Link Failure Research Articles

Adaptive prescribed time control for quadrotor formation with stochastic links failure

AbstractThe formation control for multiple quadrotors subject to maintaining the formation configuration and collision avoidance in the situation of stochastic links failure is investigated in this paper. First, the distributed formation controller is designed, the position controller is developed to manage the desired formation of position, and the attitude controller is developed to control the translation and rotation movements of the quadrotor. Then, in order to avoid the collisions between multiple quadrotors and the obstacles, a potential energy function method is introduced into the quadrotor formation control combined with the nest adaptive control. Inspired by the design of event trigger controller, a communication compensation controller is designed to ensure the stability of quadrotor formation under the condition of random communication interruption and recovery. Moreover, a prescribed time function is designed, which means the convergence time of the formation system can be set in advance. The prescribed time stability of the formation control system is proved by Lyapunov theory. Finally, the simulation results verify the effectiveness and superiority of this method.

Anticipating and Repairing Link Failures in AODV Protocol

Mobile ad hoc networks are wireless networks with particular characteristics: they are infrastructure-free, self-organizing, and self-configuring. MANETs represent an active research area with different problems. Several routing protocols are available to ensure data flow in MANETs. Due to the nature of MANETs, providing quality of service is a challenging issue that is often ignored in existing routing protocols. In general, on-demand routing protocols are characterized by higher latency and lower overhead. However, in this paper, the authors propose a new extension to the AODV routing protocol called AR-AODV. This approach provides QOS support in terms of packet loss, routing overhead, end-to-end delay, and packet delivery ratio. The proposed method aims to anticipate link failures by estimating when a link is likely to break, using Lagrange interpolation. Then the route is repaired a few seconds prior to the failure. The effects of mobility, congestion, and traffic load are explored and analyzed, and the results obtained confirm that the proposed AR-AODV protocol outperforms the basic AODV.

Effective multi-controller management and adaptive service deployment strategy in multi-access edge computing environment

With the popularity of emerging mobile services such as virtual reality and online gaming, users expect higher quality immersive experiences. In response to the communication reliability and latency performance problems between controllers and switches due to single link failure in the network, a framework for multi-access edge computing systems based on software-defined networking (SDN) has emerged, and this paper proposes a reliable placement method for controllers based on deployment cost and link failure probability. Reliability and worst-case propagation delay model based on link failure probability is constructed. A controller placement strategy with minimum network cost is solved by a controller layout algorithm based on density-peak clustering. A mobile-aware service adaptive deployment method is also proposed in this paper to address the problems of long user-perceived delays and high migration costs due to the limitations of edge server resources and coverage areas and the dynamic nature of user movement. A mobility model based on the probability density function of sojourn time and a service deployment overhead model based on user mobility are constructed. A quantum ant colony algorithm is used to solve the service adaptive deployment scheme. The proposed algorithm is compared with the benchmark algorithm using the vehicle detection benchmark application service. Experimental results show that the proposed controller placement algorithm weighs the deployment cost, worst-case propagation delay and reliability of controllers and gives a reasonable number of controllers and their locations. The proposed deployment algorithm can optimise the quality of the user experience.

An Intersection-Based Routing Scheme Using Q-Learning in Vehicular Ad Hoc Networks for Traffic Management in the Intelligent Transportation System

Vehicular ad hoc networks (VANETs) create an advanced framework to support the intelligent transportation system and increase road safety by managing traffic flow and avoiding accidents. These networks have specific characteristics, including the high mobility of vehicles, dynamic topology, and frequent link failures. For this reason, providing an efficient and stable routing approach for VANET is a challenging issue. Reinforcement learning (RL) can solve the various challenges and issues of vehicular ad hoc networks, including routing. Most of the existing reinforcement learning-based routing methods are incompatible with the dynamic network environment and cannot prevent congestion in the network. Network congestion can be controlled by managing traffic flow. For this purpose, roadside units (RSUs) must monitor the road status to be informed about traffic conditions. In this paper, an intersection-based routing method using Q-learning (IRQ) is presented for VANETs. IRQ uses both global and local views in the routing process. For this reason, a dissemination mechanism of traffic information is introduced to create these global and local views. According to the global view, a Q-learning-based routing technique is designed for discovering the best routes between intersections. The central server continuously evaluates the created paths between intersections to penalize road segments with high congestion and improve the packet delivery rate. Finally, IRQ uses a greedy strategy based on a local view to find the best next-hop node in each road segment. NS2 software is used for analyzing the performance of the proposed routing approach. Then, IRQ is compared with three methods, including IV2XQ, QGrid, and GPSR. The simulation results demonstrate that IRQ has an acceptable performance in terms of packet delivery rate and delay. However, its communication overhead is higher than IV2XQ.

