7,900 research outputs found
Estimating Dynamic Traffic Matrices by using Viable Routing Changes
Abstract: In this paper we propose a new approach for dealing with the ill-posed nature of traffic matrix estimation. We present three solution enhancers: an algorithm for deliberately changing link weights to obtain additional information that can make the underlying linear system full rank; a cyclo-stationary model to capture both long-term and short-term traffic variability, and a method for estimating the variance of origin-destination (OD) flows. We show how these three elements can be combined into a comprehensive traffic matrix estimation procedure that dramatically reduces the errors compared to existing methods. We demonstrate that our variance estimates can be used to identify the elephant OD flows, and we thus propose a variant of our algorithm that addresses the problem of estimating only the heavy flows in a traffic matrix. One of our key findings is that by focusing only on heavy flows, we can simplify the measurement and estimation procedure so as to render it more practical. Although there is a tradeoff between practicality and accuracy, we find that increasing the rank is so helpful that we can nevertheless keep the average errors consistently below the 10% carrier target error rate. We validate the effectiveness of our methodology and the intuition behind it using commercial traffic matrix data from Sprint's Tier-1 backbon
Network tomography based on 1-D projections
Network tomography has been regarded as one of the most promising
methodologies for performance evaluation and diagnosis of the massive and
decentralized Internet. This paper proposes a new estimation approach for
solving a class of inverse problems in network tomography, based on marginal
distributions of a sequence of one-dimensional linear projections of the
observed data. We give a general identifiability result for the proposed method
and study the design issue of these one dimensional projections in terms of
statistical efficiency. We show that for a simple Gaussian tomography model,
there is an optimal set of one-dimensional projections such that the estimator
obtained from these projections is asymptotically as efficient as the maximum
likelihood estimator based on the joint distribution of the observed data. For
practical applications, we carry out simulation studies of the proposed method
for two instances of network tomography. The first is for traffic demand
tomography using a Gaussian Origin-Destination traffic model with a power
relation between its mean and variance, and the second is for network delay
tomography where the link delays are to be estimated from the end-to-end path
delays. We compare estimators obtained from our method and that obtained from
using the joint distribution and other lower dimensional projections, and show
that in both cases, the proposed method yields satisfactory results.Comment: Published at http://dx.doi.org/10.1214/074921707000000238 in the IMS
Lecture Notes Monograph Series
(http://www.imstat.org/publications/lecnotes.htm) by the Institute of
Mathematical Statistics (http://www.imstat.org
Consistent Sensor, Relay, and Link Selection in Wireless Sensor Networks
In wireless sensor networks, where energy is scarce, it is inefficient to
have all nodes active because they consume a non-negligible amount of battery.
In this paper we consider the problem of jointly selecting sensors, relays and
links in a wireless sensor network where the active sensors need to communicate
their measurements to one or multiple access points. Information messages are
routed stochastically in order to capture the inherent reliability of the
broadcast links via multiple hops, where the nodes may be acting as sensors or
as relays. We aim at finding optimal sparse solutions where both, the
consistency between the selected subset of sensors, relays and links, and the
graph connectivity in the selected subnetwork are guaranteed. Furthermore,
active nodes should ensure a network performance in a parameter estimation
scenario. Two problems are studied: sensor and link selection; and sensor,
relay and link selection. To solve such problems, we present tractable
optimization formulations and propose two algorithms that satisfy the previous
network requirements. We also explore an extension scenario: only link
selection. Simulation results show the performance of the algorithms and
illustrate how they provide a sparse solution, which not only saves energy but
also guarantees the network requirements.Comment: 27 pages, 17 figure
Traffic matrix estimation on a large IP backbone: a comparison on real data
This paper considers the problem of estimating the point-to-point
traffic matrix in an operational IP backbone. Contrary to previous studies, that have used a partial traffic matrix or demands estimated from aggregated Netflow traces, we use a unique data set of complete traffic matrices from a global IP network measured over five-minute intervals. This allows us to do an accurate data analysis on the time-scale of typical link-load measurements and enables us to make a balanced evaluation of different traffic matrix estimation techniques. We describe the data collection infrastructure, present spatial and temporal demand distributions, investigate the stability of fan-out factors, and analyze the mean-variance relationships between demands. We perform a critical evaluation of existing and novel methods for traffic matrix estimation, including recursive fanout estimation, worst-case bounds, regularized estimation techniques, and methods that rely on mean-variance relationships. We discuss the weaknesses and strengths of the various methods, and highlight differences in the results for the European and American subnetworks
Network Tomography: Identifiability and Fourier Domain Estimation
The statistical problem for network tomography is to infer the distribution
of , with mutually independent components, from a measurement model
, where is a given binary matrix representing the
routing topology of a network under consideration. The challenge is that the
dimension of is much larger than that of and thus the
problem is often called ill-posed. This paper studies some statistical aspects
of network tomography. We first address the identifiability issue and prove
that the distribution is identifiable up to a shift parameter
under mild conditions. We then use a mixture model of characteristic functions
to derive a fast algorithm for estimating the distribution of
based on the General method of Moments. Through extensive model simulation and
real Internet trace driven simulation, the proposed approach is shown to be
favorable comparing to previous methods using simple discretization for
inferring link delays in a heterogeneous network.Comment: 21 page
A Survey on Wireless Security: Technical Challenges, Recent Advances and Future Trends
This paper examines the security vulnerabilities and threats imposed by the
inherent open nature of wireless communications and to devise efficient defense
mechanisms for improving the wireless network security. We first summarize the
security requirements of wireless networks, including their authenticity,
confidentiality, integrity and availability issues. Next, a comprehensive
overview of security attacks encountered in wireless networks is presented in
view of the network protocol architecture, where the potential security threats
are discussed at each protocol layer. We also provide a survey of the existing
security protocols and algorithms that are adopted in the existing wireless
network standards, such as the Bluetooth, Wi-Fi, WiMAX, and the long-term
evolution (LTE) systems. Then, we discuss the state-of-the-art in
physical-layer security, which is an emerging technique of securing the open
communications environment against eavesdropping attacks at the physical layer.
We also introduce the family of various jamming attacks and their
counter-measures, including the constant jammer, intermittent jammer, reactive
jammer, adaptive jammer and intelligent jammer. Additionally, we discuss the
integration of physical-layer security into existing authentication and
cryptography mechanisms for further securing wireless networks. Finally, some
technical challenges which remain unresolved at the time of writing are
summarized and the future trends in wireless security are discussed.Comment: 36 pages. Accepted to Appear in Proceedings of the IEEE, 201
Optimisation of Mobile Communication Networks - OMCO NET
The mini conference âOptimisation of Mobile Communication Networksâ focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University.
The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing
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