Route Computation Research Articles

An Experimental Study for Radial-Gated Ogee Spillway Discharge and Comparison with Other Model Studies

The book "Design of Small Dams" is a renowned reference publication used for the design of dams in America and it is commonly used in Turkey as well. Flood spillways of many dams are ogee profile radial-gated weirs, and accurate calculation of discharges passing over a partially open radial-gated ogee spillway is important in flood routing computations. Because each spillway and its appurtenant structures have geometrical and hydraulic properties peculiar to their own, a generalizable method for calculating the discharge over a partially open radial-gated spillway should not be realistic. Therefore, many laboratory experimental studies are done both in America and in Turkey for more accurate calculation of spillway discharge of dams individually. With the objective of comparing the results of these studies and the method given in that book another experimental study is performed as summarized in this paper. For this purpose, experiments for the partially open radial gate in a laboratory setup having a 95 mm high, 10 cm wide ogee spillway model with an adjustable radial gate of proportionate dimensions both placed in a 5 meter long, 10 cm wide and 30 cm high channel are done with many combinations of flow rates and gate openings as allowed by the physical dimensions of the open channel setup available in our laboratory and the maximum flow rate its pump can generate. Taking a scale ratio of 1/100, the relative differences of the discharge coefficients given in the above-mentioned book from the experimental discharge coefficients measured in this study for the same configurations are found to be between +3% and +34%. Although the discharge coefficients determined by this experimental study are not too deviant from those obtained from 15 laboratory model studies done by the United States Bureau of Reclamation and the United States Army Corps of Engineers in America and seven such studies done by the State Water Works in Turkey, it is concluded that the discharge – head relationship of a radial-gated spillway of a dam should be determined by a laboratory experiment on a model of not too small a scale, and a generalized method cannot be applicable to all spillways in the world as a whole.

EdgeCross: Cloud Scale Traffic Management at Peering Edges

Cloud providers deployed dozens of PoPs and data centers globally to serve billions of geo-distributed users. The traffic management at peering edges has become a key capability of cloud network operators to meet the diverse demands of users. With the rapid growth of cloud applications, users have recently announced new performance requirements, e.g., achieving latency as low as possible instead of maintaining a specified delay. The conventional inter-domain bandwidth allocation approach, which aims to reduce the high operating expenditures of bandwidth usage, fails to meet these new requirements. We further reveal that the flow scheduling among PoPs may fail due to the limited link capacity hidden by the cloud private backbone network controller. Therefore we seek a new traffic management at peering edges. We propose a new controller framework, EdgeCross, that satisfies not only users' emerging demands but maintains low operating costs. The large number of fine-grain application-aware flows and the consideration of backbone links' capacity lead to very high complexity of routing computation and verification for the controller. EdgeCross introduces a two-phase operation that first achieves the low-expense bandwidth allocation according to the standard 95th percentile billing model and then allocates specified flows to peering edges based on users' requirements. EdgeCross further reduces large memory consumption by proposing an effective routing table compression approach. The evaluation based on a production network with 16 PoPs has shown that EdgeCross can successfully process the routes of 1 billion flows in 10 seconds, reduce the average delay for performance-sensitive flows by 2 milliseconds compared to traditional BGP, and is able to save the bandwidth cost by 10-26% compared to the state-of-the-art Cascara.

Optimal Path Calculation for Multi-ring Based Packet–Optical Transport Networks

Multi-domain optical transport networks are inherently non-interoperable and require integrated orchestration and path provisioning mechanisms at the network-wide level. Moreover, ensuring the network’s survivability is a critical issue. While the MPLS-TP (multi-protocol label switching-transport profile) defines various protection and recovery mechanisms as standards, it does not address methods for calculating and selecting protection and recovery paths. Therefore, an algorithm is needed to calculate and set up paths to ensure quick protection and recovery across the entire integrated network, minimizing conflicts in protection and recovery at the packet optical integrated network level. In this paper, we proposed an algorithm that calculates and sets a path that enables rapid protection and recovery in an MPLS-TP network composed of a multi-ring-mesh topology. To this end, this study proposes the concept of a transparent node (T-node) for calculating link-disjoint SPF (shortest path first) in a multi-ring network with dual or more rings. A T-node is a node in a ring with more than a dual ring and indicates that the node has been used once in route calculation. Therefore, during path calculation, a T-node can be used as a source node and an intermediate node but not as a destination node.

