scholarly journals General Identifiability Condition for Network Topology Monitoring with Network Tomography

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4125 ◽  
Author(s):  
Shengli Pan ◽  
Zongwang Zhang ◽  
Zhiyong Zhang ◽  
Deze Zeng ◽  
Rui Xu ◽  
...  
Keyword(s):  
Network Topology ◽  
Network Monitoring ◽  
Time Varying ◽  
Network Tomography ◽  
Accurate Knowledge ◽  
Cross Traffic ◽  
End To End ◽  
The Cross

Accurate knowledge of network topology is vital for network monitoring and management. Network tomography can probe the underlying topologies of the intervening networks solely by sending and receiving packets between end hosts: the performance correlations of the end-to-end paths between each pair of end hosts can be mapped to the lengths of their shared paths, which could be further used to identify the interior nodes and links. However, such performance correlations are usually heavily affected by the time-varying cross-traffic, making it hard to keep the estimated lengths consistent during different measurement periods, i.e., once inconsistent measurements are collected, a biased inference of the network topology then will be yielded. In this paper, we prove conditions under which it is sufficient to identify the network topology accurately against the time-varying cross-traffic. Our insight is that even though the estimated length of the shared path between two paths might be “zoomed in or out” by the cross-traffic, the network topology can still be recovered faithfully as long as we obtain the relative lengths of the shared paths between any three paths accurately.

Adaptive Monitor Placement for Near Real-time Node Failure Localisation in Wireless Sensor Networks

2022 ◽  
Vol 18 (1) ◽  
pp. 1-41
Author(s):  
Pamela Bezerra ◽  
Po-Yu Chen ◽  
Julie A. McCann ◽  
Weiren Yu
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Real Time ◽  
State Of The Art ◽  
Global Analysis ◽  
Wireless Sensor ◽  
Greedy Heuristics ◽  
Network Tomography ◽  
Topology Changes ◽  
End To End

As sensor-based networks become more prevalent, scaling to unmanageable numbers or deployed in difficult to reach areas, real-time failure localisation is becoming essential for continued operation. Network tomography, a system and application-independent approach, has been successful in localising complex failures (i.e., observable by end-to-end global analysis) in traditional networks. Applying network tomography to wireless sensor networks (WSNs), however, is challenging. First, WSN topology changes due to environmental interactions (e.g., interference). Additionally, the selection of devices for running network monitoring processes (monitors) is an NP-hard problem. Monitors observe end-to-end in-network properties to identify failures, with their placement impacting the number of identifiable failures. Since monitoring consumes more in-node resources, it is essential to minimise their number while maintaining network tomography’s effectiveness. Unfortunately, state-of-the-art solutions solve this optimisation problem using time-consuming greedy heuristics. In this article, we propose two solutions for efficiently applying Network Tomography in WSNs: a graph compression scheme, enabling faster monitor placement by reducing the number of edges in the network, and an adaptive monitor placement algorithm for recovering the monitor placement given topology changes. The experiments show that our solution is at least 1,000× faster than the state-of-the-art approaches and efficiently copes with topology variations in large-scale WSNs.

Design Gap Acceptance for Right-Turning Vehicles Based on Vehicle Acceleration Capabilities

2015 ◽  
Vol 2521 (1) ◽  
pp. 12-21 ◽  
Author(s):  
Essam Dabbour
Keyword(s):  
Regression Models ◽  
Field Measurements ◽  
Data Logger ◽  
Sight Distance ◽  
Design Guide ◽  
Traffic Stream ◽  
Cross Traffic ◽  
Vehicle Acceleration ◽  
The Cross ◽  
The Right

The current geometric design guide provides a methodology to analyze intersection sight distance for right-turning vehicles at signalized and two-way stop-controlled intersections based on the gaps accepted by the majority of drivers as measured from the field. That methodology is based mainly on driver behavior without considering the actual capabilities of the turning vehicle when accelerating from rest to the speed of the cross-traffic stream. This paper introduced the new design gap concept, which was based on the actual distance and time needed for the turning vehicle to accelerate to the same speed of the cross-traffic stream to avoid a collision. The acceleration capabilities of the turning vehicle were based on field measurements collected by GPS data logger devices that recorded the positions and instantaneous speeds of different turning vehicles at 1-s intervals; based on that, regression models were developed to establish an acceleration profile for a typical vehicle turning to the right from rest. Design tables were provided to help road designers select appropriate design gaps needed for different design speeds and grades of the crossing roadways. In comparison to the new design gaps, the measured gaps used in design were found to be generally inadequate. After implementation of the newly developed design gaps, turning drivers will potentially be able to accelerate comfortably without forcing other drivers in the cross-traffic stream to reduce their speeds or to change lanes to avoid colliding with turning vehicles.

scholarly journals Time-Varying Risk Aversion and the Profitability of Carry Trades: Evidence from the Cross-Quantilogram

