Link Failure Research Articles

Fault Tolerance Management Implementation from Medium-to-Large-Scale Networks

As network infrastructures grow in complexity, ensuring high availability and resilience becomes critical, especially for medium-to-large scale networks. This study focuses on the development and implementation of fault tolerance management within Software-defined networking (SDN) environments, aimed at minimizing downtime and enhancing network reliability. SDN’s centralized control and dynamic programmability provide an ideal framework for implementing efficient fault detection and recovery mechanisms. The proposed model leverages real-time monitoring, redundancy protocols, and adaptive rerouting strategies to mitigate the impact of node or link failures. Key components of the model include failover mechanisms, load balancing, and traffic rerouting algorithms, designed to maintain seamless network operations during failures. Through simulation and testing, the model demonstrates significant improvements in network resilience, reducing recovery time and ensuring uninterrupted service delivery. This research provides a comprehensive guide to implementing fault-tolerant networks using SDN, offering scalable solutions that can be adapted to various network sizes and configurations. The findings emphasize the potential of SDN to revolutionize fault management in modern network infrastructures, making it a crucial consideration for future network design and operations.

An efficient multi-criteria cell selection handover mechanism for Vehicle-to-Everything (V2X)

The deployment of cost-effective small cells to create ultra-high-density (UDN) heterogeneous networks in 5 G networks has emerged as a potentially effective strategy for enhancing network coverage and optimising resource allocation. However, UDN makes network selection more challenging due to the densification of small cells in 5 G and their heterogeneity. This research presents an efficient small cell selection handover mechanism for 5 G V2X networks. The proposed mechanism uses a Multiple Criteria Decision Making (MCDM) technique for the handover best cell selection to improve the overall performance. The proposed handover mechanism is context sensitive and it adapts to changing network conditions, ensuring efficient handovers during high-speed vehicular movement. Furthermore, the mechanism incorporates the concept of small cell Stay Time, which may reduce unnecessary handovers. The simulation results reveal that the proposed mechanism outperforms traditional handover techniques and Handover Decision-making Algorithm (HDMA) mechanisms significantly in terms of reducing the number of frequent handovers, minimizing link failures, and minimizing ping-pong with an average of 66 % reduction for unnecessary handovers.

FRRL: A reinforcement learning approach for link failure recovery in a hybrid SDN

Network failures, especially link failures, happen frequently in Internet Service Provider (ISP) networks. When link failures occur, the routing policies need to be re-computed and failure recovery usually takes a few minutes, which degrades the network performance to a great extent. Therefore, a proper failure recovery scheme that can realize a fast and timely routing policy computation needs to be designed. In this paper, we propose FRRL, a Reinforcement Learning (RL) approach to intelligently perceive network failures and timely compute the routing policy for improving the network performance when link failure happens. Specifically, to perceive the link failures, we design a Topology Difference Vector (TDV) encoder module in FRRL for encoding the topology structure with link failures. To efficiently compute the routing policy when link failures happen, we integrate the TDV in the agent training for learning the map between the encoded failure topology structure and routing policies. To evaluate the performance of our proposed method, we conduct experiments on three network topologies and the experimental results demonstrate that our proposed method has superior performance when link failures happen compared to other methods.

Assessing the role of sustainable water bodies in urban drainage systems to mitigate urban flooding: A case study of Gurgaon, Haryana, India

For the last 5 years, Gurgaon a city in India has been facing an issue of urban flooding due to illicit encroachments over the local waterbodies, poor drainage system and increasing rainfall. In this study, Remote sensing data are employed to find the most flooded areas identified using Partial Least Square Regression and 18 new retention ponds are proposed to build a Sustainable Drainage System (SuDS) in open space and barren lands. In SWMM, the Urban Drainage System (UDS) model is simulated using 24-h rainfall hyetograph from hourly PERSIANN-CSS rainfall data (yearly rainfall events) and 7-h rainfall hyetograph from half-hourly IMERG Global Precipitation Data (extreme rainfall events) from 2000 to 2023. After comparing both UDS and SuDS in SWMM, it is found that the flood volume has decreased significantly from 240 CMS to 180 CMS (for yearly rainfall) and 500 CMS to 350 CMS (7-h rainfall hyetograph). The study also compares the structural resilience of the drainage system under the conditions of no link failure and single link failure scenarios. In no failure situation, 20% more resilience has been achieved for yearly rainfall and 10% more for extreme rainfall events. In single link failure conditions, SuDS is helping to reach 20–47% resilience for yearly rainfall events and 7–30% resilience for extreme rainfall events. Thus, this study helps to achieve SDGs 11 and 13 to build a resilient and climate-adaptive urban drainage in Gurgaon. The study gives significant insights regarding the competency of urban waterbodies to city planners and policymakers.