Fuzzy Logic System Assisted Sensing Resource Allocation for Optical Fiber Sensing and Communication Integrated Network.

With the development of information transmission, there is an increasing demand for state monitoring of fiber-optic communication networks to improve the security and self-healing ability of the network. Distributed optical fiber sensing is one of the most attractive methods because it can achieve real-time detection of the whole network without additional sensing heads. However, when the sensing network is introduced into the communication network, the failure probability should be efficiently suppressed with limited sensing resources. In this paper, the fuzzy logic system is used to evaluate the impact of different sensing resource allocation on optical cable network quality. The link failure probability and path failure probability under the condition of uniform and non-uniform sensing resource allocation are simulated and analyzed, respectively. As shown in the analysis results, the failure probability under non-uniform allocation is significantly lower than under uniform allocation. In this paper, we discussed and addressed the allocation of the optical fiber sensing and communication integrated (OFSCI) network with the limited sensing resource for the first time. The results are helpful to develop an allocation strategy for optical fiber sensing and a communication integrated network with a higher robustness.

Evaluation of the robustness of SDN controllers ONOS and ODL

Software-defined networking relies on a controller as its main element. Therefore, it is critical for the controller to present features that improve the overall network performance while ensuring continuity of service despite failures. OpenDayLight (ODL) and Open Network Operating System (ONOS) are two important open-source distributed controllers. The objective of this study is to evaluate how ODL and ONOS deal with different failure scenarios in both their data and control planes. In the data plane, the evaluation analyzes both link and switch failures and recovers, different flows in reactive and proactive modes. In the distributed control plane throughput and latency are considered when the number of switches and controllers increase, the time to synchronize the network operations among the controllers, the time for a master to recover from failure, and whether network operations are reinstalled after the failure of a master controller. Our evaluation shows that ONOS achieves better results than ODL in the data plane, and ODL surpasses ONOS in the experiments performed in the control plane.

A solution technique to cascading link failure prediction

The study of complex networks is a new powerful tool that can provide a profitable skeleton to better elucidate technology-related phenomena and interactions between components of real-world networks However, it is not easy to predict the communal behavior of such systems from their elements and on the other hand, the failure of one or few elements can trigger the failure of other elements throughout the network, resulting in network breakdown and catastrophic events at large scales. Therefore, developing predictive mathematical techniques to examine complex networks is one of the biggest challenges of the present time. Knowing that link failure prediction is less studied in the OR literature, the present study articulates a method to predict link failures in complex networks, which is primarily based on Bayesian Belief Networks (BBN) and TOPSIS. The method aims to predict failures based on the affective factors of failures in networks. To this end, effective factors of failures are first detected, and then the graph of the relationship of factors along with their weight is determined. After all, the method provides the prediction for future damaged components. The functionality of the method is validated by an extensive computational analysis employing simulation in scale-free, random, and actual international aviation networks and its performance is compared with other benchmark algorithms. The results and sensitivity analysis experiments arrive at prominent managerial insights and efficacious implications and show that our method can generate high-quality solutions in many networks.

Locating Link Failures in WSNs via Cluster Consensus and Graph Decomposition

With the popularization of network equipment and the rapid development of information technology, the scale and complexity of wireless sensor networks (WSNs) continue to expand. How to effectively locate link failures has become a challenging problem in WSNs. In this paper, we propose a novel method of locating link failures based on distributed cluster consensus protocol and graph decomposition technique. In our method, the initial data is injected into sensor nodes for distributed interactions, and then link failures can be located by observing and comparing the output data of the nodes. The proposed method is suitable for the situations with both single-link failure and multi-link failures, and has no limitations on the number, distribution and correlation of link failures. Necessary and sufficient conditions are provided to guarantee the accuracy of the proposed method in locating link failures. At last, the effectiveness of the proposed method is verified by both real and simulation experiments.

Proactive Load Balancing Through Constrained Policy Optimization for Ultra-Dense Networks

Designing an intelligent self-organizing network (SON) architecture is challenging for future wireless networks. To meet the needs of SON, the reactive self-organizing model of the traditional network needs to be transformed into an active and interactive one. Due to the user mobility and small coverage of cells in ultra-dense networks (UDNs), the network load usually becomes unbalanced, leading to deteriorated network performance, such as low throughput, radio link failure, and poor user experience. Therefore, the technique of mobility load balancing (MLB) is critical to ensuring a seamless user experience among cells. This letter proposes an active and interactive MLB strategy for UDNs, which transforms the original reactive MLB into a forward-aware and active one. In particular, user mobility is first predicted based on the Bayesian additive regression tree (BART). Then, with the mobility predictions, the joint mobility robust optimization and MLB problem subject to users’ rate constraint is solved via safe reinforcement learning. The pertaining simulation results show that the proposed method can improve the network performance and realize intelligent mobile management for future UDNs.