Evaluating the Accuracy of Machine Learning, Deep Learning and Hybrid Algorithms for Flood Routing Calculations

Evaluating the Accuracy of Machine Learning, Deep Learning and Hybrid Algorithms for Flood Routing Calculations

UAV Routing for Enhancing the Performance of a Classifier-in-the-loop

Some human-machine systems are designed so that machines (robots) gather and deliver data to remotely located operators (humans) through an interface to aid them in classification. The performance of a human as a (binary) classifier-in-the-loop is characterized by probabilities of correctly classifying objects (or points of interest) as a true target or a false target. These two probabilities depend on the time spent collecting information at a point of interest (POI), known as dwell time. The information gain associated with collecting information at a POI is then a function of dwell time and discounted by the revisit time, i.e., the duration between consecutive revisits to the same POI, to ensure that the vehicle covers all POIs in a timely manner. The objective of the routing problem for classification is to route the vehicles optimally, which is a discrete problem, and determine the optimal dwell time at each POI, which is a continuous optimization problem, to maximize the total discounted information gain while visiting every POI at least once. Due to the coupled discrete and continuous problem, which makes the problem hard to solve, we make a simplifying assumption that the information gain is discounted exponentially by the revisit time; this assumption enables one to decouple the problem of routing with the problem of determining optimal dwell time at each POI for a single vehicle problem. For the multi-vehicle problem, since the problem involves task partitioning between vehicles in addition to routing and dwell time computation, we provide a fast heuristic to obtain high-quality feasible solutions.

An Analysis of Routing Protocol in Mobile Adhoc Networks and its Applications

Each hub in a mobile ad-hoc network (MANET) sends parcels bound for different hubs in the network by means of remote (radio transmission) transmission on a wilful premise, making it a self-beginning, powerful network made up of mobile hubs. Multi-bounce transferring is the essential inspiration for the advancement of ad hoc networking. Remote Ad hoc networks, otherwise called foundation less networks, can be set up rapidly and effectively using radio waves as the network's transmission channel. There is no principal server or referee in an ad hoc network. Every hub in a MANET is fit for playing out its own networking errands, for example, routing and bundle sending, in a decentralized and independent style. Uses of routing protocols like Ad hoc On-request Distance Routing Protocol (AODV), Dynamic Source Routing (DSR), Transiently Requested Routing Calculation (TORA), and Enhanced Connection State Routing (OLSR) are fascinating in light of the fact that routing is the focal issue in MANETs. These routing protocols are simulated using OPNET, and their effectiveness is investigated using various metrics. With the use of metrics, the most efficient channel for data transfer can be determined. The results demonstrate the viability of AODV and TORA for topology-changing in large-scale networks using a variety of criteria.

Exploring the role of blockchain technology, warehouse automation, smart routing, and cloud computing in logistics performance

ABSTRACT Recently, there has been a heightened emphasis on employing smart technologies for evaluating logistics performance, and hence we have considered four key technologies, namely blockchain technology (BT), warehouse automation (WA), smart routing (SR), and cloud computing (CC), and measured their aggregate impacts on logistics performance. We have considered these four technologies as exogenous constructs, and the logistics performance factors as endogenous constructs. First, we generate 23 items or indicators under those technologies and measure their loadings to determine how strongly they are linked to each technology. Next, we determine how strongly technologies are linked to logistics performance factors. Finally, we test our theoretical model using partial least squares structured equation modeling (PLS-SEM). Our findings confirm that the constructs and indicators in our theoretical framework meet the criteria, supported by PLS-SEM results and fit indices. This study advances logistics theory by highlighting smart technology adoption in practice and supporting institutional theory.

Investigating the influence of e-navigation and S-100 over the computation of the weather route

The weather route presents significant potential in identifying the most efficient route for vessels, enabling the optimisation of distance, time, or fuel consumption. Although there have been notable advancements in most technologies in recent years, weather routing has failed to keep pace with these developments. The purpose of this document is to determine whether a specific IMO’s concept called e-navigation may have an impact on vessel’s route optimisation. Today, mariners are required to integrate heterogeneous data types (hydrographic, weather, oceanographic, etc.) that are transmitted on board via various devices and in diverse formats in order to execute weather routing. When e-navigation becomes operational, relevant marine-related data will be accessible in a standardised format on a unified device. The objective of this work is to examine the potential of S-100 in the context of weather routing and route optimisation for the benefit of navigation. There are two methodologies discussed: manual and automated. A range of S-100-based products will be evaluated in relation to a variety of optimisation algorithms in order to predict whether e-navigation could be advantageous in addressing the weather routing problem. Constraints regarding the implementation of electronic navigation and the mariners’ duties are outlined in the final part of the paper.

