3D Urban Environment Research Articles

Line-of-Sight Probability Models for 5G/6G Millimeter-Wave Networks: A Review

In the fifth-generation (5G) and future wireless networks, coverage planning for millimeter-wave (mm-wave) networks in a three-dimensional (3D) urban environment depends on the communication channel and line-of-sight (LoS) probability models. This is especially true when there are big stationary obstructions. The current literature lacks a complete review of the existing LoS probability models. To bridge this gap, this paper reviews modeling approaches and mathematical models of the LoS probability. We discovered that, due to the complexity of modeling a large number of 3D layouts, most existing models have focused on a few urban layouts. We developed a 3D synthetic urban layout generator that is based on game engine technology to simulate any ITU-R P.1410 urban layouts and collect the ray-tracing. We provided a proof-of-concept to show that the game technology could handle the complexity of simulating ray-tracing in 3D urban environments. Moreover, we addressed some of the biggest challenges and gaps in the current LoS probability literature and suggested some valid opportunities.

Identification of Typhoon-Vulnerable Areas and Countermeasures in High-Density Coastal Cities: The Case of Macau

Typhoons are extremely severe weather events which seriously threaten the safety of people’s lives and properties. Therefore, identifying and controlling typhoon disaster hazards have become important research topics. The spatial–temporal characteristics of typhoons are analysed using the typhoon disaster data in Macau from 2000 to 2020. Computational fluid dynamics (CFD) numerical simulation is adopted to understand the 3D urban wind environment. Moreover, the ‘exposure, sensitivity and adaptation’ evaluation model is applied to construct the study framework. To calculate urban disaster vulnerability, the Create Fishnet tool is used to divide the city of Macau into 470 grids. The principal component analysis method is used to reveal the factors that significantly affect the typhoon’s vulnerable areas. Result shows that 31.27% of grids are severely vulnerable. In addition, six principal components are identified, including indicators such as population density, building area ratio, mean elevation and wind speed. This study verifies the feasibility of wind speed data obtained by CFD in the typhoon evaluation model. Moreover, it provides a reliable reference guide for future urban microlevel studies.

Multi-IRS-Assisted mmWave UAV-BS Network for Coverage Extension.

In the era of Industry 5.0, advanced technologies like artificial intelligence (AI), robotics, big data, and the Internet of Things (IoT) offer promising avenues for economic growth and solutions to societal challenges. Digital twin technology is important for real-time three-dimensional space reproduction in this transition, and unmanned aerial vehicles (UAVs) can support it. While recent studies have explored the potential applications of UAVs in nonterrestrial networks (NTNs), bandwidth limitations have restricted their utility. This paper addresses these constraints by integrating millimeter wave (mmWave) technology into UAV networks for high-definition video transmission. Specifically, we focus on coordinating intelligent reflective surfaces (IRSs) and UAV networks to extend coverage while maintaining virtual line-of-sight (LoS) conditions essential for mmWave communication. We present a novel approach for integrating IRS into Beyond 5G/6G networks to enhance high-speed communication coverage. Our proposed IRS selection method ensures optimal communication paths between UAVs and user equipment (UE). We perform numerical analysis in a realistically modeled 3D urban environment to validate our approach. Our results demonstrate significant improvements in the received SNR for multiple UEs upon the introduction of IRSs, and they confirm the feasibility of coverage extension in mmWave UAV networks.

Deep learning based prediction of urban air mobility noise propagation in urban environment.

A deep learning based method is proposed to predict the urban air mobility (UAM) noise propagation in the urban environment. This method aims to efficiently estimate the noise impact of UAM flights on the complex urban area. The noise hemisphere was created via the comprehensive multirotor noise assessment framework to determine the noise level of UAM. The noise propagation to a randomly generated three-dimensional (3D) urban area was then calculated using the ray tracing method, including atmospheric attenuation and multiple reflections. 45 000 two-dimensional noise maps were used to train and evaluate the modified convolutional neural network. The results demonstrated high accuracy, with a root mean square error of only 2.56 dB compared to the ray tracing method, while reducing computation time by more than 1800 times. This model was applied to analyze the noise impact of various UAM flight conditions and landing scenarios at a vertiport. This deep learning approach is a fast method with adequate accuracy for predicting UAM noise impact in 3D urban environments. Also, it can inform the development of noise based strategies for UAM operations.

