Dynamic Routing Method Research Articles

DAR-DRL: A dynamic adaptive routing method based on deep reinforcement learning

Mobile-centric wireless networks offer users a diverse range of services and experiences. However, existing intelligent routing methods often struggle to make suitable routing decisions during dynamic network changes, significantly limiting transmission performance. This paper proposes a dynamic adaptive routing method based on Deep Reinforcement Learning (DAR-DRL) to effectively address these challenges. First, to accurately model network state information in complex and dynamically changing routing tasks, we introduce a link-aware graph learning model (LA-GNN) that efficiently senses network information of varying structures through a hierarchical aggregated message-passing neural network. Second, to ensure routing reliability in dynamic environments, we design a hop-by-hop routing strategy featuring a large acceptance domain and a reliability guarantee reward function. This mechanism adaptively avoids routing holes and loops across various network scenarios while enhancing the robustness of routing under dynamic conditions. Experimental results demonstrate that the proposed DAR-DRL method achieves the network routing task with shorter end-to-end delays, lower packet loss rates, and higher throughput compared to existing mainstream methods across common dynamic network scenarios, including cases with dynamic traffic variations, random link failures (small topology changes), and significant topology alterations.

A energy efficiency optimization routing processing method for Linear Wireless Sensor Networks

Linear wireless Sensor networks (LWSNs) are extensively utilized to monitor linear infrastructure such as railways, bridges, mines, and borders. In the large-scale deployment of sensor networks, extending network life and improving network energy balance remains a significant challenge. The existing routing protocols adopt the path planning method based on the routing information database, which can effectively transmit data packets. However, the methods consume additional energy when establishing and maintaining routing tables, significantly reducing the nodes’ lifespan and leading to unstable data transmission quality. This paper proposes a dynamic random multipath routing method (DRMRM) for LWSNs. The technique combines the node depth and residual energy models to select the optimal next-hop relay node without relying on the transmission routing table. At the same time, we designed a data loss retransmission mechanism and a data loop retreat mechanism to prevent data packets from reaching a dead end. The experimental results demonstrate that our routing method is superior to existing energy consumption balance and network lifespan protocols.

Communication channel parameters estimation using a ray tracing model in heterogeneous networks

Currently, modern networks LTE, Wi-Fi, 5GNR are combined into a single heterogeneous network, which leads to an increase in intra-system interference. According to the integrated optimization method, to distribute data flows in a heterogeneous network, taking into account the intra-system interference influence, the collective dynamic routing method is used. At the analysis stage of this method it is necessary to estimate the channel data rate in all valid routes. To obtain correct channel data rates estimates, it is necessary to reliably estimate the channel matrix. Today, the ray tracing model is widely used to estimate the channel matrix. The verification results of the using ray tracing model given in [1] show the need for additional research to increase the channel matrix estimates reliability. Therefore increasing the reliability of channel matrix transmission coefficients estimates by analyzing the influence of the number of rays taken into account is the actual task. The work analyzes the influence of the number of rays taken into account on the reliability of estimating the complex transmission coefficients of the channel matrix for the cases of the presence and absence of line of sight be-tween the transmitter and the receiver. Expressions are obtained to take into account the influence of diffracted, singly reflected and passed through an obstacle rays. A program has been developed to find all possible rays arriving at the receiver location point and determine their parameters based on a digital 3D map indicating the map objects materials. Using the developed program, it is shown that taking into account the influence of additional rays makes it possible to increase the channel matrix estimation reliability, and therefore, the modified ray tracing model can be used to estimate channel data rates when applying the collective dynamic routing method in heterogeneous net-works.

