Resource-constrained Data Collection and Fusion for Identifying Weak Distributed Patterns in Networks
Abstract
This project addressed the problems of detection, localization and estimation of weak and distributed patterns of activation in a large-scale network given access to direct, compressive and adaptive noisy node measurements. Precise information-theoretic limits were identified for these problems that provide necessary conditions on how the signal-to-noise ratio required scales as a function of the number of measurements, the graph size, connectivity and properties such as cut-size of the activated vertices, under a graph-structured normal means model. By leveraging highly inter-disciplinary tools from machine learning, statistics, signal processing and optimization, fast methods were developed that nearly achieve the information-theoretic limits, for general graph structures and classes of activation patterns. Development of such state-of-the-art methods that are both computationally and statistically efficient is crucial to advance AFOSR's ability to monitor, understand and secure modern large-scale networks that are vulnerable to covert attacks.
Document Details
- Document Type
- Technical Report
- Publication Date
- Oct 15, 2013
- Accession Number
- ADA591821
Entities
People
- Aarti Singh
Organizations
- Carnegie Mellon University