Resilient Routing Research Articles

Resilient Routing Mechanism for Wireless Sensor Networks With Deep Learning Link Reliability Prediction

Wireless sensor networks play an important role in Internet of Things systems and services but are prone and vulnerable to poor communication channel quality and network attacks. In this paper we are motivated to propose resilient routing algorithms for wireless sensor networks. The main idea is to exploit the link reliability along with other traditional routing metrics for routing algorithm design. We proposed firstly a novel deep-learning based link prediction model, which jointly exploits Weisfeiler-Lehman kernel and Dual Convolutional Neural Network (WL-DCNN) for lightweight subgraph extraction and labelling. It is leveraged to enhance self-learning ability of mining topological features with strong generality. Experimental results demonstrate that WL-DCNN outperforms all the studied 9 baseline schemes over 6 open complex networks datasets. The performance of AUC (Area Under the receiver operating characteristic Curve) is improved by 16% on average. Furthermore, we apply the WL-DCNN model in the design of resilient routing for wireless sensor networks, which can adaptively capture topological features to determine the reliability of target links, especially under the situations of routing table suffering from attack with varying degrees of damage to local link community. It is observed that, compared with other classical routing baselines, the proposed routing algorithm with link reliability prediction module can effectively improve the resilience of sensor networks while reserving high-energy-efficiency.

NNIR: N-Non-Intersecting-Routing Algorithm for Multi-Path Resilient Routing in Telecommunications Applications

In this paper, we will present a N-Non-Intersecting-Routing (NNIR) algorithm which is used to reduce the cost of resilient routing in telecommunications problems. Resilient Routing is the connections between two locations in a graph through the use of N completely independent routes. Resilient Routing is applicable in a wide variety of domains including telecommunications, logistics and embedded systems design. The proposed NNIR algorithm increase the cost of the primary route by taking a less optimal route, thus freeing a more optimal route for the resilient routes, in turn reducing the total cost of both routes. This is achieved through the use of a Genetic Algorithm, Dijkstra’s Algorithm and the repair operator. The proposed NNIR shows an average improvement of 34.2% when compared to Dijkstra’s Algorithm (one of the most widely used algorithm routing). Similarly, there is an average improvement of 34.2% when compared to A* (another popular shortest path algorithm). Additionally, there is an average improvement of 26.9% when compared to Simulated Annealing (a popular evolutionary technique used within routing problems). In this paper we show how NNIR performs within two different routing domains (telecommunications routing and road routing), and compares it against three other routing techniques to solve the resilient routing problem.

Secure Energy Efficient Attack Resilient Routing Technique for Zone based Wireless Sensor Network

Security and Energy efficiency are two key factors to be contemplated in the design of applications based on wireless sensor networks (WSNs). Optimization of energy consumption is obligatory for an increased life time of the network. Without security, attackers can disrupt the entire operation of sensor network by instigating diverse attacks like message tampering, message dropping either in partial or whole and message flooding etc. This work proposes a secure energy efficient attack resilient routing technique for zone based wireless sensor network with proactive detection of malicious zones and mitigation from the attacks. Different from earlier works on detecting each malicious node, this work cleaves the network to zones and allots a probabilistic fuzzy score to model the success ratio of packet propagation through the zone. The routing is adaptive to ongoing residual energy and security risks. A Firm decision cannot be made on the frameworks influencing the life time of the network considering it may influence the operations of network. Experimentation of the proposed solution is done in NS2 and contrasted with the existing solutions to prove the effectiveness of the approach.