Modification of the collective dynamic routing method in heterogeneous networks

In the heterogeneous networks, a complex signal-interference environment is formed, characterized by a intra-system interference high level, which significantly reduces the heterogeneous networks throughput. One of the ways to increase such networks throughput by effectively reducing the intra-system interference influence is to use the integrated optimization method of these networks. However, its application is hampered by the complexity of implementing the collective dynamic routing method that underlies it. Therefore, modification of the collective dynamic routing method in relation to large heterogeneous networks is relevant. As a result of the application analysis of the collective dynamic routing method for optimizing the data flows distribution in heterogeneous networks, it is shown that the main limitation of this method applicability is the computational complexity of finding the optimal data transmission route. For this value the corresponding expressions are obtained. A modification of the collective dynamic routing method has been developed, in which the influence of intra-system interference is taken into account at the subnet segment level, and data distribution between subnets is carried out at the zone level. Using the obtained expressions, it is shown that the computational complexity of implementing the collective dynamic routing method in this case depends only on the number of users in the zone, which ensures the scalability of such a solution. Therefore, the use of the developed modification of the collective dynamic routing method will make it possible to use it to increase the throughput of large heterogeneous networks.

A Multiinterest and Social Interest-Field Framework for Financial Security

Online payment has become an influential method of transaction. While improving convenience, this method of payment also brings great financial risks. Prevalidating transactions can be effective in reducing the number of frauds. For this reason, recommendation algorithms have been introduced to measure the credibility of transactions by predicting users’ ratings of items. However, most algorithms deal with the relationships in social networks without distinction, mixing positive and negative information into the recommender system, which brings huge noise. And, they only generate a single interest representation for each user to measure the similarity between users and spread interest, ignoring the diversity of user interests. Moreover, they did not consider the propagation of different interests would be different. In this article, we propose a multiinterest and social interest-field framework (MISIF) for social recommendations in financial security, which introduces capsule networks into social recommendation and extends the traditional single-interest representation to user multiinterest embedding by dynamic routing (DR) and other methods to improve the expressiveness of user embedding. After that, we construct social interest fields to integrate social interests based on multiinterest embedding, which alleviates the noise in social networks and user data sparsity problems. Finally, we aggregate user multiinterest embedding and additional information through neural networks to obtain the final prediction scores. Experiments with three publicly available datasets show that our proposed MISIF framework outperforms the state-of-the-art social recommendation methods.

Novel Modification of Integrated Optimization Method for Sensor's Communication in Wi-Fi Public Networks.

A novel modification of IP networks integrated optimization method for heterogeneous networks, for example, the seamless Wi-Fi network serving simultaneously mobile users and wireless sensors, has been developed in this article. The mutual influence of signal reception, frequency-territorial planning, and routing procedures in heterogeneous networks have been analyzed in the case of simultaneous data transmission by both mobile users and wireless sensors. New principles for the listed procedures interaction and the basic functions for their describing are formulated. A novel modification of the integrated optimization method and its algorithm have been developed. The developed method's effectiveness has been analyzed for the IEEE 802.11ax network segment. Its result showed that the network load was decreased by an average of 20%, the data rate over the network as a whole increased for users and sensors by an average of 25% and 40%, respectively, and the sensors' lifetime increased by an average of 20% compared to the novel modification of the Collective Dynamic Routing method.

Heart Murmur Classification Using a Capsule Neural Network.

The healthcare industry has made significant progress in the diagnosis of heart conditions due to the use of intelligent detection systems such as electrocardiograms, cardiac ultrasounds, and abnormal sound diagnostics that use artificial intelligence (AI) technology, such as convolutional neural networks (CNNs). Over the past few decades, methods for automated segmentation and classification of heart sounds have been widely studied. In many cases, both experimental and clinical data require electrocardiography (ECG)-labeled phonocardiograms (PCGs) or several feature extraction techniques from the mel-scale frequency cepstral coefficient (MFCC) spectrum of heart sounds to achieve better identification results with AI methods. Without good feature extraction techniques, the CNN may face challenges in classifying the MFCC spectrum of heart sounds. To overcome these limitations, we propose a capsule neural network (CapsNet), which can utilize iterative dynamic routing methods to obtain good combinations for layers in the translational equivariance of MFCC spectrum features, thereby improving the prediction accuracy of heart murmur classification. The 2016 PhysioNet heart sound database was used for training and validating the prediction performance of CapsNet and other CNNs. Then, we collected our own dataset of clinical auscultation scenarios for fine-tuning hyperparameters and testing results. CapsNet demonstrated its feasibility by achieving validation accuracies of 90.29% and 91.67% on the test dataset.

