Channel Model of Molecular Communication via Diffusion in a Vessel-like Environment Considering a Partially Covering Receiver

By considering potential health problems that a fully covering receiver may cause in vessel-like environments, the implementation of a partially covering receiver is needed. To this end, distribution of hitting location of messenger molecules (MM) is analyzed within the context of molecular communication via diffusion with the aim of channel modeling. The distribution of these MMs for a fully covering receiver is analyzed in two parts: angular and radial dimensions. For the angular distribution analysis, the receiver is divided into 180 slices to analyze the mean, standard deviation, and coefficient of variation of these slices. For the axial distance distribution analysis, Kolmogorov- Smirnov test is applied for different significance levels. Also, two different implementations of the reflection from the vessel surface (i.e., rollback and elastic reflection) are compared and mathematical representation of elastic reflection is given. The results show that MMs have tendency to spread uniformly beyond a certain ratio of the distance to the vessel radius. By utilizing the uniformity, we propose a channel model for the partially covering receiver in vessel-like environments and validate the proposed model by simulations

Diffusive molecular communication in a biological spherical environment with partially absorbing boundary

Diffusive molecular communication (DMC) is envisioned as a promising approach to help realize healthcare applications within bounded biological environments. In this paper, a DMC system within a biological spherical environment (BSE) is considered, inspired by bounded biological sphere-like structures throughout the body. As a biological environment, it is assumed that the inner surface of the sphere’s boundary is fully covered by biological receptors that may irreversibly react with hitting molecules. Moreover, information molecules diffusing in the sphere may undergo a degradation reaction and be transformed to another molecule type. Concentration Green’s function (CGF) of diffusion inside this environment is analytically obtained in terms of a convergent infinite series. By employing the obtained CGF, the information channel between transmitter and transparent receiver of DMC in this environment is characterized. Interestingly, it is revealed that the information channel is reciprocal, i.e., interchanging the position of receiver and transmitter does not change the information channel. Results indicate that the conventional simplifying assumption that the environment is unbounded may lead to an inaccurate characterization in such biological environments

Influence of Red Blood Cells on Channel Characterization in Cylindrical Vasculature

Molecular communication via diffusion (MCvD) expects Brownian motions of the information molecules to transmit information. However, the signal propagation largely depends on the geometric characteristics of the assumed flow model, i.e., the characteristics of the environment, design, and position of the transmitter and receiver, respectively. These characteristics are assumed to be lucid in many ways by either consideration of one-dimensional diffusion, unbounded environment, or constant drift. In reality, diffusion often occurs in blood-vessel-like channels. To this aim, we try to study the effect of the biological environment on channel performance. The Red-Blood Cells (RBCs) found in blood vessels enforces a higher concentration of molecules towards the vessel walls, leading to better reception. Therefore, in this paper we derive an analytical expression of Channel Impulse Response (CIR) for a dispersion-advection-based regime, contemplating the influence of RBCs in the model and considering a point source transmitter and a realistic design of the receiver

Cooperative molecular communication in drift-induced diffusive cylindrical channel

A cooperative molecular communication (CMC) system is considered inside a cylindrical-shaped channel where a few cooperative nodes (CNs) are intermediately placed between a transmitter (TX) and a fusion center (FC). The expressions for the maximum achievable rate and probability of error at the FC considering AND and OR rules are derived. Furthermore, the performance of the CMC system in a cylindrical channel is compared with the direct and CN-assisted systems. The CMC system with randomly-placed CNs is also analyzed and compared with the uniformly-placed CNs, and it is found that a lower probability of error is obtained in the case of uniform placement of CNs. Furthermore, the system performance as a function of radial displacement of TX and FC under constant flow is compared with that under laminar flow and a higher probability of error is observed under laminar flow. The increased probability of error under laminar flow occurs due to the fact that the drift velocity decreases towards the walls of the cylindrical channel. The analytical expressions are verified using Monte-Carlo simulations

Channel model of molecular communication via diffusion in a vessel-like environment Considering a Partially Covering Receiver

By considering potential health problems that a fully covering receiver may cause in vessel-like environments, the implementation of a partially covering receiver is needed. To this end, distribution of hitting location of messenger molecules (MM) is analyzed within the context of molecular communication via diffusion with the aim of channel modeling. The distribution of these MMs for a fully covering receiver is analyzed in two parts: angular and radial dimensions. For the angular distribution analysis, the receiver is divided into 180 slices to analyze the mean, standard deviation, and coefficient of variation of these slices. For the axial distance distribution analysis, Kolmogorov-Smirnov test is applied for different significance levels. Also, two different implementations of the reflection from the vessel surface (i.e., rollback and elastic reflection) are compared and mathematical representation of elastic reflection is given. The results show that MMs have tendency to spread uniformly beyond a certain ratio of the distance to the vessel radius. By utilizing the uniformity, we propose a channel model for the partially covering receiver in vessel-like environments and validate the proposed model by simulations.Postprint (published version

