Abstract
Vehicular Ad-Hoc Networks (VANETs) enable information sharing among vehicles, enhancing the safety of vehicle operations and holding significant practical value in preventing and managing events such as traffic congestion and accidents. However, effectively distributing beacon messages in complex and dynamic traffic environments presents a major challenge. Therefore, this paper introduces VANETs, where the strategy for communication link duration involves improving transmission range by increasing transmission power. However, under dense traffic conditions, boosting transmission power may lead to high interference levels and increased network overhead. Consequently, dynamic adjustment of power based on varying traffic density has become a common strategy.
This paper was supported by the National Natural Science Foundation of China (No. 92159102), the Natural Science Foundation of Jiangxi Province (No. 20232ACB205001), Support Plan for Talents in Gan Poyang – Academic and Technical Leader Training Project in Major Disciplines (No. 20232BCJ22025).
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References
Araujo, G.B., Peixoto, M.L., Sampaio, L.N.: NDN4IVC: a framework for simulating and testing of applications in vehicular named data networking. arXiv preprint arXiv:2107.00715 (2021)
Kamel, J., Wolf, M., Van Der Hei, R.W., Kaiser, A., Urien, P., Kargl, F.: VeReMi extension: a dataset for comparable evaluation of misbehavior detection in VANETs. In: 2020 IEEE International Conference on Communications (ICC), ICC 2020, pp. 1–6. IEEE (2020)
Al Mallah, R., Quintero, A., Farooq, B.: Distributed classification of urban congestion using VANET. IEEE Trans. Intell. Transp. Syst. 18(9), 2435–2442 (2017)
Eckhoff, D., Sommer, C.: A multi-channel IEEE 1609.4 and 802.11p EDCA model for the veins framework. In: Proceedings of 5th ACM/ICST International Conference on Simulation Tools and Techniques for Communications, Networks and Systems: 5th ACM/ICST International Workshop on OMNet++, Desenzano, Italy, 19–23 March 2012. OMNeT (2012)
Lukezic, A., Vojir, T., Cehovin Zajc, L., Matas, J., Kristan, M.: Discriminative correlation filter with channel and spatial reliability. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6309–6318 (2017)
Brümmer, N., De Villiers, E.: The BOSARIS toolkit: theory, algorithms and code for surviving the new DCF. arXiv preprint arXiv:1304.2865 (2013)
Samhat, A., Altman, Z., Fourestie, B.: Performance analysis of the IEEE 802.11 DCF with imperfect radio conditions. In: 2006 International Conference on Wireless and Mobile Communications, ICWMC 2006, p. 27. IEEE (2006)
Salama, A., Zaidi, S.A., McLernon, D., Qazzaz, M.M.: FLCC: efficient distributed federated learning on IoMT over CSMA/CA. arXiv preprint arXiv:2304.13549 (2023)
Andersen, P.A.: Deep reinforcement learning using capsules in advanced game environments. arXiv preprint arXiv:1801.09597 (2018)
Xu, K., Gerla, M., Bae, S.: How effective is the IEEE 802.11 RTS/CTS handshake in ad hoc networks. In: 2002 Global Telecommunications Conference, GLOBECOM 2002, vol. 1, pp. 72–76. IEEE (2002)
Acknowledgements
This paper was supported by the National Natural Science Foundation of China (No. 92159102), the Natural Science Foundation of Jiangxi Province (No. 20232ACB205001), Support Plan for Talents in Gan Poyang – Academic and Technical Leader Training Project in Major Disciplines (No. 20232BCJ22025).
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Shi, Q., Tu, B., Zhang, G. (2024). A Study of Adaptive Algorithm for Dynamic Adjustment of Transmission Power and Contention Window. In: Vaidya, J., Gabbouj, M., Li, J. (eds) Artificial Intelligence Security and Privacy. AIS&P 2023. Lecture Notes in Computer Science, vol 14510. Springer, Singapore. https://doi.org/10.1007/978-981-99-9788-6_16
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DOI: https://doi.org/10.1007/978-981-99-9788-6_16
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