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Adaptive Sharing of IoT Resources Through SDN-Based Microsegmentation of Services Using Mininet

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Simulation Tools and Techniques (SIMUtools 2023)

Abstract

As we gradually embrace Smart Cities and the many advantages they can potentially offer, several technical issues arise that should be properly addressed, including security, efficiency, and performance, among others. In this regard, the massive deployment of IoT devices to support the numerous potential applications can consume excessive resources if their use is not optimized; this includes the sharing of some IoT devices whose content has the potential to be shared by many potential applications. One such example are CCTV smart cameras, as their deployment is costly, has a significant impact on urban aesthetics, and also the traffic flow they generate is high. To address such issue, in this paper we propose a novel SDN framework that enables the seamless sharing of streamed CCTV camera contents among multiple users, while adequately accounting for security and privacy restrictions. In particular, we adopt the Zero Trust paradigm to have a fine granularity control of the data-sensitive contents streamed by CCTV cameras. Experiments performed in Mininet using the Ryu controller evidence the potential of our solutions, which is able to achieve the target goals in a resource efficient manner, while introducing a low network updating overhead.

This work is derived from R &D project PID2021-122580NB-I00, funded by MCIN/AEI/10.13039/501100011033 and “ERDF A way of making Europe”.

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Correspondence to José D. Padrón .

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Martínez, A., Padrón, J.D., Zambrano-Martinez, J.L., Calafate, C.T. (2024). Adaptive Sharing of IoT Resources Through SDN-Based Microsegmentation of Services Using Mininet. In: Guisado-Lizar, JL., Riscos-Núñez, A., Morón-Fernández, MJ., Wainer, G. (eds) Simulation Tools and Techniques. SIMUtools 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 519. Springer, Cham. https://doi.org/10.1007/978-3-031-57523-5_18

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  • DOI: https://doi.org/10.1007/978-3-031-57523-5_18

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