The behavior of a wireless sensor network dedicated to distributed estimation tasks may be significantly altered by the presence of nodes whose sensors are defective and produce erroneous measurements. This paper proposes and analyzes the performance of two distributed algorithms to help each node in determining whether it is equipped with a defective sensor. A node first collects data from its neighborhood, processes them to decide, using some generic local outlier detection test, whether these data contain outliers and broadcasts the result. Then, it determines the status of its own sensor using its result and those received from neighboring nodes. A single-decision and an iterative algorithm for defective sensor detection are proposed. Bounds on the performance of the single-decision algorithm are derived.A theoretical analysis of the probability of error and of the equilibrium of the iterative algorithm is provided for a wide class of local outlier detection tests. The trade-off between false alarm probability and detection probability is characterized theoretically and by simulation. MAC-layer issues, as well as the effect of packet losses are accounted for.

To guarantee its integrity, a wireless sensor network needs to efficiently detect faulty nodes producing erroneous measurements. This paper proposes a fully distributed fault detection algorithm. A node first collects the measurements of its neighborhood, processes them to decide whether they contain outliers, and broadcasts the result. Then, it decides autonomously about its functioning status. The detection algorithm is proposed in two variants, depending on the proportion of faulty nodes in the network. A theoretical analysis of the probability of error and of the convergence of the algorithm is provided. The tradeoff between false alarm probability and detection probability is characterized using simulation. 1

Practical schemes for distributed video coding with side information at the decoder need to consider non-standard correlation models in order to take non-stationarities into account. In this paper we introduce two correlation models for Gaussian sources, the GaussianBernoulli-Gaussian (GBG) and the Gaussian-Erasure (GE) models, and evaluate lower and upper bounds on their rate-distortion functions. Provided that the probability of impulse noise or of erasures remains small, these bounds remain close to the rate-distortion function for Gaussian correlation. Two practical schemes for the GE correlation model are also presented, with performance about 1.5 dB away from the upper bound.

In this paper, the distributed computation of confidence regions for parameter estimation is considered. Some information diffusion strategies are proposed and compared in terms of the required number of data exchanges to get the corresponding region. The effects of algorithms truncation is also addressed. As support for the theoretical part, numerical results are presented.