Fault resilient routing based on moth flame optimization scheme for underwater wireless sensor networks

Fault resilient routing is a typical issue for underwater wireless sensor networks ($$ UWSNs $$) due to contact of underwater creatures, mobility of nodes and natural disaster. In the existing studies, most of the researchers introduced routing scheme for routing the packets towards the base station through a cluster head. However, these schemes use static or mobile nodes for deployment, but nodes move from its original position to another position and get stuck in a particular region due to ocean current, natural disaster and environmental interference. Therefore, this leads to high energy depletion, link failure, disjoint path and overloaded data. To resolve this issue, we have proposed fault resilient routing based on moth flame optimization ($$ MFO $$) scheme for transferring the packets towards the base station through autonomous underwater vehicles ($$ AUVs $$). In this scheme, $$ AUVs $$ is used instead of cluster head to avoid the reclustering, overloading problem and it also feasible for large scale networks. There may be a disjoint path issue, so we have deployed additional mobile nodes with the help of $$ AUVs $$ within the network. Further, a novel fitness function is integrated with the $$ MFO $$ scheme to overcome the link failure problem. The proposed scheme is applied to select the best forwarding node for transmitting the packets towards the nearest $$ AUVs $$ using multi-hop acoustic links. In $$ UWSNs $$, $$ AUVs $$ is placed to move the packets towards the $$ BS $$ from lower level to upper level. Performance evaluation of proposed scheme shows better result in terms of fault resilient, residual energy, network lifetime, packet delivery ratio and convergence rate than the existing scheme under the different network scenario.

Routing and Scheduling of Mobile Power Sources for Distribution System Resilience Enhancement

Mobile power sources (MPSs), including electric vehicle fleets, truck-mounted mobile energy storage systems, and mobile emergency generators, have great potential to enhance distribution system (DS) resilience against extreme weather events. However, their dispatch is not well investigated. This paper implements resilient routing and scheduling of MPSs via a two-stage framework. In the first stage, i.e., before the event, MPSs are pre-positioned in the DS to enable rapid pre-restoration, in order to enhance survivability of the electricity supply to critical loads. DS network is also proactively reconfigured into a less impacted or stressed state. A two-stage robust optimization model is constructed and solved by the column-and-constraint generation algorithm to derive first-stage decisions. In the second stage, i.e., after the event, MPSs are dynamically dispatched in the DS to coordinate with conventional restoration efforts, so as to enhance system recovery. A novel mixed-integer programming model that resolves different timescales of MPS dispatch and DS operation, coupling of road and power networks, etc., is formulated to optimize dynamic dispatch of MPSs. Case studies conducted on IEEE 33-node and 123-node test systems demonstrate the proposed method’s effectiveness in routing and scheduling MPSs for DS resilience enhancement.

Reversing the trajectory of school disengagement? Lessons from the analysis of Warsaw youth’s educational trajectories

The theoretical framework of the paper combines the notions of school disengagement and educational trajectories. Our current research has demonstrated that several trajectories of school disengagement can be distinguished: unanticipated crisis, parabola, downward spiral, boomerang, resilient route, shading out. The text focuses on two trajectory types – the parabola, when youngsters facing increasing school disengagement are provided with substantial support and their trajectory changes its direction, and the downward spiral, which despite the support leads to further school disengagement and school leaving. Analysing the educational biographies of students from secondary schools in Warsaw, we focus on their perceptions of the support provided by different formal and informal sources. Investigating the protective factors and successful interventions might thus be useful in fostering the educational success of youth at risk. An analysis of the trajectories might be treated as guidance as to how to offset the negative impact of social and educational inequalities and hence to reverse the negative direction in one’s educational trajectory. The text is based on qualitative analysis of data obtained within an international research project: individual semi-structured interviews with Polish students at risk of early school leaving and youngsters who left school early.

Evaluating the dynamic resilience process of a regional water resource system through the nexus approach and resilience routing analysis