Economies ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 18
Author(s):  
Riza Demirer ◽  
Rangan Gupta ◽  
Hossein Hassani ◽  
Xu Huang
Keyword(s):  
Risk Aversion ◽  
Predictive Power ◽  
Investor Sentiment ◽  
Time Varying ◽  
Carry Trade ◽  
Currency Markets ◽  
High Quantiles ◽  
Predictive Role ◽  
The Cross ◽  
Directional Predictability

This paper examines the predictive power of time-varying risk aversion over payoffs to the carry trade strategy via the cross-quantilogram methodology. Our analysis yields significant evidence of directional predictability from risk aversion to daily carry trade returns tracked by the Deutsche Bank G10 Currency Future Harvest Total Return Index. The predictive power of risk aversion is found to be stronger during periods of moderate to high risk aversion and largely concentrated on extreme fluctuations in carry trade returns. While large crashes in carry trade returns are associated with significant rises in investors’ risk aversion, we also found that booms in carry trade returns can be predicted at high quantiles of risk aversion. The results highlight the predictive role of extreme investor sentiment in currency markets and regime specific patterns in carry trade returns that can be captured via quantile-based predictive models.

scholarly journals A Review of Advanced Algebraic Approaches Enabling Network Tomography for Future Network Infrastructures

2020 ◽  
Vol 12 (2) ◽  
pp. 20 ◽  
Author(s):  
Grigorios Kakkavas ◽  
Despoina Gkatzioura ◽  
Vasileios Karyotis ◽  
Symeon Papavassiliou
Keyword(s):  
Network Coding ◽  
Technology Development ◽  
Network Monitoring ◽  
Future Internet ◽  
Network Architectures ◽  
Network Tomography ◽  
Fundamental Operation ◽  
Qualitative Comparison ◽  
Algebraic Approaches

Network tomography has emerged as one of the lean approaches for efficient network monitoring, especially aiming at addressing the ever-increasing requirements for scaling and efficiency in modern network architectures and infrastructures. In this paper, we explore network coding and compressed sensing as enabling technologies in the context of network tomography. Both approaches capitalize on algebraic tools for achieving accuracy while allowing scaling of operation as the size of the monitored network increases. Initially, a brief overview of the tomographic problems and the related classification of methods is provided to better comprehend the problems encountered and solutions provided to date. Subsequently, we present representative approaches that employ either one of the aforementioned technologies and we comparatively describe their fundamental operation. Eventually, we provide a qualitative comparison of features and approaches that can be used for further research and technology development for network monitoring in future Internet infrastructures.

scholarly journals Cross-Language End-to-End Speech Recognition Research Based on Transfer Learning for the Low-Resource Tujia Language

Symmetry ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 179 ◽  
Author(s):  
Chongchong Yu ◽  
Yunbing Chen ◽  
Yueqiao Li ◽  
Meng Kang ◽  
Shixuan Xu ◽  
...  
Keyword(s):  
Speech Recognition ◽  
Transfer Learning ◽  
Short Term Memory ◽  
Recognition System ◽  
Language Recognition ◽  
Low Resource ◽  
End To End ◽  
The Cross ◽  
Hidden Layer ◽  
Cross Language

To rescue and preserve an endangered language, this paper studied an end-to-end speech recognition model based on sample transfer learning for the low-resource Tujia language. From the perspective of the Tujia language international phonetic alphabet (IPA) label layer, using Chinese corpus as an extension of the Tujia language can effectively solve the problem of an insufficient corpus in the Tujia language, constructing a cross-language corpus and an IPA dictionary that is unified between the Chinese and Tujia languages. The convolutional neural network (CNN) and bi-directional long short-term memory (BiLSTM) network were used to extract the cross-language acoustic features and train shared hidden layer weights for the Tujia language and Chinese phonetic corpus. In addition, the automatic speech recognition function of the Tujia language was realized using the end-to-end method that consists of symmetric encoding and decoding. Furthermore, transfer learning was used to establish the model of the cross-language end-to-end Tujia language recognition system. The experimental results showed that the recognition error rate of the proposed model is 46.19%, which is 2.11% lower than the that of the model that only used the Tujia language data for training. Therefore, this approach is feasible and effective.

Marriage and housework

2020 ◽  
Author(s):  
By Cristina Borra ◽  
Martin Browning ◽  
Almudena Sevilla
Keyword(s):  
Time Varying ◽  
Cross Sectional ◽  
Housework Time ◽  
Married Individuals ◽  
The Cross ◽  
The Difference ◽  
Time Invariant

Abstract This article provides insights into the gains of forming a couple by estimating how much of the difference in housework between single and married individuals is causal and how much is due to selection. Time-varying observed variables and time-invariant heterogeneity explains about half of the observed differences in housework documented in the cross-sectional data. There remains a genuine one-and-a-half-hour increase per week in housework time for each partner, with women specializing in routine and men in non-routine housework tasks.

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