4,151 research outputs found
VIQID: a no-reference bit stream-based visual quality impairment detector
In order to ensure adequate quality towards the end users at all time, video service providers are getting more interested in monitoring their video streams. Objective video quality metrics provide a means of measuring (audio)visual quality in an automated manner. Unfortunately, most of the current existing metrics cannot be used for real-time monitoring due to their dependencies on the original video sequence. In this paper we present a new objective video quality metric which classifies packet loss as visible or invisible based on information extracted solely from the captured encoded H.264/AVC video bit stream. Our results show that the visibility of packet loss can be predicted with a high accuracy, without the need for deep packet inspection. This enables service providers to monitor quality in real-time
SENATUS: An Approach to Joint Traffic Anomaly Detection and Root Cause Analysis
In this paper, we propose a novel approach, called SENATUS, for joint traffic
anomaly detection and root-cause analysis. Inspired from the concept of a
senate, the key idea of the proposed approach is divided into three stages:
election, voting and decision. At the election stage, a small number of
\nop{traffic flow sets (termed as senator flows)}senator flows are chosen\nop{,
which are used} to represent approximately the total (usually huge) set of
traffic flows. In the voting stage, anomaly detection is applied on the senator
flows and the detected anomalies are correlated to identify the most possible
anomalous time bins. Finally in the decision stage, a machine learning
technique is applied to the senator flows of each anomalous time bin to find
the root cause of the anomalies. We evaluate SENATUS using traffic traces
collected from the Pan European network, GEANT, and compare against another
approach which detects anomalies using lossless compression of traffic
histograms. We show the effectiveness of SENATUS in diagnosing anomaly types:
network scans and DoS/DDoS attacks
Networked Computing in Wireless Sensor Networks for Structural Health Monitoring
This paper studies the problem of distributed computation over a network of
wireless sensors. While this problem applies to many emerging applications, to
keep our discussion concrete we will focus on sensor networks used for
structural health monitoring. Within this context, the heaviest computation is
to determine the singular value decomposition (SVD) to extract mode shapes
(eigenvectors) of a structure. Compared to collecting raw vibration data and
performing SVD at a central location, computing SVD within the network can
result in significantly lower energy consumption and delay. Using recent
results on decomposing SVD, a well-known centralized operation, into
components, we seek to determine a near-optimal communication structure that
enables the distribution of this computation and the reassembly of the final
results, with the objective of minimizing energy consumption subject to a
computational delay constraint. We show that this reduces to a generalized
clustering problem; a cluster forms a unit on which a component of the overall
computation is performed. We establish that this problem is NP-hard. By
relaxing the delay constraint, we derive a lower bound to this problem. We then
propose an integer linear program (ILP) to solve the constrained problem
exactly as well as an approximate algorithm with a proven approximation ratio.
We further present a distributed version of the approximate algorithm. We
present both simulation and experimentation results to demonstrate the
effectiveness of these algorithms
Cognition-Based Networks: A New Perspective on Network Optimization Using Learning and Distributed Intelligence
IEEE Access
Volume 3, 2015, Article number 7217798, Pages 1512-1530
Open Access
Cognition-based networks: A new perspective on network optimization using learning and distributed intelligence (Article)
Zorzi, M.a , Zanella, A.a, Testolin, A.b, De Filippo De Grazia, M.b, Zorzi, M.bc
a Department of Information Engineering, University of Padua, Padua, Italy
b Department of General Psychology, University of Padua, Padua, Italy
c IRCCS San Camillo Foundation, Venice-Lido, Italy
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View references (107)
Abstract
In response to the new challenges in the design and operation of communication networks, and taking inspiration from how living beings deal with complexity and scalability, in this paper we introduce an innovative system concept called COgnition-BAsed NETworkS (COBANETS). The proposed approach develops around the systematic application of advanced machine learning techniques and, in particular, unsupervised deep learning and probabilistic generative models for system-wide learning, modeling, optimization, and data representation. Moreover, in COBANETS, we propose to combine this learning architecture with the emerging network virtualization paradigms, which make it possible to actuate automatic optimization and reconfiguration strategies at the system level, thus fully unleashing the potential of the learning approach. Compared with the past and current research efforts in this area, the technical approach outlined in this paper is deeply interdisciplinary and more comprehensive, calling for the synergic combination of expertise of computer scientists, communications and networking engineers, and cognitive scientists, with the ultimate aim of breaking new ground through a profound rethinking of how the modern understanding of cognition can be used in the management and optimization of telecommunication network
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