Dijkstra's Shortest Path Algorithm Research Articles

Development of Multi-Strip Image Mosaicking for KOMPSAT-3A Images

Abstract. High-resolution satellite imagery has a limitation in terms of coverage area. This limitation presents challenges for extensive-scale analysis at regional or national levels. To maximize the utility of high-resolution satellite imagery, the implementation of image mosaicking techniques is essential. In this paper, we have developed seamline extraction techniques and relative geometric correction optimized for high-resolution satellite imagery. Ultimately, we proposed a multi-strip image mosaicking method for KOMPSAT-3A (Korea Multi-Purpose Satellite-3A) images. We applied the Dijkstra's shortest path algorithm to efficiently extract seamlines. we also performed image registration based on feature matching and homography transformation to correct the relative geometric errors between input images. We conducted experiments with our methods using 29 scenes from KOMPSAT-3A L1G data. The results indicated high relative geometric accuracy, with an average error of 1.63 pixels. Furthermore, we were able to obtain high-quality seamless mosaic images. Our proposed method is expected to enhance the utility of KOMPSAT-3A imagery for large-scale environmental and urban analysis and to provide more accurate and comprehensive data.

Electric vehicle charging navigation strategy in coupled smart grid and transportation network: A hierarchical reinforcement learning approach

Most of the existing electric vehicle (EV) charging navigation methods do not simultaneously take into account the electric vehicle charging destination optimization and path planning. Moreover, they are unable to provide online real-time decision-making under a variety of uncertain factors. To address these problems, this paper first establishes a bilevel stochastic optimization model for EV charging navigation considering various uncertainties, and then proposes an EV charging navigation method based on the hierarchical enhanced deep Q network (HEDQN) to solve the above stochastic optimization model in real-time. The proposed HEDQN contains two enhanced deep Q networks, which are utilized to optimize the charging destination and charging route path of EVs, respectively. Finally, the proposed method is simulated and validated in two urban transportation networks. The simulation results demonstrate that compared with the Dijkstra shortest path algorithm, single-layer deep reinforcement learning algorithm, and traditional hierarchical deep reinforcement learning algorithm, the proposed HEDQN algorithm can effectively reduce the total charging cost of electric vehicles and realize online real-time charging navigation of electric vehicles, that shows excellent generalization ability and scalability.

Mobile application for finding the best route on campus

The actual task of developing a mobile application for finding the optimal route on a university campus is considered. The mathematical basis for designing a mobile application is the task of finding the shortest route in a graph whose vertices are classrooms, laboratories, libraries, departments, dean's offices, etc. in the buildings of the university.The analysis of the main characteristics of existing mobile applications for walking navigation has been carried out, their advantages and disadvantages have been identified. The well-known algorithms for finding the shortest path in a graph are considered: breadth-first search, greedy algorithm, and Dijkstra shortest path algorithm. For the task under consideration, a software implementation and an auxiliary service have been developed to create maps and bring them to a format in which a graph is automatically created for the correct operation of the application. The novelty of the proposed development lies in the possibility of displaying, adding and editing various maps. Further improvement of the developed mobile application can be carried out in the direction of increasing the accuracy of navigation by combining algorithms for finding paths in a graph, as well as in translating maps from 2D to 3D. The results of the study can be used to find the best routes in other universities.

Fuzzy‐based unequal clustering protocol for underwater wireless sensor networks

SummaryUnderwater wireless sensor network (UWSN) is a network made up of underwater sensor nodes, anchor nodes, surface sink nodes or surface stations, and the offshore sink node. Energy consumption, limited bandwidth, propagation delay, high bit error rate, stability, scalability, and network lifetime are the key challenges related to underwater wireless sensor networks. Clustering is used to mitigate these issues. In this work, fuzzy‐based unequal clustering protocol (FBUCP) is proposed that does cluster head selection using fuzzy logic as it can deal with the uncertainties of the harsh atmosphere in the water. Cluster heads are selected using linguistic input variables like distance to the surface sink node, residual energy, and node density and linguistic output variables like cluster head advertisement radius and rank of underwater sensor nodes. Unequal clustering is used to have an unequal size of the cluster which deals with the problem of excess energy usage of the underwater sensor nodes near the surface sink node, called the hot spot problem. Data gathered by the cluster heads are transmitted to the surface sink node using neighboring cluster heads in the direction of the surface sink node. Dijkstra's shortest path algorithm is used for multi‐hop and inter‐cluster routing. The FBUCP is compared with the LEACH‐UWSN, CDBR, and FBCA protocols for underwater wireless sensor networks. A comparative analysis shows that in first node dies, the FBUCP is up to 80% better, has 64.86% more network lifetime, has 91% more number of packets transmitted to the surface sink node, and is up to 58.81% more energy efficient than LEACH‐UWSN, CDBR, and FBCA.

