Going for large scale with nano-wireless simulations

Boillot, Nicolas; Dhoutaut, Dominique; Bourgeois, Julien

doi:10.1145/2800795.2800828

Cited by 6 publications

(3 citation statements)

References 7 publications

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“…Vouivre [31] is a C++ simulator library for nanonetworks in Dynamic Physical Rendering Simulator (DPRSim). Vouivre solves the scalability issue by removing the heavy DPRSim features, and thus can simulate tens of thousands of nodes in the nanonetwork.…”

Section: Vouivrementioning

confidence: 99%

3D dynamic ring-based forwarder selection to improve packet delivery in ultra-dense nanonetworks

Hoteit,

Dedu,

Dhoutaut

et al. 2024

Nano Communication Networks

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Section: Vouivrementioning

confidence: 99%

3D dynamic ring-based forwarder selection to improve packet delivery in ultra-dense nanonetworks

Hoteit,

Dedu,

Dhoutaut

et al. 2024

Nano Communication Networks

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“…Nano-Sim [17] and TeraSim [18] are heavy and can only simulate networks of up to to around one thousand nodes. Vouivre [19] can simulate tens of thousands of nodes in the nanonetwork, but it does not study the effect of the packet payload and bits. None of the three have any support for visualization.…”

Section: A Available Simulation Softwarementioning

confidence: 99%

Influence of beta and source packet rate on electromagnetic nanocommunications

Hoteit¹,

Dedu²,

Seah

et al. 2023

2022 IEEE 28th International Conference on Parallel and Distributed Systems (ICPADS)

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Nanotechnology permits the manipulation of materials at nanoscale. Contrarily to traditional ad hoc wireless networks, electromagnetic nanonetworks are massively dense networks of highly-constrained nanodevices, connected by a ultra high-capacity terahertz channel (in the order of Tb/s). Like in traditional networks, congestion and collisions can appear in nanonetworks. However, they have a different meaning, and the parameters influencing them are also different. A parameter specific to nanonetworks is beta (β), which expresses the ratio of time between two consecutive bits and the bit length, and is sometimes called the symbol rate. No paper in the literature has evaluated this parameter, neither congestion and collisions in a dense network. Therefore, in this paper we study the influence of this parameter together with source packet rate to congestion and collisions in the context of a multi-flow nanonetwork. We conclude that lower source rates and lower β values result in less congestion and thus a good packet delivery to the destination.

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“…Vouivre [13] is a discrete event simulation library that can also work in stand-alone mode. It simulates individual packets and propagation delay.…”

Section: Implementation and Simulation Softwarementioning

confidence: 99%

Efficient Density Estimation Algorithm for Ultra Dense Wireless Networks

Arrabal

Dhoutaut

Dedu

2018

2018 27th International Conference on Computer Communication and Networks (ICCCN)

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Extremely dense wireless network topologies gradually become a reality, especially through wireless sensors networks and more recently nanonetworks. Electromagnetic nanonetworks are expected to allow a very large amount of extremely small and capability-limited devices to communicate with each others. In nanonetworks, even in a communication range limited to tens of centimeters, thousands of neighbors can be found. Information diffusion and routing protocols would greatly benefit from having an accurate estimation of the density of nodes. However, in this context, most traditional wireless communication protocols are not suited. We propose Density Estimator for Dense Networks (DEDeN), a distributed algorithm able to provide the required density estimation. It allows confidence tuning and can cope with an extreme range of local densities. A formal analysis of DEDeN is provided and corroborated by extensive simulations. DEDeN interest is then demonstrated through application to two information diffusion protocols tailored for very dense networks, and also to a routing protocol specific to nanonetworks.

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Going for large scale with nano-wireless simulations

Cited by 6 publications

References 7 publications

3D dynamic ring-based forwarder selection to improve packet delivery in ultra-dense nanonetworks

3D dynamic ring-based forwarder selection to improve packet delivery in ultra-dense nanonetworks

Influence of beta and source packet rate on electromagnetic nanocommunications

Efficient Density Estimation Algorithm for Ultra Dense Wireless Networks

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