Routing In Networks Research Articles

Concurrent Entanglement Routing for Quantum Networks: Model and Designs

Concurrent Entanglement Routing for Quantum Networks: Model and Designs

Optimal control of linear cost networks

We present a method for optimal control with respect to a linear cost function for positive linear systems with coupled input constraints. We show that the Bellman equation giving the optimal cost function and resulting sparse state feedback for these systems can be stated explicitly, with the solution given by a linear program. Our framework admits a range of network routing problems with underlying linear dynamics. These dynamics can be used to model traditional graph-theoretical problems like shortest path as a special case, but can also capture more complex behaviors. We provide an asynchronous and distributed value iteration algorithm for obtaining the optimal cost function and control law.

Fragmentation and ISRS-Aware Survivable Routing, Band, Modulation, and Spectrum Allocation Algorithm in Multi-Band Elastic Optical Networks

Fragmentation and ISRS-Aware Survivable Routing, Band, Modulation, and Spectrum Allocation Algorithm in Multi-Band Elastic Optical Networks

Optical routing: link failure detection-aided optimal routing in elastic optical networks using hybrid deep learning with target-based feature pool formation

ABSTRACT The Elastic Optical Network (EON) is a kind of optical network where a large amount of data signals are given into the channel of optical fibre by modulating with the optical signal. One of the most crucial tasks for optical networks is resource allocation and routing, which has a significant impact on resource effectiveness and network capacity. Deep learning-based routing and resource allocation have piqued the interest of researchers to a considerable extent. Thus, this paper aims to develop efficient link failure detection in EONs with hybrid deep learning strategies. Initially, the data aggregation is performed to combine the multiple link information from EONs. The aggregated link data is given into the hybrid detection model named as Enhanced Deep Learning Network (EDLNet), where the One-Dimensional Convolutional Neural Network (1D-CNN) is used for extracting the features and used for generating the target-based feature pool. This feature pool is utilised in the Long Short-Term Memory (LSTM) network for detecting link failures. The parameter optimization is done by the hybrid 1DCNN-LSTM network with the support of the Hybrid Red Deer with Sewing Training-based Optimisation (HRD-STO) to achieve accurate detection results. The detected failed link is mitigated at the time of routing, and only reliable links are used to attain certain objective constraints. These objective constraints include shortest path, link reliability and stability, throughput and delay for enhancing the efficiency of the EONs. The replication analysis reveals the effectiveness of the proposed link failure detection model by comparing it with the conventional models.

Optimal Node Routing Layouts in Mobile Adhoc Network

Mobile Adhoc Network (MANET) is a self-organizing network of various network devices responsible for communication. In MANET, new layouts are adopted for energy optimization, security implementation, traffic minimization and overhead reduction. MANET are aggressive in nature. This requires modification in network routing protocols for performance enhancement. This paper presents the detailed survey of different layouts involved in the implementation of network routing protocols in MANET. The survey is classified into four phases involved in MANET routing applications namely- routing protocols, neighborhood discovery, distributed or centralized hash tables, and bloom filter. Firstly, the classification of routing protocols based on various traffic models presents the MANET configuration. Secondly, a neighborhood discovery mechanism conveys the enhancement in the routing configurations. Thirdly, application of hash table operation (distributed/ centralized) to MANET to improve the matching efficiencies and computational speed. Finally, the application of bloom filter concept to improve service discovery rate and reduce the fake identities. Traditionally, the unpredictability occurs during the prediction of optimal node routing due to the dynamic nature of the MANET. Moreover, the presence of ejected malicious node in another cluster disgraced the link stability. This survey conveys the problems such as storage overhead, mobility nature, malicious nodes, and false positive rates in the traditional methods. The detailed survey leads to the stone of integration of Distributed Hash Table (DHT) with a bloom filter to reduce the problems in traditional research works. Index Terms – Performance Optimization, Bloom Filter, Centralized Hash Table (CHT), Distributed Hash Table (DHT), Energy Consumption, Mobile Ad- hoc Network (MANET), Quality of Service (QoS), Routing.

