Routing In Networks Research Articles

Optimized identification for critical metro stations from urban resilience perspective

Urban rail transit systems and metro-led underground space contribute to urban resilience. The critical level of metro-led underground space in a metro network demands different resilience requirements, thereby rendering the identification of critical metro stations before the evaluation of the resilience contribution essential. This study optimized the identification of critical metro stations in terms of network model construction and evaluation methods to suit urban resilience perspective. The optimized metro network model considered the influence of transfers, difference between above-ground and underground routes, and cost between adjacent stations, to construct an undirected weighted graph of a metro network. Three evaluation metrics: node betweenness, network efficiency loss, and route redundancy, were adopted for a comprehensive identification method of critical stations. The optimized identification method was applied to the Shanghai Metro network (402 stations and 481 links by 2023). The betweenness identified the downtown stations to be more critical, whereas the network efficiency loss and route redundancy identified the critical stations at downtown-suburban connections. Moreover, the vulnerability and distribution of above-ground stations influenced the identification result of critical stations. The identification results are expected to facilitate the resilience evaluation of metro-led underground space, to examine whether the contribution is balanced with the demand.

Joint Network Function Placement and Routing Optimization in Dynamic Software-Defined Satellite-Terrestrial Integrated Networks

Joint Network Function Placement and Routing Optimization in Dynamic Software-Defined Satellite-Terrestrial Integrated Networks

Low Earth Orbit Satellite Network Routing Algorithm Based on Graph Neural Networks and Deep Q-Network

Low Earth orbit (LEO) satellite networks are characterized by rapid topological changes, numerous network nodes and varying states of node resource constraints, which have resulted in traditional routing algorithms no longer being suitable for LEO satellite network routing. Therefore, this paper proposes an inductive learning architecture based on Graph Sample and Aggregate (GraphSAGE), which can significantly reduce the number of topology nodes to be trained, thereby reducing the computational complexity of the nodes. Then deep reinforcement learning (DRL) is employed for the continuous learning optimization of routing algorithms, and its generalization is improved by selecting GraphSAGE to construct the DRL agent. In the proposed graph neural-network-based routing optimization algorithm for LEO satellite networks, each Deep Q-Network (DQN) agent independently generates the hidden states of the nodes through the GraphSAGE model and uses them as inputs to the DRL model to make routing decisions. After a simulation and comparison, the proposed algorithm not only improves the overall network throughput, but also reduces the average end-to-end delay. The average throughput of the proposed algorithm increases by 29.47% and 18.42% compared to that of Dijkstra and the DQN, respectively. The average end-to-end delay is reduced by 39.76% and 15.29%, respectively, and can also adapt to changing topologies.

ER-OCN: Toward efficient network routing in ocean city based on deep reinforcement learning

Smart ocean cities are crucial to future city development, and offshore networks are an essential part of smart ocean cities. Nowadays, communication in offshore networks is becoming increasingly important. However, the offshore networks are highly heterogeneous, with low reliability, narrow bandwidth, and dramatic dynamics. Offshore networks are more challenging to manage than traditional terrestrial networks, and the standard quality of service routing faces significant challenges. Currently, artificial intelligence provides a way to meet the requirements. This paper proposes ER-OCN (Efficient Routing in Ocean City Networks), a two-stage routing optimization scheme driven by artificial intelligence to cope with the complex offshore networks environment. The first stage will calculate the flow paths based on Lagrangian relaxation. Besides, the ocean environment is under dramatic dynamics, leading to unpredictable network topology or link bandwidth changes. Thus, the second stage deployed a deep reinforcement learning algorithm to monitor the environment in real-time and update the routing strategy when needed. We conducted experiments based on actual network topology and traffic data sets of offshore platforms’ network systems. The results prove that our scheme improves communication cost performance by 36.61% compared to traditional methods.