Space/Frequency-Division-Based Full-Duplex Data Transmission Method for Multihop Underwater Acoustic Communication Networks

Underwater acoustic communication networks (UACNs) have been widely utilized in recent years because of the growing interest in interactive information in the deep ocean. Compared with traditional radio wireless networks, UACNs are characterized by complex and dynamic 3-D network topology and longer signal propagation delay. Therefore, recent studies on UACNs usually apply dynamic routes in data communication to adapt to complex UACN structure. However, the approaches are hardly adequate for UACN scenarios with high-traffic requirements. This is because dynamic routing methods, such as opportunistic routing, usually require external contention costs to control the routing paths, which leads to decreased network throughput. Accordingly, this article focuses on enabling high-speed acoustic communications for underwater application scenarios with high-traffic requirements, and proposes an underwater data transmission method using the multichannel full-duplex (FD) communication technique. The proposed method applies the underwater orthogonal frequency division multiple access (OFDM) technique, that uses collision-free channels to relay data in multihop routes for simultaneous transmission and reception. Unlike the traditional communication methods of UACNs with dynamic routing, the proposed one uses static routes when transporting data over hop-by-hop paths to achieve stable and uninterrupted FD communication. The approach also includes a directional forwarding-based route request method and an elimination-based channel evaluation proposal, which purpose to reduce the overheads from exploring routes and enable collision-free FD communications. The performance analysis of the proposed method is under simulated conditions of high-traffic UACN scenarios, and the results show that it has superior performance compare to the classical underwater data transmission methods.

Efficient route discovery method in MANETs and packet loss reduction mechanisms

In the route of expansion in mobiles, quantity of nodes in the mobile ad-hoc networks (MANETs) must be expanded. The MANETs being active in nature and it starts issues in deciding the most ideal route for packets. In this paper an attempt to propose another routing method which consolidates the properties of both static and dynamic routing method from that point tries to take out the issues innate in the system through size-based routing is done. The paper concentrates on efficient route discovery process for secure data transfer with less ratio of packet loss. For the minimisation of assault and packet dropping different authors manufactured different technique such strategy is node validation, distant analysis scheme, ACK-based method, status-based plan and motivating force dependent plan. ACK-based plan endured an issue of monstrous overhead because of additional affirmation packet and it likewise endured choice equivocalness if asked for node decline to send back acknowledgement. In this paper we utilises 2-ACK-based plan utilising protected channel for conquering the issue of choice equivocalness for asked for node, enhanced node confirmation and limit packet dropping in ad-hoc network.

Implementation of Quality of Service-Oriented Distributed Routing Protocol using Fuzzy Logic in Mobile Ad Hoc Networks

A mobile ad hoc network (MANET) is a type of wireless network made up of nodes that are both wireless and mobile. This type of wireless network is a non-infrastructure network (without a central manager such as router, servers, etc.) that is hard to manage; providing the Quality of Service (QoS) is also difficult. The fuzzy logic is one of the effective methods required to achieve high QoS in MANET. We propose an Ant-colony Based Cluster-head (ABC) selection a protocol based on artificial intelligence fuzzy logic techniques in this paper. With the proposed protocol, we divide the network into clusters, then, choose a cluster head which is a node having high residual energy, the most trusted; this is achieved using Ant-colony optimization (ACO) algorithm thanks to the use of a probability function which determines the likelihood of various nodes to be selected as cluster-heads. Using the Network Simulator Version-2 (NS-2), we evaluated our Proposed Protocol with ones existing in literature; Quality of Service-Ad-hoc On-Demand Distance Vector (QoS-AODV), Bandwidth aware Multi-path Routing protocol in mobile Ad Hoc networks (BMR) and Dynamic Multi-Path Source Routing Method (MSR). We conducted various experimental evaluations varying both the number of the total mobile nodes in the network and the number of receivers from a single sender; all simulation outcomes revealed that the Proposed protocol; Quality of Service-Oriented Distributed Routing Protocol using Fuzzy Logic and clustering techniques in Mobile Ad Hoc Networks (QODFL) outperformed the existing ones as compared to existing protocols, it was able to increase both the Packet Delivery Ratio by 10.5% and throughput by 6.7 % , and the End-to-End Delivery ratio by 7.7%.

