End-to-End Network Traffic Reconstruction Via Network Tomography Based on Compressive Sensing

2014 ◽  
Vol 23 (3) ◽  
pp. 709-730 ◽  
Author(s):  
Laisen Nie ◽  
Dingde Jiang ◽  
Lei Guo
Keyword(s):  
Compressive Sensing ◽  
Network Traffic ◽  
Network Tomography ◽  
End To End

A Compressive Sensing-Based Approach to End-to-End Network Traffic Reconstruction

2020 ◽  
Vol 7 (1) ◽  
pp. 507-519 ◽  
Author(s):  
Dingde Jiang ◽  
Wenjuan Wang ◽  
Lei Shi ◽  
Houbing Song
Keyword(s):  
Compressive Sensing ◽  
Network Traffic ◽  
End To End

Hubble: An End to End Approach for Anomaly Detection in Network Traffic

2021 ◽  
Author(s):  
Shiwei Wang ◽  
Haizhou Du ◽  
Lin Liu ◽  
Zhenyu Lin
Keyword(s):  
Anomaly Detection ◽  
Network Traffic ◽  
End To End

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.

Mixed Estimation Approach to EndtoEnd Network Traffic

2014 ◽  
Vol 43 (7) ◽  
pp. 706023 ◽  
Author(s):  
蒋定德 JIANG Dingde ◽  
赵祖耀 ZHAO Zuyao ◽  
许宏伟 XU Hongwei ◽  
王兴伟 WANG Xingwei
Keyword(s):  
Network Traffic ◽  
End To End ◽  
Mixed Estimation

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.

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