The increasing magnitude and frequency of undesirable events, driven by climate and anthropogenic changes, have given rise to various approaches for quantifying the resilience of regional water resource systems. However, the deficiencies of these approaches in describing linkages among subsystems and disturbance-dependent resilience have hindered the assessment and prediction of resilience in water resource management. The nexus approach enables the propagation of a disturbance to be simulated (a process called surrogate disturbance generation). An approach analogous to a unit hydrograph is developed, and resilience routing (strain flow routing), which is a novel framework and model of the dynamic resilience process, is proposed for the evaluation of a regional water resource system. The proposed framework and model are applied to the Jinghong regional water resource system. Taking a pollution event as a disturbance, the responses of the water supply, fishery and electricity subsystems are simulated to test the validity of the proposed methods. The linkages among subsystems are determined according to the sink-source dynamic using the nexus approach, and the levels of surrogate disturbance transformed from the disturbance event can be quantified by the processes of dynamic resilience evaluation. The shape of the dynamic resilience process is quantified by the parameters of unit resilience routing with disturbance independence and reflects the characteristics of the system responding to the disturbance. The proposed method helps to assess the adaptive capacity of a water system to alleviate and regulate disturbances. Furthermore, after the calibration and validation of the assumptions of linearity inherent in the method, it can also be used to predict the dynamic resilience processes of every subsystem in response to any disturbance event affecting a regional water resource system.

An automatic restoration scheme for switch-based networks

This paper presents a fully automated distributed resilient routing scheme for switch-based or new generation router based networks. The failure treatment is done locally and other nodes in the network do not need to undertake special actions. In contrast to conventional IP routing schemes, each node routes the traffic on the basis of the entering arc and of the destination. The resulting constraint is that two flows to the same destination entering in a node by a common arc have to merge after this arc. It is shown that this is sufficient for dealing with all single link failure situations, assuming that the network is symmetric and two-link connected. Two heuristic approaches are proposed to handle the corresponding dimensioning problem for large network instances. The proposed method generalizes some methods of literature (Wang and Nelakuditi, 2007; Xi and Chao, 2007) and provides more cost-efficient solutions.

Risk Minimization Routing Against Geographically Correlated Failures

Regional failures, such as natural disaster or malicious attack, have become a major threat to the construction of future reliable communication network. The regional failures usually cause a large number of disconnected nodes simultaneously and influence the network for a long time. However, a routing scheme that is resilient to such geographically correlated failures is still unexplored. In this paper, we provide a comprehensive study of the disaster resilient routing dealing with the regional failure in operational IP backbone networks. It is notable that the path with minimal risk (i.e., minimal failure probability) is not necessarily the shortest path. The main challenge of finding such paths is that regional failure is unpredictable in terms of time, location, and the affected area. To this end, in combination with the computational geometry tool, we develop effective algorithms to find the minimal risk path between end node pairs to tolerate random regional failures. We show that in contrast to the conventional shortest path, a little longer path can be more effective to the disasters. After selecting such a path as the primary path, we turn to find a secondary backup path. In contrast to the conventional single link/node failure, a regional failure disrupts a large number of network components, simultaneously. As a result, how to find backup paths for re-establishment of the corrupted paths will raise a novel fairness issue. Specifically, during the backup path allocation, we focus on routing fairness to bound the worst-case user experience. A metric is proposed based on which an ILP is formulated. The extensive simulations validate that such an issue is non-negligible in face of regional failure scenarios.

Load-Optimal Local Fast Rerouting for Dense Networks

Reliable and highly available computer networks must implement resilient fast rerouting mechanisms: upon a link or node failure, an alternative route is determined quickly, without involving the network control plane. Designing such fast failover mechanisms capable of dealing with multiple concurrent failures, however, is challenging, as failover rules need to be installed proactively , i.e., ahead of time, without knowledge of the actual failures happening at runtime. Indeed, only little is known today about the design of resilient routing algorithms. This paper introduces a general framework to reason about and design local failover algorithms that minimize the resulting load after failover on dense networks, beyond destination-based routing. We show that due to the inherent locality of the failover decisions at runtime, the problem is fundamentally related to the field of distributed algorithms without coordination . We derive an intriguing lower bound on the inherent network load overhead any local fast failover scheme that will introduce in the worst case, even though globally seen, much more balanced traffic allocations exist. We then present different randomized and deterministic failover algorithms and analyze their overhead load. In particular, we build upon the theory of combinatorial designs and develop a novel deterministic failover mechanism based on symmetric block design theory , which tolerates a maximal number of link failures while ensuring low loads.

