NDLI: Sensor Selection for Minimizing Worst-Case Prediction Error

Content Provider	IEEE Xplore Digital Library
Author	Das, A. Kempe, D.
Copyright Year	2008
Description	Author affiliation: Southern California Univ., Los Angeles, CA (Das, A.; Kempe, D.)
Abstract	We study the problem of choosing the "best" subset of k sensors 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 the values at two sensors can differ by at most a monotonically increasing, concave function of their distance. The goal is then to select a subset of sensors so as to minimize the prediction error, assuming that the actual values at unsampled sensors are worst-case subject to the constraints imposed by their distances from sampled sensors. Even selecting sensors for the optimal prediction of the mean, maximum or minimum is NP-hard; we present approximation algorithms to select near-optimal subsets of k sensors that minimize the worst-case prediction error. In general, we show that for any aggregate function satisfying certain concavity, symmetry and monotonicity conditions, the sensor selection problem can be modeled as a k-median clustering problem, and solved using efficient approximation algorithms designed for k-median clustering. Our theoretical results are complemented by experiments on two real-world sensor data sets; our experiments confirm that our algorithms lead to prediction errors that are usually less than the (normalized) standard deviation of the test data, using only around 10% of the sensors.
Starting Page	97
Ending Page	108
File Size	427593
Page Count	12
File Format	PDF
ISBN	9780769531571
DOI	10.1109/IPSN.2008.40
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-04-22
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Chemical sensors Extraterrestrial phenomena sensor selection Sensor phenomena and characterization Extraterrestrial measurements spatial correlation Temperature sensors estimation Aggregates sensor placement Clustering algorithms Approximation algorithms Biosensors Monitoring
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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