Routing In Networks Research Articles

Design and evaluation of Swift routing for payment channel network

Payment Channel Networks (PCNs) are a promising alternative to improve the scalability of a blockchain network. A PCN employs off-chain micropayment channels that do not need a global block confirmation procedure, thereby sacrificing the ability to confirm transactions instantaneously. PCN uses a routing algorithm to identify a path between two users who do not have a direct channel between them to settle a transaction. The performance of most of the existing centralized path-finding algorithms does not scale with network size. The rapid growth of Bitcoin PCN necessitates considering distributed algorithms. However, the existing decentralized algorithms suffer from resource underutilization. We present a decentralized routing algorithm, Swift, focusing on fee optimization. The concept of a secret path is used to reduce the path length between a sender and a receiver to optimize the fees. Furthermore, we reduce a network structure into combinations of cycles to theoretically study fee optimization with changes in cloud size. The secret path also helps in edge load sharing, which results in an improvement of throughput. Swift routing achieves up to 21% and 63% in fee and throughput optimization, respectively. The results from the simulations follow the trends identified in the theoretical analysis.

Secure opportunistic based void-hold routing for underwater acoustic sensor networks

Secure opportunistic based void-hold routing for underwater acoustic sensor networks

Optimal data transmission for decentralized IoT and WSN based on Type-2 Fuzzy Harris Hawks Optimization

Due to the widespread availability of services and smart devices, the decentralized internet of things and wireless sensor network-based systems (DIoT and WSN) have drawn the attention of academics and researchers and are still under constant development. Many challenges face the DIoT and WSN networks and need to be solved. The main issue among these challenges is reducing energy consumption in order to increase network lifetime. Energy-efficient routing protocols and the clustering method are the best solutions to this problem. In this paper, Type-2 Fuzzy Harris Hawks Optimization (T2FHHO) is used to choose the best path (route) from the source (cluster head (CH)) to the destination nodes (base station (BS)). Along with this, we propose a new fitness function to find the next hop based on residual energy, distance, traffic, and buffer size. Then, we apply the T2FHHO method to get the best CH. This protocol seeks to increase the long-term reward earned by each node. The best path in terms of the minimum cost obtained using the proposed method is found. The two main contributions of this study are as follows: firstly, we present a new efficient routing approach that minimizes costs and maximizes efficiency. Secondly, we apply the T2FHHO to the network parameters of energy consumption, network lifetime, and convergence curves. The results indicate that T2FHHO outperforms competing methods. The proposed method holds the promise of significantly advancing the state of the art in energy-efficient routing for DIoT and WSN networks, with significant implications in various sectors and applications, ensuring extended network lifetimes and sustainable IoT implementations.

Network Routing Optimization Using Tabu Search Algorithm in Dynamic Routing

Internet penetration is increasing along with the need for data packages for communication such as social media, chatting, video conferencing and others. On large-scale networks such as the Internet, dynamic routing is used to build routing protocol information in the routing table automatically. Currently, Djikstra's algorithm is used to solve the shortest path problem in dynamic routing. In this research, the optimization of the algorithm is carried out in determining the best path or trajectory. One of the optimization algorithms is the Tabu Search Algorithm which can guide heuristic local search procedures to explore the solution area outside the local optimum point. This optimization is assessed from the test parameters measured from the smallest cost. The data analyzed is in the form of bandwidth and topological flow. From the results of tracing the path of data packets sent through 9 routers using the Tabu Search algorithm with the parameters namely number of Neighbor Solutions = 50, Length of tabu list = 10, Maximum Number of Iterations = 100, the result of the path matrix value is 180.9676. The path taken is router 0-2-4-8-9

Wireless sensor network routing optimization based on improved ant colony algorithm in the Internet of Things

The Internet of Things (IoT) connects devices, enabling real-time data acquisition, automation, and collaboration. Wireless sensor networks are one of the important components of the Internet of Things, consisting of many wireless sensor nodes distributed in space. These nodes can perceive environmental information and transmit it to other nodes through wireless communication. In wireless sensor network routing optimization methods, improved ant colony algorithm can be used to find the optimal routing scheme. Ant colony algorithm simulates the behavior of ants in the process of searching for food, and optimizes factors such as transfer probability and pheromone concentration to enable ants to find the shortest path. In wireless sensor networks, node positions can be used as reference nodes and anchor nodes, combined with the objective function of wireless sensor network routing optimization, and improved ant colony algorithm can be used to solve the optimal path, thus obtaining the optimal wireless sensor network routing optimization scheme. Through experimental results, it can be found that the proposed method performs well in terms of energy consumption, transmission delay, number of dead nodes, and network throughput. These optimization results have positive implications for the sustainable development and practical application of the Internet of Things, which can improve the development of the digital economy and enhance the construction of smart cities.

