Delaunay Triangulation Research Articles

Estimation of the cavity volume in the gasification zone for underground coal gasification under different oxygen flow conditions

In this study, the impact of oxygen flow rate on the cavity of the gasification zone was analyzed through two experiments using a coaxial hole model. The oxygen flow rate was controlled between 3 L/min and 5 L/min for experiment one and between 7.5 L/min and 15 L/min for experiment two. For the estimation of the cavity volume of the gasification zone in UCG, the stoichiometric method, the Delaunay triangulation approach, and the voxelization method were used. The actual gasification zone cavity volume change was obtained by dividing the coal consumption monitored by electronic mass balance by the apparent density of the coal. After analyzing and comparing the cavity volumes of the gasification zone obtained by the three estimation methods with the actual cavity volume of the gasification zone, the results showed that the stoichiometric method had an estimation error of less than 6.5 %. The Delaunay triangulation approach could reduce the estimation error for the convex cavity volume of the gasification zone to within 10 %. The voxelization method could reduce the estimation error for the cavity volume of the gasification zone to below 8 % and accurately describe the shape of the cavity.

A method for detecting tomato canopies’ phenotypic traits based on improved skeleton extraction algorithm

Automatic acquisition of tomato canopies’ phenotypic traits is essential for tomato varieties’ selection and scientific cultivation. Due to the infinite growth characteristics of tomato, its organ development is stochastic and the canopies' internal structure of is also complex. These make it challenging to obtain organs’ detailed phenotypic traits. Thus, this work proposed a method for detecting tomato canopies’ phenotypic traits based on improved skeleton extraction algorithm (ISEA). Firstly, after collecting tomato canopies' point cloud data from multiple perspectives, this work reconstructed its three-dimensional (3D) model accurately. Secondly, the least squares method was used to simplify the spatial contraction model of the Laplace Skeleton Extraction algorithm to obtain the tomatoes’ skeleton point set. On this basic, Greedy algorithm was used to optimise the Edge Collapse algorithm to extract more accurate and reliable skeleton structure. Then, in conjunction with the canopy growth characteristics of the crop and the Intrinsic Shape Signatures (ISS) principle, the simplified skeleton structure was subjected to local principal component analysis (PCA), which achieved the separation of tomatoes’ main stem and leaves. Finally, a modelling algorithm based on Delaunay triangulation was applied to construct the separated organs’ model to calculate phenotypic traits such as stem diameter, leaf area index and average leaf inclination. The calculated results were also compared with the measured values at different growth stages for performance evaluation. The average precision, average recall, average accuracy and micro F1 score of ISEA were 0.9144, 0.7751, 0.7243 and 0.8306, respectively. The overall R2 between calculated values and measured values for stem diameter, leaf area index and average leaf inclination were 0.9638, 0.9067, and 0.9428, and the root mean square errors (RMSE) were 0.3922, 0.0029, and 0.0186, respectively. As a result, the proposed method can extract a simple skeleton from the complex tomato canopy and calculate phenotypic traits accurately. It also provided solid technical support for studying the interaction about “genotype-phenotype-envirotype” in tomato.

A Hierarchical Cluster Tree Approach Leveraging Delaunay Triangulation

This research introduces a robust and reliable technique for structuring document image pages hierarchically, harnessing the power of Delaunay triangulation. Central to our approach is the formation of a cluster tree, which encapsulates the page's content through strategically exploiting layout elements arrangements and their relative distances. By applying our technique, we proficiently categorize the page into distinct clusters encompassing images, titles, and paragraphs. The consequent hierarchical framework, founded on the cluster tree, establishes a durable and trustworthy blueprint of the document layout, thereby accelerating document comprehension and examination.

The Multiscale Hybrid Method with a Localized Constraint. I. A Modified Control Volume Function for the Hybridized Mass and Momentum Equations

A new hybrid multiscaling model has been developed on the basis of the modified control volume function. Following the two-phase analogy of the same substance, the continuum and particle representations are coupled together in the framework of the mass and momentum conservation laws. The new functional form of the control volume function is elaborated by using the continuum discretization principle based on the Delaunay triangulation. The derived mass and momentum equations possess the invariant form for both micro-scale particle and large-scale continuum representations.