Feature Extraction Algorithm based Metaheuristic Optimization for Handwritten Character Recognition

Interest in feature extraction for Handwritten Character Recognition (HCR) has been growing due to numerous algorithms aimed at improving classification accuracy. This study introduces a metaheuristic approach utilizing the Honey Badger Algorithm (HBA) for feature extraction in HCR. The Freeman Chain Code (FCC) is employed for data representation. One challenge with using FCC to represent characters is that extraction results vary depending on the starting points, affecting the chain code's route length. To address this issue, a metaheuristic approach using HBA is proposed to identify the shortest route length and minimize computational time for HCR. The performance metrics of the HB-FCC extraction algorithm are route length and computation time. Experiments on the algorithm use chain code representations from the Center of Excellence for Document Analysis and Recognition (CEDAR) dataset, containing 126 uppercase letter characters. According to the results, the proposed HB-FCC method achieves a route length of 1880.28 and requires only 1.07 seconds to process the entre set of character images.

A fuel-efficient pathfinding algorithm for next-generation WIG crafts

ABSTRACT Wing-In-Ground (WIG) crafts have the advantage of being fuel-efficient by utilising the ground effect. Next-generation WIG crafts can choose flight paths that navigate over obstacles while flying at specific altitudes. However, current route computation algorithms for ships fail to account for paths that allow ships to navigate over obstacles. Leveraging fundamental path-finding approaches such as A* and Jump Point Search (JPS), our algorithms are designed to forecast routes that significantly improve operational efficiency. Validation was performed using real topographic data from Indonesia, focusing on fuel consumption metrics to evaluate the algorithms’ effectiveness. The results evaluate the superiority of our methods in optimising route selection for WIG crafts, marking a advancement in autonomous marine navigation technology.

Biphase routing scheme for optimal throughput in large-scale optical satellite networks

In the large-scale optical satellite network (LS-OSN), hundreds to thousands of low Earth orbit (LEO) satellites will be interconnected via laser links, offering global coverage characterized by high throughput and low latency. LS-OSNs present an attractive strategy to cultivate a comprehensively connected, intelligent world. However, the dynamic nature of the satellites, as they orbit the Earth, results in frequent changes in the LS-OSN topology. Thus, there is a pressing need for efficient routing algorithms that not only cater to massive traffic demands but also swiftly adapt to these constant topological changes. Traditional routing algorithms for services with specific bandwidth requirements often compromise on either computational speed or throughput efficiency. In response, this study introduces a routing scheme based on flow optimization and decomposition (FOND). This seeks to shorten the computation time while preserving optimal network throughput. Expanding upon the FOND scheme, we further devised two heuristic algorithms: the flow-based greedy path (FGP) and the flow-based greedy width (FGW). Simulation results from a 288-satellite constellation network indicate that both the FGP and FGW outpace contemporary methods in terms of the routing computation time while maintaining a consistent throughput equal to 100% of the network capacity. Notably, the FGP has exhibited an impressive capability, reducing the routing computation time to 0.23% compared to the baseline incremental-widest-path (IWP) algorithm, which operates on Dijkstra’s algorithm principles.

Research on Computing Resource Measurement and Routing Methods in Software Defined Computing First Network.

Computing resource measurement and computing routing are essential technologies in the computing first network (CFN), serving as its foundational elements. This paper introduces a Software Defined Computing First Network (SD-CFN) architecture. Building upon this framework, a Dynamic-Static Integrated Computing Resource Measurement Mechanism (DCRMM) is proposed, incorporating methods such as the entropy weight method and K-Means clustering. The DCRMM algorithm outperforms the Maximum-closest Static Algorithm (MSA) and Maximum Closest Dynamic Algorithm (MDA) in terms of node stability, node utilization, and node matching accuracy. Additionally, a Reinforcement Learning and Software Defined Computing First Networking Routing (RSCR) algorithm is presented as a software-defined computing routing solution within the SD-CFN. RSCR introduces a knowledge plane responsible for computing routing calculations. It comprehensively considers factors such as link latency, available bandwidth, and packet loss rate. Simulation experiments conducted on the GÉANT topology demonstrate that RSCR outperforms the OSPF algorithm in terms of link latency, packet loss rate, and throughput. DCRMM and RSCR offer innovative solutions for computing resource measurement and computing routing in computing first networks.

Hyperbolic-Embedding-Aided Geographic Routing in Intelligent Vehicular Networks

Intelligent vehicular networks can not only connect various smart terminals to manned or unmanned vehicles but also to roads and people’s hands. In order to support diverse vehicle-to-everything (V2X) applications in dynamic, intelligent vehicular networks, efficient and flexible routing is fundamental but challenging. Aimed to eliminate routing voids in traditional Euclidean geographic greedy routing strategies, we propose a hyperbolic-embedding-aided geographic routing strategy (HGR) in this paper. By embedding the network topology into a two-dimensional Poincaré hyperbolic disk, greedy forwarding is performed according to nodes’ hyperbolic coordinates. Simulation results demonstrated that the proposed HGR strategy can greatly enhance the routing success rate through a smaller stretch of the routing paths, with little sacrifice of routing computation time.