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 cooperative distributed droop gains adjustment in DC microgrids

Direct Current (DC) microgrids are a very promising solution to integrate renewable energy sources, energy storage systems and loads. The main goals of a microgrid controller are to achieve voltage regulation and load sharing among multiple distributed generators (DGs). Distributed control techniques are widely adopted to address these two objectives. Most of the existing distributed methods in the literature adjust the deviation caused by the conventional droop control by adding two additional terms. In this work, a new cooperative distributed controller to adjust the droop gains of DGs is proposed. The proposed method achieves average voltage regulation and proportional current sharing in a unified manner. It does not require additional corrective terms, resulting then in a simple control structure. Moreover, it relies only on exchanging one communication variable among neighboring DGs, which reduces the communication overhead. The stability of the proposed controller is analyzed using a small signal model of the system. The effectiveness of the proposed controller is evaluated under common load changes, local load variations, communication link failures and communication delays. The designed control scheme is verified through Hardware-In-the-Loop (HIL) tests using NI PXIe real-time simulator.

Monte Carlo safeguarding of key links through multiple random walks in large network

Safeguarding important links is a useful way to promote network vulnerability, especially in sparse networks where random link failures can disconnect the network. Sustaining network connectivity is the main goal of safeguarding and can be handled by selectively safeguarding links belonging to certain cuts of a network. However, due to the inherent high complexity of cut enumeration, existing algorithms can only handle small‐scale networks with limited nodes. It is important to find practical algorithms that are feasible for large‐scale graphs, as a significant portion of real‐world graphs under investigation are not as small as dozens of nodes. To address the problem, we propose to use a high‐precision approximate approach to accelerate the first step of the two‐step safeguard process and thus accelerate the whole process. A Monte Carlo algorithm is proposed to exploit efficient random paths for small cuts enumeration, which can help locate important edges with high probability in large‐scale sparse networks. These edges will then be utilized to find the approximated minimum cost edge set whose safeguarding can sustain the expected network connectivity. The algorithm is validated using various sizes of graphs of random/Barbasi–Albert scale‐free/Clustered scale‐free/unit disk/Watts–Strogatz small‐world and real‐world graphs. The experimental results show that the algorithm can perfectly sustain the network connectivity, and the acceleration ratio of more than 105 can be achieved with a little additional overhead. Specifically, in large graphs of one million nodes, approximated safeguard solutions survive at least 99.9% random link failures, and the solution time can be further reduced to dozens of seconds when the algorithm steps are easily implemented in full parallel.

Optimized wide area backup for distance relay tele-protection with security adjustment

Tele-protection schemes efficiently clear faults at line ends, but failures in the telecommunication link or relay malfunction can cause delayed fault clearance, network instability and significant power loss. Hence, this paper presents a reliable protection method as a backup for tele-protection schemes which is designed based on nearby distance relays zone pickup. The proposed protective method introduces a novel ability to adjust sensitivity to fault events through a security factor index. This method can adapt to network uncertainties or changes in topology, using one-step calculation of optimal values based on the least square error method and provides the fast adaptation to network changes. In addition, it enables the fault detection even if a relay fails at one of the faulted line terminals. Performance evaluation of the proposed method has been done based on the reliability indices through a wide simulation of fault scenarios on the IEEE 9-bus system. Considering the possible uncertainties, the test results have shown that the proposed method is more efficient with 98.5 % dependability compared to the conventional tele-protection schemes. While this index is evaluated at 90 % and 92.5 % in the PUTT and POTT schemes, respectively.

Residual multiscale attention based modulated convolutional neural network for radio link failure prediction in 5G

