Stability and contagion measures for spatial extreme value analyzes

Fonseca, Cecília; Ferreira, Helena; Pereira, Luísa; Martins, Ana Paula

doi:10.14736/kyb-2014-6-0914

Cited by 3 publications

(5 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…More generally, examples of GRFs include wireless channels [37], speech processing [38], natural phenomena (temperature, rainfall intensity etc.) [3], [39], and recently in [24], [25].…”

Section: A Related Work On Spatial Field Reconstruction In Sensor Nementioning

confidence: 97%

“…Such models have recently become popular due to their mathematical tractability and accuracy [33], [44], [45]. The degree of the spatial correlation in the process increases with the decrease of the separation between two observing locations and can be accurately modelled as a Gaussian random field 1 [3], [24], [25], [29], [31], [37]. A Gaussian process (GP) defines a distribution over a space of functions and it is completely specified by the equivalent of sufficient statistics for such a process, and is formally defined as follows.…”

Section: A Spatial Gaussian Random Fields Backgroundmentioning

confidence: 99%

See 1 more Smart Citation

Spatial Field Reconstruction and Sensor Selection in Heterogeneous Sensor Networks With Stochastic Energy Harvesting

Zhang

Nevat²,

Peters³

et al. 2018

IEEE Trans. Signal Process.

View full text Add to dashboard Cite

We address the two fundamental problems of spatial field reconstruction and sensor selection in heterogeneous sensor networks. We consider the case where two types of sensors are deployed: the first consists of expensive, high quality sensors; and the second, of cheap low quality sensors, which are activated only if the intensity of the spatial field exceeds a pre-defined activation threshold (eg. wind sensors). In addition, these sensors are powered by means of energy harvesting and their time varying energy status impacts on the accuracy of the measurement that may be obtained. We account for this phenomenon by encoding the energy harvesting process into the second moment properties of the additive noise, resulting in a spatial heteroscedastic process. We then address the following two important problems: (i) how to efficiently perform spatial field reconstruction based on measurements obtained simultaneously from both networks; and (ii) how to perform query based sensor set selection with predictive MSE performance guarantee. We first show that the resulting predictive posterior distribution, which is key in fusing such disparate observations, involves solving intractable integrals. To overcome this problem, we solve the first problem by developing a low complexity algorithm based on the spatial best linear unbiased estimator (S-BLUE). Next, building on the S-BLUE, we address the second problem, and develop an efficient algorithm for query based sensor set selection with performance guarantee. Our algorithm is based on the Cross Entropy method which solves the combinatorial optimization problem in an efficient manner. We present a comprehensive study of the performance gain that can be obtained by augmenting the high-quality sensors with low-quality sensors using both synthetic and real insurance storm surge database known as the Extreme Wind Storms Catalogue.

show abstract

“…More generally, examples of GRFs include wireless channels [37], speech processing [38], natural phenomena (temperature, rainfall intensity etc.) [3], [39], and recently in [24], [25].…”

Section: A Related Work On Spatial Field Reconstruction In Sensor Nementioning

confidence: 97%

Section: A Spatial Gaussian Random Fields Backgroundmentioning

confidence: 99%

Spatial Field Reconstruction and Sensor Selection in Heterogeneous Sensor Networks With Stochastic Energy Harvesting

Zhang

Nevat²,

Peters³

et al. 2018

IEEE Trans. Signal Process.

View full text Add to dashboard Cite

show abstract

“…The degree of the spatial correlation in the process increases with the decrease of the separation between two observing locations and can be accurately modelled as a Gaussian random field 1 [4], [13], [14], [37], [38], [44]- [47]. A Gaussian process (GP) defines a distribution over a space of functions and it is completely specified by the equivalent of sufficient statistics for such a process, and is formally defined as follows.…”

Section: A Spatial Gaussian Random Fields Backgroundmentioning

confidence: 99%

“…Application of such problems are presented in [4], [37] and an application in wireless communications is presented in [38]. 3) Spatial classification: the task is to perform binary classification of the spatial field.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Spatially Correlated Random Fields in Heterogeneous Wireless Sensor Networks

Nevat

Peters

Septier

et al. 2015

IEEE Trans. Signal Process.

View full text Add to dashboard Cite

International audienceWe develop new algorithms for spatial field re- construction, exceedance level estimation and classification in heterogeneous (mixed analog & digital sensors) Wireless Sensor Networks (WSNs). We consider spatial physical phenomena which are observed by a heterogeneous WSN, meaning that it consists partially of sparsely deployed high-quality sensors and partially of low-quality sensors. The high-quality sensors transmit their (continuous) noisy observations to the Fusion Centre (FC), while the low-quality sensors first perform a simple thresholding operation and then transmit their binary values over imperfect wireless channels to the FC. The resulting observations are mixed continuous and discrete (1-bit decisions) observations, and are combined in the FC to solve the inference problems. We first formulate the problem of spatial field reconstruction, exceedance level estimation and classification in such heterogeneous networks. We show that the resulting posterior predictive distribution, which is key in fusing such disparate observations, involves intractable integrals. To overcome this problem, we develop an algorithm that is based on a multivariate series expansion approach resulting in a Saddle-point type approximation. We then present comprehensive study of the performance gain that can be obtained by augmenting the high-quality sensors with low-quality sensors using real data of insurance storm surge database known as the Extreme Wind Storms Catalogue

show abstract

“…A wireless sensor network (WSN) has attracted considerable attention, because of its great potential in various applications such as battlefield surveillance, traffic, security, weather forecasts [1][2][3], health care, and home automation. Each sensor makes a local binary decision in a WSN that has N distributed sensors and a fusion center (FC).…”

Section: Introductionmentioning

confidence: 99%

An algorithm based on logistic regression with data fusion in wireless sensor networks

Liu

Luo

Qin

et al. 2017

J Wireless Com Network

View full text Add to dashboard Cite

A decision fusion rule using the total number of detections reported by the local sensors for hypothesis testing and the total number of detections that report "1" to the fusion center (FC) is studied for a wireless sensor network (WSN) with distributed sensors. A logistic regression fusion rule (LRFR) is formulated. We propose the logistic regression fusion algorithm (LRFA), in which we train the coefficients of the LRFR, and then use the LRFR to make a global decision about the presence/absence of the target. Both the fixed and variable numbers of decisions received by the FC are examined. The fusion rule of K out of N and the counting rule are compared with the LRFR. The LRFA does not depend on the signal model and the priori knowledge of the local sensors' detection probabilities and false alarm rate. The numerical simulations are conducted, and the results show that the LRFR improves the performance of the system with low computational complexity.

show abstract

Stability and contagion measures for spatial extreme value analyzes

Cited by 3 publications

References 17 publications

Spatial Field Reconstruction and Sensor Selection in Heterogeneous Sensor Networks With Stochastic Energy Harvesting

Spatial Field Reconstruction and Sensor Selection in Heterogeneous Sensor Networks With Stochastic Energy Harvesting

Estimation of Spatially Correlated Random Fields in Heterogeneous Wireless Sensor Networks

An algorithm based on logistic regression with data fusion in wireless sensor networks

Contact Info

Product

Resources

About