Link Failure Research Articles

FlowerCast: Efficient Time-sensitive Multicast in Wireless Sensor Networks with Link Uncertainty

This article studies time-sensitive multicast in wireless sensor networks (WSNs) with link uncertainty, where information from the source needs to be delivered to multiple receivers within an imposed delay constraint. Prior art on static WSNs minimizes the multicast delay via the construction of a multicast tree that approximates the Steiner tree in length, which, however, may be invalidated by the time-varying network topology of WSNs with uncertain link states. Moreover, for multicast in WSNs with link uncertainty, the possible link failure necessitates a suitable measurement of the uncertain communication distance and calls for the performance guarantee in both delay and delivery ratio. In this work, by modeling a WSN as a random graph with each link associated with a transmission probability, we propose FlowerCast, an efficient multicast scheme, to jointly minimize the expected multicast delay and to maximize the expected delivery ratio of multicast under delay constraint. The core of FlowerCast is to quantify the uncertain communication distance by the expected transmission delay of a time-varying path, based on which a delay-optimal multicast tree is constructed in accordance with the directionality of delay. Candidate paths with a high expected delivery ratio and low expected delay are then selected in a distributed manner to conditionally connect adjacent multicast members and thus transform the multicast tree into a multicast flower. Despite the NP-hardness of optimal candidate paths’ addition, the transformation with the highest expected delivery ratio of multicast under delay constraint can be guaranteed through a pseudo-polynomial time derandomization-based greedy approach. We further demonstrate the time and energy efficiency of FlowerCast through asymptotic analysis. To make full use of the possible overlapping links in a multicast flower, a hybrid routing strategy is presented to wisely switch between sequential routing and synchronous routing for extra enhancement of the multicast performance. Extensive experiments on various datasets verify the superiority of FlowerCast and hybrid routing over baselines and indicate their wide applicability to practical scenarios.

Recovering Power Grids Using Strategies Based on Network Metrics and Greedy Algorithms.

For this study, we investigated efficient strategies for the recovery of individual links in power grids governed by the direct current (DC) power flow model, under random link failures. Our primary objective was to explore the efficacy of recovering failed links based solely on topological network metrics. In total, we considered 13 recovery strategies, which encompassed 2 strategies based on link centrality values (link betweenness and link flow betweenness), 8 strategies based on the products of node centrality values at link endpoints (degree, eigenvector, weighted eigenvector, closeness, electrical closeness, weighted electrical closeness, zeta vector, and weighted zeta vector), and 2 heuristic strategies (greedy recovery and two-step greedy recovery), in addition to the random recovery strategy. To evaluate the performance of these proposed strategies, we conducted simulations on three distinct power systems: the IEEE 30, IEEE 39, and IEEE 118 systems. Our findings revealed several key insights: Firstly, there were notable variations in the performance of the recovery strategies based on topological network metrics across different power systems. Secondly, all such strategies exhibited inferior performance when compared to the heuristic recovery strategies. Thirdly, the two-step greedy recovery strategy consistently outperformed the others, with the greedy recovery strategy ranking second. Based on our results, we conclude that relying solely on a single metric for the development of a recovery strategy is insufficient when restoring power grids following link failures. By comparison, recovery strategies employing greedy algorithms prove to be more effective choices.

A Distributed Autonomous Mission Planning Method for the Low-Orbit Imaging Constellation

With the improvement of satellite autonomy, multi-satellite cooperative mission planning has become an important application. This requires multiple satellites to interact with each other via inter-satellite links to reach a consistent mission planning scheme. Considering issues such as inter-satellite link failure, external interference, and communication delay, algorithms should minimize communication costs as much as possible. The CBBA algorithm belongs to a fully distributed multi-agent task allocation algorithm, which has been introduced into multi-satellite autonomous task planning scenarios and achieved good planning results. This paper mainly focuses on the communication problem, and proposes an improved algorithm based on it, which is called c-CBBA. The algorithm is designed with task preemption strategy and single-chain strategy to reduce the communication volume. The task preemption strategy is an accelerated convergence mechanism designed for the convergence characteristics of CBBA, while the single-chain strategy is a communication link pruning strategy designed for the information exchange characteristics of satellites. Experiments in various scenarios show that the algorithm can effectively reduce communication volume while ensuring the effectiveness of task planning.