In the realm of the 5 G environment, Radio Access Networks (RANs) are integral components, comprising radio base stations communicating through wireless radio links. However, this communication is susceptible to environmental variations, particularly weather conditions, leading to potential radio link failures that disrupt services. Addressing this, proactive failure prediction and resource allocation adjustments become crucial. Existing approaches neglect the relationship between weather changes and radio communication, lacking a holistic view despite their effectiveness in predicting radio link failures for one day. Therefore, the Dynamic Arithmetic Residual Multiscale attention-based Modulated Convolutional Neural Network (DARMMCNN) is proposed. This innovative model considers radio link data and weather changes as key metrics for predicting link failures. Notably, the proposed approach extends the prediction span to 5 days, surpassing the limitations of existing one-day prediction methods. In this, input data is collected from the Radio Link Failure (RLF) prediction dataset. Then, the distance correlation and noise elimination are used to improve the quality and relevance of the data. Following that, the sooty tern optimization algorithm is used for feature selection, which contributes to link failures. Next, a multiscale residual attention modulated convolutional neural network is applied for RLF prediction, and a dynamic arithmetic optimization algorithm is accomplished to tune the weight parameter of the network. The proposed work obtains 79.03 %, 65.93 %, and 67.51 % of precision, recall, and F1-score, which are better than existing techniques. The analysis shows that the proposed scheme is appropriate for RLF prediction.

Design and Optimization of Direct-Drive Drilling Permanent Magnet Synchronous Motor

Abstract A direct-drive drilling electric drilling tool is developed due to the problems of long transmission link, low efficiency, and high failure rate of traditional electric drilling tools. In this paper, the key components of the direct-drive drilling permanent magnet synchronous motor (DPMSM) are designed and calculated, and the robust parameters optimization is performed by Taguchi method with efficiency and cogging torque as the optimization objectives. The air-gap length, thickness of permanent magnets, pole-arc coefficient, and width of slot top are selected as significant factors, and the impact of the optimized parameters on the efficiency and cogging torque of the DPMSM is calculated with orthogonal experiment to obtain the optimal combination of factors. Comparing the DPMSM’s performance before and after optimization, the cogging torque is reduced by 80.1% and the efficiency is improved by 0.24% after optimization. Finite element simulation of the electromagnetic characteristics of the optimized DPMSM under no-load and load conditions is carried out to verify the rationality of the optimization. Calculate the main losses of the DPMSM and the heat generation rate of key components. The analysis model of the DPMSM’s temperature field is established, and the temperature field simulation is carried out with and without drilling fluid respectively to further verify the rationality of the motor’s design. This work can promote the engineering application of downhole direct-drive electric drilling tools and provide technical support for the innovation of drilling equipments for marine energy.

Event-triggered distributed consensus control of heterogeneous multi-agent system under communication and actuator faults

In this paper, a heterogeneous leader-follower multi-agent system is studied under simultaneous time-varying communication faults and actuator faults. First, the state of the leader is modeled as the closed-loop reference model (CRM) where the states of the direct-connected followers are fed to the leader to improve the leader-follower tracking capability. An event-triggered communication mechanism is designed for the agent information sharing among its neighbors so as to reduce the communication burden. The designed event-triggered mechanism, capable of adjusting the triggering interval threshold within a certain range, can be applied in practical multi-robot collaborative control to accommodate the varying requirements for different triggering intervals. Considering the time-varying communication link failures, a new distributed event-triggered observer is designed for each follower to estimate the system states so as to reduce the state error, whereas the adaptive distributed event-triggered estimators are further designed for the non-directly connected followers to estimate the coefficient matrix of the leader system. Then, an estimator is designed for the actuator fault estimation to mitigate their impact on the system consistency. Finally, an adaptive control strategy is proposed to ensure the consistency of the leader-follower system under the time-varying communication link faults and actuator faults. It is also shown that Zeno behavior is excluded for each agent and the effectiveness of the proposed adaptive event-triggered control strategy is verified on a heterogeneous multi-agent system.

A Robust Routing Protocol in Cognitive Unmanned Aerial Vehicular Networks.

The adoption of UAVs in defence and civilian sectors necessitates robust communication networks. This paper presents a routing protocol for Cognitive Radio Unmanned Aerial Vehicles (CR-UAVs) in Flying Ad-hoc Networks (FANETs). The protocol is engineered to optimize route selection by considering crucial parameters such as distance, speed, link quality, and energy consumption. A standout feature is the introduction of the Central Node Resolution Factor (CNRF), which enhances routing decisions. Leveraging the Received Signal Strength Indicator (RSSI) enables accurate distance estimation, crucial for effective routing. Moreover, predictive algorithms are integrated to tackle the challenges posed by high mobility scenarios. Security measures include the identification of malicious nodes, while the protocol ensures resilience by managing multiple routes. Furthermore, it addresses route maintenance and handles link failures efficiently, cluster formation, and re-clustering with joining and leaving new nodes along with the predictive algorithm. Simulation results showcase the protocol's self-comparison under different packet sizes, particularly in terms of end-to-end delay, throughput, packet delivery ratio, and normalized routing load. However, superior performance compared to existing methods, particularly in terms of throughput and packet transmission delay, underscoring its potential for widespread adoption in both defence and civilian UAV applications.