Forest restoration in southern Amazonia: Soil preparation triggers natural regeneration

Forest regeneration in abandoned pastures in Amazonia has been well studied, but active restoration of non-resilient pastures has not. In this work, we evaluated large-scale active restoration of intensively used pastures in southern Amazonia, where the highest deforestation rates are observed. With the construction of the Jirau Dam in the Madeira river (state of Rondônia, Brazil), a 3000-ha forest buffer zone has been established. This area was previously covered by African pasture grasses for cattle ranching. Eight mixed-species planting sites with variable grass management and ranging from 6 to 60 months post-planting were measured three times over 2.5 years. We also tested a gradient of restoration intensity in an experimental planting: (i) no intervention (control), (ii) harrowing and herbicides to control grasses, and (iii) harrowing and herbicides plus tree planting. Our goals were to understand the initial trajectory of actively restored sites, the role of harrowing and herbicide application in triggering natural regeneration, and the role of seedling planting on the initial vegetation structure. All tree species ≥30 cm in height were sampled in five circular 10-m-radius plots per site. Plant cover was also monitored using the step-point method. At 18 months, stem density ranged from 2500 to 14,490 ind·ha−1, demonstrating that density increased suddenly in most sites through colonization, although this was highly variable. Tree cover reached 81% in five years, virtually eliminating grass cover after 36 months. Recruits contributed more to basal area than planted seedlings. Although 17 species, on average, were planted in restoration sites, the number of species at the sites steadily increased over time, at a rate of 7 species·yr−1 per 1570 m2. Cecropia spp. and Trema micrantha recruits had the highest stem densities and basal areas. Harrowing and grass control were enough to trigger succession in sites where natural regeneration was not taking place, shifting these sites to the highly resilient route of early secondary forest succession in the Amazon. Future restoration efforts should use a combination of methods, first evaluating the potential for natural regeneration and then gradually eliminating barriers.

Toward fast calculation of communication paths for resilient routing

Utilization of alternate communication paths is a common technique to provide protection of transmission against failures of network nodes/links. However, a noticeable delay is encountered when calculating the relevant sets of disjoint paths using the available algorithms (e.g., using Bhandari's approach). This, in turn, may have a serious impact on the ability of a network to serve dynamic demands (i.e., characterized by a relatively short duration time). To provide a solution to this problem, in this article we introduce an approach to pre‐compute the sets of disjoint paths in advance to be able to start serving the demands shortly after their arrival. Our approach is based on the observation that the issue of establishing a set of node‐disjoint paths is equivalent to the problem of determining the cheapest cycle in the network topology traversing the end nodes of a demand. In particular, we propose a generalization of this scheme assuming that any pair of node‐disjoint paths can be obtained by means of merging a number of basic cycles defined for a network topology. A new method to calculate the cheapest end‐to‐end cycles based on the so called basic cycles is introduced, which, as verified for real network topologies, reduces up to 70% the time needed to establish node‐disjoint paths (compared with the results obtained for the reference Bhandari's scheme). © 2017 Wiley Periodicals, Inc. NETWORKS, Vol. 70(4), 308–326 2017

Routing at Large Scale: Advances and Challenges for Complex Networks

A wide range of social, technological and communication systems can be described as complex networks. Scale-free networks are one of the well known classes of complex networks in which nodes� degrees follow a power-law distribution. The design of scalable, adaptive and resilient routing schemes in such networks is very challenging. In this article we present an overview of required routing functionality, categorize the potential design dimensions of routing protocols among existing routing schemes, and analyze experimental results and analytical studies performed so far to identify the main trends/trade-offs and draw main conclusions. Besides traditional schemes such as hierarchical/shortest-path path-vector routing, the article pays attention to advances in compact routing and geometric routing since they are known to significantly improve scalability in terms of memory space. The identified trade-offs and the outcomes of this overview enable more careful conclusions regarding the (un-)suitability of different routing schemes to large-scale complex networks and provide a guideline for future routing research.