Robust Multiple Unmanned Aerial Vehicle Network Design in a Dense Obstacle Environment

Highly robust networks can resist attacks, as when some UAVs fail, the remaining UAVs can still transmit data to each other. In order to improve the robustness of a multi-UAV network, most methods construct the network by adjusting the positions of the UAVs and adding a large number of links. However, having a large number of links greatly consumes communication resources and increases serious signal interference. Therefore, it is necessary to study a method that can improve robustness and reduce the number of links. In this paper, we propose a method that consists of combining formation control and link selection, which can work in a distributed manner. For formation control, our method keeps the UAVs compact in the obstacle environment through an improved artificial potential field. The compact formation enables UAVs to have a large number of neighbors. For link selection, reinforcement learning is used to improve the robustness of the network and reduce the number of network edges. In the simulation of the 3D urban environment, three failure modes are used to verify the robustness of the network. The experimental results show that even if the number of links is reduced by 20%, the networks designed by our method still have strong robustness.

A Model for Urban Environment Instance Segmentation with Data Fusion.

Fine-grained urban environment instance segmentation is a fundamental and important task in the field of environment perception for autonomous vehicles. To address this goal, a model was designed with LiDAR pointcloud data and camera image data as the subject of study, and the reliability of the model was enhanced using dual fusion at the data level and feature level. By introducing the Markov Random Field algorithm, the Support Vector Machine classification results were optimized according to the spatial contextual linkage while providing the model with the prerequisite of the differentiation of similar but foreign objects, and the object classification and instance segmentation of 3D urban environments were completed by combining the Mean Shift. The dual fusion approach in this paper is a method for the deeper fusion of data from different sources, and the model, designed more accurately, describes the categories of items in the environment with a classification accuracy of 99.3%, and segments the different individuals into groups of the same kind of objects without instance labels. Moreover, our model does not have high computational resource and time cost requirements, and is a lightweight, efficient, and accurate instance segmentation model.

UAV Communication Recovery under Meteorological Conditions

Our study proposes a UAV communications recovery strategy under meteorological conditions based on a ray tracing simulation of excessive path loss in four distinct three-dimensional (3D) urban environments. We start by reviewing the air-to-ground propagation loss model under meteorological conditions, as well as the specific attenuation of rain, fog, and snow, and we propose a new expression for line-of-sight (LoS) probability. Using the two frequency bands of 28 GHz and 71 GHz, we investigate the impact of specific attenuation caused by different weather conditions and analyze the relationship between the radius of the UAV coverage area and the elevation angle. Furthermore, we investigate the effects of the rainfall rate, liquid water density, and snowfall rate on the maximum coverage area and optimal height of the UAV. Eventually, we propose a strategy that involves compensating for the maximum path loss and adjusting the UAV’s position to recover the coverage of the UAV to ground users. Our results show that rain has the greatest impact on the UAV’s coverage area and optimum height among the three types of weather conditions. For various weather conditions, relative to Region 1, the percentage reduction in the maximum coverage radius of Region 2 to Region 4 increases gradually, and the extent of each increase is approximately 10%. Moreover, after adding the compensated path loss, the coverage radius of the UAV in the four regions is restored to a value slightly larger than that before the rain. In addition, rain caused the greatest reduction in UAV coverage for suburban environments and the lowest for high-rise urban environments.

Leveraging LiDAR for smart cities climate change resilient: A solar potential case study in a developing area

Smart cities and solar energy are crucial components in climate change adaptation and sustainability. The interrelation with United Nations Sustainable Development Goals (SDGs) based on the 7th SDG goal, Affordable and Clean Energy for solar energy, and the 11th SDG goal, Sustainable Cities and Communities for smart cities, catalyze the research in this field. Unfortunately, there are some gaps in integrating smart cities and solar energy, especially in the elevation model and solar potential accuracy factors. It is essential because the highly accurate integrated model is enormously beneficial, especially for stakeholders. Therefore, this study is leveraging the Light Detection and Ranging (LiDAR) technology to provide high-resolution datasets for elevation models and create a precise 3D urban environment model for resilient integrated smart cities climate change. Digital terrain, including Digital Terrain Model and Digital Surface Model, were developed based on precise LiDAR point clouds before two vital surfaces, building rooftops and land parcels, were treated as a fundamental surface for solar potential calculation. The solar potential Return of Investment rate was classified and integrated with the 3D urban environment model. This 3D urban environment model helps visualize and provides a more specific and coherent picture of reality. The potential application of LiDAR data in climate change adaptation and sustainability that can be linked using various technologies and scales is also demonstrated in this study.

