Abstract
Due to difficult and costly to build a real-world test-bed, simulation is still the main method in the research area of mobile ad hoc networks (MANETs). Depict a exactly mobile scene or select a proper mobility model is very important to the performance of the simulation. In this paper, we propose a new mobility model based on Markov chain, we call it Time-Speed Markov Random Path (TSMRP) mobility model, which can reflect more classic mobile scene and has better independent parameters controllability. The flexible movement manners of the nodes and the algorithm of TSMRP are derived and analyzed. In order to test the TSMRP effectively, we implement and compare it to the MRP model and the RWP model by NS2, which are widely used in the research area of MANETs. The simulation result shows that, according to choose different probability parameters can effectively simulate different scenarios, and also more stable than the other two models. The results also shows that, different mobility models have great effects on the performance of routing protocol in MANETs.
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Acknowledgement
This paper is funded by the National Natural Science Foundation of China (61966009, U1811264, U1711263, 61803113), the Guangxi Natural Science Foundation (2019GXNSFBA245059, 2019GXNSFBA245049).
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Liu, T., Gu, T., Chang, L., Zhao, H. (2020). A New Mobility Model Based on Markov Chain for MANETs. In: Chen, X., Yan, H., Yan, Q., Zhang, X. (eds) Machine Learning for Cyber Security. ML4CS 2020. Lecture Notes in Computer Science(), vol 12488. Springer, Cham. https://doi.org/10.1007/978-3-030-62463-7_19
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DOI: https://doi.org/10.1007/978-3-030-62463-7_19
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