Numerical simulation of deformable droplets in three-dimensional, complex-shaped microchannels

The physics of drop motion in microchannels is fundamental to provide insights when designing applications of drop-based microfluidics. In this paper, we develop a boundary-integral method to simulate the motion of drops in microchannels of finite depth with flat walls and fixed depth but otherwise arbitrary geometries. To reduce computational time, we use a moving frame that follows the droplet throughout its motion. We provide a full description of the method, including our channel-meshing algorithm, which is a combination of Monte Carlo techniques and Delaunay triangulation, and compare our results to infinite-depth simulations. For regular geometries of uniform cross section, the infinite-depth limit is approached slowly with increasing depth, though we show much faster convergence by scaling with maximum vs average velocities. For non-regular channel geometries, features such as different branch heights can affect drop partitioning, breaking the symmetric behavior usually observed in regular geometries. Moreover, non-regular geometries also present challenges when comparing the results for deep and infinite-depth channels. To probe inertial effects on drop motion, the full Navier–Stokes equations are first solved for the entire channel, and the tabulated solution is then used as a boundary condition at the moving-frame surface for the Stokes flow inside the moving frame. For moderate Reynolds numbers up to Re = 5, inertial effects on the undisturbed flow are small even for more complex geometries, suggesting that inertial contributions in this range are likely small. This work provides an important tool for the design and analysis of three-dimensional droplet-based microfluidic devices.

A QoS-Aware Energy-Efficient Chimp Optimization Routing Protocol with Efficient Sensor Node Deployment Strategy in Underwater Acoustic Sensor Network

As an extension of wireless sensor networks in the underwater environment, Underwater Acoustic Sensor Networks (UASNs) have led to a broad consideration of academicians. One of the problems that lower UWSN effectiveness in terms of network lifespan is premature energy depletion. It might be caused by the network nodes using different amounts of energy. In UASNs, the effectiveness and dependability of data transfer remain so adverse because of the intricate underwater environment in diverse ocean applications like surveilling atypical submarine oil pipelines. Inspired by the significance of UASNs’ quality of service in several implementations, this study proffers a metaheuristic optimization algorithm (AG) called Chimp Optimization-based Routing Protocol (CH-ORP) for UASNs obtaining intricate features of underwater (UW) medium into concern like 3D changing topology, high propagation delay, node mobility, and density, and, also, cluster head nodes’ rotation mechanism. Initially, the entire network (NW) paradigm has been considered as a three-dimensional cube out of a grid point of view, and the three-dimensional cube has been split into several little cubes by employing Delaunay Triangulation. The optimization has been carried out for lessening node failure rate and NW energy consumption rate by ideally placing the sensor nodes in UW acoustic communication. The NW topology’s steadiness has been assured by the AG, and this optimizes the node redeployment scheme by computing the fitness function for all nodes. The proffered AG’s simulation substantiations have been performed for exhibiting the CH-ORP’s efficiency that executes finer when compared to the advanced methodologies concerning energy efficiency, reliability, and end-to-end delay. It has been found that the proposed Ch-ORP achieves 698 packets received with 28% of energy consumption, 156-sec Network delay, 257 packet loss, 97.23% of PDR, and 1256 Mbps of Network throughput.

Invariant Feature Encoding for Contact Handprints Using Delaunay Triangulated Graph

Contact-based biometric applications primarily use prints from a finger or a palm for a single instance in different applications. For access control, there is an enrollment process using one or more templates which are compared with verification images. In forensics applications, randomly located, partial, and often degraded prints acquired from a crime scene are compared with the images captured from suspects or existing fingerprint databases, like AFIS. In both scenarios, if we need to use handprints which include segments from the finger and palm, what would be the solution? The motivation behind this is the concept of one single algorithm for one hand. Using an algorithm that can incorporate both prints in a common processing framework can be an alternative which will have advantages like scaling to larger existing databases. This work proposes a method that uses minutiae or minutiae-like features, Delaunay triangulation and graph matching with invariant feature representation to overcome the effects of rotation and scaling. Since palm prints have a large surface area with degradation, they tend to have many false minutiae compared to fingerprints, and the existing palm print algorithms fail to tackle this. The proposed algorithm constructs Delaunay triangulated graphs (DTG) using minutiae where Delaunay triangles form from minutiae, and initiate a collection of base triangles for opening the matching process. Several matches may be observed for a single triangle match when two images are compared. Therefore, the set of initially matched triangles may not be a true set of matched triangles. Each matched triangle is then used to extend as a sub-graph, adding more nodes to it until a maximum graph size is reached. When a significant region of the template image is matched with the test image, the highest possible order of this graph will be obtained. To prove the robustness of the algorithm to geometrical variations and working ability with extremely degraded (similar to latent prints) conditions, it is demonstrated with a subset of partial-quality and extremely-low-quality images from the FVC (fingerprint) and the THUPALMLAB (palm print) databases with and without geometrical variations. The algorithm is useful when partial matches between template and test are expected, and alignment or geometrical normalization is not accurately possible in pre-processing. It will also work for cross-comparisons between images that are not known a priori.