Autonomous Scheduling for Reliable Transmissions in Industrial Wireless Sensor Networks

Deploying Internet of Things (IoT) on low-power lossy wireless sensor/actuator networks (LLN) in harsh industrial environments presents challenges such as dynamic link qualities due to noise, signal attenuations and spurious interferences. However, the critical demand for industrial applications is reliability of data delivery on low-cost low-power sensor/actuator devices. To address these issues, this paper proposes a fully autonomous scheduling approach, called Auto-Sched, which ensures reliability of data delivery for both downlink and uplink traffic scheduling and enhances network robustness against node/link failures. To ensure reliability, Auto-Sched assigns retransmission time slots based on the reliability constraints of the communication link. To avoid collision issues, Auto-Sched creates an upward pipeline-like communication schedule for uplink end-to-end data delivery, and a downward pipeline-like communication schedule for downlink scheduling. For enhancing network robustness, we propose a simple algorithm for real-time autonomous schedule reconstruction, when node or link failures occur, with minimal influence on communication overhead. Performance evaluations quantified the performance of our proposed approaches under a variety of scenarios comparing them with existing approaches.

Predicting and Recovering Link Failure Localization Using Competitive Swarm Optimization for DSR Protocol in MANET

Portable impromptu organization is a self-putting together, major construction-less, independent remote versatile hub that exists without even a trace of a determined base station or government association. MANET requires no extraordinary foundation as the organization is unique. Multicasting is an urgent issue in correspondence organizations. Multicast is one of the effective methods in MANET. In multicasting, information parcels from one hub are communicated to a bunch of recipient hubs all at once, at a similar time. In this research work, Failure Node Detection and Efficient Node Localization in a MANET situation are proposed. Localization in MANET is a main area that attracts significant research interest. Localization is a method to determine the nodes’ location in the communication network. A novel routing algorithm, which is used for Predicting and Recovering Link Failure Localization using a Genetic Algorithm with Competitive Swarm Optimization (PRLFL-GACSO) Algorithm is proposed in this study to calculate and recover link failure in MANET. The process of link failure detection is accomplished using mathematical modelling of the genetic algorithm and the routing is attained using the Competitive Swarm optimization technique. The result proposed MANET method makes use of the CSO algorithm, which facilitates a well-organized packet transfer from the source node to the destination node and enhances DSR routing performance. Based on node movement, link value, and endwise delay, the optimal route is found. The main benefit of the PRLFL-GACSO Algorithm is it achieves multiple optimal solutions over global information. Further, premature convergence is avoided using Competitive Swarm Optimization (CSO). The suggested work is measured based on the Ns simulator. The presentation metrix are PDR, endwise delay, power consumption, hit ratio, etc. The presentation of the proposed method is almost 4% and 5% greater than the present TEA-MDRP, RSTA-AOMDV, and RMQS-ua methods. After, the suggested method attains greater performance for detecting and recovering link failure. In future work, the hybrid multiway routing protocols are presented to provide link failure and route breakages and liability tolerance at the time of node failure, and it also increases the worth of service aspects, respectively.

Blocking aware offline survivable path provisioning of connection requests in elastic optical networks

Survivability of Elastic Optical Networks (EONs) from a link failure is an important problem. Shared Backup Path Protection (SBPP) is widely used for protection against a link failure. Backup Spectrum Reservation with Multi Path Protection (BSR-MPP) was introduced to increase backup spectrum sharing by reserving backup spectrum and employing protection with multiple backup paths. We propose a new polynomial-time strategy, Blocking Aware Survivable Path Provisioning (BASPP) that shares backup resources like SBPP and BSR-MPP but can reduce request blocking when opportunity of sharing resources does not exist. Since we address the offline problem of Survivable Routing, Modulation and Spectrum Allocation in EONs, we also propose an Integer Linear Program formulation. Results show appreciable reduction in bandwidth blocking ratio by using BASPP over existing strategies.

