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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 manoeuvring object. The paper concludes with a discussion of the limitations of the presented technique and an indication of future avenues for enhancement.

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 non-increasing 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.

Abstract-Information about the topology and link-level characteristics of a network is critical for many applications including network diagnostics and management. However, this information is not always directly accessible; subnetworks may not cooperate in releasing information and widespread local measurement can be prohibitively expensive. Network tomographic techniques obviate the need for network cooperation, but the majority assume probing from a single source, which imposes scalability limitations because sampling traffic is concentrated on network links close to the source. We describe a multiple source, end-toend sampling architecture that uses coordinated transmission of carefully engineered multi-packet probes to jointly infer logical topology and estimate link-level performance characteristics. We commence by demonstrating that the general multiple source, multiple destination tomography problem can be formally reduced to the two source, two destination case, allowing the immediate generalization of any sampling techniques developed for the simpler, smaller scenario. We then describe a method for testing whether links are shared in the topologies perceived by individual sources, and describe how to fuse the measurements in the shared case to generate more accurate estimates of the link-level performance statistics.Index Terms-Internet tomography, end-to-end measurements, active probing, topology discovery, loss rate estimation.

This study reports on monthly scans of healthy patient volunteers with the clinical prototype of a microwave imaging system. The system uses time-domain measurements, and incorporates a multistatic radar approach to imaging. It operates in the 2-4 GHz range and contains 16 wideband sensors embedded in a hemispherical dielectric radome. The system has been previously tested on tissue phantoms in controlled experiments. With this system prototype, we scanned 13 patients (26 breasts) over an eight-month period, collecting a total of 342 breast scans. The goal of the study described in this paper was to investigate how the system measurements are impacted by multiple factors that are unavoidable in monthly monitoring of human subjects. These factors include both biological variability (e.g., tissue variations due to hormonal changes or weight gain) and measurement variability (e.g., inconsistencies in patient positioning, system noise). For each patient breast, we process the results of the monthly scans to assess the variability in both the raw measured signals and in the generated images. The significance of this study is that it quantifies how much variability should be anticipated when conducting microwave breast imaging of a healthy patient over a longer period. This is an important step toward establishing the feasibility of the microwave radar imaging system for frequent monitoring of breast health.