scholarly journals Shifting network tomography toward a practical goal

2011 ◽  
Author(s):  
Denisa Ghita ◽  
Can Karakus ◽  
Katerina Argyraki ◽  
Patrick Thiran
Keyword(s):  
Network Tomography

Identify Congested Links with Network Tomography Under Multipath Routing

2018 ◽  
Vol 27 (2) ◽  
pp. 409-429 ◽  
Author(s):  
Shengli Pan ◽  
Yingjie Zhou ◽  
Zhiyong Zhang ◽  
Song Yang ◽  
Feng Qian ◽  
...  
Keyword(s):  
Multipath Routing ◽  
Network Tomography

Network Tomography for Efficient Monitoring in SDN-Enabled 5G Networks and Beyond: Challenges and Opportunities

2021 ◽  
Vol 59 (3) ◽  
pp. 70-76
Author(s):  
Grigorios Kakkavas ◽  
Adamantia Stamou ◽  
Vasileios Karyotis ◽  
Symeon Papavassiliou
Keyword(s):  
Network Tomography ◽  
5G Networks ◽  
Challenges And Opportunities

Fisher Information-based Experiment Design for Network Tomography

2015 ◽  
Vol 43 (1) ◽  
pp. 389-402 ◽  
Author(s):  
Ting He ◽  
Chang Liu ◽  
Ananthram Swami ◽  
Don Towsley ◽  
Theodoros Salonidis ◽  
...  
Keyword(s):  
Fisher Information ◽  
Experiment Design ◽  
Network Tomography

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.

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