Hybrid tree-and-multiple-ring radio-over-fiber transmission system for a 5G network in metropolitan areas

What we believe to be a novel radio-over-fiber (RoF) transmission system with a hybrid tree-and-multiple-ring network structure is developed and experimentally demonstrated to support broadband wireless signal transmission among the central office (CO) and densely distributed 5G base stations (BS) in urban areas. With the assistance of an advanced network structure and a unique single-line bidirectional optical add/drop multiplexer (SBOADM), the transmission system can utilize a hybrid tree-and-ring-topology fiber network to simultaneously support multiple 5G BS-Groups constructed as sub-ring-networks. Furthermore, the system manager is able to simultaneously utilize wavelength/time division multiplexing techniques to support the connected BSs and to dynamically embed extra sub-ring-networks to the main-ring-network or extra BSs to the sub-ring-network by using the RoF transmission system. Needing no backup fiber link, the transmission system enjoys intrinsic robust resilience against fiber link failures. Experimental results proved that the proposed RoF transmission system enables prompt recovery of interrupted optical fiber connection and guarantees the expected quality of service (QoS) by adjusting a few preinstalled optical switches when fiber link failure happens in the main-ring-network, in each sub-ring-network, or even in both of the above scenarios. The induced power penalty for maintaining unchanged bit error rate performance is less than 2 dB. In sum, the proposed transmission system proves to be a great network architecture suitable for supporting 5G signal transmission between the CO and BSs in an urban area due to the dynamically extensible network size and the well-protected transmission that ensures the QoS by simple operation.

A Graph reinforcement learning based SDN routing path selection for optimizing long-term revenue

Software-Defined Network (SDN) paradigm decouples control plane from data plane and provides a logically-centralized control to whole underlying network, which enables the controller to centrally configure the network, and has demonstrated great potential in fully utilizing network capacity. However, the traditional SDN does not consider the accumulative long-term revenue in routing configuration for network service provider (SP). For example, it is prone to choosing the path passing through important nodes, which may increase the possibility of future congestion, and hamper the SP’s long-term revenue. Deep reinforcement learning (DRL) has brought about the transformative advances in decision-making when facing uncertainty. As a well-known type of DRL, Deep Q-network (DQN) can explore optimal routing schemes from high dimensional network state. However, most of existing DRL based methods fail to provide satisfactory configuration when confronted with a topology unseen during training. Meanwhile, many existing network optimization schemes ignore the impact of nodes and global network attributes on the routing configuration. Consequently, they may perform poorly in real scenarios. In this paper, we propose a graph reinforcement learning (GRL) based SDN routing selection scheme, GN-DQN for optimizing SP’s accumulative long-term revenue. First, besides the link embeddings, GN-DQN explicitly learns the representations of the routers and globe network topology with full graph neural network block. Then, through utilizing the formed network representation as the state, DQN is used to learn to sequentially choose the optimal routing path from the candidate path set to maximize the accumulative long-term revenue. Thorough experiments on multiple real network topologies show that our proposed GN-DQN outperforms other GRL based and traditional routing selection schemes, and can generalize over arbitrary topologies. Moreover, GN-DQN also demonstrates great robustness to the link failure scenario.

TIVC: An Efficient Local Search Algorithm for Minimum Vertex Cover in Large Graphs.

The minimum vertex cover (MVC) problem is a canonical NP-hard combinatorial optimization problem aiming to find the smallest set of vertices such that every edge has at least one endpoint in the set. This problem has extensive applications in cybersecurity, scheduling, and monitoring link failures in wireless sensor networks (WSNs). Numerous local search algorithms have been proposed to obtain "good" vertex coverage. However, due to the NP-hard nature, it is challenging to efficiently solve the MVC problem, especially on large graphs. In this paper, we propose an efficient local search algorithm for MVC called TIVC, which is based on two main ideas: a 3-improvements (TI) framework with a tiny perturbation and edge selection strategy. We conducted experiments on real-world large instances of a massive graph benchmark. Compared with three state-of-the-art MVC algorithms, TIVC shows superior performance in accuracy and possesses a remarkable ability to identify significantly smaller vertex covers on many graphs.

