Distributed on-line multidimensional scaling for self-localization in wireless sensor networks

The present work considers the localization problem in wireless sensor networks formed by fixed nodes. Each node seeks to estimate its own position based on noisy measurements of the relative distance to other nodes. In a centralized batch mode, positions can be retrieved (up to a rigid transformation) by applying Principal Component Analysis (PCA) on a so-called similarity matrix built from the relative distances. In this paper, we propose a distributed on-line algorithm allowing each node to estimate its own position based on limited exchange of information in the network. Our framework encompasses the case of sporadic measurements and random link failures. We prove the consistency of our algorithm in the case of fixed sensors. Finally, we provide numerical and experimental results from both simulated and real data. Simulations issued to real data are conducted on a wireless sensor network testbed.Comment: 32 pages, 5 figures, 1 tabl

Distributed Robust Learning

We propose a framework for distributed robust statistical learning on {\em big contaminated data}. The Distributed Robust Learning (DRL) framework can reduce the computational time of traditional robust learning methods by several orders of magnitude. We analyze the robustness property of DRL, showing that DRL not only preserves the robustness of the base robust learning method, but also tolerates contaminations on a constant fraction of results from computing nodes (node failures). More precisely, even in presence of the most adversarial outlier distribution over computing nodes, DRL still achieves a breakdown point of at least $\lambda^*/2$, where $\lambda^*$ is the break down point of corresponding centralized algorithm. This is in stark contrast with naive division-and-averaging implementation, which may reduce the breakdown point by a factor of $k$ when $k$ computing nodes are used. We then specialize the DRL framework for two concrete cases: distributed robust principal component analysis and distributed robust regression. We demonstrate the efficiency and the robustness advantages of DRL through comprehensive simulations and predicting image tags on a large-scale image set.Comment: 18 pages, 2 figure

Measuring corruption: perception surveys or victimization surveys?

While methodologies and survey techniques recorded progress over the years, corruption measurement remains a many-headed monster. Since 2003 and the first publication of Transparency International's Global Corruption Barometer, researchers have access to population's feeling about the corruption scourge across institutions. Thereby, wider room emerged for populations' perceptions in the field of corruption quantification. In this paper, we analyze the gulf separating perceived corruption from experienced bribe situations using global household surveys in a Panel dataset. We show that the gap between these two types of data can be wide and unevenly distributed across countries. Introducing further objective and subjective data we try to puzzle out perception mechanisms.Corruption, Global Corruption Barometer, Governance, CPI, Transparency International, Corruption measurement, Perception indicators, Press freedom, Econometrics, Panel Data, Household surveys.

Measuring corruption: perception surveys or victimization surveys? Towards a better comprehension of populations’ perception mechanisms: press freedom, confidence and gossip

While methodologies and survey techniques recorded progress over the years, corruption measurement remains a many-headed monster. Since 2003 and the first publication of Transparency International’s Global Corruption Barometer, researchers have access to population’s feeling about the corruption scourge across institutions. Thereby, wider room emerged for populations’ perceptions in the field of corruption quantification. In this paper, we analyze the gulf separating perceived corruption from experienced bribe situations using global household surveys in a Panel dataset. We show that the gap between these two types of data can be wide and unevenly distributed across countries. Introducing further objective and subjective data we try to puzzle out perception mechanisms. Bien que les techniques d’enquête et les méthodologies se soient améliorées au fil des années, la mesure corruption demeure problématique. Depuis 2003 et la première publication du Baromètre Mondial de la Corruption par Transparency International, les chercheurs ont dorénavant accès aux perceptions des populations pour évaluer l’étendue de la corruption au sein de différentes administrations. Dans cet article, nous analysons l’écart entre les perceptions de la corruption et l’expérience concrète de celle-ci en utilisant des données de panel issues d’enquêtes ménages menées à une échelle mondiale. Nous comparons ainsi, au sein même des populations, les écarts entre expériences et perceptions de la corruption, afin d’isoler au mieux les mécanismes à l’oeuvre dans la construction des perceptions. Nous montrons alors que les écarts entre ces deux types de donnée peuvent être importants et inégalement distribués.(Full text in english)

Distributed static linear Gaussian models using consensus

Algorithms for distributed agreement are a powerful means for formulating distributed versions of existing centralized algorithms. We present a toolkit for this task and show how it can be used systematically to design fully distributed algorithms for static linear Gaussian models, including principal component analysis, factor analysis, and probabilistic principal component analysis. These algorithms do not rely on a fusion center, require only low-volume local (1-hop neighborhood) communications, and are thus efficient, scalable, and robust. We show how they are also guaranteed to asymptotically converge to the same solution as the corresponding existing centralized algorithms. Finally, we illustrate the functioning of our algorithms on two examples, and examine the inherent cost-performance tradeoff

Panda: Neighbor Discovery on a Power Harvesting Budget

Object tracking applications are gaining popularity and will soon utilize Energy Harvesting (EH) low-power nodes that will consume power mostly for Neighbor Discovery (ND) (i.e., identifying nodes within communication range). Although ND protocols were developed for sensor networks, the challenges posed by emerging EH low-power transceivers were not addressed. Therefore, we design an ND protocol tailored for the characteristics of a representative EH prototype: the TI eZ430-RF2500-SEH. We present a generalized model of ND accounting for unique prototype characteristics (i.e., energy costs for transmission/reception, and transceiver state switching times/costs). Then, we present the Power Aware Neighbor Discovery Asynchronously (Panda) protocol in which nodes transition between the sleep, receive, and transmit states. We analyze \name and select its parameters to maximize the ND rate subject to a homogeneous power budget. We also present Panda-D, designed for non-homogeneous EH nodes. We perform extensive testbed evaluations using the prototypes and study various design tradeoffs. We demonstrate a small difference (less then 2%) between experimental and analytical results, thereby confirming the modeling assumptions. Moreover, we show that Panda improves the ND rate by up to 3x compared to related protocols. Finally, we show that Panda-D operates well under non-homogeneous power harvesting

Spatial Whitening Framework for Distributed Estimation

Designing resource allocation strategies for power constrained sensor network in the presence of correlated data often gives rise to intractable problem formulations. In such situations, applying well-known strategies derived from conditional-independence assumption may turn out to be fairly suboptimal. In this paper, we address this issue by proposing an adjacency-based spatial whitening scheme, where each sensor exchanges its observation with their neighbors prior to encoding their own private information and transmitting it to the fusion center. We comment on the computational limitations for obtaining the optimal whitening transformation, and propose an iterative optimization scheme to achieve the same for large networks. We demonstrate the efficacy of the whitening framework by considering the example of bit-allocation for distributed estimation.Comment: 4 pages, 2 figures, this paper has been presented at CAMSAP 2011; Proc. 4th Intl. Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP 2011), San Juan, Puerto Rico, Dec 13-16, 201