Stochastically Consistent Caching and Dynamic Duty Cycling for Erratic Sensor Sources

We present a novel dynamic duty cycling scheme to maintain stochastic consistency for caches in sensor networks. To reduce transmissions, base stations often maintain caches for erratically changing sensor sources. Stochastic consistency guarantees the cache-source deviation is within a pre-specified bound with a certain confidence level. We model the erratic sources as Brownian motions, and adaptively {\it predict} the next cache update time based on the model. By piggybacking the next update time in each regular data packet, we can dynamically adjust the relaying nodes' duty cycles so that they are awake before the next update message arrives, and are sleeping otherwise. Through simulations, we show that our approach can achieve very high source-cache fidelity with low power consumption on many real-life sensor data. On average, our approach consumes 4-5 times less power than GAF~\cite{gaf}, and achieves 50\% longer network lifetime

Segregation by Racial and Demographic Group: Evidence from the San Francisco Bay Area

This paper considers residential segregation by race and by type of household in 1970 and 1980. The paper presents entropy indices of segregation for the San Francisco Bay Area and its five metropolitan areas. The methodology permits an investigation of the effects of group definition upon segregation measures, and an analysis of the degree of independence in the segregation of households by race and demographic group. The results indicate that the levels of segregation by race and by household type have declined modestly during the 1970s, at least in this region. More importantly, however, the results indicate a remarkable independence in the spatial distribution of households by race and demographic group. Only a very small fraction of the observed levels of segregation by race could be ’explained’ by the prior partitioning of households by demographic group. The principal results of the analysis are invariant to changes in the definition of racial or household groups

Detection of inconsistencies in geospatial data with geostatistics

Almost every researcher has come through observations that “drift” from the rest of the sample, suggesting some inconsistency. The aim of this paper is to propose a new inconsistent data detection method for continuous geospatial data based in Geostatistics, independently from the generative cause (measuring and execution errors and inherent variability data). The choice of Geostatistics is based in its ideal characteristics, as avoiding systematic errors, for example. The importance of a new inconsistent detection method proposal is in the fact that some existing methods used in geospatial data consider theoretical assumptions hardly attended. Equally, the choice of the data set is related to the importance of the LiDAR technology (Light Detection and Ranging) in the production of Digital Elevation Models (DEM). Thus, with the new methodology it was possible to detect and map discrepant data. Comparing it to a much utilized detections method, BoxPlot, the importance and functionality of the new method was verified, since the BoxPlot did not detect any data classified as discrepant. The proposed method pointed that, in average, 1,2% of the data of possible regionalized inferior outliers and, in average, 1,4% of possible regionalized superior outliers, in relation to the set of data used in the study

Patterns in random walks and Brownian motion

We ask if it is possible to find some particular continuous paths of unit length in linear Brownian motion. Beginning with a discrete version of the problem, we derive the asymptotics of the expected waiting time for several interesting patterns. These suggest corresponding results on the existence/non-existence of continuous paths embedded in Brownian motion. With further effort we are able to prove some of these existence and non-existence results by various stochastic analysis arguments. A list of open problems is presented.Comment: 31 pages, 4 figures. This paper is published at http://link.springer.com/chapter/10.1007/978-3-319-18585-9_