Resource-Efficient Distributed Deep Neural Networks Empowered by Intelligent Software-Defined Networking

Contemporary machine learning methods have evolved from conventional algorithms to deep neural networks (DNNs) that are computation- and data- intensive. Thus, they are suitable to be deployed in the cloud that can offer high computational capacity and scalable resources. However, the cloud computing paradigm is not optimal for delay- and energy-sensitive applications. To mitigate these problems, a battery of distributed DNNs have been proposed to allow a fast inference with device-edge-cloud synergy. Furthermore, although distributed deployment of DNNs on real communication networks is an important research topic, the legacy network architecture cannot meet the requirements of these distributed deep neural networks due to the complicated management and manual configuration, etc. To cope with these requirements, we develop a novel and explicit Intelligent Software Defined Networking (ISDN) that aims to manage the bandwidth and computing resources across the network via the SDN paradigm. We first identify the difficulties of deploying distributed intelligent computing in the current network architecture. Then, we explain how to address these problems by introducing the ISDN architecture. Specifically, we develop a dynamic routing method to enable Quality-of-Service (QoS) communication based on the SDN paradigm and propose a Markov Decision Process (MDP) based dynamic task offloading model to achieve the optimal offloading policy of DNN tasks. We develop a simulation platform based on Mininet to measure its performance advantages over traditional architectures. Extensive experimental results show that compared with the traditional network architecture, our architecture based on the SDN paradigm can perform better in terms of both network throughput and resource utilization.

Novel Modification of the Collective Dynamic Routing Method for Sensors' Communication in Wi-Fi Public Networks.

The widespread use of the Internet of Things makes it relevant to use public IP networks for simultaneous access by both users and wireless sensors. To achieve this, a significant reduction in the subscriber devices' energy consumption is required. This paper analyzes the application features of the collective dynamic routing method both with and without the use of a robust method for estimating the channel data rate for sensors' communication in wireless public networks. Based on the analysis, a novel modification of the collective dynamic routing method has been developed that reduces the sensors' energy consumption while keeping a high data rate and short delivery time for users. An analysis of the network load, the total data transfer rate over the network, and the parameters affecting the sensors' energy consumption was carried out for a segment of a seamless IEEE 802.11ax network. The simulation demonstrated a high efficiency of using a novel modification of the collective dynamic routing method for access to users and wireless sensors.

A Novel Deep Learning-Based Cooperative Communication Channel Model for Wireless Underground Sensor Networks.

Wireless Underground Sensor Networks (WUSNs) have been showing prospective supervising application domains in the underground region of the earth through sensing, computation, and communication. This paper presents a novel Deep Learning (DL)-based Cooperative communication channel model for Wireless Underground Sensor Networks for accurate and reliable monitoring in hostile underground locations. Furthermore, the proposed communication model aims at the effective utilization of cluster-based Cooperative models through the relay nodes. However, by keeping the cost effectiveness, reliability, and user-friendliness of wireless underground sensor networks through inter-cluster Cooperative transmission between two cluster heads, the determination of the overall energy performance is also measured. The energy co-operative channel allocation routing (ECCAR), Energy Hierarchical Optimistic Routing (EHOR), Non-Cooperative, and Dynamic Energy Routing (DER) methods were used to figure out how well the proposed WUSN works. The Quality of Service (QoS) parameters such as transmission time, throughput, packet loss, and efficiency were used in order to evaluate the performance of the proposed WUSNs. From the simulation results, it is apparently seen that the proposed system demonstrates some superiority over other methods in terms of its better energy utilization of 89.71%, Packet Delivery ratio of 78.2%, Average Packet Delay of 82.3%, Average Network overhead of 77.4%, data packet throughput of 83.5% and an average system packet loss of 91%.

