14,457 research outputs found
CSWA: Aggregation-Free Spatial-Temporal Community Sensing
In this paper, we present a novel community sensing paradigm -- {C}ommunity
{S}ensing {W}ithout {A}ggregation}. CSWA is designed to obtain the environment
information (e.g., air pollution or temperature) in each subarea of the target
area, without aggregating sensor and location data collected by community
members. CSWA operates on top of a secured peer-to-peer network over the
community members and proposes a novel \emph{Decentralized Spatial-Temporal
Compressive Sensing} framework based on \emph{Parallelized Stochastic Gradient
Descent}. Through learning the \emph{low-rank structure} via distributed
optimization, CSWA approximates the value of the sensor data in each subarea
(both covered and uncovered) for each sensing cycle using the sensor data
locally stored in each member's mobile device. Simulation experiments based on
real-world datasets demonstrate that CSWA exhibits low approximation error
(i.e., less than C in city-wide temperature sensing task and
units of PM2.5 index in urban air pollution sensing) and performs comparably to
(sometimes better than) state-of-the-art algorithms based on the data
aggregation and centralized computation.Comment: This paper has been accepted by AAAI 2018. First two authors are
equally contribute
DMFSGD: A Decentralized Matrix Factorization Algorithm for Network Distance Prediction
The knowledge of end-to-end network distances is essential to many Internet
applications. As active probing of all pairwise distances is infeasible in
large-scale networks, a natural idea is to measure a few pairs and to predict
the other ones without actually measuring them. This paper formulates the
distance prediction problem as matrix completion where unknown entries of an
incomplete matrix of pairwise distances are to be predicted. The problem is
solvable because strong correlations among network distances exist and cause
the constructed distance matrix to be low rank. The new formulation circumvents
the well-known drawbacks of existing approaches based on Euclidean embedding.
A new algorithm, so-called Decentralized Matrix Factorization by Stochastic
Gradient Descent (DMFSGD), is proposed to solve the network distance prediction
problem. By letting network nodes exchange messages with each other, the
algorithm is fully decentralized and only requires each node to collect and to
process local measurements, with neither explicit matrix constructions nor
special nodes such as landmarks and central servers. In addition, we compared
comprehensively matrix factorization and Euclidean embedding to demonstrate the
suitability of the former on network distance prediction. We further studied
the incorporation of a robust loss function and of non-negativity constraints.
Extensive experiments on various publicly-available datasets of network delays
show not only the scalability and the accuracy of our approach but also its
usability in real Internet applications.Comment: submitted to IEEE/ACM Transactions on Networking on Nov. 201
A Two-step Statistical Approach for Inferring Network Traffic Demands (Revises Technical Report BUCS-2003-003)
Accurate knowledge of traffic demands in a communication network enables or enhances a variety of traffic engineering and network management tasks of paramount importance for operational networks. Directly measuring a complete set of these demands is prohibitively expensive because of the huge amounts of data that must be collected and the performance impact that such measurements would impose on the regular behavior of the network. As a consequence, we must rely on statistical techniques to produce estimates of actual traffic demands from partial information. The performance of such techniques is however limited due to their reliance on limited information and the high amount of computations they incur, which limits their convergence behavior. In this paper we study a two-step approach for inferring network traffic demands. First we elaborate and evaluate a modeling approach for generating good starting points to be fed to iterative statistical inference techniques. We call these starting points informed priors since they are obtained using actual network information such as packet traces and SNMP link counts. Second we provide a very fast variant of the EM algorithm which extends its computation range, increasing its accuracy and decreasing its dependence on the quality of the starting point. Finally, we evaluate and compare alternative mechanisms for generating starting points and the convergence characteristics of our EM algorithm against a recently proposed Weighted Least Squares approach.National Science Foundation (ANI-0095988, EIA-0202067, ITR ANI-0205294
Estimating point-to-point and point-to-multipoint traffic matrices: An information-theoretic approach
© 2005 IEEE.Traffic matrices are required inputs for many IP network management tasks, such as capacity planning, traffic engineering, and network reliability analysis. However, it is difficult to measure these matrices directly in large operational IP networks, so there has been recent interest in inferring traffic matrices from link measurements and other more easily measured data. Typically, this inference problem is ill-posed, as it involves significantly more unknowns than data. Experience in many scientific and engineering fields has shown that it is essential to approach such ill-posed problems via "regularization". This paper presents a new approach to traffic matrix estimation using a regularization based on "entropy penalization". Our solution chooses the traffic matrix consistent with the measured data that is information-theoretically closest to a model in which source/destination pairs are stochastically independent. It applies to both point-to-point and point-to-multipoint traffic matrix estimation. We use fast algorithms based on modern convex optimization theory to solve for our traffic matrices. We evaluate our algorithm with real backbone traffic and routing data, and demonstrate that it is fast, accurate, robust, and flexible.Yin Zhang, Member, Matthew Roughan, Carsten Lund, and David L. Donoh
Content-access QoS in peer-to-peer networks using a fast MDS erasure code
This paper describes an enhancement of content access Quality of Service in peer to peer (P2P) networks. The main idea is to use an erasure code to distribute the information over the peers. This distribution increases the usersâ choice on disseminated encoded data and therefore statistically enhances the overall throughput of the transfer. A performance evaluation based on an original model using the results of a measurement campaign of sequential and parallel downloads in a real P2P network over Internet is presented. Based on a bandwidth distribution, statistical content-access QoS are guaranteed in function of both the content replication level in the network and the file dissemination strategies. A simple application in the context of media streaming is proposed. Finally, the constraints on the erasure code related to the proposed system are analysed and a new fast MDS erasure code is proposed, implemented and evaluated
Predictive Duty Cycle Adaptation for Wireless Camera Networks
Wireless sensor networks (WSN) typically employ dynamic duty cycle schemes to efficiently handle different patterns of communication traffic in the network. However, existing duty cycling approaches are not suitable for event-driven WSN, in particular, camera-based networks designed to track humans and objects. A characteristic feature of such networks is the spatially-correlated bursty traffic that occurs in the vicinity of potentially highly mobile objects. In this paper, we propose a concept of indirect sensing in the MAC layer of a wireless camera network and an active duty cycle adaptation scheme based on Kalman filter that continuously predicts and updates the location of the object that triggers bursty communication traffic in the network. This prediction allows the camera nodes to alter their communication protocol parameters prior to the actual increase in the communication traffic. Our simulations demonstrate that our active adaptation strategy outperforms TMAC not only in terms of energy efficiency and communication latency, but also in terms of TIBPEA, a QoS metric for event-driven WSN
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