Electrical - Electronic Engineering / Elektrik - Elektronik Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/11
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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: 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.