A new mutually reinforcing network node and link ranking algorithm

Wang, Zhenghua; Dueñas‐Osorio, Leonardo; Padgett, Jamie E.

doi:10.1038/srep15141

Cited by 15 publications

(20 citation statements)

References 38 publications

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“…All simulations are carried out using IBM ILOG CPLEX 12.6.2 optimization studio [32]. We consider three well-known network topologies namely, DT10 (10 nodes) [33], NSFNET (14 nodes) [34], and ARPANET (20 nodes) [35]. Each value reported here is calculated as the average of five simulation runs.…”

Section: Simulation Resultsmentioning

confidence: 99%

Optimal attack-aware RWA for scheduled lightpath demands

2019

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In Transparent optical networks (TONs), the data signals remain in the optical domain for the entire transmission path, creating a virtual topology over the physical connections of optical fibers. Due to the increasingly high data rates and the vulnerabilities related to the transparency of optical networks, TONs are susceptible to different physical layer attacks, including high-power jamming attacks. Developing strategies to handle such attacks and mitigating their impact on network performance is becoming an important design problem for TONs. Some approaches for handling physical layer attacks for static and dynamic traffic in TONs have been presented in recent years. In this work, we propose an integer linear program (ILP) formulation to control the propagation of such attacks in TONs for scheduled lightpath demands, which need periodic bandwidth usage at certain predefined times. We consider both the fixed window model, where the start and end timings of the demand are known in advance, and the sliding window model, where exact start and end times are unknown but fall within a larger window. We consider a number of potential objectives for attack-aware RWA and show how the flexibility to schedule demands in time can impact these objectives, compared to both attackunaware and fixed window models. Keywords Transparent optical networks (TONs) • High-power jamming attacks • Scheduled lightpath demands (SLDs) • Integer linear program (ILP) • Routing and wavelength • Assignment (RWA)

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Section: Simulation Resultsmentioning

confidence: 99%

Optimal attack-aware RWA for scheduled lightpath demands

2019

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“…Well-designed traffic systems provide passengers with the least time consumption on traveling within destination areas through variable route selections. Reference [12] stated a super edge rank algorithm and its application in identifying opinion leaders of online public opinion supernetwork, and [13] concerned a new mutually reinforcing network node and link ranking algorithm. They applied ranking algorithms mainly on the important nodes and areas to optimize the traffic topology according to the total traffic volume.…”

Section: Relevant Workmentioning

confidence: 99%

A Node Selecting Approach for Traffic Network Based on Artificial Slime Mold

et al. 2020

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The node selecting problem of traffic network is a significant issue and is difficult to be solved. In this paper, an artificial slime mold method is proposed to help us solve the problem. First, the chief components of an artificial slime mold are introduced to simulate the foraging behavior of a true slime mold, including external food sources, plasmodium, myxamoeba, nucleus, and nutrients. Then the learning mechanism of nutrient concentration for the artificial slime mold is illustrated, though there is no brain or neuron in its body. After that, the node selecting approach is described according to the propagation capabilities of nodes. Second, the algorithm flow is designed to show how to solve this kind of complex selecting problem. The algorithm flow to select important traffic nodes by artificial slime mold is composed of 4 main steps, including initialization, food searching, feeding, and selecting for output. Third, a comprehensive example is designed and derived from references to certificate that the proposed artificial slime mold can help us select important traffic nodes by their generated traffic topologies. The contributions of this paper are important both for traffic node selecting and artificial learning mechanism in theoretical and practical aspects.

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“…As for the topology-based importance measures, this paper employs five practical element importance measures that build upon network structure, including connectivity reliability (CR) [28], PageRank [29], Normalized Wide network Ranking algorithm (NWRank) [30], degree centrality [31], and betweenness centrality [11]. The CR-based importance measure monitors the system's CR loss when an element is removed from the network, and quantifies the element importance based on loss levels.…”

Section: Comparison With Other Ranking Measuresmentioning

confidence: 99%

“…HITS) and the weight normalization feature of PageRank. In NWRank, relative weights are assigned to links based on the degree of the adjacent neighbors and their Betweenness Centrality instead of assigning the same weight to every link as assumed in PageRank[30]. Simpler metrics, such as the degree centrality, give the highest score of importance to the node with the largest number of neighbors[31,34].…”

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confidence: 99%

AC power flow importance measures considering multi-element failures

Dueñas‐Osorio

Chen

et al. 2017

Reliability Engineering & System Safety

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Quantifying the criticality of individual components of power systems is essential for overall reliability and management. This paper proposes an AC-based power flow element importance measure, while considering multi-element failures. The measure relies on a proposed AC-based cascading failure model, which captures branch overflow, bus load shedding, and branch failures, via AC power flow and optimal power flow analyses. Taking the IEEE 30, 57 and 118-bus power systems as case studies, we find that N-3 analyses are sufficient to measure the importance of a bus or branch. It is observed that for a substation bus, its importance is statistically proportional to its power demand, but this trend is not observed for power plant buses. While comparing with other reliability, functionality, and topology-based importance measures popular today, we find that a DC power flow model, although better correlated with the benchmark AC model as a whole, still fails to locate some critical elements. This is due to the DCbased models focus on real power and ignoring reactive power. The proposed importance measure is aimed to inform decision makers about important components in complex systems, while improving cascading failure prevention, system backup siting, and overall resilience.

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A new mutually reinforcing network node and link ranking algorithm

Cited by 15 publications

References 38 publications

Optimal attack-aware RWA for scheduled lightpath demands

Optimal attack-aware RWA for scheduled lightpath demands

A Node Selecting Approach for Traffic Network Based on Artificial Slime Mold

AC power flow importance measures considering multi-element failures

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