Fast UAV path planning in urban environments based on three-step experience buffer sampling DDPG

The path planning of Unmanned Aerial Vehicle (UAV) is a critical issue in emergency communication and rescue operations, especially in adversarial urban environments. Due to the continuity of the flying space, complex building obstacles, and the aircraft's high dynamics, traditional algorithms cannot find the optimal collision-free flying path between the UAV station and the destination. Accordingly, in this paper, we study the fast UAV path planning problem in a 3D urban environment from a source point to a target point and propose a Three-Step Experience Buffer Deep Deterministic Policy Gradient (TSEB-DDPG) algorithm. We first build the 3D model of a complex urban environment with buildings and project the 3D building surface into many 2D geometric shapes. After transformation, we propose the Hierarchical Learning Particle Swarm Optimization (HL-PSO) to obtain the empirical path. Then, to ensure the accuracy of the obtained paths, the empirical path, the collision information and fast transition information are stored in the three experience buffers of the TSEB-DDPG algorithm as dynamic guidance information. The sampling ratio of each buffer is dynamically adapted to the training stages. Moreover, we designed a reward mechanism to improve the convergence speed of the DDPG algorithm for UAV path planning. The proposed TSEB-DDPG algorithm has also been compared to three widely used competitors experimentally, and the results show that the TSEB-DDPG algorithm can archive the fastest convergence speed and the highest accuracy. We also conduct experiments in real scenarios and compare the real path planning obtained by the HL-PSO algorithm, DDPG algorithm, and TSEB-DDPG algorithm. The results show that the TSEB-DDPG algorithm can archive almost the best in terms of accuracy, the average time of actual path planning, and the success rate.

Automatic 3D Building Reconstruction from OpenStreetMap and LiDAR Using Convolutional Neural Networks

This paper presents the implementation of an automatic method for the reconstruction of 3D building maps. The core innovation of the proposed method is the supplementation of OpenStreetMap data with LiDAR data to reconstruct 3D urban environments automatically. The only input of the method is the area that needs to be reconstructed, defined by the enclosing points in terms of the latitude and longitude. First, area data are requested in OpenStreetMap format. However, there are certain buildings and geometries that are not fully received in OpenStreetMap files, such as information on roof types or the heights of buildings. To complete the information that is missing in the OpenStreetMap data, LiDAR data are read directly and analyzed using a convolutional neural network. The proposed approach shows that a model can be obtained with only a few samples of roof images from an urban area in Spain, and is capable of inferring roofs in other urban areas of Spain as well as other countries that were not used to train the model. The results allow us to identify a mean of 75.57% for height data and a mean of 38.81% for roof data. The finally inferred data are added to the 3D urban model, resulting in detailed and accurate 3D building maps. This work shows that the neural network is able to detect buildings that are not present in OpenStreetMap for which in LiDAR data are available. In future work, it would be interesting to compare the results of the proposed method with other approaches for generating 3D models from OSM and LiDAR data, such as point cloud segmentation or voxel-based approaches. Another area for future research could be the use of data augmentation techniques to increase the size and robustness of the training dataset.

RIS-Assisted UAV for Fresh Data Collection in 3D Urban Environments: A Deep Reinforcement Learning Approach

Dispatching flexible unmanned aerial vehicles (UAVs) to collect data from distributed Internet-of-Things devices (IoTDs) is expected to be a promising technology to support time-critical applications. However, in urban environments, the communication links between the UAV and IoTDs are prone to be frequently blocked by buildings, which severely impairs the freshness of information collected by the UAV. Thus, how to overcome urban building blockages and ensure fresh data collection is quite important but neglected in existing works. In this paper, for keeping the information fresh, we propose to utilize the reconfigurable intelligent surface (RIS) to assist the UAV in mitigating signal propagation impairments caused by building blockages. The formulated optimization problem is minimizing the age of information (AoI) of all IoTDs by jointly optimizing the UAV trajectory, IoTD scheduling and discrete phase shifts of the RIS. It is a mixed-integer non-convex problem as well as lacking the complete channel state information (CSI), thus using conventional optimization methods is intractable. To address this issue, we present an effective and robust deep reinforcement learning (DRL)-based scheme called “SAC-AO-RIS,” where a soft actor-critic (SAC) algorithm with recent-prioritized experience replay is designed for learning highly stable policies of UAV trajectory and IoTD scheduling, and an alternating optimization (AO) algorithm is leveraged for solving RIS phase shifts. Finally, simulation results demonstrate the effectiveness and superiority of our proposed scheme compared with other baseline approaches.