A Boltzmann machine optimizing dynamic routing for FANETs

Routing optimization for FANETs is a kind of NP-hard problem in the field of combinatorial optimization that is simple to model but difficult to solve. The quality of routing has a direct impact on the network quality of FANETs, and the design of routing protocols becomes a very challenging topic. In this paper, we study the characteristics of dynamic routing, combine the characteristics of FANETs themselves, and use the energy of nodes, bandwidth, link stability, etc., as the metric of routing and use a Boltzmann machine for routing search to form an optimized dynamic routing protocol. The NS3 simulator is used to compare and study the traditional MANET dynamic routing methods AODV and DSR, and the simulation results show that the routes obtained by using the Boltzmann machine search are better than those of AODV and DSR in many aspects, such as end-to-end average delay, average route survival time and control overhead.

Swarm intelligence-based green optimization framework for sustainable transportation

Traffic congestion is one of the most critical issues in developing sustainable transportation in smart cities. As the Internet of Things evolves, connected vehicle technology has arisen as an essential research topic in smart, sustainable transportation. This study investigates a decentralized green traffic optimization framework by pushing swarm intelligence into connected vehicles to mitigate traffic congestion. We present a dynamic traffic routing method based on ant species’ swarm intelligence for connected vehicles so that they can communicate with each other and their surrounding environment via digital pheromones to perform routing decision-making in a decentralized manner. Traditional pheromones attract other vehicles to move to the optimal path, which will soon be congested if many vehicles travel on that path concurrently. To overcome this limitation, we propose the concept of repelling pheromone, which generates a repulsive force among vehicles so that their travel paths are distributed throughout a road network, resulting in a congestion-free road network. The proposed method is validated in the Simulation of Urban Mobility platform. Simulation findings reveal that the proposed method outperforms baseline methods in mitigating traffic congestion, reducing average fuel consumption and emissions by 13–19% and the average trip duration by 19–28%.

Analisis dan Perancangan Simulasi Perbandingan Kinerja Jaringan Komputer Menggunakan Metode Protokol Routing Statis, Open Shortest Path First (OSPF) dan Border Gateway Protocol (BGP) (Studi Kasus Tanri Abeng University)

Network is a communication between two devices to communicate with each other either using wire or wireless media. The LAN network topology used at Tanri Abeng Unversity still uses a static routing topology, where the routing process is still done manually, so that if there is an addition of a new network, it will be difficult to configure the routing table. In this research, a network topology with dynamic routing methods will be built that will help improve network performance at Tanri Abeng University. The purpose of this research is to analyze the comparison of network performance at Tanri Abeng Unversity in sending data packets using static routing protocols, OSPF, and BGP based on the parameters used. In this research also, the ping process was carried out using the cisco packet tracer 100 times with the size of the data packet sent of 32 bytes for each routing method to be compared. Then in this research, the test scenario was carried out eight times on each parameter of throughput, packet loss, delay, and jitter in the hope of determining which routing method is more optimal among static routing, OSPF, and BGP at Tanri Abeng University. The test results show that BGP routing for throughput and packet loss parameters is superior to other routing methods, while OSPF routing for delay and jitter parameters is superior to other routing methods.

Energy‐aware dynamic‐link load balancing method for a software‐defined network using a multi‐objective artificial bee colony algorithm and genetic operators

Information and communication technology (ICT) is one of the sectors that have the highest energy consumption worldwide. It implies that the use of energy in the ICT must be controlled. A software-defined network (SDN) is a new technology in computer networking. It separates the control and data planes to make networks more programmable and flexible. To obtain maximum scalability and robustness, load balancing is essential. The SDN controller has full knowledge of the network. It can perform load balancing efficiently. Link congestion causes some problems such as long transmission delay and increased queueing time. To overcome this obstacle, the link load balancing strategy is useful. The link load-balancing problem has the nature of NP-complete; therefore, it can be solved using a meta-heuristic approach. In this study, a novel energy-aware dynamic routing method is proposed to solve the link load-balancing problem while reducing power consumption using the multi-objective artificial bee colony algorithm and genetic operators. The simulation results have shown that the proposed scheme has improved packet loss rate, round trip time and jitter metrics compared with the basic ant colony, genetic-ant colony optimisation, and round-robin methods. Moreover, it has reduced energy consumption.