Wind dynamic and energy-efficiency path planning for unmanned aerial vehicles in the lower-level airspace and urban air mobility context

Unmanned aerial vehicles (UAVs) have been extensively used in urban environments for logistics, parcel delivery and surveillance, and the development of air taxi services. Given the dynamic nature of urban air mobility in terms of decision-making time limit, wind dynamics and other external factors, one should consider their safe and efficient operations in an urban context. Therefore, we propose an energy-efficient path-planning model for UAVs under large and complex urban environments and wind dynamics. The proposed method adopted Voronoi Diagram to decompose the complex urban environment into a simplified network model, given the presence of no-fly zones and restricted areas as obstacles. One could obtain the feasible initial path by solving the network model using the Dijkstra shortest path algorithm concerning the distance matrix. Given the nonlinearity of energy consumption along a path. We further model the UAV energy consumption and propose an efficient particle swarm optimisation (PSO) metaheuristic algorithm to achieve better solution quality. Compared to traditional PSO, the proposed algorithm achieved a 5% to 22% improvement under different wind scenarios by simulating real-life situations. In conclusion, the proposed method can achieve safe UAV operations with sufficient separation and less energy consumption.

Fleet Management and Vehicle Routing Plan using Dijkstra's Shortest Path Algorithm

Fleet Management and Vehicle Routing Plan using Dijkstra's Shortest Path Algorithm

The Floyd‐Warshall all‐pairs shortest paths algorithm for disconnected and very sparse graphs

AbstractThe Floyd‐Warshall algorithm is the most popular algorithm for determining the shortest paths between all vertex pairs in a graph. It is a very simple and an elegant algorithm. However, for graphs without any negative weighted edges, using Dijkstra's shortest path algorithm for every vertex as a source vertex to produce all‐pairs shortest paths works significantly better than the Floyd‐Warshall algorithm, especially for large graphs. Furthermore, for graphs with negative weighted edges, with no negative cycle, in general Johnson's algorithm also performs better than the Floyd‐Warshall algorithm for large graphs. Johnson's algorithm first transforms the graph into a non‐negative one by using the Bellman‐Ford algorithm, then applies the Dijkstra's algorithm to the transformed graph. Thus, mainly, the Floyd‐Warshall algorithm is quite inefficient, especially for large graphs. In this paper, we show a simple improvement on the Floyd‐Warshall algorithm that will increases its efficiency, especially for very sparse graphs (i.e., the number of its edges is less than the number of its vertices), so it can be used instead of more complicated alternatives. We also show that our approach is also very effective for denser disconnected graphs. Since the new algorithm modifies the original Floyd‐Warshall algorithm, it is mainly aimed for directed graphs without negative cycles. Most programmers prefer to implement the Floyd‐Warshall algorithm over more complicated but more efficient alternatives for solving all‐pairs shortest path problems. In this work, we show that without the addition of any complicated data structures, the performance of the Floyd‐Warshall algorithm can be improved very easily. Our practical approach works even better than its alternatives for large sparse graphs.

Energy-Aware Virtual Network Migration for Internet of Things Over Fiber Wireless Broadband Access Network

The virtualized fiber wireless broadband access networks (V-FiWi) paradigm, effectively embedding heterogeneous virtual networks (VNs) originated from the service provider (SP) into shared substrate network (SN) provided by infrastructure provider (InP), plays a tremendous role in meeting the differentiated requirements between wireless frontend subnetwork and fiber backhaul subnetwork to achieve the interoperability of heterogeneous resource allocation. However, most of the existing V-FiWi integration systems mainly focused on the virtual network request (VNR) acceptance ratio, InP revenue, and substrate resource utilization, and they ignored the crucial issue called higher energy consumption cost, which was resulted from the imbalanced consumption of the substrate resource between the arrival and departure of VNR. In this article, we devote to exploring the energy-aware virtual network migration (EA-VNM) problem over the FiWi access technology, aiming to reoptimize the energy consumption while maintaining the high InP revenue and the large substrate resource utilization. In response to this issue, we first represent the service-oriented V-FiWi broadband access network architecture from the perspective of computing, storage, and network resource constraints, in which a migration model consisting of migration node and migration time is explained in detail. Then, we propose an enhanced KM-based energy-aware node migration (EKM-ENM) algorithm to economize on more bandwidth resource. More specially, the EA-VNM technology consists of network topology attributes and global network resources-based node-ranking measurement (NRM) phase, maximum weight matching-based node migration phase, and energy-aware link migration phase via Dijkstra shortest path algorithm. Finally, a rather large number of simulations are analyzed and evaluated numerically. Simulation results suggest that the proposed EKM-ENM algorithm outperforms the traditional embedding algorithms in terms of saving energy cost, decreasing time complexity, and improving VNR acceptance ratio.