An Innovative Prediction of Link Failure and Node Lifetime in Mobile Ad Hoc Networks Using Grey Wolf Optimization

The Path Restoration System in Mobile Ad hoc Network (MANET) was tough due to the changing environment. Data packets are lost if a link is broken while delivering information, and the system is vulnerable to various assaults. Considering this, we propose the Grey Wolf Optimization technique (GWO) to predict connection failure, link and node lifetime before broadcasting packets to avoid packet loss. To define the path, we used the Route Information Protocol (RIP). Following that, GWO is manually played; with this method, this research forecasts the node and lifetime, and achieves a packet delivery ratio of 0.7. The proposed Gray-Wolf algorithm achieves an efficient packet transmission rate and improves the early detection of links and node lifetimes to maintain path stability for data transmission. The proposed model reduces end-to-end delay, overhead, and packet drop. It improves the residual energy of nodes and the packet delivery ratio. Grey Wolf Optimization is one of many examining boosting methods activated by the grouping within the wolf family and the special hunting techniques used by grey wolves. As a result, the Grey Wolf optimization method was used to find the optimal result by mocking the overall characteristics of the grey wolf colony.

Mobility Management UAV‐based Grouping routing protocol in flying ad hoc networks for biomedical applications

SummaryWireless body area networks play a vital role in the medical field by saving human lives. It senses the human body condition and transmits it to the respective doctors. The faster the communication, the sensed data can be transmitted faster and the patients in emergency can be treated on time. Therefore, to increase the speed of communication, Unmanned Aerial Vehicles (UAV) are used in biomedical applications. In this flying ad hoc network, there is a possibility of link failure due to the high mobility of UAV. To overcome this issue, a new routing protocol, Mobility Management UAV‐based Grouping (MMUG), for biomedical data transmission from one ground station to another via UAV is suggested in this research. The UAV creates the grouping system based on the coverage range of the group head. Group head selects the node that lies closer to the ground station. Group head continuously monitors the mobility of the unmanned aerial vehicles that lie in the coverage range and thus helps to maintain the link stability. The mobility issue will be overcome by using this proposed scheme, and it manages the mobility. The mobility management helps to increase the network performance by increasing the delivery rate and throughput and reducing the drop rate and overhead. Simulation results show that this routing protocol achieves 86% delivery rate and 1.95 Mbps throughput and reduces the drop rate by 14%, control overhead by 23%, and routing overhead by 20%. Moreover, this model achieves higher network performance compared to the existing techniques.

AI powered solution for radio link failure prediction based on link features and weather forecast

Radio link sustainability gets affected by weather adversities such as snow, fog, cloud, rain, thunderstorm, etc. A proactive solution in radio link failure scenarios is necessary to overcome economic loss and maintain the Quality of Service (QoS). To address the issue, our work contributes towards building
 a machine-learning-based solution to predict the radio link failure when generic regional weather forecast data, key performance indices of radio link and spatial nature of the data are available. After rigorous data preprocessing, ensembling models like logistic regression, random forest, light BGM, XGBoost and gradient boosting classifiers were trained to predict the Radio Link Failure (RLF) for two cases i.e., day-1-predict and day-5-predict. Since it is a classification use case, the metrics used for our work are precision, recall, and F1 score. The performance of the gradient boosting classifier was better as compared to the other models with an F1 score of 0.95 for both day-1-predict and day-5-predict.

An optimized routing scheme for congestion avoidance using mobile nodes in Wireless Sensor Network

Wireless Sensor Network (WSN) consists of a group of dynamic sensor nodes such nodes form a volatile topology. When an event occurs the dynamic nodes WSN communicate wirelessly from source to destination node without any infrastructure. Dynamic sensor nodes are vital characteristics of WSN where link breakage occurs recurrently and change in topology. Due to this overall network Quality of Service (QoS) degrades due to link failure. Whenever an event in the network experience packet loss due to link failure which leads one of the causes for congestion. However predictable network tries to discover alternate path whenever the link failure occurs on demand which does not have the overall network information thus increasing the communication overhead, delay, and high energy consumption. To address these issues this problem paper proposes an optimized routing scheme known as low latency optimal link-state routing (LL-OLSR) for mobile in WSN which proactively monitors the established path and connects the source to destination with minimum cost with optimal energy. LL-OLSR algorithm finds the disjoint link where it is detected between two nodes and also maximizes the packet delivery ratio by minimizing path delay using connectivity range and queuing delay by which congestion is avoided. Simulation experiment of proposed LL-OLSR scheme is carried on event-driven simulation tool –NS2 and QoS parameters are evaluated by comparing with existing routing scheme.

