Electrical - Electronic Engineering / Elektrik - Elektronik Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/11
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Research Project Moleküler haberleşmede moleküler bilginin tespiti ve tahmini(2017) Atakan, BarışMoleküler haberlesme bilgi tasıyıcısı olarak molekülleri kullanan bir haberlesme paradigmasıdır. Yeryüzündeki en eski ve en yaygın haberlesme yöntemi olan moleküler haberlesme tek hücreli canlılardan çok hücreli hayvan ve bitkilere kadar bütün canlıların hayati fonksiyonlarını sürdürebilmeleri için vazgeçilmezdir. Dogada var olanların yanı sıra günümüzde moleküler haberlesme moleküler sinyalleri algılayıp yorumlayabilen nano ve biyoteknoloji tabanlı birçok yeni uygulamanın da önemli bilesenleri arasına girmistir. Bu proje kapsamında moleküler haberlesme ile ilgili gerçeklestirilen çalısmalar bes ana bölüme ayrılabilir. Birinci bölümde kanal hafızalı difüzyona dayalı moleküler haberlesmenin basarımının anlasılabilmesi için analitik basarım degerlendirmeleri ve ilgili benzetim deneyleri yapılmıstır. Basarım degerlendirmesi için gelistirilen analitik modelin dogrulugu rastgele yürüyüs (random walk) benzetimleri ile gösterilmistir. Deneyler bazı moleküler haberlesme senaryoları için kanal hafızasının basarımı azaltmak yerine arttırdıgını göstermistir. Ikinci bölümde hafızalı moleküler haberlesme kanalının eszamansız oldugu durumlar için analitik basarım degerlendirmeleri yapılmıstır. Moleküler haberlesmenin verici tarafından gönderilen bir isaret (beacon) sembolünün tespiti ile baslatıldıgı düsünülmüstür. Bu isaret sembolü için kullanılan salım miktarı ve difüzyon katsayısı gibi parametrelerin uygun seçimi ile eszamansız moleküler haberlesmenin neredeyse eszamanlı kadar iyi basarım saglayabildigi gösterilmistir. Üçüncü bölümde bir moleküler kaynagın yerini ve salım hızını bulabilmek için teorik bir tahmin yöntemi gelistirilerek basarımı benzetim deneyleri ile gösterilmistir. Dördüncü bölümde ise moleküler sinyallerin alıcıda yeniden olusturulması için analitik bir model olusturularak sinyal bozulmasının analitik ifadesi türetilmistir. Yine benzetim deneyleri ile bozulmanın hangi parametrelerden nasıl etkilendigi arastırılarak en iyi alıcı tasarımı gerçeklestirilmistir. Son olarak besinci bölümde bir moleküler kaynak ve mikroislemci ile kontrol edilen bir algılayıcıdan olusan moleküler haberlesme sistemi gerçeklenmistir. Gerçeklenen sistem kullanılarak difüzyon katsayısı ve kaynak ile alıcı arasındaki mesafe tahmini için yöntemler önerilerek basarımları gösterilmistir. Burada özetlenen projenin bes bölümünde moleküler alıcı mükemmel absorblayıcı (perfect absorber) olarak düsünülmüstür. Mükemmel alıcı birçok açıdan yüzey reseptörlü alıcıların performansını da yansıtabildigi için performans degerlendirmelerinde reseptörlü alıcılar kapsam dısında bırakılmıstır.Article Citation - WoS: 4Citation - Scopus: 4Mobile human ad hoc networks: A communication engineering viewpoint on interhuman airborne pathogen transmission(Elsevier, 2022) Güleç, Fatih; Atakan, Barış; Dressler, FalkoA number of transmission models for airborne pathogens transmission, as required to understand airborne infectious diseases such as COVID-19, have been proposed independently from each other, at different scales, and by researchers from various disciplines. We propose a communication engineering approach that blends different disciplines such as epidemiology, biology, medicine, and fluid dynamics. The aim is to present a unified framework using communication engineering, and to highlight future research directions for modeling the spread of infectious diseases through airborne transmission. We introduce the concept of mobile human ad hoc networks (MoHANETs), which exploits the similarity of airborne transmission-driven human groups with mobile ad hoc networks and uses molecular communication as the enabling paradigm. In the MoHANET architecture, a layered structure is employed where the infectious human emitting pathogen-laden droplets and the exposed human to these droplets are considered as the transmitter and receiver, respectively. Our proof-of-concept results, which we validated using empirical COVID-19 data, clearly demonstrate the ability of our MoHANET architecture to predict the dynamics of infectious diseases by considering the propagation of pathogen-laden droplets, their