8 research outputs found
Diffusion Adaptation over Networks under Imperfect Information Exchange and Non-stationary Data
Adaptive networks rely on in-network and collaborative processing among
distributed agents to deliver enhanced performance in estimation and inference
tasks. Information is exchanged among the nodes, usually over noisy links. The
combination weights that are used by the nodes to fuse information from their
neighbors play a critical role in influencing the adaptation and tracking
abilities of the network. This paper first investigates the mean-square
performance of general adaptive diffusion algorithms in the presence of various
sources of imperfect information exchanges, quantization errors, and model
non-stationarities. Among other results, the analysis reveals that link noise
over the regression data modifies the dynamics of the network evolution in a
distinct way, and leads to biased estimates in steady-state. The analysis also
reveals how the network mean-square performance is dependent on the combination
weights. We use these observations to show how the combination weights can be
optimized and adapted. Simulation results illustrate the theoretical findings
and match well with theory.Comment: 36 pages, 7 figures, to appear in IEEE Transactions on Signal
Processing, June 201
Diffusion Strategies Outperform Consensus Strategies for Distributed Estimation over Adaptive Networks
Adaptive networks consist of a collection of nodes with adaptation and
learning abilities. The nodes interact with each other on a local level and
diffuse information across the network to solve estimation and inference tasks
in a distributed manner. In this work, we compare the mean-square performance
of two main strategies for distributed estimation over networks: consensus
strategies and diffusion strategies. The analysis in the paper confirms that
under constant step-sizes, diffusion strategies allow information to diffuse
more thoroughly through the network and this property has a favorable effect on
the evolution of the network: diffusion networks are shown to converge faster
and reach lower mean-square deviation than consensus networks, and their
mean-square stability is insensitive to the choice of the combination weights.
In contrast, and surprisingly, it is shown that consensus networks can become
unstable even if all the individual nodes are stable and able to solve the
estimation task on their own. When this occurs, cooperation over the network
leads to a catastrophic failure of the estimation task. This phenomenon does
not occur for diffusion networks: we show that stability of the individual
nodes always ensures stability of the diffusion network irrespective of the
combination topology. Simulation results support the theoretical findings.Comment: 37 pages, 7 figures, To appear in IEEE Transactions on Signal
Processing, 201
Robust Distributed Parameter Estimation in Wireless Sensor Networks
abstract: Fully distributed wireless sensor networks (WSNs) without fusion center have advantages such as scalability in network size and energy efficiency in communications. Each sensor shares its data only with neighbors and then achieves global consensus quantities by in-network processing. This dissertation considers robust distributed parameter estimation methods, seeking global consensus on parameters of adaptive learning algorithms and statistical quantities.
Diffusion adaptation strategy with nonlinear transmission is proposed. The nonlinearity was motivated by the necessity for bounded transmit power, as sensors need to iteratively communicate each other energy-efficiently. Despite the nonlinearity, it is shown that the algorithm performs close to the linear case with the added advantage of power savings. This dissertation also discusses convergence properties of the algorithm in the mean and the mean-square sense.
Often, average is used to measure central tendency of sensed data over a network. When there are outliers in the data, however, average can be highly biased. Alternative choices of robust metrics against outliers are median, mode, and trimmed mean. Quantiles generalize the median, and they also can be used for trimmed mean. Consensus-based distributed quantile estimation algorithm is proposed and applied for finding trimmed-mean, median, maximum or minimum values, and identification of outliers through simulation. It is shown that the estimated quantities are asymptotically unbiased and converges toward the sample quantile in the mean-square sense. Step-size sequences with proper decay rates are also discussed for convergence analysis.
Another measure of central tendency is a mode which represents the most probable value and also be robust to outliers and other contaminations in data. The proposed distributed mode estimation algorithm achieves a global mode by recursively shifting conditional mean of the measurement data until it converges to stationary points of estimated density function. It is also possible to estimate the mode by utilizing grid vector as well as kernel density estimator. The densities are estimated at each grid point, while the points are updated until they converge to a global mode.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201