ARPMEC: an adaptive mobile edge computing-based routing protocol for IoT networks

The Internet of Things (IoT) networks comes with many challenges, especially in network architecture designs. IoT is populated by several kinds of devices with different characteristics that are autonomously managed. These devices do not have enough resources and they require to process data in real-time. Hence, there is a need to design suitable architectures for IoT networks that are as efficient as possible. Previously, Cloud Computing (CC) seemed to provide a good solution of processing data from IoT networks. Recently, Mobile Edge Computing (MEC) seems to be offering a better solution than CC by ensuring a better Quality of Services (QoS) provisioning. As a result, many MEC solutions have emerged for QoS improvement in IoT networks. These solutions mainly focus on device resource management without considering data routing from an end-user device to another, especially when the latter are mobile and need to communicate with each other. In this paper, we propose to design an adaptive routing protocol for a MEC-based network to manage efficiently, the end-user devices’ energy consumption during data routing. The proposed adaptive Mobile Edge Computing-based protocol consists of two main phases: firstly, we subdivide the network’s objects into clusters by exploiting a link quality prediction algorithm. Secondly, we route the data to their destination adaptively by considering the object’s movement during the routing process. As presented in the simulation results, our protocol outperforms other existing routing protocols for IoT networks in terms of energy consumption. We then propose the use of our solution for data routing in IoT networks that require huge data processing and forwarding.

Development of a routing method for ground-air Ad-Hoc network of special purpose

The object of the study is the process of forming control decisions to ensure the operation of the ground-air communication network routing subsystem based on neural network algorithms. The carried-out research is based on the application of the numerical-analytical approach to the selection of modern scientific and applied solutions for building management models for promising Ad-Hoc communication networks. In the Google Collab simulation environment, using the Python programming language, it was possible: firstly, to simulate the operation of a ground-to-air communication network based on previously obtained models and a routing process management system based on the FA-OSELM algorithm. Secondly, in accordance with the scenario of route construction and maintenance described in the article, to experimentally determine the communication metrics of the proposed method of intelligent routing of the ground-air Ad-Hoc special-purpose network, in order to assess its efficiency, adequacy and reliability of the results obtained. Thus, in order to evaluate the effectiveness of the proposed solutions, a comparative analysis of the application of three existing routing methods (FLCA, Q-Routing, Neuro Routing) used in Ad-Hoc networks relative to the developed method was conducted. The result of the experiment showed that the proposed routing method MAODV-FA-OSELM provides significant advantages over analogs. Thus, the method exhibits the best network throughput (2.12e+06), the lowest average network latency (0.12), the lowest packet loss (6.32), the lowest bit error rate (2.41), and the lowest overhead (0.10e+06). However, it should be noted that a promising direction of further research may be the study of the computational complexity of the routing management process and the determination of the minimum allowable representative sample of initial data to ensure online decision-making.

A comprehensive review on vehicular ad-hoc networks routing protocols for urban and highway scenarios, research gaps and future enhancements

A comprehensive review on vehicular ad-hoc networks routing protocols for urban and highway scenarios, research gaps and future enhancements

Financial and Economic Situation and Development Features of Aviation Industry Entities

The relevance of the problem is determined by the fact that under the conditions of sanction pressure on the economy of the Russian Federation and periodic restrictions on the movement of air vehicles in international airspace, gaps arise in the continuity of functioning of aviation industry entities, creating barriers to obtaining and maximizing profi ts. The purpose of the study is to analyze the fi nancial and economic situation of aviation industry entities and determine the conditions for their further development. Materials and research methods — the systematic approach and situational analysis used in writing the article were based on processing the resources of information retrieval systems: GARANT, Consultant Plus. Also, the use of abstraction, grouping and series of dynamics of revenue, cost and profi t of the largest airlines in Russia made it possible to identify the priorities and causes of the fi nancial decline of the leaders of the national aviation market. As a result of the analysis of indicators of transport work and the aircraft fleet of the Russian air transport system, the reasons and possibilities for ensuring the availability of air transportation through the implementation of government support measures, including subsidizing the transportation of citizens and the development of regional transportation, were established. The article provides facts based on the results of the analysis of the Comprehensive Program of the current state and development of the aviation industry of the Russian Federation until 2030, among which important attention is paid to the need to achieve a gradual reduction in the number of foreign-made aircraft fl eet. The article reflects that the priority factors influencing the determination of consumer choice in the aviation market are: effective on-board technologies; network route offers and various loyalty programs for regular customers of passenger services. The authors of the article identified the reasons for the decline of PJSC Aeroflot, which was experiencing fi nancial difficulties and the risk of losses. The directions for further development of the largest airlines in Russia have been determined, taking into account «targeted» subsidizing measures and improving measures of state support for subjects of the Russian airline business.