Protection Techniques using Resource Delayed Release for SDN-based OTN over WDM Networks

The availability and reliability of optical backbone links are very important to ensure the efficient operation of the Internet. To address the issue of data loss due to optical link failures, there is a need for an optimal recovery strategy so that the traffic can be rerouted on a backup path to the destination. This paper builds on top of our prior research efforts (Yavary Mehr et al., 2022; Zhou et al., 2017) which introduced the concept of Resource Delayed Release (RDR) by adding a new state called ”idle state” which begins when the channel has completed carrying its services so that the next request can be carried immediately instead of waiting for a new channel to be established. While RDR improves the network performance by reducing the service provisioning time and blocking probability, it does not handle link failures which are quite common in optical networks. Therefore, enhancing RDR with protection strategies will make the network more reliable and thus we investigate this topic in this work.In this paper, we evaluate four different protection methods for single link failure recovery in WDM networks (Path Protection (PP), Partial Path Protection (PPP), Segment Protection (SegP) and Link Protection (LP)) with two different routing approaches namely Shortest Path (SPath) and Greedy (G) algorithm under uniform and non-uniform traffic generated using real traffic traces collected from a local Internet Service Provider (ISP). Special attention while evaluating these protection strategies was paid to the optimization of the amount of remaining bandwidth. The performance evaluation of the network under uniform and non-uniform traffic was done over the NSFNet and COST239 topologies by employing the metrics of link and network utilization, Blocking Probability (BP), Bandwidth Blocking Probability (BBP), Recovery Time (RT) and Service Provisioning Time (SPT). Our results show that the PPP method performs the best in terms of reducing BP, BBP, and SPT compared with PP, LP, and SegP in all three topologies while utilizing RDR.

Vertex and Link Residual Closeness of Graphs of Given Fractional Matching Number

Vertex and link residual closeness are two new measures of network vulnerability due to the failure of individual vertices and links, respectively. We identify those graphs with maximum vertex/link residual closeness over all [Formula: see text]-vertex connected graphs with fixed fractional matching number.

Efficient Distance and Connectivity Based Traffic Density Stable Routing Protocol for Vehicular Ad Hoc Networks

Vehicle ad-hoc networks (VANETs) have become a prominent research topic in recent years due to rapid dynamic topology, high vehicle mobility, frequent link failures and significant delay constraints. The mobility of the vehicle nodes increases, and the overhead of control traffic due to the high dynamics of the VANET. Efficient routing algorithms are necessary for VANETs to ensure reliable transmission (VANETs). This work proposes a Traffic Density Stable Routing Protocol based on Connection- and Distance (TDSRP-DC) to avoid data packet collisions at intersections and an adaptive routing schedule based on the selection at every instant. Our approach is based on vehicle-to-vehicle communication. Ground vehicles identify the most appropriate next junction and transfer the data packet to the receiver to find the optimal multi-hop route. It relies on transferring the data between all vehicles to estimate real-time traffic fluctuations sporadically. Network formation, neighbor realization, fitness value prediction, and routing methodology are the conceptual process needed in this research. Valid parameters for finding the optimum path in the conceptual model include node distance, node speed, node azimuth, link stability, and link reliability. The suggested solution’s simulation was performed in comparison to traditional algorithms. Compared to existing TFOR algorithms on complex traffic, the proposed TDSRP-DC provides tremendous improvements based on packet delivery rate (10%) and performance (35%). The demonstrated results and comparisons illustrate the proposed routing protocol’s significance with enhanced quality of service performance.

Multiagent Q-Learning-Based Mobility Management for Multi-Connectivity in mmWAVE Cellular Systems.

