Sequential Loss Tomography Using Compressed Sensing

Route referencing and ordering for synchronization-free delay tomography in wireless networks

“…This network is generated according to the random graph theory [34], where the degree of the node is adjusted to be more than 2 for satisfying 1-identifiability, and the most left and most right nodes are selected as S and D, respectively. In addition, according to the method proposed in [7], 15 routes between S and D are selected, which are summarized in Table 1. Note that we have the relationships among Q, J, and I as Q ≈ J/2 and I ≈ J/2 for the network in Fig.…”

“…We consider delay tomography in a simple scenario [7,13] for an easy-to-understand explanation, where a single source node S and a single destination node D are assigned out of the boundary nodes, and S has established several routes to D before a tomography session. We define a set of the routes as R = {r 1 , r 2 , · · · , r i , · · · , r I }, where I denotes the number of routes, and define a set of links which constitutes r i as L i .…”

“…The probability that multiple anomaly simultaneously occur is considered to be relatively low, so compressed sensing has successfully brought high efficiency into delay tomography for the aforementioned challenge of the active anomaly detection, by reducing the number of measurement routes as compared to the number of links in a given network, that is, by reducing the number of probing packets. Similar to the previous study [6][7][8], provided that the sparsity of the desired solution can be exploited, the primary objective of this paper is to identify few links giving extraordinarily large delays as anomaly, which is referred to as "abnormal links. "…”

“…Especially when packet route delays are measured, network tomography is referred to as "delay tomography, " and recently, it has been applied to the problem of anomaly identification in networks since it alleviates cooperation of internal nodes. In the problem, tomography schemes are often formulated as underdetermined linear systems [6][7][8], and compressed sensing [9,10] is utilized to solve it, which is a promising technique for reconstructing a finite-dimensional sparse vector based on its linear measurements with dimension smaller than the size of the unknown sparse vector. The probability that multiple anomaly simultaneously occur is considered to be relatively low, so compressed sensing has successfully brought high efficiency into delay tomography for the aforementioned challenge of the active anomaly detection, by reducing the number of measurement routes as compared to the number of links in a given network, that is, by reducing the number of probing packets.…”

Reflective Network Tomography Based on Compressed Sensing

“…In this subsection we define transmissive network tomography via some examples [4]- [9] which are characterized by transmissive end-to-end path measurements. Fig.…”