High-fidelity entanglement routing in quantum networks

Quantum networks, endowed with their distinct quantum properties, are poised to emerge as indispensable platforms for the implementation of quantum information technology applications in the near future. Within the realm of quantum networks, the transmission of remote quantum information presents a pivotal and intricate challenge, commonly referred to as the entanglement routing problem. While previous efforts have delved into the intricacies of entanglement routing, there has been a notable oversight concerning the crucial parameter of entanglement fidelity. Hence, this paper introduces a novel Purification-enabled Entanglement Routing Algorithm (PERA). Initially, PERA utilizes the network throughput as the routing metric and ensures fidelity guarantees on all nodes in the link through a hop-by-hop purification process. Subsequently, acknowledging the probabilistic nature of entanglement purification, a strategy involving multiple attempts at purification is employed. Finally, a comprehensive utility function is deployed to address the path selection problem for multiple source–destination pairs. Numerical simulations confirm that PERA establishes entanglement connections with outstanding fidelity guarantees. Concurrently, PERA outperforms the baseline algorithm in terms of network throughput and resource utilization.

FPGA-based Inference Parallelization for Onboard RL-based Routing in Dynamic LEO Satellite Networks

FPGA-based Inference Parallelization for Onboard RL-based Routing in Dynamic LEO Satellite Networks

Design of Smart Home System based on Wireless Sensor Network Link Status Awareness Algorithm

When wireless sensor networks are used in smart homes, the connection state will be unstable due to signal masking attenuation. This will cause low packet rate, high time delay and high cost in the network. In this paper, a network routing algorithm for wireless sensing based on connection conditions is designed. Secondly, the expected number of sends is proposed to evaluate the stability of links. Based on this, the following network signal delivery situation is forecasted in real time and quickly. According to the estimated expected number of transmissions, the path is dynamically corrected to effectively avoid attenuation in the channel and achieve optimal system performance. Experimental results show that the method proposed in this paper can improve the efficiency of message sending and reduce the routing cost under the condition of masking effect.

A survey on routing and load-balancing mechanisms in software-defined vehicular networks

A survey on routing and load-balancing mechanisms in software-defined vehicular networks

Trust Enabled Cluster Aware Routing Protocol Design with Energy Considerations using Fuzzy Strategies

In this modern era, wireless communication technologies are getting high impact over network industries. Due to this drastic development of communication technologies, a network needs to be more precise with certain metric such as: energy consumption, throughput, trust management, performance and lifetime. These are all the primary concern while handling the transactions with wireless communication systems. In this paper, a new approach of network routing protocol is introduced, which will replace the issues found in existing routing protocols and provide good support while communication between entities. The proposed paper introduces a new routing protocol called Modified Adhoc On-Demand Distance Vector (MAODV), in which the classic AODV protocol supports mobile nodes with several impacts such as mobility, failure prevention, loss management and so on. However, the issues in industry still remains, so, that a new approach or routing protocol is required to overcome such issues. This is the concern to develop a new routing protocol for establishing communication between mobile nodes or wireless devices. The drastic development of communication devices requires some latest technologies to provide range free communication model with the assistance of internet support. A term, 'Internet of Things (IoT)', which rules the world now-a-days through its power of connectivity and establishing the bridge between communication devices around the globe without any range restrictions. In this paper, a cluster based routing strategy is designed with the help of fuzzy assistance. The cluster based routing protocols divides the range of communication into clusters and electing the cluster head based on the communication needs and provides a range free communication model with the hands of IoT support. This cluster based routing model provides support for both inter and intra-cluster communications, which all will be brief in further sections. The proposed model of wireless communication assures the following things such as Improved Energy Efficiency, reduced packet loss and delay, improved throughput ratio and lifetime over resulting scenario with the help of a new algorithm called Fuzzy Assisted Cluster enabled Routing Model (FACeRM) with the association of new routing protocol MAODV.