Capsule Networks With Residual Pose Routing.

Capsule networks (CapsNets) have been known difficult to develop a deeper architecture, which is desirable for high performance in the deep learning era, due to the complex capsule routing algorithms. In this article, we present a simple yet effective capsule routing algorithm, which is presented by a residual pose routing. Specifically, the higher-layer capsule pose is achieved by an identity mapping on the adjacently lower-layer capsule pose. Such simple residual pose routing has two advantages: 1) reducing the routing computation complexity and 2) avoiding gradient vanishing due to its residual learning framework. On top of that, we explicitly reformulate the capsule layers by building a residual pose block. Stacking multiple such blocks results in a deep residual CapsNets (ResCaps) with a ResNet-like architecture. Results on MNIST, AffNIST, SmallNORB, and CIFAR-10/100 show the effectiveness of ResCaps for image classification. Furthermore, we successfully extend our residual pose routing to large-scale real-world applications, including 3-D object reconstruction and classification, and 2-D saliency dense prediction. The source code has been released on https://github.com/liuyi1989/ResCaps.

A Distributed Multicast QoS Routing Construction Approach in Information-Centric Networking

Many applications suitable for multicast transmission, such as video conferencing and live e-commerce, demand high Quality of Service (QoS) and require data delivery to be completed within specified delay constraints. Some methods have been proposed for constructing delay-constrained multicast routing based on network state. However, obtaining precise network latency can be challenging, resulting in inaccuracies in delay-constrained routing calculations and, ultimately, the inability to meet application requirements. Additionally, many methods engage in an indiscriminate exploration of potential paths in the network, causing significant message processing overhead. This paper proposes an Information-Centric Networking (ICN)-based approach for delay-constrained multicast routing. Our method dynamically constructs multicast paths from tree nodes to receivers based on real-time path status detection during the join message propagation phase. Additionally, we present a method for acquiring neighborhood state information to facilitate real-time routing decisions. To curtail indiscriminate path exploration, our approach uses the ICN Name Resolution System (NRS) to obtain and select potential optimal tree nodes. For this purpose, we design a multicast service registration and resolution mechanism using the ICN Name Resolution System (NRS). Simulation results indicate that our approach exhibits a higher success ratio and concurrently incurs lower message processing overhead than some other methods, particularly in situations with stringent delay constraints.

Fast, Scalable and Robust Centralized Routing for Data Center Networks

This paper presents a fast and robust centralized data center network (DCN) routing solution, called . For fast routing calculation, uses centralized controllers to collect/disseminate the network’s link-states (LS), and offload the actual routing calculation onto each switch. Observing that the routing changes can be classified into a few fixed patterns in DCNs which have regular topologies, we simplify each switch’s routing calculation into a table-lookup manner, i.e., comparing LS changes with pre-installed base topology and updating routing paths according to predefined rules. As such, the routing calculation time at each switch only needs 10s of us even in a large network topology containing 10K+ switches. For efficient controller fault-tolerance, purposely uses reporter switch to ensure the LS updates successfully delivered to all affected switches. As such, can use multiple stateless controllers and little redundant traffic to tolerate failures, which incurs little overhead under normal case, and keeps 10s of ms fast routing reaction time even under complex data-/control-plane failures. We design, implement and evaluate with extensive experiments on Linux-machine controllers and white-box switches. provides <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim$</tex-math> </inline-formula> 1200x and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim$</tex-math> </inline-formula> 100x shorter convergence time than current distributed protocol BGP and the state-of-the-art centralized routing solution, respectively. Furthermore, Primus maintains good routing controllability/manageability thanks to its centralized architecture, which enables us to build several advanced routing features in our testbed, including routing failure visualization and weighted-cost-multi-path routing.

