communications societies (INFOCOM’99), Vol. 3, New York.

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

    Google Scholar 

  23. Ahmed, S., Karmakar, G. C., & Kamruzzaman, J. (2010). An environment-aware mobility model for wireless ad hoc network. Computer Networks, 54(2010), 1470–1489.

    Article  MATH  Google Scholar 

  24. Jardosh, A., Belding-Royer, E., Almeroth, K., & Suri, S. (2005). Real-world environment models for mobile network evaluation. IEEE Journal on Selected Areas in Communications, 23(2005), 622–632.

    Article  Google Scholar 

  25. Tian, J., Haehner, J., Becker, C., Stepanov, I., & Rothermel, K. (2012). Graph-based mobility model for mobile ad hoc network simulation. In Proceeding of 35th annual simulation symposium, in cooperation with the IEEE computer society and ACM, San Diego, California.

  26. Shohrab-Hossain, M., & Atiquzzaman, M. (2009). Stochastic properties and application of city section mobility model. In Proceedings of the 28th IEEE conference on global telecommunications (GLOBECOM’09), Piscataway, NJ, USA.

  27. Papageorgiou, C., Birkos, K., Dagiuklas, T., & Kotsopoulos, S. (2012). Modeling human mobility in obstacle-constrained ad hoc networks. Ad Hoc Networks, 10(2012), 421–434.

    Article  Google Scholar 

  28. Pilu C., Miriam D. I., Andrea M., Gianluca R., & Paola V.(2009). Spatial node distribution of manhattan path based random waypoint mobility models with applications. In Proceedings of the 16th international conference on structural information and communication Complexity, Piran, Slovenia (pp. 154–166).

  29. Wei, W., Xiaohong, G., Beizhan, W., & Yaping, W. (2010). A novel mobility model based on semi-random circular movement in mobile ad hoc networks. Information Sciences, 180(2010), 399–413.

    Google Scholar 

  30. Cavalcanti, E. R., & Spohn, M. A. (2010). Improved spatial and temporal mobility metrics for mobile ad hoc networks. In The 4th international conference on mobile ubiquitous computing, systems, services and technologies (pp. 189–195).

  31. Karyotis, V., Manolakos, A., & Papavassiliou, A. S. (2010). On topology control and non-uniform node deployment in ad hoc networks. In 8th IEEE international conference on pervasive computing and communications workshops, Mannheim, Germany (pp. 522–527).

  32. Lalar, S. (2013). Obstacle detection sensors: A survey. International Journal of Current Engineering and Technology, 3(5), 2138–2142.

    Google Scholar 

  33. Jiméneza, F. Eugenio, & Naranjob, J. (2011). Improving the obstacle detection and identification algorithms of a laserscanner-based collision avoidance system. Transportation Research Part C: Emerging Technologies, 19(4), 658–672.

    Article  Google Scholar 

  34. Yan, L., Ding, X., Zheng, Y., Kong, J., & Liu, J. (2013). A novel identification method of obstacles based on multi-sensor data fusion in forest. Sensors & Transducers, 155(8), 39–46.

    Google Scholar 

  35. Zhou, F., Song, B., & Tian, G. (2011). Bezier curve based smooth path planning for mobile robot. Journal of Information & Computational Science, 8(12), 2441–2450.

    Google Scholar 

  36. Thakur, G. S., & Helmy, A. (2013). COBRA: A framework for the analysis of realistic mobility models. In IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), Turin (pp. 145–150).

  37. Danoy, G., Matthias R., & Brust, P. B. (2015). Connectivity stability in autonomous multi-level UAV swarms for wide area monitoring. In The Fifth ACM international symposium on development and analysis of intelligent vehicular networks and application, Cancun, Mexico (pp. 1–8).

  38. Yujun, Z., Qi, H., Zhongcheng, L.,& Jie, T.(2012). Design and performance study of a Topology-Hiding Multipath Routing protocol for mobile ad hoc networks. In Proceedings of INFOCOM, Orlando, Florida, USA, Vol. 131, no. 5 (pp. 10–18).