Multi‐lingual encryption technique using Unicode and Riemann zeta function and elliptic curve cryptography for secured routing in wireless sensor networks

AbstractSecure routing and communication with confidentiality based on encryption of texts in multiple natural languages are challenging issues in wireless sensor networks which are widely used in recent applications. The existing works on Elliptic Curve Cryptography based secured routing algorithms are focused only on the encryption and decryption of single language text encrypted over a Prime finite field. In this article, a new algorithm called Multi‐Language ECC encrypted Secure Routing algorithm with trust management is proposed, in order to ensure confidentiality and integrity which focuses on the encryption of plain text using Riemann's zeta function and Elliptic Curve Cryptography for improving the key strength which is applied for encryption over a range of multi languages namely Tamil, English, Hindi French and German which are supported by Unicode and routing the text security. From the experiments conducted using the proposed multi‐lingual encryption algorithm with network routing, we prove that the suggested method provides greater security than the current secure routing algorithms due to the use of Zeta function and Gamma function with ECC key and trust management. but also boasts reduced complexity compared to other existing multi‐lingual encryption algorithms.

Decentralized Protocol for Organizing Sustainable Interaction between Subscribers in Networks with High Dynamics of Topology Changes

Emergency services often have to carry out rescue and liquidation operations in the absence of centralized communications. The inability to ensure stable communication between members of the rescue unit significantly reduces the quality of work. Moreover, in modern realities, stable communication means not only voice exchange, which can be provided by shortwave radio transmitters, but also intensive exchange of large volumes of traffic. The use of standard solutions based on standard network equipment (Wi-Fi, satellite communications, etc.) and existing algorithms for ensuring quality of service in the conditions under consideration does not allow quickly ensuring information exchange between heterogeneous subscribers. Moreover, operation in high-Hz bands can be very difficult in the presence of obstacles, which reduces the overall coverage area and the quality of data transmission. We propose a network layer routing protocol designed to organize decentralized communication in an emergency service department, where subscribers have different degrees of mobility and types of transmitted traffic. This protocol includes algorithms for connecting to the network, detecting optimal and alternative communication routes, and transmitting and balancing traffic along the found routes. The original route search algorithm analyzes the performance of communication channels and determines all possible paths for transmitting traffic between subscribers. Using the route evaluation function based on gradient boosting of decision trees, optimal and alternative communication routes are formed, and when transmitting data, traffic balancing is performed based on the received information. An experimental study of the proposed protocol showed an improvement in the speed of deployment and quality of service in scenarios with varying degrees of subscriber mobility.

A method for analyzing the impact of SEUs on satellite networks from the perspective of distributed routing

Single event upset (SEU) refers to the change in the state of a digital circuit caused by a single, energetic particle passing through the sensitive area of semiconductor components. In the space environment, cosmic rays and high-energy particles may lead to SEUs, which can cause failures of routing functions, such as forwarding errors, in satellite networks. This paper focuses on the impact of SEUs on satellite networks from the perspective of distributed routing. The influence of SEUs on a single satellite can easily spread to the entire satellite network through distributed routing, which can influence the reliability of the satellite network. We propose a framework for the reliability analysis of satellite networks under the impact of SEUs. According to the influence of SEUs on the execution of routing programs in different routing phases, we present the error propagation model of satellite networks for understanding SEUs in satellite network routing. We found that all the influences on program execution ultimately converge on data forwarding and can be reflected in the proposed path topology graph. Therefore, we propose an algorithm that can evaluate the impact of SEUs on satellite networks by analyzing the path topology graphs in both normal and SEU scenarios. The feasibility and effectiveness of the proposed method is demonstrated by experiments. The proposed method could provide a new perspective and theoretical basis for research on secure routing mechanisms and program/instruction reinforcement.

