Literature Database Entry


Fatih Güleç and Baris Atakan, "Fluid dynamics-based distance estimation algorithm for macroscale molecular communication," Elsevier Nano Communication Networks, vol. 28, pp. 100351, June 2021.


Many species, from single-cell bacteria to advanced animals, use molecular communication (MC) to share information with each other via chemical signals. Although MC is mostly studied in microscale, new practical applications emerge in macroscale. It is essential to derive an estimation method for channel parameters such as distance for practical macroscale MC systems which include a sprayer emitting molecules as a transmitter (TX) and a sensor as the receiver (RX). Due to the similarity between sneezing/coughing and spraying mechanisms, these practical systems have the potential to be applied in modeling airborne pathogen (viruses, bacteria, etc.) transmission with a MC perspective where an infected human emitting pathogen-laden droplets is considered as a TX. In this paper, a novel approach based on fluid dynamics is proposed for the derivation of the distance estimation in practical MC systems. According to this approach, transmitted molecules are considered as moving and evaporating droplets in the MC channel. With this approach, the Fluid Dynamics-based Distance Estimation (FDDE) algorithm which predicts the propagation distance of the transmitted droplets by updating the diameter of evaporating droplets at each time step is proposed. FDDE algorithm is validated by experimental data. The results reveal that the distance can be estimated by the fluid dynamics approach which introduces novel parameters such as the volume fraction of droplets in a mixture of air and liquid droplets and the beamwidth of the TX. Furthermore, the effect of the evaporation is shown with the numerical results.

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Fatih Güleç
Baris Atakan

BibTeX reference

    author = {G{\"{u}}le{\c{c}}, Fatih and Atakan, Baris},
    doi = {10.1016/j.nancom.2021.100351},
    title = {{Fluid dynamics-based distance estimation algorithm for macroscale molecular communication}},
    pages = {100351},
    journal = {Elsevier Nano Communication Networks},
    issn = {1878-7789},
    publisher = {Elsevier},
    month = {6},
    volume = {28},
    year = {2021},

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