Recursive state estimation for delayed complex networks with random link failures and stochastic inner coupling under cyber attacks

This paper focuses on the filtering issue regarding a class of uncertain time-delayed complex networks (CNs) subject to random link failures (RLF) and stochastic inner coupling (SIC) under cyber attacks, where the attacks occur in a random way with uncertain occurrence probabilities. The phenomena of RLF and SIC are described via a Bernoulli distributed random variable and a Gaussian noise, respectively. The former reflects the presence or absence of connections between different nodes, while the latter presents the uncertainty of the inner coupling strength. In addition, denial of service attacks (DoSAs) and false data injection attacks (FDIAs) are both taken into account in the discussed cyber space, where the switching behavior of attack modes is described by two independent Bernoulli distributed random variables. A novel recursive estimator is constructed with consideration of RLF, uncertain time-delay, SIC and cyber attacks, wherein a state estimation error covariance upper bound (SEECUB) is obtained. And such upper bound is minimized via reasonable design of the estimator gain. Through theoretical deduction and analysis, it is proved that the presented SEECUB is uniformly bounded under certain conditions. Finally, the availability of the proposed filtering strategy and the gained results are verified through simulation examples.

Robust Optimization Research of Cyber-Physical Power System Considering Wind Power Uncertainty and Coupled Relationship.

To mitigate the impact of wind power uncertainty and power-communication coupling on the robustness of a new power system, a bi-level mixed-integer robust optimization strategy is proposed. Firstly, a coupled network model is constructed based on complex network theory, taking into account the coupled relationship of energy supply and control dependencies between the power and communication networks. Next, a bi-level mixed-integer robust optimization model is developed to improve power system resilience, incorporating constraints related to the coupling strength, electrical characteristics, and traffic characteristics of the information network. The upper-level model seeks to minimize load shedding by optimizing DC power flow using fuzzy chance constraints, thereby reducing the risk of power imbalances caused by random fluctuations in wind power generation. Furthermore, the deterministic power balance constraints are relaxed into inequality constraints that account for wind power forecasting errors through fuzzy variables. The lower-level model focuses on minimizing traffic load shedding by establishing a topology-function-constrained information network traffic model based on the maximum flow principle in graph theory, thereby improving the efficiency of network flow transmission. Finally, a modified IEEE 39-bus test system with intermittent wind power is used as a case study. Random attack simulations demonstrate that, under the highest link failure rate and wind power penetration, Model 2 outperforms Model 1 by reducing the load loss ratio by 23.6% and improving the node survival ratio by 5.3%.

A Framework for 5G Network Slicing Optimization using 2-Edge-Connected Subgraphs for Path Protection

Emerging telecommunications technologies require robust frameworks for efficient network slicing. We propose a network-slicing model that aims to optimize the deployment of virtual networks on a physical network topology. Our model ensures compliance with 5G requirements, incorporating latency and capacity constraints on virtual links. Selecting slices with cost and resource requirements on the computing nodes is optimized using a Knapsack problem with revenue maximization. We propose a path protection algorithm to deal with link failures by constructing a 2-edge-connected subgraph (or two link-disjoint Steiner trees) for each slice to provide both primary and backup paths. Simulation results include comparison with existing solutions by metrics such as latency, revenue, resource utilization, number of protected slices, and computation time, providing valuable insights for network planners operating in diverse and dynamic environments. Key contributions include efficient resource allocation using the Knapsack problem, enhanced network resilience via 2-edge-connected subgraphs for path protection, and realistic simulation experiments on SNDlib dataset topologies. The simulation results show that the proposed framework improves computational efficiency compared to the recent related solutions, particularly in large network topologies where k-connected function slicing (KC- FS) subgraph embeddings take approximately 3.5 times more computation time.

