Electrical - Electronic Engineering / Elektrik - Elektronik Mühendisliği
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Article Citation - WoS: 7Citation - Scopus: 9Fluid dynamics-based distance estimation algorithm for macroscale molecular communication(Elsevier, 2021) Güleç, Fatih; Atakan, BarışMany 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. (C) 2021 Elsevier B.V. All rights reserved.Article Citation - WoS: 21Citation - Scopus: 25Distance Estimation Methods for a Practical Macroscale Molecular Communication System(Elsevier, 2020) Güleç, Fatih; Atakan, BarışAccurate estimation of the distance between the transmitter (TX) and the receiver (RX) in molecular communication (MC) systems can provide faster and more reliable communication. In addition, distance information can be used in determining the location of the molecular source in practical applications such as monitoring environmental pollution. Existing theoretical models in the literature are not suitable for distance estimation in a practical scenario. Furthermore, deriving an analytical model is a nontrivial problem, since the liquid in the TX is sprayed as droplets rather than molecules, these droplets move according to Newtonian mechanics, the size of the droplets change during their propagation and droplet-air interaction causes unsteady flows. Therefore, five different practical methods comprising three novel data analysis based methods and two supervised machine learning (ML) methods, Multivariate Linear Regression (MLR) and Neural Network Regression (NNR), are proposed for distance estimation at the RX side. In order to apply the ML methods, a macroscale practical MC system, which consists of an electric sprayer without a fan, alcohol molecules, an alcohol sensor and a microcontroller, is established, and the received signals are recorded. A feature extraction algorithm is proposed to utilize the measured signals as the inputs in ML methods. The numerical results show that the ML methods outperform the data analysis based methods in the root mean square error sense with the cost of complexity. The nearly equal performance of MLR and NNR shows that the input features such as peak time, peak concentration and the energy of the received signal have a highly linear relation with the distance. Moreover, the peak time based estimation, which is one of the proposed data analysis based methods, yields better results with respect to the other proposed four methods, as the distance increases. Given the experimental data and fluid dynamics theory, a possible trajectory of the molecules between the TX and RX is given. Our findings show that distance estimation performance is jointly affected by unsteady flows and the non-linearity of the sensor. According to our findings based on fluid dynamics, it is evaluated that fluid dynamics should be taken into account for more accurate parameter estimation in practical macroscale MC systems. (C) 2020 Elsevier B.V. All rights reserved.