Optimal Routing in Wireless Sensor Networks for Advancing IoT Efficiency and Sustainability using Enhanced Ant Colony Algorithm with machine learning approaches

This research study aims to investigate the incorporation of machine learning tools, such as Q-learning, Genetic Algorithms, Unsupervised Learning, and Ensemble Learning, into Enhanced Ant Colony Algorithm to assess the impacts of such incorporation on the WSN’s performance. Ten experimental trials were conducted on each to analyze the accuracy, precision, and F1 score results. It was observed that Q-learning achieves an average accuracy of 0.867; precision of 0.842; and F1 score of 0.854, making it highly adaptable and efficient in making routing decisions. The GA presented average accuracy of 0.833; precision of 0.812; and F1 score of 0.821 which show that the tool is highly robust in evolutionary optimization. Unsupervised learning machine performances indicate that the mean accuracy, precision, and F1 score for the model are 0.875, 0.856, and 0.865, respectively . As for ES with multi source models, the model showed the highest performance of 0.898, 0.882, and 0.891 in accuracy, precision, and F1 score, respectively . This study is thus very valuable in the backend regarding application of machine learning tools into routing optimization algorithms and efforts geared towards WSN efficiencies and sustainability..

Multi-Agent Deep Reinforcement Learning for Packet Routing in Tactical Mobile Sensor Networks

Multi-Agent Deep Reinforcement Learning for Packet Routing in Tactical Mobile Sensor Networks

Intelligent Routing in Directional Ad Hoc Networks Through Predictive Directional Heat Map From Spatio-Temporal Deep Learning

By applying a simple shortest/minimum-cost routing algorithm, the mobile ad-hoc network (MANET) with heavy data transmissions may be easily congested if multiple routing paths meet at the same relay node. A path may be unable to provide high throughput since some of its path nodes are helping other paths to forward the heavy traffic. Therefore, those busy nodes should be avoided when a new path is to be established. The task of optimal path seeking becomes more challenging when a MANET is equipped with directional antennas that may cause directional interference with neighboring receivers. The motivation of our research is to build an intelligent proactive routing scheme for MANETs with directional antennas. Our directional routing protocol considers not only the global traffic distribution in different areas of the MANET, but also the properties of directional antennas. It uses spatio-temporal deep learning algorithm to predict the next-time snapshot of directional heat map (DHM), which shows the traffic density distribution in each network location as well as the coverage of each directional antenna. Such a DHM will be used to identify the optimal path that can avoid congested areas as well as the interference from all neighboring directional links. Furthermore, an optimization algorithm is designed to perform optimal path selection. It is able to automatically split a single path into multiple ones that later on get converged again into one path, if the path needs to go around a congested area. Therefore, our routing scheme achieves better quality-of-service (QoS) performance than traditional routing mechanisms such as AODV and OLSR.

