Distributed Mean-Field Estimation and Detection in Correlated Gaussian Random Fields Using Sensor Networks
نویسندگان
چکیده
We propose distributed methods for estimating and detecting the mean of a correlated Gaussian random field observed by a sensor network. The random-field correlations are assumed to follow a conditional autoregressive (CAR) model. First, a distributed maximum likelihood (ML) estimator of the mean field is derived. We then develop batch and sequential detectors for testing the hypothesis that the mean field is greater than a specified level. We also derive exact and approximate performance measures for our methods. Numerical examples demonstrate the performance of the proposed approach.
منابع مشابه
Outlier Detection in Wireless Sensor Networks Using Distributed Principal Component Analysis
Detecting anomalies is an important challenge for intrusion detection and fault diagnosis in wireless sensor networks (WSNs). To address the problem of outlier detection in wireless sensor networks, in this paper we present a PCA-based centralized approach and a DPCA-based distributed energy-efficient approach for detecting outliers in sensed data in a WSN. The outliers in sensed data can be ca...
متن کاملTracking performance of incremental LMS algorithm over adaptive distributed sensor networks
in this paper we focus on the tracking performance of incremental adaptive LMS algorithm in an adaptive network. For this reason we consider the unknown weight vector to be a time varying sequence. First we analyze the performance of network in tracking a time varying weight vector and then we explain the estimation of Rayleigh fading channel through a random walk model. Closed form relations a...
متن کاملA Robust Distributed Estimation Algorithm under Alpha-Stable Noise Condition
Robust adaptive estimation of unknown parameter has been an important issue in recent years for reliable operation in the distributed networks. The conventional adaptive estimation algorithms that rely on mean square error (MSE) criterion exhibit good performance in the presence of Gaussian noise, but their performance drastically decreases under impulsive noise. In this paper, we propose a rob...
متن کاملSpatial prediction with mobile sensor networks using Gaussian processes with built-in Gaussian Markov random fields
In this paper, a new class of Gaussian processes is proposed for resource-constrained mobile sensor networks. Such a Gaussian process builds on a GMRFwith respect to a proximity graph over a surveillance region. The main advantages of using this class of Gaussian processes over standard Gaussian processes defined by mean and covariance functions are its numerical efficiency and scalability due ...
متن کاملENERGY AWARE DISTRIBUTED PARTITIONING DETECTION AND CONNECTIVITY RESTORATION ALGORITHM IN WIRELESS SENSOR NETWORKS
Mobile sensor networks rely heavily on inter-sensor connectivity for collection of data. Nodes in these networks monitor different regions of an area of interest and collectively present a global overview of some monitored activities or phenomena. A failure of a sensor leads to loss of connectivity and may cause partitioning of the network into disjoint segments. A number of approaches have be...
متن کامل