A Novel Analytic Method of Automatically Assessing Beamline Geometric Range Variations for CBCT-Guided Intensity-Modulated Proton Therapy (IMPT) for Localized Prostate Cancer

A novel image-processing analytics software was introduced to quantitatively assess the beamline geometric range variations for proton spots around the planning target in daily CBCT-guided prostate IMPT. Three hundred eighty-seven consecutive full-CBCT alignment images of 11 localized prostate cancer IMPT cases were retrospectively analyzed to quantify the range variations of proton beamlines using a home-grown software. Each patient treatment comprises two proton scanning beams. All cases had a history of a plan revision with a re-simulated CT set. With this novel method the patient body surfaces are reconstructed by generating triangle mesh surfaces using a python Delaunay tessellation method. A python ray casting algorithm is utilized to determine the surface offset of the aligned CBCT relative to the planning CT. The proton beamlines are extrapolated from all the beam spots within 10 mm-enlarged planning target volume. The maximal geometric range variation was used to score the selected percentile of the included beamlines of the beam. The accuracy of this analytic software was satisfactorily validated prior to its application to this retrospective investigation. In practice, the proton range uncertainties are routinely estimated from their geometric range variations measured on a setup CBCT. The computation or processing time is about 60 seconds for each CBCT - planning CT analysis on a prostate IMPT positioning. The geometric range variations in the 95th percentile beamlines of 774 fractional beams or 387 aligned CBCT images are 3.29 ± 1.43 mm. As tabulated in table-1, worsened beam range variations of setup CBCTs were reduced by 2.60 ± 0.30 mm in the succeeding 2 fractions post using re-simulated setups. In this serial, an averaged 1.41 ± 0.10 mm range variation reduction was seen by re-simulation throughout the course. The geometric range variations of beamlines in prostate IMPT were comprehensively analyzed, for the first time, using a novel analytic software based on aligned daily CBCTs. The results of this study suggest that the re-simulated setups can be an effective way to reduce beamline range variations for localized prostate IMPT. Incorporation of this process for other IMPT treatment sites, as well as a Gamma-indexed analysis will be added in our immediate future research.

Study on the Measurement Method of Wheat Volume Based on Binocular Structured Light

In this paper, we propose a grain volume measurement method based on binocular structured light to address the need for fast and high-precision grain volume measurement in grain stocks. Firstly, we utilize speckle structured light imaging to tackle the image matching problem caused by non-uniform illumination in the grain depot environment and the similar texture of the grain pile surface. Secondly, we employ a semi-global stereo matching algorithm with census transformation to obtain disparity maps in grain bins, which are then converted into depth maps using the triangulation principle. Subsequently, each pixel in the depth map is transformed from camera coordinates to world coordinates using the internal and external parameter information of the camera. This allows us to construct 3D cloud data of the grain pile, including the grain warehouse scene. Thirdly, the improved European clustering method is used to achieve the segmentation of the three-dimensional point cloud data of the grain pile and the scene of the grain depot, and the pass-through filtering method is used to eliminate some outliers and poor segmentation points generated by segmentation to obtain more accurate three-dimensional point cloud data of the grain pile. Finally, the improved Delaunay triangulation method was used to construct the optimal topology of the grain surface continuous triangular mesh, and the nodes of the grain surface triangular mesh were projected vertically to the bottom of the grain warehouse to form several irregular triangular prisms; then, the cut and complement method was used to convert these non-plane triangular prisms into regular triangular prisms that could directly calculate the volume. The measured volume of the pile is then obtained by calculating the volume of the triangular prism. The experimental results indicate that the measured volume has a relative error of less than 1.5% and an average relative error of less than 0.5%. By selecting an appropriate threshold, the relative standard deviation can be maintained within 0.6%. The test results obtained from the laboratory test platform meet the requirements for field inspection of the granary.