Automated feeder routing for underground electricity distribution networks based on aerial images

The process towards a carbon-neutral society requires a substantial amount of investment into electricity distribution networks for integrating more sustainable technologies such as photovoltaic systems, electric vehicles, heat pumps, and others. In order to execute the large number of network expansion measures associated with this effort, strategic and operational planning of distribution networks benefit from higher degrees of automation. A key component in these planning processes is the routing of feeder segments, i.e., the task of finding feasible and cost-efficient paths for underground power lines between two geographic locations, such as residential buildings and transformer stations. In this paper, we present FEEDAIR, a novel and innovative approach to automate feeder routing based exclusively on aerial images. FEEDAIR integrates deep neural networks, which encode geoinformation from aerial images, and A* search, which identifies effective feeder routes based on this encoding. We train and evaluate FEEDAIR using a large data set of more than 7000 real-world electricity distribution network feeder segments. Thereby, we compare FEEDAIR to state-of-the-art approaches from related work and find that the feeder segments generated by FEEDAIR are on par or superior with regard to several characteristics and metrics. FEEDAIR achieves this performance by relying exclusively on aerial images, whereas competing approaches require data on geocoded land cover and estimated construction cost, which is not as widely available and is frequently based on potentially subjective expert opinion.

A methodology for feasibility analyses of district heating networks: A case study applied to greenhouse crops

In this article, the preliminary routing, sizing, and cost structure of a heat network that supplies energy, in an agrarian district, to greenhouses located around a cogeneration plant in the municipality of El Ejido (Almería) is presented. A genetic algorithm is employed to select a set of points of consumption (plots of land)–which indirectly determine the network routing over the road network–resulting in a project with a better net present value. To obtain the routing, the Steiner tree problem is solved through an approximation that involves several algorithms derived from graph theory and the use of georeferenced information. The design is constrained to ensure technical feasibility, so that the available thermal power from the plant, averaged for hourly periods over a year, is greater than the demand from the greenhouses. The said demand is calculated through an empty-greenhouse climate model, considering temperature setpoints compatible with the most common crops in the region. According to the results, a network of total length equal to 1.3464 km and an average pipe diameter of 0.1532 m would be required to annually supply 8621.4 MWh of heat to 12 points of consumption. The net present value of the project would be 6 417 900 €, considering a 3% discount rate and useful life of 30 years, with an initial investment cost of 1 723 300 € and an annual operation cost of 15 800 €year−1. A sensibility analysis of the variables with greater economic impact is also presented.

An effective deep-Q learning scheme for QoS improvement in physical layer of software-defined networks

The demands of diverse applications necessitate specific communication requirements, such as jitter, packet loss ratio (PLR), and latency, at the end-to-end (E2E) level within the physical layer of software-defined networking (SDN). In heterogeneous network environments, E2E routes traverse multiple domains with varying quality-of-service (QoS) classes of traffic, posing challenges in meeting unique E2E QoS criteria. Hence the performance on the E2E path is significant to meet the needs of applications. Hence, the issue of providing services on the E2E route is challenging due to several QoS classes in a domain on the source-to-destination route. This paper presents an SDN architecture leveraging Deep Q-learning (DQL) to ascertain the appropriate QoS class for E2E routes in software-defined heterogeneous networks, thereby reducing overall E2E delay. The proposed model with DQL is compared with benchmark reinforcement learning. The proposed framework is validated using various real Internet topologies such as Abilene, USNet, and OS3E, assessing not only E2E delay but also jitter, PLR, and throughput, showcasing its efficacy in optimizing network performance regarding these performance metrics.

A Multipolicy Deep Reinforcement Learning Approach for Multiobjective Joint Routing and Scheduling in Deterministic Networks

A Multipolicy Deep Reinforcement Learning Approach for Multiobjective Joint Routing and Scheduling in Deterministic Networks

Optimal carrier Selection Based on Reliability Under Delivery Spoilage and Cost Constraint

In a highly competitive market environment, outsourcing of logistics service is one of the most significant strategies for enterprises to enhance competitiveness. However, how to construct an efficient and reliable logistics network by selecting suitable logistics carriers is a complex but important practical issue. In general, there are multiple carriers to provide freight service along each route of a logistics network. Moreover, the transportation capacity of each carrier has multiple states due to various factors, which can be portrayed by a capacity probability distribution. In addition, delivery spoilage and cost constraint are the key indicators affecting carrier selection. Therefore, this paper studies the logistics carrier selection problem from the perspective of multi-state network reliability under delivery spoilage and cost constraint, and apply the minimal path (MP) method to solve the problem. The practicality of the algorithm is verified by example analysis.

