28,008 research outputs found
A Network Coding Approach to Loss Tomography
Network tomography aims at inferring internal network characteristics based
on measurements at the edge of the network. In loss tomography, in particular,
the characteristic of interest is the loss rate of individual links and
multicast and/or unicast end-to-end probes are typically used. Independently,
recent advances in network coding have shown that there are advantages from
allowing intermediate nodes to process and combine, in addition to just
forward, packets. In this paper, we study the problem of loss tomography in
networks with network coding capabilities. We design a framework for estimating
link loss rates, which leverages network coding capabilities, and we show that
it improves several aspects of tomography including the identifiability of
links, the trade-off between estimation accuracy and bandwidth efficiency, and
the complexity of probe path selection. We discuss the cases of inferring link
loss rates in a tree topology and in a general topology. In the latter case,
the benefits of our approach are even more pronounced compared to standard
techniques, but we also face novel challenges, such as dealing with cycles and
multiple paths between sources and receivers. Overall, this work makes the
connection between active network tomography and network coding
Active Topology Inference using Network Coding
Our goal is to infer the topology of a network when (i) we can send probes
between sources and receivers at the edge of the network and (ii) intermediate
nodes can perform simple network coding operations, i.e., additions. Our key
intuition is that network coding introduces topology-dependent correlation in
the observations at the receivers, which can be exploited to infer the
topology. For undirected tree topologies, we design hierarchical clustering
algorithms, building on our prior work. For directed acyclic graphs (DAGs),
first we decompose the topology into a number of two-source, two-receiver
(2-by-2) subnetwork components and then we merge these components to
reconstruct the topology. Our approach for DAGs builds on prior work on
tomography, and improves upon it by employing network coding to accurately
distinguish among all different 2-by-2 components. We evaluate our algorithms
through simulation of a number of realistic topologies and compare them to
active tomographic techniques without network coding. We also make connections
between our approach and alternatives, including passive inference, traceroute,
and packet marking
Passive network tomography for erroneous networks: A network coding approach
Passive network tomography uses end-to-end observations of network
communication to characterize the network, for instance to estimate the network
topology and to localize random or adversarial glitches. Under the setting of
linear network coding this work provides a comprehensive study of passive
network tomography in the presence of network (random or adversarial) glitches.
To be concrete, this work is developed along two directions: 1. Tomographic
upper and lower bounds (i.e., the most adverse conditions in each problem
setting under which network tomography is possible, and corresponding schemes
(computationally efficient, if possible) that achieve this performance) are
presented for random linear network coding (RLNC). We consider RLNC designed
with common randomness, i.e., the receiver knows the random code-books all
nodes. (To justify this, we show an upper bound for the problem of topology
estimation in networks using RLNC without common randomness.) In this setting
we present the first set of algorithms that characterize the network topology
exactly. Our algorithm for topology estimation with random network errors has
time complexity that is polynomial in network parameters. For the problem of
network error localization given the topology information, we present the first
computationally tractable algorithm to localize random errors, and prove it is
computationally intractable to localize adversarial errors. 2. New network
coding schemes are designed that improve the tomographic performance of RLNC
while maintaining the desirable low-complexity, throughput-optimal, distributed
linear network coding properties of RLNC. In particular, we design network
codes based on Reed-Solomon codes so that a maximal number of adversarial
errors can be localized in a computationally efficient manner even without the
information of network topology.Comment: 40 pages, under submission for IEEE Trans. on Information Theor
Network monitoring in multicast networks using network coding
In this paper we show how information contained in robust network codes can be used for passive inference of possible locations of link failures or losses in a network. For distributed randomized network coding, we bound the probability of being able to distinguish among a given set of failure events, and give some experimental results for one and two link failures in randomly generated networks. We also bound the required field size and complexity for designing a robust network code that distinguishes among a given set of failure events
Active Learning of Multiple Source Multiple Destination Topologies
We consider the problem of inferring the topology of a network with
sources and receivers (hereafter referred to as an -by- network), by
sending probes between the sources and receivers. Prior work has shown that
this problem can be decomposed into two parts: first, infer smaller subnetwork
components (i.e., -by-'s or -by-'s) and then merge these components
to identify the -by- topology. In this paper, we focus on the second
part, which had previously received less attention in the literature. In
particular, we assume that a -by- topology is given and that all
-by- components can be queried and learned using end-to-end probes. The
problem is which -by-'s to query and how to merge them with the given
-by-, so as to exactly identify the -by- topology, and optimize a
number of performance metrics, including the number of queries (which directly
translates into measurement bandwidth), time complexity, and memory usage. We
provide a lower bound, , on the number of
-by-'s required by any active learning algorithm and propose two greedy
algorithms. The first algorithm follows the framework of multiple hypothesis
testing, in particular Generalized Binary Search (GBS), since our problem is
one of active learning, from -by- queries. The second algorithm is called
the Receiver Elimination Algorithm (REA) and follows a bottom-up approach: at
every step, it selects two receivers, queries the corresponding -by-, and
merges it with the given -by-; it requires exactly steps, which is
much less than all possible -by-'s. Simulation results
over synthetic and realistic topologies demonstrate that both algorithms
correctly identify the -by- topology and are near-optimal, but REA is
more efficient in practice
A convolutional autoencoder approach for mining features in cellular electron cryo-tomograms and weakly supervised coarse segmentation
Cellular electron cryo-tomography enables the 3D visualization of cellular
organization in the near-native state and at submolecular resolution. However,
the contents of cellular tomograms are often complex, making it difficult to
automatically isolate different in situ cellular components. In this paper, we
propose a convolutional autoencoder-based unsupervised approach to provide a
coarse grouping of 3D small subvolumes extracted from tomograms. We demonstrate
that the autoencoder can be used for efficient and coarse characterization of
features of macromolecular complexes and surfaces, such as membranes. In
addition, the autoencoder can be used to detect non-cellular features related
to sample preparation and data collection, such as carbon edges from the grid
and tomogram boundaries. The autoencoder is also able to detect patterns that
may indicate spatial interactions between cellular components. Furthermore, we
demonstrate that our autoencoder can be used for weakly supervised semantic
segmentation of cellular components, requiring a very small amount of manual
annotation.Comment: Accepted by Journal of Structural Biolog
Wireless industrial monitoring and control networks: the journey so far and the road ahead
While traditional wired communication technologies have played a crucial role in industrial monitoring and control networks over the past few decades, they are increasingly proving to be inadequate to meet the highly dynamic and stringent demands of today’s industrial applications, primarily due to the very rigid nature of wired infrastructures. Wireless technology, however, through its increased pervasiveness, has the potential to revolutionize the industry, not only by mitigating the problems faced by wired solutions, but also by introducing a completely new class of applications. While present day wireless technologies made some preliminary inroads in the monitoring domain, they still have severe limitations especially when real-time, reliable distributed control operations are concerned. This article provides the reader with an overview of existing wireless technologies commonly used in the monitoring and control industry. It highlights the pros and cons of each technology and assesses the degree to which each technology is able to meet the stringent demands of industrial monitoring and control networks. Additionally, it summarizes mechanisms proposed by academia, especially serving critical applications by addressing the real-time and reliability requirements of industrial process automation. The article also describes certain key research problems from the physical layer communication for sensor networks and the wireless networking perspective that have yet to be addressed to allow the successful use of wireless technologies in industrial monitoring and control networks
- …