OPTIMAL MONITORING OF SENSOR NETWORKS
Abstract
Sensor networks form a key part of the growing infrastructure for making informed decisions concerning available and potential data. Data can take many forms (e.g., telemetry, environmental, geostatistical, financial, etc.), and in all cases, a potentially massive amount of data can be collected, and then used to make decisions about safety, for example. Networks have limited bandwidth, and there are enormous computational challenges for rapidly processing data and even storing it. Because of these challenges, it is very important to identify the most important sources-locations for monitoring, so that we can make optimal decisions. As sensor networks become larger and larger it becomes more important to determine a manageable subset of sensors from which to collect and analyze data for the basis of making decisions. The Maximum Entropy Sampling Problem provides a mathematically rigorous, implementable framework for the problem of optimally selecting such a subset sensors, or more general data sources, that is independent of the application domain.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Jan 14, 2022
- Source ID
- FA95501910175
Entities
People
- Jon Lee
Organizations
- Air Force Office of Scientific Research
- Board of Regents of the University of Michigan
- United States Air Force