Resilient Routing Overlay Network Construction with Super-Relay Nodes

Overlay routing has emerged as a promising approach to improve reliability and efficiency of the Internet. The key to overlay routing is the placement and maintenance of the overlay infrastructure, especially, the selection and placement of key relay nodes. Spurred by the observation that a few relay nodes with high betweenness centrality can provide more optimal routes for a large number of node pairs, we propose a resilient routing overlay network construction method by introducing Super-Relay nodes. In detail, we present the K-Minimum Spanning Tree with Super-Relay nodes algorithm (SR-KMST), in which we focus on the selection and connection of Super-Relay nodes to optimize the routing quality in a resilient and scalable manner. For the simultaneous path failures between the default physical path and the overlay backup path, we also address the selection of recovery path. The objective is to select a proper one-hop recovery path with minimum cost in path probing and measurement. Simulations based on a real ISP network and a synthetic Internet topology show that our approach can provide high-quality overlay routing service, while achieving good robustness.

Simulation-based multi-objective model for supply chains with disruptions in transportation

Unpredictable disruptions (e.g., accidents, traffic conditions, among others) in supply chains (SCs) motivate the development of decision tools that allow designing resilient routing strategies. The transportation problem, for which a model is proposed in this paper, consists of minimizing the stochastic transportation time and the deterministic freight rate. This paper extends a stochastic multi-objective minimum cost flow (SMMCF) model by proposing a novel simulation-based multi-objective optimization (SimMOpt) solution procedure. A real case study, consisting of the road transportation of perishable agricultural products from Mexico to the United States, is presented and solved using the proposed SMMCF-Continuous/SimMOpt solution framework. In this case study, time variability is caused by the inspection of products at the U.S.-Mexico border ports of entry. The results demonstrate that this framework is effective and overcomes the limitations of the multi-objective stochastic minimum cost flow problem (which becomes intractable for large-scale instances). We model a supply chain for perishable products imported from Mexico to U.S.We compare the results from the deterministic and the discretized stochastic problem.We propose an efficient solution procedure when attributes are discretely distributed.We discuss the limitations of this model when attributes are continuously distributed.We propose an efficient solution procedure when attributes are continuously distributed.

A Resilient Routing Algorithm with Formal Reliability Analysis for Partially Connected 3D-NoCs

3D ICs can take advantage of a scalable communication platform, commonly referred to as the Networks-on-Chip (NoC). In the basic form of 3D-NoC, all routers are vertically connected. Partially connected 3D-NoC has emerged because of physical limitations of using vertical links. Routing is of great importance in such partially connected architectures. A high-performance, fault-tolerant and adaptive routing strategy with respect to the communication flow among the cores is crucial while freedom from livelock and deadlock has to be guaranteed. In this paper we introduce a new routing algorithm for partially connected 3D-NoCs. The routing algorithm is adaptive and tolerates the faults on vertical links as compared to the predesigned routing algorithms. Our results show a $40-50\%$ improvement in the fraction of intact inter-level communications when the fault tolerant algorithm is used. This routing algorithm is light-weight and has only one virtual channel along the $Y$ dimension.

Resilient routing implementation in 2D mesh NoC

With the rapid shrinking of technology and growing integration capacity, the probability of failures in Networks-on-Chip (NoCs) increases and thus, fault tolerance is essential. Moreover, the unpredictable locations of these failures may influence the regularity of the underlying topology, and a regular 2D mesh is likely to become irregular. Thus, for these failure-prone networks, a viable routing framework should comprise a topology-agnostic routing algorithm along with a cost-effective, scalable routing mechanism able to handle failures, irrespective of any particular failure patterns. Existing routing techniques designed to route irregular topologies efficiently lack flexibility (logic-based), scalability (table-based) or relaxed switch design (uLBDR-based). Designing an efficient routing implementation technique to address irregular topologies remains a pressing research problem. To address this, we present a fault resilient routing mechanism for irregular 2D meshes resulting from failures. To handle irregularities, it avoids using routing tables and employs a few fixed configuration bits per switch resulting in a scalable approach. Experiments demonstrate that the proposed approach is guaranteed to tolerate all locations of single and double-link failures and most multiple failures. Also, unlike uLBDR it is not restricted to any particular switching technique and does not replicate any extra messages. Along with fault tolerance, the proposed mechanism can achieve better network performance in fault-free cases. The proposed technique achieves graceful performance degradation during failure. Compared to uLBDR, our method has 14% less area requirements and 16% less overall power consumption.