System of system design-for-resilience heuristics derived from forestry case study variants

System of Systems connect complex technological systems (e.g. power plants, transportation, and infrastructure) to provide additional services. Failures in one constituent system, however, can often cascade to other systems within the network. Decision makers need repeatable, generalizable System of System design-for-resilience approaches. Current design-for-resilience approaches, however, are often not generalizable, do not fully leverage network structure as a design parameter, and require designers to identify possible faults before they occur. These gaps motivate our research question: How can biologically inspired design be used to increase System of Systems resilience to unexpected link failure? We hypothesize that examining a set of 20 possible design variants for the same case study will reveal correlations between Ecological Network Analysis metrics (a subset of graph theory) and increased resilience. These correlations are then used to identify 13 design-for-resilience heuristics. Heuristics are simplified design guidelines that improve, but do not necessarily optimize, decision making during the design process. This work provides two contributions. First, the heuristics provide an approach to both design for increased resilience and evaluate potential System of Systems designs. A discussion of when the heuristics are applicable is presented (i.e. initial graph theory metric value limitations). Secondly, validation tests provide evidence that the heuristics can be used to increase resilience for networks other than the case study. This article provides a crucial step by utilizing Ecological Network Analysis to identify and test a set of design-for-resilience heuristics, an important step to developing a repeatable, network-based, fault agnostic approach to design Systems of Systems with increased resilience.

Ring based hybrid FSO- fiber optic architecture utilizing same wavelengths for downstream and upstream transmission with FN/DN protection capability

This paper proposes a reliable hybrid 4 × 10 Gbps fiber optic-FSO based ring architecture. The proposed architecture aims to provide reliable and bandwidth-efficient transmission. The proposed architecture has a fault protection capability in presence of the link failure occurring at the feeder network (FN) and distribution network (DN) towards the central office (CO) and optical networking unit (ONU) respectively. To avail the benefits of free space optics (FSO), it is integrated with the optical fiber at the user end. The architecture transmits the data on the ring in only a clockwise direction for both downstream and upstream transmission and therefore enables the utilization of the same wavelengths without any interference. i.e. the proposed architecture improves the bandwidth utilization and needs only half of the spectrum. In the proposed architecture, four 10 Gbps signals are propagated through a 20 km single-mode fiber (SMF) and a 200 m length of free-space/optical fiber link for the downstream and upstream transmissions. We also compared the signal transmission performance of architecture in terms of bit error rate (BER) for different weather conditions of the FSO link at the user side. The architecture works well for the different weather conditions and can transmit data through the FSO link for upto 700 m and 200 m for clear and heavy rain weather conditions respectively. Protection against faults in the fiber links; improves the survivability and reliability of the network. The proposed architecture is useful in rural and urban hilly areas where signal transmission interrupts frequently due to various geographical challenges.

Trade-offs between Risk and Operational Cost in SDN Failure Recovery Plan

We consider the problem of SDN flow optimization in the presence of a dynamic probabilistic link failures model. We introduce a metric for path risk, which can change dynamically as network conditions and failure probabilities change. As these probabilities change, the end-to-end path survivability probability may drop, i.e., its risk may rise. The main objective is to reroute at-risk end-to-end flows with the minimum number of flow operation so that a fast flow recovery is guaranteed. We provide various formulations for optimizing network risk versus operational costs and examine the trade-offs in flow recovery and the connections between operational cost, path risk, and path survival probability. We present our suboptimal dynamic flow restoration methods and evaluate their effectiveness against the Lagrangian relaxation approach. Our results show a significant improvement in operational cost against a shortest-path approach.

