Sensor Selection for Minimizing Worst-Case Prediction Error

We study the problem of choosing the "best'' subset of ksensors to sample from among a sensor deployment of n > k sensors, in order to predict aggregate functions over all the sensor values. The sensor data being measured are assumed to be spatially correlated, in the sense that th...

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Vydáno v:	Information Processing in Sensor Networks; Proceedings: International Conference on Information Processing in Sensor Networks, 2008: St. Louis, MO s. 97 - 108
Hlavní autoři:	Das, Abhimanyu, Kempe, David
Médium:	Konferenční příspěvek
Jazyk:	angličtina
Vydáno:	Washington, DC, USA IEEE Computer Society 22.04.2008 IEEE
Edice:	ACM Conferences
Témata:	Aggregates Approximation algorithms Biosensors Chemical sensors Clustering algorithms Computer systems organization > Dependable and fault-tolerant systems and networks > Availability Computer systems organization > Dependable and fault-tolerant systems and networks > Maintainability and maintenance Computer systems organization > Dependable and fault-tolerant systems and networks > Reliability estimation Extraterrestrial measurements Extraterrestrial phenomena General and reference > Cross-computing tools and techniques > Measurement General and reference > Cross-computing tools and techniques > Metrics General and reference > Cross-computing tools and techniques > Reliability Hardware > Communication hardware, interfaces and storage > Signal processing systems Monitoring Sensor phenomena and characterization sensor placement sensor selection spatial correlation Temperature sensors estimation spatial correlation sensor selection sensor placement
ISBN:	9780769531571, 0769531571
On-line přístup:	Získat plný text
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