Effective mobility management is crucial for efficient operation of next-generation cellular systems in the millimeter wave (mmWave) band. Massive multiple-input-multiple-output (MIMO) systems are seen as necessary to overcome the significant path losses in this band, but the highly directional beam makes the channels more susceptible to radio link failures due to blockages. To meet stringent capacity and reliability requirements, multi-connectivity has attracted significant attention. This paper proposes a multiagent distributed Q learning-based mobility management scheme for multi-connectivity in mmWave cellular systems. A hierarchical structure is adopted to address the model complexity and speed up the learning process. The performance is assessed using a realistic measurement data set collected from Wireless Insite in an urban area and compared with independent Q learning and a heuristic scheme in terms of handover probability and spectral efficiency.

Efficient Routing in VANETs Using MRRP Algorithm

Designing a reliable routing protocol for Vehicular Ad hoc Network (VANET) poses considerable challenges due to certain unique challenges inherently present in Vehicular Ad hoc Network (VANET) topology. Some of them are needed for vehicles acting as nodes having to abide by traffic rules, uncertain inter-vehicular speed variations that may affect link stability etc. Designing a routing protocol capable of dealing with multiple limiting conditions such as long congestion periods, link failures and handoffs is a challenging task, where most of the existing multipath routing protocol shows poor performance. In this paper, the proposed Multipath Route Restoration Protocol (MRRP)is aimed at providing a robust communication channel in case of link failure between nodes. This is realized by focusing on better route maintenance for the protocol. In a wireless network, a routing protocol determines the particular ways in which routers connect. In a wireless network, as the number of hops in a wireless communication path increases, various signal factors such as interference and path loss degrade the network performance. however, sending data over a longer distance will reduce throughput. Furthermore, link stability is substantially impacted by the unpredictable movement of vehicles. Multipath routing is regarded as a potential solution to improve packet delivery and end-to-end delay in VANETs.

Design of an In-Band Control Plane for Automatic Bootstrapping and Fast Failure Recovery in P4 Networks

In this paper, we present the design of an in-band control plane system for P4 networks. The proposed in-band control plane performs automatic switch bootstrapping, network status monitoring, topology discovery and fast failover. The authentication and registration of newly connected switches are managed by the controller without manual intervention. The system can recover from any single link or single node failure in a short period of time. The recovery control paths for each single link and single node failure are prepared in advance. We propose an algorithm that takes recovery time into account for preplanning the recovery paths. In addition to handling any single failure, the proposed control plane is survivable even if there are multiple failures in the network. We implement an emulated network to evaluate the proposed control plane system. The experimental results show that the system successfully provides all the target functions.

Failure Detector Based on Vehicle Movement Prediction in Vehicular Ad-Hoc Networks

Vehicular ad hoc network (VANETs) improves road safety and efficiency by organizing vehicles and infrastructure to provide a platform for application deployment. The availability of vehicles and infrastructure is critical to the operation of applications. Accurate failure detector (FD) has been one of the fundamental components for maintaining high availability in VANETs. However, it is hard to find the vehicle failure accurately and timely due to the dynamic nature of VANETs caused by the high mobility of vehicles and communications link failures. Therefore, it is important to achieve an accurate FD which can cope with the high mobility of VANETs. In this paper, we propose a dead reckoning based FD, called DR-FD. It can predict the mobility of vehicle accurately and avoid the impact of link failures on the detection results by the cooperation between vehicles. Experimental results are provided to confirm that the proposed DR-FD method can achieve at most 20% reduction in detection time, 30% improvement in mistake rate and 20% improvement in overhead.