Topological packing statistics of living and nonliving matter.

Complex disordered matter is of central importance to a wide range of disciplines, from bacterial colonies and embryonic tissues in biology to foams and granular media in materials science to stellar configurations in astrophysics. Because of the vast differences in composition and scale, comparing structural features across such disparate systems remains challenging. Here, by using the statistical properties of Delaunay tessellations, we introduce a mathematical framework for measuring topological distances between general three-dimensional point clouds. The resulting system-agnostic metric reveals subtle structural differences between bacterial biofilms as well as between zebrafish brain regions, and it recovers temporal ordering of embryonic development. We apply the metric to construct a universal topological atlas encompassing bacterial biofilms, snowflake yeast, plant shoots, zebrafish brain matter, organoids, and embryonic tissues as well as foams, colloidal packings, glassy materials, and stellar configurations. Living systems localize within a bounded island-like region of the atlas, reflecting that biological growth mechanisms result in characteristic topological properties.

Improved Routing on the Delaunay Triangulation

Improved Routing on the Delaunay Triangulation

Geometric Analysis of Free and Accessible Volume in Atmospheric Nanoparticles.

Computer-generated atomistic microstructures of atmospheric nanoparticles are geometrically analyzed using Delaunay tessellation followed by Monte Carlo integration to compute their free and accessible volume. The nanoparticles studied consist of cis-pinonic acid (a biogenic organic aerosol component), inorganic ions (sulfate and ammonium), and water. Results are presented for the free or unoccupied volume in different domains of the nanoparticles and its dependence on relative humidity and organic content. We also compute the accessible volume to small penetrants such as water molecules. Most of the free volume or volume accessible to a penetrant as large as a water molecule is located in the domains occupied by organics. In contrast, regions dominated by inorganics do not have any cavities with sizes larger than 1 Å. Solid inorganic domains inside the particle are practically impermeable to any small molecule, thereby offering practically infinite resistance to diffusion. A guest molecule can find diffusive channels to wander around within the nanoparticle only through the aqueous and organic-rich domains. The largest pores are observed in nanoparticles with high levels of organic mass and low relative humidity. At high relative humidity, the presence of more water molecules reduces the empty space in the inner domains of the nanoparticle, since areas rich in organic molecules (which are the only ones where appreciable pores are found) are pushed to the outer area of the particle. This, however, should not be expected to affect the diffusive process as transport through the aqueous phase inside the particle will be, by default, fast due to its fluid-like nature.

Bedrock mapping based on terrain weighted directed graph convolutional network using stream sediment geochemical samplings

To explore an efficient strategy for intelligent bedrock mapping that can be applied in the areas with coexisting Quaternary coverages and bedrock outcrops, a graph convolutional network (GCN) was implemented for bedrock classification using stream sediment geochemical samplings in the Chahanwusu River area, Qinghai Province, China. The sampling points were organized into a terrain weighted directed graph (TWDG) using Delaunay triangulation to capture the upstream-downstream relationships among the geochemical sampling points. The experimental results indicate that the semi-supervised GCN models, only using 20% of the labeled sampling points, achieved accuracies of 68.20% and 78.31% in ten-type and five-type bedrock discrimination, respectively. In conclusion, it is feasible to map the bedrock type through the concentrations of elements on the stream sediment geochemical sampling points. The proposed data-driven GCN bedrock classification method not only improves the efficiency of bedrock mapping but also may be applied in a large area.

Nondestructive detection of key phenotypes for the canopy of the watermelon plug seedlings based on deep learning

Nondestructive measurement technology of phenotype can provide substantial phenotypic data support for applications such as seedling breeding, management, and quality testing. The current method of measuring seedling phenotypes mainly relies on manual measurement which is inefficient, subjective and destroys samples. Therefore, the paper proposes a nondestructive measurement method for the canopy phenotype of the watermelon plug seedlings based on deep learning. The Azure Kinect was used to shoot canopy color images, depth images, and RGB-D images of the watermelon plug seedlings. The Mask-RCNN network was used to classify, segment, and count the canopy leaves of the watermelon plug seedlings. To reduce the error of leaf area measurement caused by mutual occlusion of leaves, the leaves were repaired by CycleGAN, and the depth images were restored by image processing. Then, the Delaunay triangulation was adopted to measure the leaf area in the leaf point cloud. The YOLOX target detection network was used to identify the growing point position of each seedling on the plug tray. Then the depth differences between the growing point and the upper surface of the plug tray were calculated to obtain plant height. The experiment results show that the nondestructive measurement algorithm proposed in this paper achieves good measurement performance for the watermelon plug seedlings from the 1 true-leaf to 3 true-leaf stages. The average relative error of measurement is 2.33% for the number of true leaves, 4.59% for the number of cotyledons, 8.37% for the leaf area, and 3.27% for the plant height. The experiment results demonstrate that the proposed algorithm in this paper provides an effective solution for the nondestructive measurement of the canopy phenotype of the plug seedlings.