The flow of axonal information among hippocampal sub-regions 2: patterned stimulation sharpens routing of information transmission.

The sub-regions of the hippocampal formation are essential for episodic learning and memory formation, yet the spike dynamics of each region contributing to this function are poorly understood, in part because of a lack of access to the inter-regional communicating axons. Here, we reconstructed hippocampal networks confined to four subcompartments in 2D cultures on a multi-electrode array that monitors individual communicating axons. In our novel device, somal, and axonal activity was measured simultaneously with the ability to ascertain the direction and speed of information transmission. Each sub-region and inter-regional axons had unique power-law spiking dynamics, indicating differences in computational functions, with abundant axonal feedback. After stimulation, spiking, and burst rates decreased in all sub-regions, spikes per burst generally decreased, intraburst spike rates increased, and burst duration decreased, which were specific for each sub-region. These changes in spiking dynamics post-stimulation were found to occupy a narrow range, consistent with the maintenance of the network at a critical state. Functional connections between the sub-region neurons and communicating axons in our device revealed homeostatic network routing strategies post-stimulation in which spontaneous feedback activity was selectively decreased and balanced by decreased feed-forward activity. Post-stimulation, the number of functional connections per array decreased, but the reliability of those connections increased. The networks maintained a balance in spiking and bursting dynamics in response to stimulation and sharpened network routing. These plastic characteristics of the network revealed the dynamic architecture of hippocampal computations in response to stimulation by selective routing on a spatiotemporal scale in single axons.

Traffic grooming with greedy-based priority routing and wavelength assignment for passive optical networks

Abstract Today, in passive optical networks (PON) the major issue is call blocking and it is getting worse as there is an increase in the number of connection requests but the wavelength channels in fiber links are limited. In this research, greedy-based priority routing and wavelength assignment traffic grooming (GPRWATG) technique is proposed aimed at reducing call blockage. In this scheme, to avoid optical–electrical–optical correspondence, firstly the grooming of connection requests with same source destination (s–d) is performed. According to the priority of these groomed connection requests, wavelength assignment and routing is assigned. This approach not only addresses the call blocking issue but also aligns with industry demands for improved network infrastructure. The proposed work performance is analyzed for blocking probability (BP), congestion, and its performance is compared with the non-priority-based routing and wavelength assignment traffic grooming (NPRWATG) and priority-based routing and wavelength assignment traffic grooming (PRWATG) schemes. The proposed method has 23.6 % lower congestion as compared to PRWATG and 21 % lower congestion as compared to NPRWATG. Also, the BP of GPRWATG is 26 % less than PRWATG and 21 % less than NPRWATG. Thus, it can be analyzed that by using the proposed technique, the BP as well as the congestion of the network altogether is reduced in comparison to the existing state-of-art techniques.

Node utilization index-based data routing and aggregation protocol for energy-efficient wireless sensor networks

Node utilization index-based data routing and aggregation protocol for energy-efficient wireless sensor networks

A Java Application for Teaching Graphs in Undergraduate Courses

Graph theory is a common topic that is introduced as part of the curricula of computing courses such as Computer Science, Computer Engineering, Data Science, Information Technology and Software Engineering. Understanding graphs is fundamental for solving many real-world problems, such as network routing, social network analysis, and circuit design; however, many students struggle to grasp the concepts of graph theory, as they often have difficulties in visualising and manipulating graphs. To overcome these difficulties, educational software can be used to aid in the teaching and learning of graph theory. This work focuses on the development of a Java system for graph visualisation and computation, called MaGraDa (Graphs for Discrete Mathematics), that can help both students and teachers of undergraduate or high school courses that include concepts and algorithms related to graphs. A survey on the use of this tool was conducted to explore the satisfaction level of students on a Discrete Mathematics course taken as part of a Computer Engineering degree at the University of Alicante (Spain). An analysis of the results showed that this educational software had the potential to enhance students’ understanding of graph theory and could enable them to apply these concepts to solve practical problems in the field of computer science. In addition, it was shown to facilitate self-learning and to have a significant impact on their academic performance.

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.

AF-FDS: An Accurate, Fast, and Fine-Grained Detection Scheme for DDoS Attacks in High-Speed Networks With Asymmetric Routing

Distributed Denial of Service (DDoS) attacks have posed severe threats to the Internet. Although researchers have proposed many DDoS detection schemes, there are still some challenging issues. Traditional per-flow-based DDoS methods are impractical for massive amounts of high-speed network traffic due to the huge resource consumption. In addition, existing methods are not designed to take into account the widespread asymmetric routing in high-speed networks, resulting in false positives when these methods are deployed on the Internet. Furthermore, existing methods can not achieve a good trade-off between detection accuracy and granularity when detecting hybrid DDoS attacks. This paper proposes an Accurate, Fast, and Fine-grained Detection Scheme (AF-FDS) for DDoS attacks in high-speed networks with asymmetric routing. We select features based on the characteristics of DDoS attacks and design a data structure Double Composite Structure Sketch (DCSS). DCSS can achieve fast recording and extraction of the selected features from the sampled traffic. Experimental results using real-world traces in a 10Gbps network with asymmetric routing show that AF-FDS can detect nine types of DDoS attacks at a fine-grained level within 15 seconds with over 98.0% precision and recall, even at a sampling rate of 1/1024. Furthermore, the comparison with several state-of-the-art methods illustrates that AF-FDS can detect DDoS attacks with a lower false positive rate (FPR) and shorter alarm time in asymmetric routing scenarios.