Enhanced Entropy‐Based Resource Searching in Unstructured P2P Networks

How to find desired resources efficiently and accurately is one fundamental challenge of any unstructured P2P networks, which is mainly involved some difficulties in the P2P overlay topology, data representation in peers and routing mechanism. In this paper, we address the issue of resilient routing in unstructured P2P networks. An efficient algorithm called Query routing tree (QRT) based on maximum mutual information is proposed to improve the performance of resource searching, which has tightly associated the resource contents of peers with the logical links in P2P network that makes the query messages forwarded more effectively in similar peers and can hit more target resources faster. Additionally, we present an optimized routing scheme with the query conditions taken into account, to obtain the optimal routing tree with the minimal information gain from the candidate tree set so as to adapt to different query types more flexibly. The simulation results show the proposed QRT can reduce the search cost more effectively and maintain higher targets hit rate than existing typical algorithms such as Flooding, k-RW and APS. Finally, our optimized scheme is also proved to conduct high searching performance with nicer self-adaptability and scalability in unstructured P2P networks.

Experimental validation of resilient tree-based greedy geometric routing

Geometric routing is an alternative for IP routing based on longest prefix matching. Using this routing paradigm, every node in the network is assigned a coordinate and packets are forwarded towards their intended destination following a distance-decreasing policy (greedy forwarding). This approach makes the routers significantly more memory-efficient compared to the current IP routers. In this routing, greedy embeddings are used to guarantee a 100% successful delivery to every destination in the network. Most of the existing proposals lack resiliency mechanisms to react efficiently to network changes. We propose a distributed algorithm to calculate a greedy embedding based on a spanning tree of the network. In this algorithm, nodes are triggered to re-calculate their coordinates upon a change in the topology such as link or node failures. The advantage of this approach is that it recovers from topology failures within a very short period of time. We further extend the algorithm to generate backups to apply protection in distributed setups. Different trade-offs and trends of re-convergence for geometric routing have been evaluated in an emulation environment. Realistic results are achieved through emulation as no model or abstraction is involved. The proposed routing scheme is implemented in Quagga routing software and new elements are developed in Click modular router to enable greedy forwarding. For the first time, the performance of this scheme is evaluated through emulation on a large topology of 1000 nodes and the results are compared with BGP. The experimental results indicate that the proposed scheme has interesting characteristics in terms of convergence time upon a change in the network topology.

Robustness of airline alliance route networks

The aim of this study is to analyze the robustness of the three major airline alliances’ (i.e., Star Alliance, oneworld and SkyTeam) route networks. Firstly, the normalization of a multi-scale measure of vulnerability is proposed in order to perform the analysis in networks with different sizes, i.e., number of nodes. An alternative node selection criterion is also proposed in order to study robustness and vulnerability of such complex networks, based on network efficiency. And lastly, a new procedure – the inverted adaptive strategy – is presented to sort the nodes in order to anticipate network breakdown. Finally, the robustness of the three alliance networks are analyzed with (1) a normalized multi-scale measure of vulnerability, (2) an adaptive strategy based on four different criteria and (3) an inverted adaptive strategy based on the efficiency criterion. The results show that Star Alliance has the most resilient route network, followed by SkyTeam and then oneworld. It was also shown that the inverted adaptive strategy based on the efficiency criterion – inverted efficiency – shows a great success in quickly breaking networks similar to that found with betweenness criterion but with even better results.