Research on the Unit Black-Start Strategy Considering Recovery Path and Start Sequence

Large-scale power outage events bring serious economic losses to national and social development and cause bad impacts, and the reasonable formulation of a unit black-start strategy is the basis of power outage recovery. Firstly, the Dijkstra shortest path algorithm is used to search for the optimal recovery path of the unit to be started after a major blackout occurs, and secondly, a comprehensive index of the unit start sequence considering the unit characteristics and unit distance is defined to guide the unit start sequence formulation by combining the unit capacity, climbing rate, start power, and other characteristics with the recovery time and capacitance value of the recovery path. Finally, based on the unit start sequence and optimal recovery path, and taking into account the unit start constraints, a complete unit black-start strategy is proposed, and the effectiveness of this strategy is verified by IEEE30 node system simulation. The results show that the proposed unit black-start strategy can reduce the unit recovery time and improve the recovery success rate.

Evaluating an Application Aware Distributed Dijkstra Shortest Path Algorithm in Hybrid Cloud/Edge Environments

To increase the flexibility and the dynamism of communication networks, Software Defined Networking (SDN) has emerged as a challenging approach to decouple control and data planes, using a logically centralized controller able to manage the underlying network resources. However, traditional network solutions can not be always used in SDN. In this paper, we deal with routing issues in the setup of dynamic SDN spanning Fog/Edge and IoT systems for supporting the new generation of applications. In particular, we present a modified version of Dijkstra's routing algorithm that can optimize complex routing metrics and uses MapReduce to speed up the configuration of routers in a software-defined network. The system can optimize the packet routing accordingly to different parameters including, e.g., hops, latency, and energy efficiency policies. To show the effective benefits of the proposed solution, we performed evaluations on the revised MapReduce version of the Dijkstra routing algorithm considering a highly scalable network topology with thousands of virtual nodes.

FPGA-based vehicle real-time path planning system

In order to realize the requirements of low latency and low power consumption in real-time path planning for private vehicles, this paper designs an FPGA accelerator based on real-time road information and Dijkstra's shortest path algorithm, which is applied to the path planning system of vehicle edge computing. The system is designed using Xilinx High-Level Synthesis (HLS) compiler and is implemented in the programming logic of a Xilinx Zynq FPGA. The experiment is carried out on a city map, and the results show that the system has lower circuit area and power consumption, and its computing performance is 3.8 times that of ARM, which is suitable for edge computing platform.

Research on Path Planning for Reducing Vibration Fatigue of Precision Equipment Transportation

Abstract This paper analyzes the safety and reliability problems of large precision instruments and equipment in highway transportation, aiming at reducing the vibration fatigue damage of equipment to the greatest extent from the perspective of road planning. Based on the dynamic response model of the equipment, the relationship between the ground spectrum response and the cumulative fatigue damage of the equipment is analyzed. Based on the method of probability and statistics, an equipment fatigue prediction model is established. Based on the study of the relationship between the roughness of pavement grade and the reliability of equipment, the low-risk path planning is proposed. This paper adapts the Dijkstra shortest path algorithm. Not only the basic information such as the length of the highway is considered but also the road factors related to the reliability of the equipment and the environmental factors affecting the driving safety are considered. Try to avoid the adverse environment which has a great impact on the reliability of the equipment, reduce the vibration fatigue of the equipment in the process of transportation, and improve the quality of transportation. Using geographic information system secondary development, database, global positioning system positioning, and other technologies, this paper provides users with the shortest path and low-risk path planning for reference, which has a certain practical significance for highway transportation of high reliability products such as vulnerable products, precision instruments, and special equipment.