Shortest Path Routing Performance Evaluation over SDN Environment

Static routing has a manual configuration setup system, and the scope of static routing in an SDN network is just for small networks. The solution to this problem rises up with the new technology defined as software-defined networking (SDN) based on shortest path first dynamic routing. SDN has the facility of a centralized controller that smooth the controls and routes computation over a data packet. The performance analysis of SDN networks that have SDN switches connected to the network based on the shortest path first protocol are simulated on Mininet. The POX controller with Mininet programming feature for creating smart topologies was chosen. In this research, the SDN network using Dijkstra’s algorithm, Bellman-Ford algorithm, extended Dijkstra’s algorithm and Floyd Warshall Algorithm were implemented. The quality factors of SDN created by using four algorithms are measured in terms of delay, jitter, latency, packet loss, transmit, received, throughput, and bandwidth based on experimental results and European Telecommunications Standards Institute (ETSI) data. The performance parameters of SDN network topology created using Dijkstra’s, bellman ford, extended Dijkstra’s, and Floyd Warshall algorithms were compared and the experimental results showed that Bellman-Ford algorithm is better in terms of performance parameters than the other three algorithms.

Coordination and Optimization Control Framework for Vessels Platooning in Inland Waterborne Transportation System

Vessels sailing in a single platoon could reduce resistance from the perspective of the whole platoon and the individual vessel, and contribute to improving energy benefits. Moreover, transportation energy costs and traffic efficiency are essential indicators for measuring waterborne transportation systems. We attempt to minimize transportation energy costs by coordinating platoon formation using a distributed framework of controllers. A large-scale coordinated vessel platooning program is proposed to minimize transportation energy costs and optimize traffic efficiency while guaranteeing safety. The control framework covers routing, energy consumption-dependent cooperative platooning decision and speed optimization based on graph search algorithm, cluster analysis, optimal control approach and model predictive control. Firstly, a local scheduling strategy combined with the leader vessel selection algorithm is adopted. Furthermore, we used cluster analysis to create a series of mergeable vessel platooning sets. Then, we used the mathematical planning method and a two-step hybrid optimal control approach to calculate the improvement and optimization of each vessel platoon’s path and speed. Finally, the scalability of the scheduling strategy is elucidated. In a simulation of large scale inland waterborne network, savings surpassed 3.5% when six hundreds vessels participated in the system. These simulation results reveal that the scheduling strategy coordinating vessels into vessel platooning, which improves transportation efficiency as well as descends cost, comparing to a fixed origin route in the waterway network.

A Novel Routing Control Method Using Federated Learning in Large-Scale Wireless Mesh Networks

Currently, the volume of communication by mobile terminals are increasing owing to 5G and other technologies. A robust network and appropriate routing control methods are requied to transmit information in unstable wireless communication environments and avoid congestion. Therefore, in recent years, numerous studies have been conducted on wireless mesh networks (WMNs), which provide a fault-tolerant communication environment by securing multiple communication paths and whose topology can be freely configured and extended. Additionally, machine learning routing is attracting attention as a new routing method for wireless communication environments. However, when performing machine learning on a large WMN, the learning time increases and rapid routing control may be impossible. In this study, we apply federated learning to machine learning and propose a machine-learning-based routing method that can be applied to large-scale WMNs. Furthermore, experimental results demonstrate the effectiveness of the proposed method in various environments: congestion avoidance is achieved in a large-scale WMN by machine-learning routing using federated learning. This study is expected to serve as a basis for significant progress in the realization of large-scale WMNs as wireless communication infrastructure.

Review on greedy algorithm

The greedy algorithm is a commonly used algorithm design idea that can provide efficient solutions to many practical problems. This paper aims to review and summarize the basic ideas, characteristics and application fields of greedy algorithms, and discuss their advantages and limitations. Firstly, the basic concepts of greedy algorithms are introduced, including the greedy selection properties and optimal substructures. Then, some classic greedy algorithms such as the backpack problem, the activity selection problem, and the minimum spanning tree problem are introduced, and the concept of time complexity is introduced. Next, the application of greedy algorithms in practical problems, such as scheduling problems, network routing, and graph generation, will be discussed. Finally, the advantages of the greedy algorithm and the limitation of the inability to obtain the global optimal solution will be evaluated, and the improvement direction combined with other algorithms will be proposed.

