Mark Coates

Distributed particle filters for sensor networks

This paper describes two methodologies for performing distributed particle filtering in a sensor network. It considers the scenario in which a set of sensor nodes make multiple, noisy measurements of an underlying, time-varying state that describes the monitored system. The goal of the proposed algorithms is to perform on-line, distributed estimation of the current state at multiple sensor nodes, whilst attempting to minimize communication overhead. The first algorithm relies on likelihood factorization and the training of parametric models to approximate the likelihood factors. The second algorithm adds a predictive scalar quantizer training step into the more standard particle filtering framework, allowing adaptive encoding of the measurements. As its primary example, the paper describes the application of the quantization-based algorithm to tracking a maneuvering object. The paper concludes with a discussion of the limitations of the presented technique and an indication of future avenues for enhancement.

Distributed Average Consensus With Dithered Quantization

In this paper, we develop algorithms for distributed computation of averages of the node data over networks with bandwidth/power constraints or large volumes of data. Distributed averaging algorithms fail to achieve consensus when deterministic uniform quantization is adopted. We propose a distributed algorithm in which the nodes utilize probabilistically quantized information, i.e., dithered quantization, to communicate with each other. The algorithm we develop is a dynamical system that generates sequences achieving a consensus at one of the quantization values almost surely. In addition, we show that the expected value of the consensus is equal to the average of the original sensor data. We derive an upper bound on the mean-square-error performance of the probabilistically quantized distributed averaging (PQDA). Moreover, we show that the convergence of the PQDA is monotonic by studying the evolution of the minimum-length interval containing the node values. We reveal that the length of this interval is a monotonically nonincreasing function with limit zero. We also demonstrate that all the node values, in the worst case, converge to the final two quantization bins at the same rate as standard unquantized consensus. Finally, we report the results of simulations conducted to evaluate the behavior and the effectiveness of the proposed algorithm in various scenarios.

Network delay tomography

The substantial overhead of performing internal network monitoring motivates techniques for inferring spatially localized information about performance using only end-to-end measurements. In this paper, we present a novel methodology for inferring the queuing delay distributions across internal links in the network based solely on unicast, end-to-end measurements. The major contributions are: 1) we formulate a measurement procedure for estimation and localization of delay distribution based on end-to-end packet pairs; 2) we develop a simple way to compute maximum likelihood estimates (MLEs) using the expectation-maximization (EM) algorithm; 3) we develop a new estimation methodology based on recently proposed nonparametric, wavelet-based density estimation method; and 4) we optimize the computational complexity of the EM algorithm by developing a new fast Fourier transform implementation. Realistic network simulations are carried out using network-level simulator ns-2 to demonstrate the accuracy of the estimation procedure.

Multivariate Online Anomaly Detection Using Kernel Recursive Least Squares

High-speed backbones are regularly affected by various kinds of network anomalies, ranging from malicious attacks to harmless large data transfers. Different types of anomalies affect the network in different ways, and it is difficult to know a priori how a potential anomaly will exhibit itself in traffic statistics. In this paper we describe an online, sequential, anomaly detection algorithm, that is suitable for use with multivariate data. The proposed algorithm is based on the kernel version of the recursive least squares algorithm. It assumes no model for network traffic or anomalies, and constructs and adapts a dictionary of features that approximately spans the subspace of normal behaviour. The algorithm raises an alarm immediately upon encountering a deviation from the norm. Through comparison with existing block-based offline methods based upon Principal Component Analysis, we demonstrate that our online algorithm is equally effective but has much faster time-to-detection and lower computational complexity. We also explore minimum volume set approaches in identifying the region of normality.

Distributed Average Consensus using Probabilistic Quantization

In this paper, we develop algorithms for distributed computation of averages of the node data over networks with bandwidth/power constraints or large volumes of data. Distributed averaging algorithms fail to achieve consensus when deterministic uniform quantization is adopted. We propose a distributed algorithm in which the nodes utilize probabilistically quantized information to communicate with each other. The algorithm we develop is a dynamical system that generates sequences achieving a consensus, which is one of the quantization values. In addition, we show that the expected value of the consensus is equal to the average of the original sensor data. We report the results of simulations conducted to evaluate the behavior and the effectiveness of the proposed algorithm in various scenarios.