Path advisor

The shortest path in both the two dimensional (2D) plane and the three dimensional (3D) terrain is extensively used both in industry and academia. Although there are some map visualization tools for viewing the shortest path in 2D and 3D views, we find two limitations: (1) they are not applicable for map applications with obstacles (such as the wall in a building), and (2) they look unrealistic and strange when a road network approach is blindly adopted. Motivated by this, we developed a web-based multi-functional campus map tool called Path Advisor , which allows users to visualize the shortest path in the 2D view, the bird's eye view and the virtual reality view (VR view). Path Advisor uses Dijkstra's shortest path algorithm and breadth-first tree in the 2D view, and the weighted shortest surface path algorithm in the bird's eye view and the VR view. We shot a video for demonstrating Path Advisor at https://youtu.be/ZgdjyXXHwqg.

A scalable private Bitcoin payment channel network with privacy guarantees

While Bitcoin heavily dominates the cryptocurrency markets, its use in micropayments is still a challenge due to long transaction confirmation times and high fees. Recently, the concept of off-chain transactions is introduced that led to the idea of establishing a payment channel network called Lightning Network (LN), which utilizes multi-hop payments. Off-chain links provide the ability to make instant payments without a need to writing to Blockchain. However, LN's design still favors fees, and it is creating hub nodes or relays that defeat the purpose of Blockchain. In addition, it is still not reliable, as not all transactions are guaranteed to be delivered to their destinations. These issues hinder its wide adoption by retailers. To address this issue, in this paper, we argue that the retailers could create a private payment channel network among them to serve their business needs, just like the concept of private Blockchains. The goal is to build a pure peer-to-peer topology that will prevent the formation of hub nodes while also eliminating the need for any relays to increase the robustness of the payments. Assuming off-chain links as edges and retailers as nodes, we formulate the problem as a multi-flow commodity problem where transactions represent the commodities from various sources to destinations. As the multi-flow commodity problem is NP-Complete, we propose a heuristic approach that utilizes Dijkstra's shortest path algorithm for payments in a dynamic way by updating the edge weights when new paths need to be found. The order of transactions is randomized to provide fairness among the retailers. We further extend this approach to guarantee the privacy of payments by forcing all the payments to travel at least three hops. We utilized k-shortest path algorithm to choose from k options that will meet our criterion. The evaluations indicate that the proposed heuristic comes close to an optimal solution while providing scalability and guaranteeing user privacy.

РАСЧЕТ И ВИЗУАЛИЗАЦИЯ КАРТОГРАФИЧЕСКИХ МАРШРУТОВ С ИСПОЛЬЗОВАНИЕМ ПРОГРАММНОГО ОБЕСПЕЧЕНИЯ QGIS И PGROUTING

Recently, new tools have been created for working with geodata, which are used in various fields of human activity. Software for network analysis and routing solutions is of particular importance. The software product pgRouting is an example, distributed under the GPLv2 license. This program extends the capabilities of PostGIS / PostgreSQL geospatial databases. The article discusses the general principles of constructing routes on the graphs of the road network. It describes how to work with the geospatial database and the pgRouting software for building a route. The purpose of the work is to build a correct rout of a road graph in routing areas with a big number of objects and a poorly developed road network. The problem is solved by software pgRouting and QGIS on the basis of the Dijkstra shortest path algorithm, Johnson and Floyd-Warshall algorithms and allows you to solve the traveling salesman problem, and many others. The task is solved by means of software pgRouting и QGIS. As an experiment the article shows the solution for the task in which it is not enough to use only a road graph for building a correct route. Such situations may occur when routing the areas with a big number of objects and a poorly developed road network. In the process of the experiment described in the article it was found out that software pgRouting together with QGIS allows to rather effectively solve the task on calculation and visualization of the shortest route between two points on the map.

Three-dimensional spatial reconstruction of coronary arteries based on fusion of intravascular optical coherence tomography and coronary angiography.

We present a three-dimensional (3D) spatial reconstruction of coronary arteries based on fusion of intravascular optical coherence tomography (IVOCT) and digital subtraction angiography (DSA). Centerline of vessel in DSA images is exacted by multi-scale filtering, adaptive segmentation, morphology thinning and Dijkstra's shortest path algorithm. We apply the cross-correction between lumen shapes of IVOCT and DSA images and match their stenosis positions to realize co-registration. By matching the location and tangent direction of the vessel centerline of DSA images and segmented lumen coordinates of IVOCT along pullback path, 3D spatial models of vessel lumen are reconstructed. Using 1121 distinct positions selected from eight vessels, the correlation coefficient between 3D IVOCT model and DSA image in measuring lumen radius is 0.94% and 97.7% of the positions fall within the limit of agreement by Bland-Altman analysis, which means that the 3D spatial reconstruction IVOCT models and DSA images have high matching level.