An online transfer learning based multifactorial evolutionary algorithm for solving the clustered Steiner tree problem

The rapid increase in the number and computing power of devices interacting through the network over the past few years has led to a growing trend of decentralized clustering network architecture. The Clustered Steiner Tree Problem (CluSteiner) is a recently introduced NP-Hard problem and is assessed to be the core of efficient multicast routing in such networks. However, research on this problem in terms of theory and algorithmic design is still in its infancy as current solving approaches are limited in the literature. Lately, the Multifactorial Evolutionary Algorithm (MFEA) has been applied to solve several clustering-structure network design problems for the reason that these problems rarely exist independently and are often deployed concurrently in practice. To effectively transfer knowledge between problems while solving them simultaneously, this paper proposes an approach based on the novel data-driven MFEA-II to deal with the CluSteiner problem. In the proposal, an efficient encoding and a two-level decoding mechanism are introduced, combined with a module capable of online learning the amount of knowledge transferred between tasks that help the algorithm to explore potential regions of the search space. Experiments are undertaken on various test instances to evaluate the efficacy of the proposed algorithm. The empirical results indicate that our proposal can perform well on both metric and non-metric graphs with the statistical tests used to verify its efficiency over other baseline algorithms regarding the solution quality and convergence trend.

Petal Region Analysis of Improved Petal Ant Routing for Mobile Ad Hoc Networks

Petal Region Analysis of Improved Petal Ant Routing for Mobile Ad Hoc Networks

An enhanced bio‐inspired energy‐efficient localization routing for mobile wireless sensor network

SummaryEnvironmental disasters such as earthquakes, tsunamis, floods, and landslides disrupt natural resources, human casualties, and so on. Early warning and alert of such events will help for preparedness to avoid human and economic causalities. Managing a disaster or emergency scenario is a difficult endeavor. These episodes of mass devastation, whether caused by natural disasters or man‐made disasters, result in massive losses of money, property, and lives as a result of inadequate response by the government and management authorities. As a result, actions must be taken to avoid these circumstances from occurring by discovering the causes of these catastrophes in advance and providing swift rescue procedures once the crisis happens. In a disaster situation, communication plays a vital role, and a lot of people turn towards it as a source of information conveyance/delivery for controlling the situation. Even these communication networks are damaged and incur tremendous losses during disasters. All the communication networks, including mobile wireless sensor networks (WSNs) deployed for communication, also suffer huge damage and face many challenges during disasters. Nature appears to deal with these issues well. WSN is inspired by several biological systems and processes that have intrinsically attractive properties such as adaptation, self‐organization, and self‐healing. In this paper, we proposed an enhanced bio‐inspired energy‐efficient localization (EBEEL) routing for Mobile WSN. The main focus of the proposed EBEEL algorithm is to overcome the challenges faced by peer‐existing routing protocols such as data aggregation, limited bandwidth, scalability, data redundancy, energy consumption, packet drops, load balancing, and Quality of Service (QoS).

