Learning-Based Distributed Detection-Estimation in Sensor Networks with Unknown Sensor Defects

We consider the problem of distributed estimation of an unknown deterministic scalar parameter (the target signal) in a wireless sensor network (WSN), where each sensor receives a single snapshot of the field. We assume that the observation at each node randomly falls into one of two modes: a valid or an invalid observation mode. Specifically, mode one corresponds to the desired signal plus noise observation mode (\emph{valid}), and mode two corresponds to the pure noise mode (\emph{invalid}) due to node defect or damage. With no prior information on such local sensing modes, we introduce a learning-based distributed procedure, called the mixed detection-estimation (MDE) algorithm, based on iterative closed-loop interactions between mode learning (detection) and target estimation. The online learning step re-assesses the validity of the local observations at each iteration, thus refining the ongoing estimation update process. The convergence of the MDE algorithm is established analytically. Asymptotic analysis shows that, in the high signal-to-noise ratio (SNR) regime, the MDE estimation error converges to that of an ideal (centralized) estimator with perfect information about the node sensing modes. This is in contrast to the estimation performance of a naive average consensus based distributed estimator (without mode learning), whose estimation error blows up with an increasing SNR.Comment: 15 pages, 2 figures, submitted to TS

BIASING EMITTER LOCATION ESTIMATES VIA FALSE LOCATION INJECTION

ABSTRACT We consider the problem of a rogue introducing bias into a network estimating location under the time difference of arrival (TDOA) method. In particular we consider how a rogue by injecting only a single false sensor position can drive the network's location estimate a specified distance away from the true value. The least squares (LS) residuals is minimized to find the false location to inject given the rogue's desired distance offset. In order to illustrate the success of our method, we consider the statistical tools that the locating network might employ to handle our false information injection including least squares and in the presence of outliers robust least median squares (LMS). We show that our method can successfully bias the location estimate of an estimating network when both LS and LMS methods are used

Distributed estimation in sensor networks with imperfect model information: An adaptive learning-based approach

The paper considers the problem of distributed estimation of an unknown deterministic scalar parameter (the target signal) in wireless sensor networks (WSNs), in which each sensor receives a single snapshot of the field. The observation or sensing mode is only partially known at the corresponding n-odes, perhaps, due to their limited sensing capabilities or oth-er unpredictable physical factors. Specifically, it is assumed that the observation process at a node switches stochastically between two modes, with mode one corresponding to the desired signal plus noise observation mode (a valid obser-vation), and mode two corresponding to pure noise with no signal information (an invalid observation). With no prior information on the local sensing modes (valid or invalid), the paper introduces a learning-based distributed estimation procedure, the mixed detection-estimation (MDE) algorithm, based on closed-loop interactions between the iterative dis-tributed mode learning and estimation. The online learning (or sensing mode detection) step re-assesses the validity of the local observations at each iteration, thus refining the ongoing estimation update process. The convergence of the MDE al-gorithm is established analytically. Simulation studies show that, in the high signal-to-noise ratio (SNR) regime, the MDE estimation error converges to that of an ideal (centralized) estimator with perfect information about the node sensing modes. This is in contrast with the estimation performance of a naive average consensus based distributed estimator (with no mode learning), whose estimation error blows up with an increasing SNR. Index Terms — Distributed estimation, distributed learn-ing, adaptive, stochastic switching, sensor networks