Consideration of tactical decisions in microscopic pedestrian simulation: Algorithm and experiments

In the paper, a routing algorithm of two sub-algorithms is proposed for consideration of tactical decisions in a microscopic crowd simulation. One sub-algorithm works in the high or macroscopic level and is used to determine the departure door, which would suffer a later change, whenever pedestrians firstly enter rooms. The sub-algorithm is based on a tailored Dijkstra shortest-path algorithm with introduction of the concept of impedance conduction for considering congestion along paths. The other sub-algorithm works in the low or microscopic level and is supposed to work with any chosen microscopic crowd dynamics model. It is mainly used to model dynamic changes of pedestrians’ departure doors while they are walking inside rooms. Many mechanisms are introduced for considering practical factors affecting pedestrians’ routing behaviors, for example consideration of congestion of segments between pedestrians and doors, consideration of impact of re-selection of previous doors, etc. According to conducted numerical experiments, the proposed algorithm can well model crowd’s tactical decisions in a microscopic simulation under various scenarios, thus is a suitable method for study of crowd dynamics in a complicated environment.

Path intelligent optimization for dense crowd emergency evacuation in heritage buildings

There is a class of heritage buildings, such as museums, palaces and castles, which are closed buildings with multiple layers and exits. There are a lot of precious heritage and cultural relics in heritage buildings, and a large number of tourists need to be received every day. When suffering from emergencies such as fires, heritage buildings will be confronted with serious consequences if individuals cannot evacuate in time. In this paper, the path intelligent optimization for dense crowd emergency evacuation is proposed in heritage buildings. Firstly, based on Artificial Fish Swarm Algorithm (AFSA), agglomeration model is used when there is no guidance of guide personnel. Secondly, single-layer evacuation model is used to classify individuals into different categories based on improved K-Nearest Neighbor (KNN) algorithm. The model can help individuals choose the nearest stair and save escape time. Thirdly, based on Dijkstra shortest path algorithm, multi-layer evacuation model is used to find the shortest distance and the optimal path for individuals to reach the exit. These models are applied to the Louvre in order to verify the rationality by calculating the approximate evacuation time. In case of emergency, the algorithm proposed in this paper can be modified according to the actual heritage buildings.

Optimization Strategy of Electric Vehicles Charging Path Based on “Traffic-Price-Distribution” Mode

According to the current optimization problem of electric vehicle charging path planning, a charging path optimization strategy for electric vehicles is proposed, which is under the “traffic-price-distribution” mode. Moreover, this strategy builds an electric vehicle charging and navigation system on the basis of the road traffic network model, real-time electricity price model and distribution network model. Based on the Dijkstra shortest path algorithm and Monte Carlo time-space prediction method, it gets the optimal charging path navigation with the goal of minimizing the charging cost of electric vehicles. The simulation results in MATLAB and MATPOWER (MATLAB R2018a, MATPOWER3.1b2, PSERC, Cannell University) show that the electric vehicle charging path optimization strategy can solve the local traffic congestion problem better and improve the safety and stability of the distribution network because of the fully considering the convenience of electric vehicle charging.

Analysis of security and energy efficiency for shortest route discovery in low‐energy adaptive clustering hierarchy protocol using Levenberg‐Marquardt neural network and gated recurrent unit for intrusion detection system

AbstractWireless sensor network (WSN) is a collection of a huge number of autonomous sensor nodes having capabilities such as sensing, processing, and manipulation. In any WSN, routing protocols are the backbone for performing all type tasks such as sensing, controlling, and transmission of packets in ubiquitous environment. In this article, a LEACH protocol with Levenberg‐Marquardt neural network (LEACH‐LMNN) is considered to analyze the overall network lifetime. The aim of LEACH‐LMNN protocol comprises two parts: selection of cluster head node using LMNN approach and the second part is to locate the shortest path from the cluster‐head node to base‐station node adopting various route discovery algorithms, that is, breadth‐first search, Bellman‐Ford, and Dijkstra. The simulation result shows that the LEACH‐LMNN protocol with the Dijkstra shortest path algorithm outperforms other route discovery algorithms. In addition to this, this work also analyzes normal and anomaly detection based on intrusion detection system in wireless sensor networks using gated mechanism, that is, long short‐term memory (LSTM) and gated recurrent unit (GRU) in deep learning models. The proposed model achieves the highest detection rate of 97.84% for GRU and 97.85% for LSTM as well as improves the false positive rate (FPR) of 5.87% and 3.88% FPR for GRU and LSTM, respectively.