This paper focuses on the problem of the distributed estimation of a parameter vector based on noisy observations regularly acquired by the nodes of a wireless sensor network and assuming that some of the nodes have faulty sensors. We propose two online schemes, both centralized and distributed, based on the Expectation-Maximization (EM) algorithm. These algorithms are able to identify and disregard the faulty nodes, and provide a refined estimate of the parameters each time instant after a new set of observations is acquired. Simulation results demonstrate that the centralized versions of the proposed online algorithms attain the same estimation error as the centralized batch EM, whereas the distributed versions come very close to matching...
In this paper, we consider the problem of simultaneously classifying sensor types and estimating hid...
We present an online algorithm for hypothesis testing from correlated observations obtained from a n...
In this paper, we address the problem of simultaneous classification and estimation of hidden parame...
This paper focuses on the problem of the distributed estimation of a parameter vector based on noisy...
We address the problem of distributed estimation of a vector-valued parameter performed by a wireles...
We address the problem of distributed estimation of a parameter from a set of noisy observations col...
We address the problem of distributed estimation of a parameter from a set of noisy observations col...
We address the problem of distributed estimation of a parameter from a set of noisy observations col...
This paper addresses the problem of distributed estimation of a parameter vector in the presence of ...
This paper addresses the problem of distributed estimation of a parameter vector in the presence of ...
Estimating the unknown parameters of a statistical model based on the observations collected by a se...
The paper considers the problem of distributed estimation of an unknown deterministic scalar paramet...
A major issue in distributed wireless sensor networks (WSNs) is the design of efficient distributed ...
In this paper, a distributed method for fault detection using sensor networks is proposed. Each sens...
In this paper, we consider the problem of simultaneously classifying sensor types and estimating hid...
In this paper, we consider the problem of simultaneously classifying sensor types and estimating hid...
We present an online algorithm for hypothesis testing from correlated observations obtained from a n...
In this paper, we address the problem of simultaneous classification and estimation of hidden parame...
This paper focuses on the problem of the distributed estimation of a parameter vector based on noisy...
We address the problem of distributed estimation of a vector-valued parameter performed by a wireles...
We address the problem of distributed estimation of a parameter from a set of noisy observations col...
We address the problem of distributed estimation of a parameter from a set of noisy observations col...
We address the problem of distributed estimation of a parameter from a set of noisy observations col...
This paper addresses the problem of distributed estimation of a parameter vector in the presence of ...
This paper addresses the problem of distributed estimation of a parameter vector in the presence of ...
Estimating the unknown parameters of a statistical model based on the observations collected by a se...
The paper considers the problem of distributed estimation of an unknown deterministic scalar paramet...
A major issue in distributed wireless sensor networks (WSNs) is the design of efficient distributed ...
In this paper, a distributed method for fault detection using sensor networks is proposed. Each sens...
In this paper, we consider the problem of simultaneously classifying sensor types and estimating hid...
In this paper, we consider the problem of simultaneously classifying sensor types and estimating hid...
We present an online algorithm for hypothesis testing from correlated observations obtained from a n...
In this paper, we address the problem of simultaneous classification and estimation of hidden parame...