Distributed resilient consensus: a non-parametric approach

In this paper, two parameter-independent fault-tolerant consensus algorithms are proposed to address the consensus problem in the presence of misbehaving agents. The first algorithm relies on adaptively estimating the number of faulty agents in the network by using a distributed fault-detection scheme. It is shown that this algorithm converges if the network of non-faulty agents is ( f+1)-robust, where f is the number of faulty agents in the network. The second algorithm is a non-parametric Mean-Subsequence-Reduced algorithm whose convergence is guaranteed if the network of non-faulty nodes is ( f+1)-robust and all non-faulty nodes have the same number of in-neighbours. Neither algorithm requires initial knowledge on the number of faulty agents in the network. The efficacy of the algorithms are illustrated with simulation results.

Download Full-text

Toward a Faster Fault Tolerant Consensus to Maintain Data Consistency in Collaborative Environments

International Journal of Cooperative Information Systems ◽

10.1142/s0218843017500022 ◽

2017 ◽

Vol 26 (03) ◽

pp. 1750002

Author(s):

Fouad Hanna ◽

Lionel Droz-Bartholet ◽

Jean-Christophe Lapayre

Keyword(s):

Network Model ◽

Fault Tolerant ◽

Data Consistency ◽

Consensus Algorithm ◽

Simulation Platform ◽

Consensus Problem ◽

Collaborative Environments ◽

Consensus Algorithms ◽

Shared Data ◽

Simultaneous Process

The consensus problem has become a key issue in the field of collaborative telemedicine systems because of the need to guarantee the consistency of shared data. In this paper, we focus on the performance of consensus algorithms. First, we studied, in the literature, the most well-known algorithms in the domain. Experiments on these algorithms allowed us to propose a new algorithm that enhances the performance of consensus in different situations. During 2014, we presented our very first initial thoughts to enhance the performance of the consensus algorithms, but the proposed solution gave very moderate results. The goal of this paper is to present a new enhanced consensus algorithm, named Fouad, Lionel and J.-Christophe (FLC). This new algorithm was built on the architecture of the Mostefaoui-Raynal (MR) consensus algorithm and integrates new features and some known techniques in order to enhance the performance of consensus in situations where process crashes are present in the system. The results from our experiments running on the simulation platform Neko show that the FLC algorithm gives the best performance when using a multicast network model on different scenarios: in the first scenario, where there are no process crashes nor wrong suspicion, and even in the second one, where multiple simultaneous process crashes take place in the system.

Download Full-text

Improvement of Persistent Scatterer Interferometry to Detect Large Non-Linear Displacements with the 2π Ambiguity by a Non-Parametric Approach

Remote Sensing ◽

10.3390/rs11212467 ◽

2019 ◽

Vol 11 (21) ◽

pp. 2467

Author(s):

Ogushi ◽

Matsuoka ◽

Defilippi ◽

Pasquali

Keyword(s):

Data Processing ◽

Observation Data ◽

Maximum Magnitude ◽

Parametric Approach ◽

Persistent Scatterer Interferometry ◽

Surface Displacements ◽

Persistent Scatterer ◽

Non Linear ◽

Simulation Results ◽

Non Parametric

Persistent scatterer interferometry (PSI) is commonly applied to monitor surface displacements with millimetric precision. However, this technique still has trouble estimating non-linear displacements because the algorithm is designed for the slow and linear displacements. Additionally, there is a variety of non-linear displacement types, and finding an appropriate displacement model for PSI is still assumed to be a fairly large task. In this paper, the conventional PSI technique is extended using a non-parametric non-linear approach (NN-PSI), and the performance of the extended method is investigated by simulations and actual observation data processing with TerraSAR-X. In the simulation, non-linear displacements are modeled by the magnitudes and periods of the displacement, and the evaluation of NN-PSI is conducted. According to the simulation results, the maximum magnitude of the displacement that can be estimated by NN-PSI is two and a half times the magnitude of the SAR sensor’s wavelength (2.5λ that is roughly equivalent to 8 cm for X-band, 14 cm for C-band, and 60 cm for L-band), and the period of the displacement is about three months. However, this displacement cannot be reconstructed by the conventional PSI due to the limitation, known as the 2π displacement ambiguity. The result of the observation data processing shows that a large displacement with the 2π ambiguity can be estimated by NN-PSI as the simulation results show, but the conventional PSI cannot reconstruct it. In addition, a different approach, Small BAseline Subset (SBAS), is applied to the same data to ensure the accuracy of results, and the correlation between NN-PSI and SBAS is 0.95, while that between the conventional PSI and SBAS is –0.66. It is concluded that NN-PSI enables the reconstruction of non-linear displacements by the non-parametric approach and the expansion of applications to measure surface displacements that could not be measured due to the limitations of the traditional PSI methods.

Download Full-text