Random pore-network development methodology based on Voronoi and Delaunay tessellations for residual coal under axial stress

Coal spontaneous combustion (CSC) in the gob areas is considered to be an important safety issue in the coal industries. Numerical simulation is a reasonable tool for predicting and preventing this problem. The commonly applied finite-volume and finite-element techniques are computationally expensive when they must account for pore-scale phenomena. A pore-network model (PNM) has the advantage of lower computational cost while also simulating heat and mass transfers in porous media at scale with reasonable accuracy. This study provides a method for extending pore networks to packed coal particles with a wide spectrum of size distributions, and to generate pore networks of arbitrary size within a range. This method was developed based on the Voronoi and Delaunay tessellations theories. Statistical data on the size distribution, porosity, and permeability of packed coal particles were collected experimentally. This method requires a relatively low network generation cost for the preservation of the voids that are formed among larger particles and characterize the effect of fine particles filling in throat channels by reference to a conductivity adjustment factor ηi, which depends on axial stress. The average relative error of predicted and experimental permeability does not exceed 5% when this new pore-network construction procedure is used. The predicted result holds until the volume upscaling ratio reaches 58.

Bi-sided facility location problems: an efficient algorithm for k-centre, k-median, and travelling salesman problems

This study introduces a general framework, called Bi-sided facility location, for a wide range of problems in the area of combined facility location and routing problems such as locating test centres and designing the network of supermarkets. It is based on a multi-objective optimisation model to enhance the service quality which the clients received, called client-side, and enhance the interconnection quality and eligibility among the centres, called center-side. Well-known problems such as k-median and k-centre for the client-side and the travelling salesman problem for the centre-side are taken into account in this paper. After discussing the complexity of this kind of combination, we propose a heuristic approximation algorithm to find approximation Pareto-optimal solutions for the problem. The algorithm is an efficient local search utilising geometric objects such as the Voronoi diagram and Delaunay triangulation as well as algorithms for computing approximation travelling salesman tour. In addition to the comprehensive theoretical analysis of the proposed models and algorithm, we apply the algorithm to different instances and benchmarks, and compare it with NSGA-II based on set coverage and spacing metrics. The results confirm the efficiency of the algorithm in terms of running time and providing a diverse set of efficient trade-off solutions. Highlights Introducing a general bi-side location model considering centres and clients’ utilities Discussing and proving the NP-hardness of the model in the general framework Considering two instances; k-centre and k-median for client-side and TSP for centre-side Proposing an efficient geometric-based algorithm for solving the problems Implementing, testing, and comparing the proposed algorithm on several benchmarks

Stability and machine learning applications of persistent homology using the Delaunay-Rips complex

Persistent homology (PH) is a robust method to compute multi-dimensional geometric and topological features of a dataset. Because these features are often stable under certain perturbations of the underlying data, are often discriminating, and can be used for visualization of structure in high-dimensional data and in statistical and machine learning modeling, PH has attracted the interest of researchers across scientific disciplines and in many industry applications. However, computational costs may present challenges to effectively using PH in certain data contexts, and theoretical stability results may not hold in practice. In this paper, we define, implement, and investigate a simplicial complex construction for computing persistent homology of Euclidean point cloud data, which we call the Delaunay-Rips complex (DR). By only considering simplices that appear in the Delaunay triangulation of the point cloud and assigning the Vietoris-Rips weights to simplices, DR avoids potentially costly computations in the persistence calculations. We document and compare a Python implementation of DR with other simplicial complex constructions for generating persistence diagrams. By imposing sufficient conditions on point cloud data, we are able to theoretically justify the stability of the persistence diagrams produced using DR. When the Delaunay triangulation of the point cloud changes under perturbations of the points, we prove that DR-produced persistence diagrams exhibit instability. Since we cannot guarantee that real-world data will satisfy our stability conditions, we demonstrate the practical robustness of DR for persistent homology in comparison with other simplicial complexes in machine learning applications. We find in our experiments that using DR in an ML-TDA pipeline performs comparatively well as using other simplicial complex constructions.