Channel model of molecular communication via diffusion in a vessel-like environment Considering a Partially Covering Receiver

By considering potential health problems that a fully covering receiver may cause in vessel-like environments, the implementation of a partially covering receiver is needed. To this end, distribution of hitting location of messenger molecules (MM) is analyzed within the context of molecular communication via diffusion with the aim of channel modeling. The distribution of these MMs for a fully covering receiver is analyzed in two parts: angular and radial dimensions. For the angular distribution analysis, the receiver is divided into 180 slices to analyze the mean, standard deviation, and coefficient of variation of these slices. For the axial distance distribution analysis, Kolmogorov-Smirnov test is applied for different significance levels. Also, two different implementations of the reflection from the vessel surface (i.e., rollback and elastic reflection) are compared and mathematical representation of elastic reflection is given. The results show that MMs have tendency to spread uniformly beyond a certain ratio of the distance to the vessel radius. By utilizing the uniformity, we propose a channel model for the partially covering receiver in vessel-like environments and validate the proposed model by simulations

Application of the Machine Learning Tools in the Integrity Management of Pipelines Containing Dent-gouges and Corrosions

Dent-gouges and corrosions are two of the well-known failure mechanisms that threaten the structural integrity management of oil and gas pipelines. Dent-gouges or corrosions markedly reduce the burst capacity of pipelines as a result of localized wall thickness reduction. Fitness-for-service (FFS) assessment is commonly employed to maintain the integrity of in-service pipelines containing flaws and the burst capacity evaluation is central to the FFS assessment. As the predictive accuracy of existing FFS models is generally very poor, the use of machine learning (ML) tools provides a viable option to develop burst capacity models with high accuracy. The main objective of the present thesis is to facilitate the FFS assessment of dent-gouges and corrosions based on ML tools. The first study proposes an improved burst capacity model for pipelines containing dent-gouges based on European Pipeline Research Group (EPRG) burst capacity model using full-scale burst tests by adding a correction term. The Gaussian process regression (GPR) is employed to quantify the correction term, which is a function of six non-dimensional random variables incorporating the effect of pipe and geometric properties, sizes of dent-gouges, and internal pressure loading condition. The accuracy of the improved EPRG model, i.e. EPRG-C model, is validated based on the comparison between the test and predicted burst capacities corresponding to the test data, and shown to be markedly greater than that of the EPRG model, suggesting the high effectiveness of the correction term. The second study presents a limit state-based assessment (LSBA) framework for pipelines containing dent-gouges to achieve reliability consistent outcomes. The LSBA is formulated based on the EPRG-C model proposed in the first study by assigning appropriate partial safety factors to key variables as well as the internal pressure. The calibration of partial safety factors is carried out by making the outcomes of LSBA are consistent with those of the reliability-based assessment given different pre-selected allowable failure probabilities. The failure probabilities corresponding to extensive assessment cases covering wide ranges of pipe geometric and material properties, sizes of dent-gouges and the model error are evaluated using the first-order reliability method. The validity of the calibrated partial safety factors is demonstrated using independent assessment cases and two illustrative examples. The advantages of LSBA over the deterministic assessment procedure in terms of achieving reliability-consistent assessment outcomes is further demonstrated. The third study employs a deep learning algorithm tabular generative adversarial network (TGAN) to generate synthetic burst tests by capturing the joint probability distribution based on real full-scale burst test data of corroded pipelines. Two other ML tools, random forest (RF) and extra tree (ET), are used to tune the hyper-parameters and validate the credibility of TGAN-generated data. A simple criterion is proposed to eliminate the outliers contained in the synthetic data. The results indicate that the synthetic burst test data match well with the real data, suggesting that TGAN can accurately capture the joint probability distribution of real test data and generate credible synthetic data. The fourth study develops new ML-based burst capacity models for dent-gouges with combined real and synthetic full-scale burst tests. The synthetic burst test data are generated using TGAN framework, which is proposed in the third study. The results of which are used as the basis combined with the real burst tests to develop ML burst capacity models based on three ML tools, i.e. RF, ET and GPR. The proposed models are shown to be more accurate than the models developed using real test data only. The analysis result further indicates that trained models are markedly more accurate than the semi-empirical EPRG model widely employed in the pipeline industry