Path Diversity and Survivability for the HyperX Datacenter Topology

Network survivability has been recognized as an issue of a major importance in terms of security, stability and prosperity. This paper studies fundamental properties of HyperX, an emerging topology for connecting supercomputing and datacenter networks. We focus on the establishment of paths with guaranteed survivability, allowing path availability even upon a restricted number of link failures. We first examine the availability of disjoint paths connecting a pair of input nodes. Disjoint paths guarantee path existence even upon a bounded number of link failures. We explore the inherent tradeoff between allowing slightly longer paths and the ability to extend available sets of mutually disjoint paths. Second, we study the availability of paths in a HyperX topology that already observed link failures. Such failures can increase the length of available paths or even eliminate connectivity between network parts. We also compare basic properties of shortest paths in HyperX to other datacenter topologies. Last, we provide an evaluation to illustrate path availability along with the potential impact of failures.

InFaRR: In-Network Fast ReRouting

InFaRR (In-network Fast ReRouting) is an algorithm for fast rerouting in programmable data planes. Implemented in P4, InFaRR is free of additional headers and network state heartbeats. InFaRR has four essential features not jointly found in other recovery mechanisms: Loop prevention, Pushback, Recognition and Restoration and Return to the main route. Tests in standard Fat-Tree and AB Fat-Tree topologies with failures in different scenarios showed positive results when compared to state-of-the-art algorithms in the literature. In scenarios in which the other algorithms were able to recover, InFaRR presented fewer hops to bypass the failure when the Pushback, Loop Prevention and Recognition and Restoration mechanisms were used. In scenarios with multiple failures, InFaRR successfully rerouted where the others algorithms in some cases looped. The mechanism for returning to the main route allows to verify failures in remote links, making it possible to return to the main route without intervention from the control plane. Several evaluations were done comparing the results of InFaRR with the state-of-the art mechanisms, showing the capability of our fast rerouting algorithm in dealing with failures at the line-rate.

Minimum-Weight Link-Disjoint Paths With a Bounded Number of Shared Nodes

Network protection has drawn a certain interest in network optimization. One of the most effective and widely used methods to protect networks from failures is to establish backup paths for working paths. For example, we find k node-disjoint paths between a source and a sink with one working path and k-1 backup paths. However, the demand for full protection of a network is somewhat too restrictive and there may not exist k node-disjoint paths in the network due to the limitation of geographical environments. On the other hand, the occurrence probability of node failures is usually much less than that of link failures in real-world models. To save network resources, we turn to establish link-disjoint paths allowing a few shared nodes. We study the problem of finding k link-disjoint paths between a source and a sink under the constraint that the number of nodes shared by at least r paths is at most δ, minimizing the total link weight. First, we systematically study the computational complexity of the problem with respect to three parameters k, r, and δ. Then, we build an integer linear programming for the general model and design a polynomial-time algorithm for the case that k=r by using the techniques of augmenting paths and splitting nodes. Finally, we carry out experimentations on synthetic and real networks that show the effectiveness of our algorithms in practice.

A Fault‐tolerant model for tuple space coordination in distributed environments

SummaryIn distributed systems, tuple space is one of the coordination models that significantly maximizes system performance against failure due to its space and time decoupling features. With the growing popularity of distributed computing and increasing complexity in the network, host and link failure occurs frequently, resulting in poor system performance. This article proposes a fault‐tolerant model named Tuple Space Replication (TSR) for tuple space coordination in distributed environments. The model introduces a multi‐agent system that consists of multiple hosts. Each host in a multi‐agent system comprises an agent space with a tuple space for coordination. In this model, we introduce three novel fault‐tolerant algorithms for tuple space primitives to provide coordination among hosts with tolerance to multiple links and hosts failure. The first algorithm is given for out() operation to insert tuples in the tuple space. The second algorithm is presented for rdp() operation to read any tuple from the tuple space. The third algorithm is given for inp() operation to delete or withdraw tuples from the tuple space. These algorithms use less number of messages to ensure consistency in the system. The message complexity of the proposed algorithms is analyzed and found O(n) for out(), O(1) for rdp(), and O(n) for inp() operations which is comparable and better than existing works, where n is the number of hosts. The testbed experiment reveals that the proposed TSR model gives performance improvement up to 88%, 70.94%, and 63.80% for out(), rdp(), and inp() operations compared to existing models such as FT‐SHE, LBTS, DEPSPACE, and E‐DEPSPACE.