Abstract
With the exponential increase of mobile network traffic and the diverse range of connected mobile devices, optimizing data transmission in heterogeneous networks is critical. This research proposes to integrate Random Linear Network Coding (RLNC) and Content Delivery Networks (CDN) as potential solutions to reducing latency in such networks. The proposed study will investigate how RLNC-CDN integration can be implemented to optimize data transmission with a specific focus on mobile devices. By considering variables such as network topology, traffic patterns, and device types, RLNC-CDN integration's effectiveness will be evaluated in real-world scenarios using metrics like throughput, latency, and packet loss rate. The results of this research could provide critical knowledge to improve mobile network performance and the development of future network technologies catering to the needs of heterogeneous mobile devices.
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References
Wang, Y., Wang, H., Yu, B., Ma, Y.: A strategy of CDN traffic optimization based on the technology of SDN. DEStech Trans. Comput. Sci. Eng. (2017). https://doi.org/10.12783/DTCSE/ICEITI2016/6137
Zimmermann, S., Rischke, J., Cabrera, J. A., Fitzek, F.H.P.: Journey to MARS: interplanetary coding for relieving CDNs. In: GLOBECOM 2020–2020 IEEE Global Communications Conference (2020). https://doi.org/10.1109/GLOBECOM42002.2020.9322478
Julio, Y.R., et al.: Framework to manage software quality on IIoT apps. IOP Conf. Ser. Mater. Sci. Eng. 1154, 012006 (2021)
Rivera, Y., Dario, A., Mangone, P., Castaño, S.: Análisis bibliométrico sobre Ciberseguridad: técnica de ataque de suplantación de identidad y evolución, pp. 21–36 (2022)
Hung, T., Kaushal, S.K., Hsiao, H.: Content Distribution Network for Streaming using Multiple Galois Fields, pp. 0–4 (2021)
Julio, Y.R., Garcia, I.G., Marquez, J.R.: IoT: an architecture based on recoding RLNC for IOT wireless network with erase channel. In: Advances in Intelligent Systems and Computing, vol. 1137. AISC (2020)
Rivera, J.Y., Gutiérrez García, I., Márquez, J.: Fulcrum coding performance on fog computing architecture. IEEE Lat. Am. Trans. 18, 1966–1974 (2020)
Shi, S., Fouli, K.: Random Linear Network Coding (RLNC)-Based Symbol Representation draft-heide-nwcrg-rlnc-background-00 draft-heide-nwcrg-rlnc-01. 1–17 (2019).
Acevedo, J., et al.: Hardware acceleration for RLNC: a case study based on the xtensa processor with the tensilica instruction-set extension. Electronics 7 (2018)
Wunderlich, S., Gabriel, F., Pandi, S., Fitzek, F.H.P.: We don’t need no generation—a practical approach to sliding window RLNC. In: 2017 Wireless Days, WD 2017, pp. 218–223 (2017). https://doi.org/10.1109/WD.2017.7918148
Acevedo, J., et al.: Hardware acceleration for RLNC: A case study based on the xtensa processor with the tensilica instruction-set extension. Electron. 7, 1–22 (2018)
Nguyen, V., et al.: Advanced adaptive decoder using fulcrum network codes. IEEE Access 7, 141648–141661 (2019)
Wu, H., Li, Y., Hu, Y., Tang, B., Bao, Z.: On optimizing effective rate for random linear network coding over burst-erasure relay links. IEEE Wirel. Commun. Lett. 8, 588–591 (2019)
Tassi, A., Piechocki, R.J., Nix, A.: On intercept probability minimization under sparse random linear network coding. IEEE Trans. Veh. Technol. 68, 6137–6141 (2019)
Pinto-Mangones, A.D., et al.: Evaluation of 1-minute integration time rain rate statistics in Ecuador for radio propagation applications. IEEE Antennas Wirel. Propag. Lett. 21, 1298–1302 (2022)
Pérez-García, N.A., et al.: Optimising models for prediction of tropospheric scintillation on satellite links. Electron. Lett. 56, 577–579 (2020)
Pérez-García, N., et al.: Preliminary rain rate statistics with one-minute integration time for radio propagation uses in Venezuela. Electron. Lett. 59, 59–61 (2023)
Leyva-Mayorga, I., et al.: Network-coded cooperation and multi-connectivity for massive content delivery. IEEE Access 8, 15656–15672 (2020)
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Rivera, Y. et al. (2024). Integrating Random Linear Network Coding and Content Delivery Networks for Reduced Latency in Heterogeneous Network Processing of Mobile Devices. In: Rocha, Á., Fajardo-Toro, C.H., Rodríguez, J.M.R. (eds) Developments and Advances in Defense and Security. MICRADS 2023. Smart Innovation, Systems and Technologies, vol 380. Springer, Singapore. https://doi.org/10.1007/978-981-99-8894-5_7
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DOI: https://doi.org/10.1007/978-981-99-8894-5_7
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