3D urban transport environment

The paper investigates issues related to the solution of problems in the development of public transport infrastructure in urban agglomerations. The opportunities of further development of the existing typical city public transport infrastructure have been studied. The conclusion about the possibility of reforming the existing typical urban public transport infrastructure is drawn. It is proposed to use the available airspace together with the ground and underground public transport as a direction for the development of urban public transport infrastructure. The formation of an urban transport 3D environment is studied. It is shown that the joint application of all three urban transport environments will significantly expand the potential of urban public transport infrastructure. The possibility of using ultralight aircraft as an air vehicle is considered. It is proposed to use new types of aircraft as air vehicles. The prototype of an aircraft based on cylindrical vane propellers under the code name “Cyclolet” is described. Its flight technical characteristics are given. The conclusion about the possibility of using an aircraft of the “Cyclolet” type on the basis of cylindrical vane propellers as an air vehicle in the organization of permanent public transportation in urban agglomerations in the Russian Federation and abroad is drawn.

SMART: Vision-Based Method of Cooperative Surveillance and Tracking by Multiple UAVs in the Urban Environment

UAV surveillance and tracking have attracted great enthusiasm in intelligent transportation, and various approaches have been reported up to now. However, these approaches often ignored the uncertainties in the urban environment, such as occlusion, view change, and background clutter. Ignoring these uncertain factors often leads to a reduction in surveillance performance and tracking quality. This study devotes to improving the cooperative surveillance capability of multi-UAV formation by designing different cooperative strategies in the urban environment. To be specific, a novel cooperative architecture is designed to control the observation locations of multiple UAVs throughout the formation process. For different types of interference, we introduce a novel target recognition rate of each UAV as the decision factor and design corresponding cooperative strategies to guarantee the accuracy of cooperative surveillance. Based on this architecture, we develop a vision-based method of cooperative surveillance and tracking by multiple UAVs (SMART) whose objective function is the motion cost and flight reliability of UAVs to ensure that each UAV can be in the optimal surveillance location for the target. The proposed SMART skillfully integrates the strict, elastic, and flight constraint strategies. During the execution of the multi-UAV formation, the inherent safety constraints of multiple UAVs and the designed strategies are used to solve the quadratic optimization model to adjust the locations of these UAVs. To demonstrate the superiority of our method, we conduct a 3D simulation urban environment and devise several experiments to analyze the performance of SMART on it. The experimental results demonstrate that SMART can not only maintain the high cooperative flight capability, but also provide high flexibility and fault tolerance.

Finding landmarks - An investigation of viewing behavior during spatial navigation in VR using a graph-theoretical analysis approach.

Vision provides the most important sensory information for spatial navigation. Recent technical advances allow new options to conduct more naturalistic experiments in virtual reality (VR) while additionally gathering data of the viewing behavior with eye tracking investigations. Here, we propose a method that allows one to quantify characteristics of visual behavior by using graph-theoretical measures to abstract eye tracking data recorded in a 3D virtual urban environment. The analysis is based on eye tracking data of 20 participants, who freely explored the virtual city Seahaven for 90 minutes with an immersive VR headset with an inbuild eye tracker. To extract what participants looked at, we defined "gaze" events, from which we created gaze graphs. On these, we applied graph-theoretical measures to reveal the underlying structure of visual attention. Applying graph partitioning, we found that our virtual environment could be treated as one coherent city. To investigate the importance of houses in the city, we applied the node degree centrality measure. Our results revealed that 10 houses had a node degree that exceeded consistently two-sigma distance from the mean node degree of all other houses. The importance of these houses was supported by the hierarchy index, which showed a clear hierarchical structure of the gaze graphs. As these high node degree houses fulfilled several characteristics of landmarks, we named them "gaze-graph-defined landmarks". Applying the rich club coefficient, we found that these gaze-graph-defined landmarks were preferentially connected to each other and that participants spend the majority of their experiment time in areas where at least two of those houses were visible. Our findings do not only provide new experimental evidence for the development of spatial knowledge, but also establish a new methodology to identify and assess the function of landmarks in spatial navigation based on eye tracking data.

