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A Parthenogenetic Algorithm for Deploying the Roadside Units in Vehicle Networks

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Knowledge Science, Engineering and Management (KSEM 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11062))

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Abstract

It is crucial to appropriately deploy Roadside Units (RSUs) to guarantee the QoS of Vehicle-to-Infrastructure communication. Sarubbi et al. proposed the Delta-r algorithm by using relative contact time to meet the \(\triangle _{\rho _{2}}^{\rho _{1}}\)-Deployment, which is a metric for evaluating the performance of VANET. However, the “false high” relative contact time might play a negative effect on decision-making. In this paper, an improved algorithm Delta-uc, which is based on Useful Contribution, is presented. It avoids the negative effect of “extra” contact time by retaining only the useful relative trip duration of a vehicle at an urban cell. In addition, based on the Delta-uc algorithm, an effective recombination operator is designed, and a parthenogenetic algorithm UCPGA is proposed to solve the deployment problem. Compared with algorithms Delta-r and Delta-GA, in many \(\triangle _{\rho _{2}}^{\rho _{1}}\)-Deployments, the Delta-uc and UCPGA algorithms respectively required fewer RSUs, which were proved by the experiments on the realistic mobility trace of Cologne, Germany.

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References

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Acknowledgments

This research is supported by the National Natural Science Foundation of China under Grant No. 61762015, No. 61502111, No. 61763003, No. 61662007, Guangxi Natural Science Foundation under Grant No. 2015GXNSFAA139288, No. 2016GXNSFAA380192, “Bagui Scholar” Project Special Funds, Guangxi Science Base and Talent Special Support No. AD16380008, Guangxi Collaborative Innovation Center of Multi-source Information Integration and Intelligent Processing.

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Correspondence to Jingli Wu .

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Wu, J., Wu, Y., Wang, J., Ye, Y. (2018). A Parthenogenetic Algorithm for Deploying the Roadside Units in Vehicle Networks. In: Liu, W., Giunchiglia, F., Yang, B. (eds) Knowledge Science, Engineering and Management. KSEM 2018. Lecture Notes in Computer Science(), vol 11062. Springer, Cham. https://doi.org/10.1007/978-3-319-99247-1_8

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  • DOI: https://doi.org/10.1007/978-3-319-99247-1_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-99246-4

  • Online ISBN: 978-3-319-99247-1

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