Video-on-Demand Networks: Design Approaches and Future Challenges

IP network based deployments of interactive video-on-demand (VoD) systems are today very limited in scope, but there is a strong belief among telecommunication companies that this market will expand exponentially in the next few years. In this article, we outline the components of VoD architectures and survey the current approaches to their design. We strive to identify the research challenges that must be addressed in the development of design tools that can determine how to expand upon an existing network infrastructure to support video-on-demand. The long tail of content and extensive growth in usage are expected to have a major impact on the streaming and storage requirements of such systems. Hybrid VoD architectures that incorporate peer-to-peer exchange are an extremely promising paradigm, but there are many challenges in developing operational and economically feasible peer-to-peer systems. VoD networks generate sufficient traffic that their impact should be considered in planning general network infrastructure expansions

Radio-Frequency Tomography for Passive Indoor Multitarget Tracking

Radio-frequency (RF) tomography is the method of tracking targets using received signal-strength (RSS) measurements for RF transmissions between multiple sensor nodes. When the targets are near the line-of-sight path between two nodes, they are more likely to cause substantial attenuation or amplification of the RF signal. In this paper, we develop a measurement model for multitarget tracking using RF tomography in indoor environments and apply it successfully for tracking up to three targets. We compare several multitarget tracking algorithms and examine performance in the two scenarios when the number of targets is 1) known and constant, and 2) unknown and time varying. We demonstrate successful tracking for experimental data collected from sensor networks deployed in three different indoor environments posing different tracking challenges. For the fixed number of targets, the best algorithm achieves a root-mean-squared error tracking accuracy of approximately 0.3 m for a single target, 0.7 m for two targets and 0.8 m for three targets. Tracking using our proposed model is more accurate than tracking using previously proposed observation models; more importantly, the model does not require the same degree of training.

Likelihood based hierarchical clustering

This paper develops a new method for hierarchical clustering. Unlike other existing clustering schemes, our method is based on a generative, tree-structured model that represents relationships between the objects to be clustered, rather than directly modeling properties of objects themselves. In certain problems, this generative model naturally captures the physical mechanisms responsible for relationships among objects, for example, in certain evolutionary tree problems in genetics and communication network topology identification. The paper examines the networking problem in some detail to illustrate the new clustering method. More broadly, the generative model may not reflect actual physical mechanisms, but it nonetheless provides a means for dealing with errors in the similarity matrix, simultaneously promoting two desirable features in clustering: intraclass similarity and interclass dissimilarity.

Network tomography for internal delay estimation

On-line, spatially localized information about internal network performance can greatly assist dynamic routing algorithms and traffic transmission protocols. However, it is impractical to measure network traffic at all points in the network. A promising alternative is to measure only at the edge of the network and infer internal behavior from these measurements. We concentrate on the estimation and localization of internal delays based on end-to-end delay measurements from sources to receivers. We develop an EM algorithm for computing MLE of the internal delay distributions in cases where the network dynamics are stationary over the observation period. For time-varying cases, we propose a sequential Monte Carlo procedure capable of tracking non-stationary delay characteristics. Simulations are included to demonstrate the promise of these techniques.

Passive network tomography using EM algorithms

The paper presents a new method for characterizing communication network performance based solely on passive traffic monitoring at the network edge. More specifically, we devise a novel expectation-maximization (EM) algorithm to infer internal packet loss rates (at routers inside the network) using only observed end-to-end (source to receiver) loss rates. The major contributions of this paper are three-fold: we formulate a passive monitoring procedure for network loss inference based on end-to-end packet pair observations, we develop a statistical modeling and computation framework for inferring internal network loss characteristics, and we evaluate the performance with realistic network simulations.