Distributed Estimation and Detection for Sensor Networks Using Hidden Markov Random Field Models

Abstract

We develop a hidden Markov random field (HMRF) framework for distributed signal processing in sensornetwork environments. Under this framework, spatially distributed observations collected at the sensors form a noisy realization of an underlying random field that has a simple structure with Markovian dependence. We derive iterated conditional modes (ICM) algorithms for distributed estimation of the hidden random field from the noisy measurements. We consider both parametric and nonparametric measurement-error models. The proposed distributed estimators are computationally simple, applicable to a wide range of sensing environments, and localized, implying that the nodes communicate only with their neighbors to obtain the desired results. We also develop a calibration method for estimating Markov random field model parameters from training data and discuss initialization of the ICM algorithms. The HMRF framework and ICM algorithms are applied to event-region detection. Numerical simulations demonstrate the performance of the proposed approach AbstractWe develop a hidden Markov random field (HMRF) framework for distributed signal processing in sensornetwork environments. Under this framework, spatially distributed observations collected at the sensors form a noisy realization of an underlying random field that has a simple structure with Markovian dependence. We derive iterated conditional modes (ICM) algorithms for distributed estimation of the hidden random field from the noisy measurements. We consider both parametric and nonparametric measurement-error models. The proposed distributed estimators are computationally simple, applicable to a wide range of sensing environments, and localized, implying that the nodes communicate only with their neighbors to obtain the desired results. We also develop a calibration method for estimating Markov random field (MRF) model parameters from training data and discuss initialization of the ICM algorithms. The HMRF framework and ICM algorithms are applied to event-region detection. Numerical simulations demonstrate the performance of the proposed approach.

Cooperative transmit-power estimation under wireless fading

“…In other areas related to wireless network monitoring, there have been studies on Markov model based data fusion with the goal of estimating the sensor field [6,7]; however, our work is different and more specialized to power estimation under Lognormal fading. The power estimation problem that we consider also significantly differs from powercontrol problems studied in the literature on wireless and cellular networks [9][10][11].…”

Target Tracking in Wireless Sensor Networks

“…Recently, decentralized estimation of random signals in arbitrary nonlinear and nonGaussian setups was considered in (Schizas & Giannakis, 2006), while distributed estimation of stationary Markov random fields was pursued in (Dogandzic & Zhang, 2006). Adaptive algorithms based on in-network processing of distributed observations are wellmotivated for online parameter estimation and tracking of (non)stationary signals using peer-to-peer WSNs.…”

Low computation and low latency algorithms for distributed sensor network initialization

“…However, in most cases this may not be possible even though the level sets do exist. Let α be any solution of (4). By the implicit function theorem [21], in the neighborhood of α, the level set L f (ẑ m,t ) is an n − s dimensional manifold.…”

“…These initialization algorithms are faced with the task of collecting local knowledge from individual nodes, fusing these individual components to generate global knowledge, and dispersing this global knowledge throughout the network. In [4], this problem is addressed using hidden Markov random field models. The proposed algorithm can accurately initialize the network locally but the final state estimates may not be globally known.…”