A dynamic analysis of UAV -based IoT networks for efficient communication using falcon optimization approach

Unmanned Aerial Vehicles (UAVs) play a pivotal role in Internet of Things (IoT) applications by sensing data, conducting continuous surveillance, and maintaining connectivity among users. However, their high mobility and continuous surveillance for data transmission result in high energy consumption, possibly resulting in lesser network performance, including link failure, node depletion, data loss, and high delays. To address these challenges, a novel scheme termed as 'On-Demand Cross-Layered Falcon Optimization Approach (OCLFO)' has been proposed to enhance connectivity and optimize network performance in UAV-based IoT systems. The on- demand Cross-Layer Optimization (CLO) scheme facilitates data transmission across each layer of the OSI model. CLO is applied to the design of UAVs with the integration of on-demand, based on the queuing size. Falcon Optimization Approach (FOA) has been integrated with the CLO to overcome the energy consumption drawback, and provide successful data transmission between UAV users with the help of fitness value. The fitness value depends maximum on the energy and minimum on the queuing size, and this provides the energy efficient routing path for an efficient data transmission. The proposed OCLFO aims to optimize energy consumption while ensuring successful and expedited data transmission among UAV users. Simulations have been conducted using a network simulator to analyse Quality of Service (QoS) parameters, considering high mobility scenarios by varying energy levels such as 100 J and 200 J. Compared to the existing optimization algorithms, the proposed OCLFO achieves high delivery ratio, high throughput, less delay, lesser energy consumption and enhanced link efficiency.

Enhancing Wireless Network Efficiency with the Techniques of Dynamic Distributed Load Balancing: A Distance-Based Approach.

The unique combination of the high data rates, ultra-low latency, and massive machine communication capability of 5G networks has facilitated the development of a diverse range of applications distinguished by varying connectivity needs. This has led to a surge in data traffic, driven by the ever-increasing number of connected devices, which poses challenges to the load distribution among the network cells and minimizes the wireless network performance. In this context, maintaining network balance during congestion periods necessitates effective interaction between various network components. This study emphasizes the crucial role that mobility management plays in mitigating the uneven load distribution across cells. This distribution is a significant factor impacting network performance, and effectively managing it is essential for ensuring optimal network performance in 5G and future networks. The study investigated the complexities associated with congested cells in wireless networks to address this challenge. It proposes a Dynamic Distance-based Load-Balancing (DDLB) algorithm designed to facilitate efficient traffic distribution among contiguous cells and utilize available resources more efficiently. The algorithm reacts with congested cells and redistributes traffic to its neighboring cells based on specific network conditions. As a result, it alleviates congestion and enhances overall network performance. The results demonstrate that the DDLB algorithm significantly improves key metrics, including load distribution and rates of handover and radio link failure, handover ping-pong, and failed attached requests.

A Robust Hybrid Iterative Learning Formation Strategy for Multi-Unmanned Aerial Vehicle Systems with Multi-Operating Modes

This paper investigates the formation control problem of multi-unmanned aerial vehicle (UAV) systems with multi-operating modes. While mode switching enhances the flexibility of multi-UAV systems, it also introduces dynamic model switching behaviors in UAVs. Moreover, obtaining an accurate dynamic model for a multi-UAV system is challenging in practice. In addition, communication link failures and time-varying unknown disturbances are inevitable in multi-UAV systems. Hence, to overcome the adverse effects of the above challenges, a hybrid iterative learning formation control strategy is proposed in this paper. The proposed controller does not rely on precise modeling and exhibits its learning ability by utilizing historical input–output data to update the current control input. Furthermore, two convergence theorems are proven to guarantee the convergence of state, disturbance estimation, and formation tracking errors. Finally, three simulation examples are conducted for a multi-UAV system consisting of four quadrotor UAVs under multi-operating modes, switching topologies, and external disturbances. The results of the simulations show the strategy’s effectiveness and superiority in achieving the desired formation control objectives.

Routing problem for reducing significant outages in optical networks and its system analysis

Optical communication networks are operated with great care to avoid outages. In particular, significant outages having a devastating impact on society must be avoided, and several countries specify “significance standards” for network outages. Although the impact of possible outages strongly depends on routing methods for requested connections, there has been no research specifically on these standards. This paper for the first time, to the best of our knowledge, introduces a routing problem to minimize the impact of outages on a given significance standard. We have shown this problem to be non-deterministic polynomial-time hardness, and an efficient heuristic method is proposed. This method searches for a good solution by reducing the problem to a shortest-path search, where the significance of a link failure is regarded as the link weight, and suppresses outage impact compared with other routing methods. In addition, the analysis of the system dependence among a network model, mean time to repair (MTTR) of failure elements, and significance standards suggests that the total system design considering the MTTR, significance standards, and routing method could dramatically reduce the impact of significant outages on optical networks.

Deep Learning with Hybrid Black Widow-Bird Swarm Algorithm for Link Failure Detection and Prevention in Elastic Optical Networks

Deep Learning with Hybrid Black Widow-Bird Swarm Algorithm for Link Failure Detection and Prevention in Elastic Optical Networks