reception and mobility of humans.Editorial Guest Editorial for Signal Processing Aspects of Molecular Communications(Elsevier, 2022) Atakan, Barış; Galmés, Sebastià; Haselmayr, Werner; Farsad, Nariman; Nakano, TadashiMolecular communication is the most widespread communication mechanism on the Earth since it is fundamental for all living entities from unicellular organisms to multicellular animals and plants to maintain their vital functionalities. For example, many unicellular organisms sense and react to molecular signals from their surroundings to control their life cycles. Some signaling molecules called pheromone are also extensively employed by a variety of insects to send and receive information to coordinate colony activities. Moreover, in the neuronal system, signaling molecules known as neurotransmitters are used in junction points of neuron cells to carry out many mental activities. In addition to the various molecular communication mechanisms in nature, the recent advances in nano- and biotechnology have shown that molecular communication is one of the most favorable choices to enable the interconnection of nanomachines such as engineered cells and bionanorobots. The network of such nanomachines, i.e., nanonetwork, is considered to make frontier biomedical applications a reality. In these applications, molecular communication can enable the nanomachines to share information so as to provide reliability and controllability. Furthermore, this can also allow different nanomachine populations to be coordinated to reach highly sophisticated behavior and increase the number of design possibilities.Article Citation - WoS: 3Citation - Scopus: 5Event Distortion-Based Clustering Algorithm for Energy Harvesting Wireless Sensor Networks(Springer, 2021) Al-Qamaji, Ali; Atakan, BarışWireless sensor networks (WSNs) consist of compact deployed sensor nodes which collectively report their sensed readings about an event to the Base Station (BS). In WSNs, due to the dense deployment, sensor readings can be spatially correlated and it is nonessential to transmit all their readings to the BS. Therefore, for more energy efficient, it is vital to choose which sensor node should report their sensed readings to the BS. In this paper, the event distortion-based clustering (EDC) algorithm is proposed for the spatially correlated sensor nodes. Here, the sensor nodes are assumed to harvest energy from ambient electromagnetic radiation source. The EDC algorithm allows the energy-harvesting sensor nodes to select and eliminate nonessential nodes while maintain an acceptable level of distortion at the BS. To measure the reliability, a theoretical framework of the distortion function is first derived for both single-hop and two-hop communication scenarios. Then, based on the derived theoretical framework, the EDC algorithm is introduced. Through extensive simulations, the performance of the EDC algorithm is evaluated in terms of achievable distortion level, number of alive nodes and harvested energy levels. As a result, EDC algorithm can successfully exploit both the spatial correlation and energy harvesting to improve the energy efficiency while preserving an acceptable level of distortion. Furthermore, the performance comparisons reveal that the two-hop communication model outperforms the single-hop model in terms of the distortion and energy-efficiency.Article Citation - WoS: 12Citation - Scopus: 14A Molecular Communication Perspective on Airborne Pathogen Transmission and Reception Via Droplets Generated by Coughing and Sneezing(IEEE, 2021) Güleç, Fatih; Atakan, BarışInfectious diseases spread via pathogens such as viruses and bacteria. Airborne pathogen transmission via droplets is an important mode for infectious diseases. In this paper, the spreading mechanism of infectious diseases by airborne pathogen transmission between two humans is modeled with a molecular communication perspective. An end-to-end system model which considers the pathogen-laden cough/sneeze droplets as the input and the infection state of the human as the output is proposed. This model uses the gravity, initial velocity and buoyancy for the propagation of droplets and a receiver model which considers the central part of the human face as the reception interface is proposed. Furthermore, the probability of infection for an uninfected human is derived by modeling the number of propagating droplets as a random process. The numerical results reveal that exposure time affects the probability of infection. In addition, the social distance for a horizontal cough should be at least 1.7 m and the safe coughing angle of a coughing human to infect less people should be less than -25 degrees.