A prioritized planning algorithm of trajectory coordination based on time windows for multiple AGVs with delay disturbance

PurposeIn the running of multiple automated guided vehicles (AGVs) in warehouses, delay problems in motions happen unavoidably as there might exist some disabled components of robots, the instability of networks and the interference of people walking. Under this case, robots would not follow the designed paths and the coupled relationship between temporal and space domain for paths is broken. And there is no doubt that other robots are disturbed by the ones where delays happen. Finally, this brings about chaos or even breakdown of the whole system. Therefore, taking the delay disturbance into consideration in the path planning of multiple robots is an issue worthy of attention and research.Design/methodology/approachThis paper proposes a prioritized path planning algorithm based on time windows to solve the delay problems of multiple AGVs. The architecture is a unity consisting of three components which are focused on scheduling AGVs under normal operations, delays of AGVs, and recovery of AGVs. In the components of scheduling AGVs under normal operations and recovery, this paper adopts a dynamic routing method based on time windows to ensure the coordination of multiple AGVs and efficient completion of tasks. In the component for scheduling AGVs under delays, a dynamical prioritized local path planning algorithm based on time windows is designed to solve delay problems. The introduced planning principle of time windows would enable the algorithm to plan new solutions of trajectories for multiple AGVs, which could lower the makespan. At the same time, the real-time performance is acceptable based on the planning principle which stipulates the parameters of local time windows to ensure that the computation of the designed algorithm would not be too large.FindingsThe simulation results demonstrate that the proposed algorithm is more efficient than the state-of-the-art method based on homotopy classes, which aims at solving the delay problems. What is more, it is validated that the proposed algorithm can achieve the acceptable real-time performance for the scheduling in warehousing applications.Originality/valueBy introducing the planning principle and generating delay space and local adjustable paths, the proposed algorithm in this paper can not only solve the delay problems in real time, but also lower the makespan compared with the previous method. The designed algorithm guarantees the scheduling of multiple AGVs with delay disturbance and enhances the robustness of the scheduling algorithm in multi-AGV system.

Dynamic Multicriteria Alternative Routing for single- and multi-service reservation-oriented networks and its performance

We propose a new Dynamic Multicriteria Alternative Routing (DMAR) method that applies to reservation-oriented networks. DMAR combines a dynamic alternative routing scheme with a periodic update of alternative paths according to a multicriteria algorithm that aims to balance the traffic between traffic flows in single-service networks and also between services in multi-service environments. We conducted extensive simulations to compare the performance of DMAR with that of other reference alternative routing schemes in single- and multi-service networks with several topologies and load scenarios, namely with non-stationary traffic. We show that DMAR efficiently adjusts to traffic changes while often presenting better network performance than the reference alternative routing schemes, particularly in the multicriteria sense.

Q(λ) learning-based dynamic route guidance algorithm for overhead hoist transport systems in semiconductor fabs

A learning-based dynamic routing algorithm is proposed for the overhead hoist transport (OHT) systems of semiconductor fabrication facilities (fabs). An OHT system, which consists of multiple vehicles moving at high speeds on guided rails, is the primary automated material-handling system (AMHS) in a fab. Modern large-scale fabs have hundreds of vehicles moving lots between multiple processing machines. The dynamic routing method is a route guidance method that dynamically selects the best vehicle paths under given traffic conditions and congestion levels. Building on the learning method, we develop a reinforcement learning-based dynamic routing algorithm called QLBWR(λ), which consists of a Boltzmann softmax policy and a reward function. The proposed algorithm uses real-time information to effectively guide each vehicle so that it avoids congestion and finds an efficient path. The algorithm is also designed with a low computational burden, such that the efficient route can be found for hundreds of vehicles in real time. Simulation analyses on an actual fab layout are used to compare the performance of the proposed algorithm with common static and dynamic algorithms. The results show that the proposed algorithm outperforms the benchmarking algorithms.