A new approach for training a physics-based dehazing network using synthetic images

In this study, we propose a new approach for training a physics-based dehazing network, using RGB images and depth maps gathered from a 3D urban virtual environment, with simulated global illumination and physically-based shaded materials. Since 3D scenes are rendered with depth buffers, full image depth can be extracted based on this information, using a custom shader, unlike the extraction of real-world depth maps, which tend to be sparse. Our proposed physics-based dehazing network uses generated transmission and atmospheric maps from RGB images and depth maps from the virtual environment. To make our network compatible with real-world images, we incorporate a novel strategy of using unlit image priors during training, which can also be extracted from the virtual environment. We formulate the training as a supervised image-to-image translation task, using our own DLSU-SYNSIDE (SYNthetic Single Image Dehazing Dataset), which consists of clear images, unlit image priors, transmission, and atmospheric maps. Our approach makes training stable and easier as compared to unsupervised approaches. Experimental results demonstrate the competitiveness of our approach against state-of-the-art dehazing works, using known benchmarking datasets such as I-Haze, O-Haze, and RESIDE, without our network seeing any real-world images during training. The DLSU-SYNSIDE dataset and source code can be accessed through this link: https://neildg.github.io/SynthDehazing/.

Air-to-Ground Large-Scale Channel Characterization by Ray Tracing

Through ray tracing simulation on three-dimensional (3D) urban environments, we characterize air-to-ground (A2G) channels for 5G and beyond wireless communications. In this study, we review four types of elevation angle-dependent probability of line-of-sight (LoS) expressions according to building distribution types. With channel characterization data extracted from the ray tracing (RT) simulation, LoS probability versus elevation angle agrees better with the elevation angle-dependent probability expressions of LoS that assumes the buildings are randomly distributed. Furthermore, we provide a more accurate LoS probability expression that enables better curve-fitting for the LoS probability data obtained from RT simulations. In addition, the A2G channel parameters such as LoS and non-line-of-sight (NLoS) channel path loss exponents (PLEs) and the shadow fading with UAV altitudes are obtained in four typical and realistic urban environments. The LoS PLEs increase slowly with the height of the UAV, while the NLoS one decreases significantly with the increase of the UAV height.

A Multi-Objective Coverage Path Planning Algorithm for UAVs to Cover Spatially Distributed Regions in Urban Environments

This paper presents a multi-objective coverage flight path planning algorithm that finds minimum length, collision-free, and flyable paths for unmanned aerial vehicles (UAV) in three-dimensional (3D) urban environments inhabiting multiple obstacles for covering spatially distributed regions. In many practical applications, UAVs are often required to fully cover multiple spatially distributed regions located in the 3D urban environments while avoiding obstacles. This problem is relatively complex since it requires the optimization of both inter (e.g., traveling from one region/city to another) and intra-regional (e.g., within a region/city) paths. To solve this complex problem, we find the traversal order of each area of interest (AOI) in the form of a coarse tour (i.e., graph) with the help of an ant colony optimization (ACO) algorithm by formulating it as a traveling salesman problem (TSP) from the center of each AOI, which is subsequently optimized. The intra-regional path finding problem is solved with the integration of fitting sensors’ footprints sweeps (SFS) and sparse waypoint graphs (SWG) in the AOI. To find a path that covers all accessible points of an AOI, we fit fewer, longest, and smooth SFSs in such a way that most parts of an AOI can be covered with fewer sweeps. Furthermore, the low-cost traversal order of each SFS is computed, and SWG is constructed by connecting the SFSs while respecting the global and local constraints. It finds a global solution (i.e., inter + intra-regional path) without sacrificing the guarantees on computing time, number of turning maneuvers, perfect coverage, path overlapping, and path length. The results obtained from various representative scenarios show that proposed algorithm is able to compute low-cost coverage paths for UAV navigation in urban environments.