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: 14Citation - Scopus: 15A Droplet-Based Signal Reconstruction Approach To Channel Modeling in Molecular Communication(Institute of Electrical and Electronics Engineers Inc., 2021) Güleç, Fatih; Atakan, BarışIn this paper, a novel droplet-based signal reconstruction (SR) approach to channel modeling, which considers liquid droplets as information carriers instead of molecules in the molecular communication (MC) channel, is proposed for practical sprayer-based macroscale MC systems. These practical MC systems are significant, since they can be used in order to investigate airborne pathogen transmission with biological sensors due to the similar mechanisms of sneezing/coughing and sprayer. Our proposed approach takes a two-phase flow which is generated by the interaction of droplets in liquid phase with air molecules in gas phase into account. Two-phase flow is combined with the SR of the receiver (RX) to propose a channel model. The SR part of the model quantifies how the accuracy of the sensed molecular signal in its reception volume depends on the sensitivity response of the RX and the adhesion/detachment process of droplets. The proposed channel model is validated by employing experimental data. IEEEArticle Citation - WoS: 5Citation - Scopus: 7Signal Reconstruction in Diffusion-Based Molecular Communication(Wiley, 2019) Atakan, Barış; Güleç, FatihMolecular communication (MC) is an important nanoscale communication paradigm, which is employed for the interconnection of the nanomachines (NMs) to form nanonetworks. A transmitter NM (TN) sends the information symbols by emitting molecules into the transmission medium and a receiver NM (RN) receives the information symbols by sensing the molecule concentration. In this paper, a model of how an RN measures and reconstructs the molecular signal is proposed. The signal around the RN is assumed to be a Gaussian random process instead of the less realistic deterministic approach. After the reconstructed signal is derived as a doubly stochastic poisson process, the distortion between the signal around the RN and the reconstructed signal is derived as a new performance parameter in MC systems. The derived distortion, which is a function of system parameters such as RN radius, sampling period, and the diffusion coefficient of the channel, is shown to be valid by employing random walk simulations. Then, it is shown that the original signal can be satisfactorily reconstructed with a sufficiently low level of distortion. Finally, optimum RN design parameters, namely, RN radius, sampling period, and sampling frequency, are derived by minimizing the signal distortion. The simulation results reveal that there is a trade-off among the RN design parameters which can be jointly set for a desired signal distortion.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.Conference Object Citation - WoS: 4Citation - Scopus: 3On Exploiting Spatial Correlation for Energy Harvesting Wireless Sensor Networks(Institute of Electrical and Electronics Engineers Inc., 2017) Al-Qamaji, Ali; Atakan, BarışWireless Sensor Network (WSN) is a set of inexpensive densely deployed sensor nodes with limited functionalities and scarcity in energies. The observations of sensors are forwarded directly to the Base Station (BS). In densely deployed sensors, sensing data are likely to be highly correlated in space domain, which produces unfavorable redundant readings and wasting in energy. In this paper, we propose an Event Distortion-Based Node Selection (EDNS) algorithm which exploits spatial correlation for reducing inessential sensor nodes that have correlated readings for improving Energy-Efficiency (EE) with acceptable distortion level. Furthermore, we derive a theoretical framework of distortion function for single-hop communication model to observe the advantages from energy harvesting to the accuracy level. Furthermore, the trade-off between energy consumption and distortion level is investigated.