Distributed data aggregation is an important task, allowing the decentralized determination
of meaningful global properties, which can then be used to direct the execution of other …

Quantile regression under memory constraint Page 1 The Annals of Statistics 2019, Vol. 47,
No. 6, 3244–3273 https://doi.org/10.1214/18-AOS1777 © Institute of Mathematical Statistics …

Sensor networks have recently found many popular applications in a number of different
settings. Sensors at different locations can generate streaming data, which can be analyzed …

Wireless sensor networks offer the potential to span and monitor large geographical areas
inexpensively. Sensors, however, have significant power constraint (battery life), making …

This paper provides new algorithms for distributed clustering for two popular center-based
objectives, $ k $-median and $ k $-means. These algorithms have provable guarantees and …

In-network aggregation is an essential primitive for performing queries on sensor network
data. However, most aggregation algorithms assume that all intermediate nodes are trusted …

We study the mergeability of data summaries. Informally speaking, mergeability requires
that, given two summaries on two datasets, there is a way to merge the two summaries into a …

Emerging large-scale monitoring applications require continuous tracking of complex
dataanalysis queries over collections of physicallydistributed streams. Effective solutions …

We consider the problem of tracking quantiles and range countings in wireless sensor
networks. The quantiles and range countings are two important aggregations to characterize …

Outlier detection has many important applications in sensor networks, eg, abnormal event
detection, animal behavior change, etc. It is a difficult problem since global information about …