Foraging behaviour of Pheidole latinoda: An impetus for employing a heuristic approach to address optimization challenges

The primary goal of the binary model in this study was to understand the convergence pattern of the Pheidole latinoda ants. Forager and scout ants on the hunt for food use path integration. When they find a food source, they leave a trail pheromone to alert other nest mates. Every ant starts following that trail and reinforces it on their way back home. To investigate the ant convergence pattern, binary and ternary bridges of varying lengths are used. Each bridge is built in such a way that one end is connected to a food source whilst the other end is connected to the nest. The food source is surrounded by water-filled islands. The Pheidole latinoda ant's convergence pattern has been observed following the successful installation of a bridge near the ants' nest. This species took between 1-3 and 3-4minutes to find the shortest possible path. Numerous studies looking for optimal solutions, such as those addressing the challenges of travelling salesmen, routing in communication networks, etc., may use this convergence or path optimization as their new starting point.

Research on Secure Community Opportunity Network Based on Trust Model

With the innovation of wireless communication technology and the surge of data in mobile networks, traditional routing strategies need to be improved. Given the shortcomings of existing opportunistic routing strategies in transmission performance and security, this paper proposes a community opportunistic routing decision-making method based on the trust model. This algorithm calculates the node’s trust value through the node’s historical forwarding behavior and then calculates the node’s trust value based on the trust model. Thresholds and trust attenuation divide dynamic security communities. For message forwarding, nodes in the security community are prioritized as next-hop relay nodes, thus ensuring that message delivery is always in a safe and reliable environment. On this basis, better relay nodes are further selected for message forwarding based on the node centrality, remaining cache space, and remaining energy, effectively improving the message forwarding efficiency. Through node trust value and community cooperation, safe and efficient data transmission is achieved, thereby improving the transmission performance and security of the network. Through comparison of simulation and opportunistic network routing algorithms, compared with traditional methods, this strategy has the highest transmission success rate of 81% with slightly increased routing overhead, and this algorithm has the lowest average transmission delay.

Reliability Optimization of Traffic Routing in Train Communication Networks Based on Faulty Link Diagnosis

With the increasing adoption of industrial Ethernet in train communication network (TCN), the limitations of insufficient reliability and incompatibility between different Ethernet technologies have become increasingly apparent. To address these challenges, time‐sensitive networking (TSN) has emerged as a solution, enabling multi‐network convergence and enhancing data transmission determinism and reliability through techniques such as frame replication and elimination for reliability (FRER). However, the utilization of these policies will adversely affect schedulability due to increased network load. To address the issue of link failures in TCN topologies, a routing policy based on faulty link diagnosis is proposed. The proposed policy utilizes an improved max‐min ant system (IMMAS) for diagnosing and resolving faulty links. Furthermore, a routing optimization algorithm is introduced to enhance transmission reliability by planning alternative paths for traffic. To evaluate the performance of the proposed routing algorithm, multiple sets of test cases are conducted separately under different failure scenarios to compare them with other routing policies. The results demonstrate the superior reliability of the proposed routing policy over alternative approaches. Additionally, the traffic utilizing the proposed routing policy show improved schedulability following integer linear programming (ILP) scheduling. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Performance, privacy and security issues at the TCP internet layer

The rapid growth of internet-based services and applications has revolutionized the way we communicate, interact, and conduct business. However, this widespread connectivity also brings forth numerous challenges related to performance and privacy security at the internet layer. This paper aims to provide a comprehensive analysis of the various issues that arise in this context, highlighting their impact on user experience, data protection, and network efficiency, to address performance concerns; the research paper investigates the factors affecting internet Layer. Through extensive research studies, the paper examines the impact of network congestion, routing inefficiencies, and protocol limitations on the overall performance of internet-based services. This research delves into the vulnerabilities and risks associated with the internet layer. It investigates the potential threats posed by malicious actors, including hackers, data breaches, and unauthorized access to sensitive information. The findings of this research contribute to a comprehensive understanding of the interplay between performances and privacy security issues at the internet layer. By identifying the key challenges and potential solutions, this study provides valuable insights for network administrators, service providers, policymakers, and end-users to optimize performance while ensuring robust privacy and security measures. Ultimately, this research aims to foster a safer and more efficient internet ecosystem for all stakeholders.