  39. Amol, R. K., & Nilesh, S. V.(2015). Comparative analysis of AODV and AODV-TPRD reactive routing protocol in MANET. In International conference on electrical, signals, communication and optimization (pp. 1–5).

  40. Viqar, S., & Welch, J. L. (2013). Deterministic collision free communication despite continuous motion. Ad Hoc Networks, 11(2013), 508–521.

    Article  Google Scholar 

  41. Zhu, Y., Zhang, T., Song, J., Li, X., & Nakamura, M. (2012). A new method for mobile robots to avoid collision with moving obstacle. Artificial Life and Robotics, 16(4), 507–510.

    Article  Google Scholar 

  42. Matveev, A. S., Chao, W., & Savkin, A. V. (2012). Real-time navigation of mobile robots in problems of border patrolling and avoiding collisions with moving and deforming obstacles. Robotics and Autonomous Systems, 60(6), 769–788.

    Article  Google Scholar 

  43. Jardosh, A., Belding-Royer, E. M., Almeroth, K. C., & Suri, S. (2003). Towards realistic mobility models for mobile ad hoc networks. In Proceedings of ninth annual international conference on mobile computing and networking (MobiCom 2003), San Diego, CA.

  44. Yueh-Ting, W., Wanjiun, L., Cheng-Lin, T., & Tsung-Nan, L. (2009). Impact of node mobility on link duration in multihop mobile networks. IEEE Transactions on Vehicular Technology, 58(5), 2435–2442.

    Article  Google Scholar 

  45. Oliveira, R., Luís, M., Furtado, A., Bernardoa, L., Dinisa, R., & Pinto, P. (2013). Improving path duration in high mobility vehicular ad hoc networks. Ad Hoc Networks, 11(1), 89–103.

    Article  Google Scholar 

  46. Jeongseo, P., Jinsoo, C., & Taekeun, P. (2012). The impact of disjoint multiple paths on SCTP in the connected MANET for emergency situations. IEICE Transactions on Communications, E95-B(3), 1011–1014.

    Article  Google Scholar 

  47. Kuiper, E., & Nadjm-Tehrani, S. (2006). Mobility models for UAV group reconnaissance applications. In Proceedings of the international conference on wireless and mobile communications (ICWMC’06), Bucharest, Romania.

  48. Sunho, L., Chansu, Y., & Chita, R. (2010). A realistic mobility model for wireless networks of scale-free node connectivity. International Journal of Mobile Communications, 8(3), 351–369.

    Article  Google Scholar 

  49. Ducatelle, F., Di Caro, G. A., Förster, A., et al. (2014). Cooperative navigation in robotic swarms. Swarm Intelligence, 8(1), 1–33.

    Article  Google Scholar 

  50. Yoon, J., Liu, M., & Noble, B. (2003). Sound mobility models. In Proceedings of the ACM/IEEE international conference on mobile computing and networking (MOBICOM ‘03), San Diego, CA, USA (pp. 205–216).

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Acknowledgements

The authors would like to thank the anonymous reviewers for their valuable comments and suggestions to improve the quality of the paper. This work was supported by Special research program of Shaanxi Provincial Department of Education under Grant 15JK1317, and National Natural Science Foundation of China under Grant 61201118.

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Authors and Affiliations

  1. School of Computer Science, Xi’an Polytechnic University, Xi’an, 710048, China

    Wei Wang & Mingming Wang

  2. Viterbi School of Engineering, University of Southern California, Los Angeles, CA, 90007, USA

    Jiajun Wang

  3. School of Software, Xiamen University, Xiamen, 361005, China

    Beizhan Wang & Wenjing Zhang

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  1. Wei Wang
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  2. Jiajun Wang
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Correspondence to Beizhan Wang.

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Wang, W., Wang, J., Wang, M. et al. A realistic mobility model with irregular obstacle constraints for mobile ad hoc networks. Wireless Netw 25, 487–506 (2019). https://doi.org/10.1007/s11276-017-1569-z

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