Load-balancing routing algorithms for service congestion avoidance in LEO optical satellite networks

The low-Earth-orbit optical satellite network (LOSN) is an attractive solution to realize the end-to-end service quality of large-scale services, and it is the basis of establishing the 6G mobile network with high throughput and high reliability. However, the current LOSN is practically unable to achieve such high throughput for global service access. First, the centralized deployment of ground gateways will cause a heavy traffic load in the space segment of the LOSN, which becomes the bottleneck constraining the further growth of network throughput. Second, the high-speed movement of LEO satellites will cause continuous movement of traffic load, which will affect the scalability of routing computing. In this paper, two routing algorithms are proposed to increase the throughput in the dynamic LOSN topology. The congestion-aware load balancing (CALB) algorithm is proposed to address the inter-satellite link congestion in the dynamic LOSN topology. Then, a load-balancing routing algorithm based on satellite–ground cooperation (SGC-LB) is proposed to further reduce the impact of the network bottleneck. To evaluate the performance of the proposed routing algorithm, extensive simulations were performed on a 288-satellite Walker-Star constellation with inter-satellite links. The service blockage rate and network bandwidth utilization rate are evaluated showing the effectiveness of the proposed routing algorithm. The network using the SGC-LB algorithm can accommodate 60.00%, 56.00%, 42.22%, and 33.33% more services than using the Shortest Path algorithm, Sway algorithm, CALB algorithm, and Anycast algorithm, respectively, with zero service congestion during the simulation. The SGC-LB also gains a 6.04%, 5.00%, 5.04%, and 0.77% higher network utilization rate than the Shortest Path, Sway, CALB, and Anycast algorithms, respectively.

Mitigating network adaptation and QoT prediction challenges in WDM networks

Abstract The capacity and efficiency of optical communication networks have been completely transformed by wavelength division multiplexing (WDM) technology, which allows many wavelengths to be transmitted simultaneously over a single optical fibre. Conventional QoT prediction is based on analytical models that consider physical layer characteristics including dispersion, optical power and signal-to-noise ratio. But these models frequently oversimplify complex real-world situations, which reduces their accuracy for modern high-speed WDM networks. A data-driven solution is provided by machine learning(ML), which may boost the accuracy of QoT predictions by utilising real-time measurements, historical data and a variety of network situations. The creation of a ML-based framework for QoT prediction is investigated in the current research. This research proposes an effective ML-based routing computation model that uses a non-linear autoregressive recurrent neural network (ML-RCNA-RNN) to ensure QoT for every wavelength channel in high-capacity and high-speed WDM networks. Through simulations, more accurate QoT metrics, such as bit error rate (BER) 68.42 %, QoT prediction accuracy (Q-Factor) 5.9 %, network adaption time (ms) 48.3 %, latency (ms) 0.28 % and throughput (Gbps) 14.29 %, have been obtained compared to conventional QoT predictions. These results were obtained using Gaussian noise Python simulation (GNPy). As a result, the proposed framework that makes use of GNPy demonstrates that it substantially enhances optical communication networks’ performance and dependability. This facilitates the development of high-capacity, low-latency and reliable communication infrastructure, and makes it more adaptable and able to manage the complexity of high-speed WDM optical networks while preserving signal quality in the modern digital era.

A road segment prioritization approach for cycling infrastructure

Understanding the motivators and deterrents to cycling is essential for creating infrastructure that gets more people to adopt cycling as a mode of transport. This paper demonstrates a new approach to support the prioritization of cycling infrastructure and cycling network design, accounting for cyclist preferences and the growing emphasis on ‘filtered permeability’ and ‘Low Traffic Neighborhood’ interventions internationally. The approach combines distance decay, route calculation, and network analysis methods to examine where future cycling demand is most likely to arise, how such demand could be accommodated within existing street networks, and how to ensure a fair distribution of investment. Although each of these methods has been applied to cycling infrastructure prioritization in previous research, this is the first time that they have been combined, creating an integrated road segment prioritization approach. The approach, which can be applied to other cities, is demonstrated in a case study of Manchester, resulting in cycling networks that balance directness against the need for safe and stress-free routes under different investment scenarios. A key benefit of the approach from a policy perspective is its ability to support egalitarian and cost-effective strategic cycle network planning.

Boosting flood routing prediction performance through a hybrid approach using empirical mode decomposition and neural networks: a case study of the Mera River in Ankara

Abstract Flood routing is vital in helping to reduce the impact of floods on people and communities by allowing timely and appropriate responses. In this study, the empirical mode decomposition (EMD) signal decomposition technique is combined with cascade forward backpropagation neural network (CFBNN) and feed-forward backpropagation neural network (FFBNN) machine learning (ML) techniques to model 2014 floods in Ankara, Mera River. The data are split in order to avoid the underfitting and overfitting problems of the algorithm. While establishing the algorithm, 70% of the data were divided into training, 15% testing and 15% validation. Graphical indicators and statistical parameters were used for the analysis of model performance. As a result, the EMD signal decomposition technique has been found to improve the performance of ML models. In addition, the EMD-FFBNN hybrid model showed the most accurate estimation results in the flood routing calculation. The study's outputs can assist in designing flood control structures such as levees and dams to help reduce flood risk.