Self-backhauling failure mitigation using 5G new radio

5G network operators will consider the dense deployment of small cells to increase network coverage and capacity. However, in order to install a large number of gNBs, operators will face the challenge of backhauling their traffic to the core network in a cost-effective manner. Although Integrated Access and Backhaul (IAB) using 5G new radio is a promising solution to solve the dilemma of small cells’ backhauling, densifying the network will increase the probability of failure of these links. To cope with this problem, a self-healing scheme is used to mitigate or at least alleviate the effect of backhaul failure. We propose to use IAB with the collaboration of neighboring gNBs to mitigate the failed backhaul link(s). Hence, our goal is to design a pre-planned network capable of providing the minimum rate requirements to its users in the presence of backhaul failure. We formulate a joint resource allocation/backhaul outage compensation optimization problem as a non-convex mixed integer non-linear program. Due to the difficulty of this problem, we divide this problem into two sub-problems, in addition to the use of different approximation and relaxation techniques to find an approximated sub-optimal solution. Simulation results show that the proposed scheme is capable of providing the network users an acceptable continuous, albeit slightly degraded, service during backhaul failure. The degradation percentage is inversely proportional to the network densification for single and multiple failures. Meanwhile, the Degree of Recovery (DoR), i.e., subtracting the degradation percentage from 100, of single failure scenarios range from 95% (for the best case) to 54% (for the worst case). For the multiple failures scenario, the DoR is much lower and can reach zero in case of three concurrent failures

ACOLBR: ACO Based Load Balancing Routing in MANET

Real-time data transmission is one of the objectives of MANET (mobile ad-hoc network) to handle emergencies like a forest fire, flood, and earthquake. In this scenario, quick delivery of data is itself a challenging task for MANET and there is a possibility of load imbalance due to packet transmission in between source-destination pairs via the shortest path due to congestion or control overhead. In this paper, a novel routing protocol, called ACOLBR (ACO based load balancing routing), is design to control the congestion and balancing the load among the multiple paths in between source to destination. The similarity between the environment of Ant Colony and the MANET inspires the authors to apply ACO (Ant colony optimization) technique during routing in MANET to control congestion and balance the load in the network. In our proposal, two colonies of ants (red/blue) carry their packets based on the network condition. A decision variable is designed to select red/blue ant for transferring packets based on different network parameters such as bandwidth, energy, mobility, and distance. The selection of red ant means the route where the concentration of red pheromone is maximum and reverse is true for blue ant. This protocol is also concerns about the link failure during packet transmission in a route. Simulation results using OMNET++ show that ACOLBR outperforms ARA, ANTHOCNET, FACO, AODV, DSDV, DSR, CA-ARTT, and MOAODV in terms of load balancing efficiently in the route for data transmission in between source to destination.

Comprehensive analysis on sensor node fault management schemes in wireless sensor networks

SummaryWireless sensor networks (WSNs) are ad hoc networks that consist of tiny sensor nodes deployed in the sensed environment to monitor the natural phenomenon and send the monitored data to the base station for further processing. Due to the unfriendly deployment of the sensor nodes and their resource‐constrained nature, the sensor nodes are subjected to frequent node failure. Detecting sensor node failures and recovering them is a major challenge in WSN. Due to the failure of the nodes in WSN, there exist various problems such as a change in the network topology, communication link failure, a disjoint partition of the network, and failure to transmit the data to the sink. Various authors have proposed various techniques to detect and recover from node failure in the WSN. In this paper, a comprehensive analysis of the various node failure and node recovery techniques has been carried out to highlight the advantages and limitations along with the open research challenges for addressing the failure of the nodes in an effective manner.

Enforcing Multilevel Security Policies in Unstable Networks

Multilevel security (MLS) systems control access to data by formalizing permissible and impermissible information flows between data sources and destinations (e.g., database servers and clients) fixed with distinct security labels. In computer networks, MLS systems have been used to prevent unauthorized data disclosure in shared-infrastructure settings where network hosts and devices may fall within different trust domains (e.g., in multi-tenant cloud networks or wireless mesh networks). However, current MLS systems assume static network behavior—thus preventing the network from being practically usable in the presence of dynamic network events that frequent unstable network environments, including sudden changes in traffic patterns, link failures, and topology changes as a result of device movement or intermittent device connectivity. In this paper, we introduce <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MLS-Enforcer</monospace> , a software-defined networking (SDN) controller application that can efficiently deploy network-level MLS policies while retaining the ability to securely relabel network nodes under changing topology state and network traffic demands. We model network adaptivity as an integer linear programming problem that reflects a given security policy. We then introduce heuristic relabeling algorithms that achieve near-optimal performance and are more tractable and efficient for larger networks. We validate <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MLS-Enforcer</monospace> on several network topologies and traffic loads, demonstrating that it can relabel the network to route 90%+ of flows under normal conditions and quickly converge (on the order of seconds for the heuristic algorithms) under changing needs—from small network structure changes to catastrophic failures. This shows that formally secured networks can feasibly be deployed in diverse, changing, and unpredictable environments.