Assessing the effects of 2D/3D urban morphology on the 3D urban thermal environment by using multi-source remote sensing data and UAV measurements: A case study of the snow-climate city of Changchun, China

The urban heat island (UHI) effect has many adverse impacts on natural and social environments. A better understanding of how complex urban biophysical and social structures affect UHI is crucial to improve urban thermal environments. However, few studies have investigated the effects of 2D/3D urban morphology on land surface temperature (LST) and vertical air temperature simultaneously. Using LST data retrieved from Landsat 8 images and vertical temperatures at different heights collected from unmanned aerial vehicle (UAV) measurements, this study investigated the effects of 2D and 3D urban morphology on the 3D urban thermal environment among different urban function zones (UFZs) in the snow-climate city of Changchun, China. Our main findings are: (1) the average difference in vertical temperature between heights of 1.5 and 100 m was 1.78 °C and the temperature decreasing rate was 0.027 °C/m when the height was lower than 30 m, compared to 0.014 °C/m for 30–100 m. (2) Although 3D metrics had significant impact on LST, the relationships between the 2D metrics and LST were much stronger, accounting for 61% of the variations in LST. Additionally, the 2D composition metrics had stronger correlations with LST than the configuration metrics. The influence of 3D building metrics on LST was context-dependent for different UFZs. Increasing building height could reduce LST in residential UFZs but increase LST in industrial UFZs. (3) The effect of 2D urban morphology on the vertical temperature was stronger than that on the 3D ones when the height was lower than 30 m; however, opposite results were obtained when the height was greater than 30 m. The effects of 2D or 3D metrics on vertical temperature vary depending on whether the height is larger or smaller than a threshold, which is related to the characteristics of the building height. In general, 30 m appeared to be the threshold in our study. (4) The performances of models that used both 2D and 3D metrics to explain both LST and 3D thermal environments were better than those of models that used either 2D or 3D metrics alone, especially in UFZs such as residential areas.

Path Planning and Collision Risk Management Strategy for Multi-UAV Systems in 3D Environments.

Multi-UAV systems are attracting, especially in the last decade, the attention of researchers and companies of very different fields due to the great interest in developing systems capable of operating in a coordinated manner in complex scenarios and to cover and speed up applications that can be dangerous or tedious for people: search and rescue tasks, inspection of facilities, delivery of goods, surveillance, etc. Inspired by these needs, this work aims to design, implement and analyze a trajectory planning and collision avoidance strategy for multi-UAV systems in 3D environments. For this purpose, a study of the existing techniques for both problems is carried out and an innovative strategy based on Fast Marching Square—for the planning phase—and a simple priority-based speed control—as the method for conflict resolution—is proposed, together with prevention measures designed to try to limit and reduce the greatest number of conflicting situations that may occur between vehicles while they carry out their missions in a simulated 3D urban environment. The performance of the algorithm is evaluated successfully on the basis of certain conveniently chosen statistical measures that are collected throughout the simulation runs.

Path Planning Method for UAVs Based on Constrained Polygonal Space and an Extremely Sparse Waypoint Graph

Finding an optimal/quasi-optimal path for Unmanned Aerial Vehicles (UAVs) utilizing full map information yields time performance degradation in large and complex three-dimensional (3D) urban environments populated by various obstacles. A major portion of the computing time is usually wasted on modeling and exploration of spaces that have a very low possibility of providing optimal/sub-optimal paths. However, computing time can be significantly reduced by searching for paths solely in the spaces that have the highest priority of providing an optimal/sub-optimal path. Many Path Planning (PP) techniques have been proposed, but a majority of the existing techniques equally evaluate many spaces of the maps, including unlikely ones, thereby creating time performance issues. Ignoring high-probability spaces and instead exploring too many spaces on maps while searching for a path yields extensive computing-time overhead. This paper presents a new PP method that finds optimal/quasi-optimal and safe (e.g., collision-free) working paths for UAVs in a 3D urban environment encompassing substantial obstacles. By using Constrained Polygonal Space (CPS) and an Extremely Sparse Waypoint Graph (ESWG) while searching for a path, the proposed PP method significantly lowers pathfinding time complexity without degrading the length of the path by much. We suggest an intelligent method exploiting obstacle geometry information to constrain the search space in a 3D polygon form from which a quasi-optimal flyable path can be found quickly. Furthermore, we perform task modeling with an ESWG using as few nodes and edges from the CPS as possible, and we find an abstract path that is subsequently improved. The results achieved from extensive experiments, and comparison with prior methods certify the efficacy of the proposed method and verify the above assertions.