Generating a path-search graph based on ship-trajectory data: Route search via dynamic programming for autonomous ships

Voyage optimization is critical for voyage planning and an important approach for realizing the autonomous and green operation modes for ships. In recent years, voyage optimization (also referred to as weather routing) has been extensively investigated and commercial solutions have also been launched. It is used to find the shortest path (optimum path) for voyage planning, which can help reduce the operating costs of the ship. However, accurately estimating fuel costs and validating routes within an acceptable computational time remain challenging. In this study, we devised methods to generate a path-search graph based on ship-trajectory data and determined an optimal route based on dynamic programming. The trajectory data of the vessels were extracted from their automatic identification systems, and the errors contained in the data were discarded via preprocessing. The trajectory data were subsequently simplified using the Douglas–Peucker method, following which a path-search graph was created using Delaunay triangulation and the number of vertices were reduced using the quadtree method. The search graph was validated using a high-resolution bathymetric chart, and dynamic programming was adopted to determine the optimal route for minimizing fuel costs. Changes in the operational performance caused by meteorological conditions were calculated, and the fuel costs were estimated using the wind resistance coefficient and SHOPERA-NTUA-NTU-MARIC method. The effect on power was determined by the effect of current on the vessel speed; this methodology was simulated and tested on oil tankers, container ships, and car and gas carriers. The fuel saved for each vessel compared to that used for the shortest path was estimated when sailing the transpacific and transatlantic routes; a maximum fuel reduction of approximately 7.8% was estimated. The proposed methods can be applied to various ship types and maritime zones to reduce the computation time of path-search graph generation while considering the high-resolution map and accurately estimating the fuel-oil consumption.

A dynamic large-deformation particle finite element method for geotechnical applications based on Abaqus

In this paper, the application of Abaqus-based particle finite element method (PFEM) is extended from static to dynamic large deformation. The PFEM is based on periodic mesh regeneration with Delaunay triangulation to avoid mesh distortion. Additional mesh smoothing and boundary node smoothing techniques are incorporated to improve the mesh quality and solution accuracy. The field variables are mapped from the old to the new mesh using the closest point projection method to minimize the mapping error. The procedures of the proposed Abaqus-based dynamic PFEM (Abaqus-DPFEM) analysis and its implementation in Abaqus are detailed. The accuracy and robustness of the proposed approach are examined via four illustrative numerical examples. The numerical results show a satisfactory agreement with published results and further confirm the applicability of the Abaqus-DPFEM to solving dynamic large-deformation problems in geotechnical engineering.

Ruppert's delaunay triangulation refinement scheme for optimal RSUs placement in vehicle to infrastructure communication network

Road Side Units (RSUs) are the essential component of vehicular communication for the objective of improving safety and mobility in the road transportation. RSUs are generally deployed at the roadside and more specifically at the intersections in order to collect traffic information from the vehicles and disseminate alarms and messages in emergency situations to the neighborhood vehicles cooperating with the network. However, the development of a predominant RSUs placement algorithm for ensuring competent communication in VANETs is a challenging issue due to the hindrance of obstacles like water bodies, trees and buildings. In this paper, Ruppert's Delaunay Triangulation Refinement Scheme (RDTRS) for optimal RSUs placement is proposed for accurately estimating the optimal number of RSUs that has the possibility of enhancing the area of coverage during data communication. This RDTRS is proposed by considering the maximum number of factors such as global coverage, intersection popularity, vehicle density and obstacles present in the map for optimal RSUs placement, which is considered as the core improvement over the existing RSUs optimal placement strategies. It is contributed for deploying requisite RSUs with essential transmission range for maximal coverage in the convex map such that each position of the map could be effectively covered by at least one RSU in the presence of obstacles. The simulation experiments of the proposed RDTRS are conducted with complex road traffic environments. The results of this proposed RDTRS confirmed its predominance in reducing the end-to-end delay by 21.32%, packet loss by 9.38% with improved packet delivery rate of 10.68%, compared to the benchmarked schemes.