Routing algorithms in internet of things complex network with the role of machine learning

In recent years, the growth of internet-based technologies increased at a rapid pace. The development of technologies such as the Internet of Things (IoT) influenced the enormous increase in the use of data and internet services. IoT devices use different algorithms for facilitating connectivity between devices and control them. However, ensuring smooth connectivity using protocols across a shared medium of network resources is challenging. IoT ecosystems utilize several routing algorithms to deliver the best path/ route for network traffic to control cyber physical systems. These routing algorithms allow IoT networks to use network routes, thereby increasing network traffic mobility effectively. So, a comprehensive survey is needed, which paves the path for researchers. Specifically, this survey investigates and compares the routing solutions in the IoT environment from different perspectives than current surveys such as safety, flow control of data and other essential parameters in IoT physical and nonphysical systems.

Pre- and post-processing techniques for reinforcement-learning-based routing and spectrum assignment in elastic optical networks

Research on the routing and spectrum assignment (RSA) problem has long been conducted with the aim of efficiently utilizing the frequency resources of optical networks. Given the recent progress in machine learning (ML) technology, it has been reported that the application of ML in various areas of optical network design, operation, and management has the potential to bring about new innovations such as autonomous optical network operation and highly accurate estimation of network conditions. With regard to the RSA problem, it is expected that an algorithm that achieves better accommodation efficiency than conventional heuristic methods can be realized by applying reinforcement learning (RL), which well supports training in a simulation environment. In this paper, we introduce and evaluate three techniques devised to apply RL more effectively to elastic optical networks (EONs): two pre-processing techniques called link-axis positional encoding (LPE) and slot-axis positional encoding (SPE) and a post-processing technique named the assignable boundary slot mask. First, we build a simple model in which the state data of the optical network, including frequency slot utilization, are input to the neural network of the RSA agent in RL and show that this model has difficulty outperforming the conventional heuristic RSA algorithm. Next, we build an RSA agent model with the proposed techniques and simulate the accommodation of dynamic optical paths to quantitatively demonstrate that the blocking probability can be reduced by 17% compared to the conventional heuristic.

Secure distance based multi-objective artificial rabbits algorithm for clustering and routing in cognitive radio network

Secure distance based multi-objective artificial rabbits algorithm for clustering and routing in cognitive radio network

Heuristic Path Search and Multi-Attribute Decision-Making-Based Routing Method for Vehicular Safety Messages

Efficient routing in urban vehicular networks is essential for timely and reliable safety message transmission, and the selection of paths and relays greatly affects the quality of routing. However, existing routing methods usually face difficulty in finding the globally optimal transmission path due to their greedy search strategies or the lack of effective ways to accurately evaluate relay performance in intricate traffic scenarios. Therefore, we present a vehicular safety message routing method based on heuristic path search and multi-attribute decision-making (HMDR). Initially, HMDR utilizes a heuristic path search, focusing on road section connectivity, to pinpoint the most favorable routing path. Subsequently, it employs a multi-attribute decision-making (MADM) technique to evaluate candidate relay performance. The subjective and objective weights of the candidate relays are determined using ordinal relationship analysis and the Criteria Importance Through Intercriteria Correlation (CRITIC) weighting methods, respectively. Finally, the comprehensive utility values of the candidate relays are calculated in combination with the link time and the optimal relay is selected. In summary, the proposed HMDR method is capable of selecting the globally optimal transmission path, and it comprehensively considers multiple metrics and their relationships when evaluating relays, which is conducive to finding the optimal relay. The experimental results show that even if the path length is long, the proposed HMDR method gives preference to the path with better connectivity, resulting in a shorter total transmission delay for safety messages; in addition, HMDR demonstrates faster propagation speed than the other evaluated methods while ensuring better one-hop distance and one-hop delay. Therefore, it helps to improve the performance of vehicular safety message transmission in intricate traffic scenarios, thus providing timely data support for secure driving.

Solving the routing and spectrum assignment problem, driven by combinatorial properties

AbstractThe routing and spectrum assignment problem in modern optical networks is an NP‐hard problem that has received increasing attention during the last years. The majority of existing integer linear programming models for the problem uses edge‐path formulations where variables are associated with all possible routing paths so that the number of variables grows exponentially with the size of the instance. To bypass this difficulty, precomputed subsets of all possible paths per demand are typically used, which cannot guarantee optimality of the solutions in general. Our contribution is to provide a framework for the use of edge‐path formulations to minimize the spectrum width of a solution. For that, we select an appropriate subset of paths to operate on with the help of combinatorial properties in such a way that optimality of the solution can be guaranteed. Computational results indicate that our approach is indeed promising to solve the routing and spectrum assignment problem.