Advancing Healthcare Networks: Optimizing Multi-Objective RPL for Diverse Traffic in Low-Power, Lossy Environments

RPL: Powering modern healthcare IoT Networks. The Routing Protocol for Low Power and Lossy Networks(RPL) is a protocol specifically designed for routing in networks characterized by low power and lossy connections built on IPv6. It is specifically designed for the growing use of Instantaneous IoT (Internet of Things) applications. These networks cater to the specialized requirements of Low Power (resource-constrained) and Lossy Networks(unstable networks), also known as LLNs, where routing data smoothly and prioritizing traffic types are the significant challenges. Studies have shown that standard RPL, which makes use of a set of routing rules, struggles to provide the level of performance that many IoT applications in modern-day healthcare demand. This limits RPL’s potential in scenarios where we have a mix of data traffic, which is very common in healthcare settings. To handle the diverse data traffic found in healthcare settings, our method involves distributing several instances of RPL settings throughout the network. The main objective of this strategy is to improve the effectiveness of important and critical healthcare applications. We have developed and thoroughly tested our system using Contiki, which is an open-source IoT operating system. We have three instances in our MultiInstance RPL setup, each of which is intended to handle different kinds of network traffic and provide varying Quality of Service (QoS) measures. Different sets of Objective Function (OF) rules are used by each instance to decide which route is optimal for the data it processes. We have made use of OF0 (Objective Function 0) and MRHOF (Minimum Rank with Hysteresis Objective Function). We have rigorously tested our solution. Our focus has been on metrics like delay, average energy consumption and Packet Delivery Ratio (PDR). Compared to standard RPL, our approach improved packet delivery rates and significantly reduced delays for high priority data packets. This research shows how to better address the complex data traffic needs of real-time healthcare IoT networks.

Predicting Bus Travel Time in Cheonan City Through Deep Learning Utilizing Digital Tachograph Data

Urban transportation systems are increasingly burdened by traffic congestion, a consequence of population growth and heightened reliance on private vehicles. This congestion not only disrupts travel efficiency but also undermines productivity and urban resident’s overall well-being. A critical step in addressing this challenge is the accurate prediction of bus travel times, which is essential for mitigating congestion and improving the experience of public transport users. To tackle this issue, this study introduces the Hybrid Temporal Forecasting Network (HTF-NET) model, a framework that integrates machine learning techniques. The model combines an attention mechanism with Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) layers, enhancing its predictive capabilities. Further refinement is achieved through a Support Vector Regressor (SVR), enabling the generation of precise bus travel time predictions. To evaluate the performance of the HTF-NET model, comparative analyses are conducted with six deep learning models using real-world digital tachograph (DTG) data obtained from intracity buses in Cheonan City, South Korea. These models includes various architectures, including different configurations of LSTM and GRU, such as bidirectional and stacked architectures. The primary focus of the study is on predicting travel times from the Namchang Village bus stop to the Dongnam-gu Public Health Center, a crucial route in the urban transport network. Various experimental scenarios are explored, incorporating overall test data, and weekday and weekend data, with and without weather information, and considering different route lengths. Comparative evaluations against a baseline ARIMA model underscore the performance of the HTF-NET model. Particularly noteworthy is the significant improvement in prediction accuracy achieved through the incorporation of weather data. Evaluation metrics, including root mean squared error (RMSE), mean absolute error (MAE), and mean squared error (MSE), consistently highlight the superiority of the HTF-NET model, outperforming the baseline ARIMA model by a margin of 63.27% in terms of the RMSE. These findings provide valuable insights for transit agencies and policymakers, facilitating informed decisions regarding the management and optimization of public transportation systems.

Heuristic solutions to optimize the traffic routing in space division multiplexing networks

This paper underscores the symbiosis between industrial engineering techniques and optical communication technologies, bringing valuable insights to the optimization of traffic routing in communication networks using constructive heuristics. Exploring and realizing the potential of optical fiber networks has received more attention due to the popularity of various network services and the ensuing increase in overall internet traffic. Space Division Multiplexing (SDM) stands out among the technologies designed to increase the functionality of core and urban mesh/ring networks. SDM's primary goal is to significantly increase transmission system bandwidths by effectively using the spatial dimension. The routing of traffic within flex-grid SDM optical networks has been optimized in this paper using two novel heuristic algorithms. The experimental results show that the proposed algorithms outperform the solutions presented in previous research from the literature.

PathGuard: Trustworthy Routing for Sustainable and Secure IoT-WSN Networks

PathGuard: Trustworthy Routing for Sustainable and Secure IoT-WSN Networks