Distributed localization and clustering using data correlation and the Occam's razor principle

Pankaj K. Agarwal; Alon Efrat; Chris Gniady; Joseph S.B. Mitchell; Valentin Polishchuk; Girishkumar R. Sabhnani

doi:10.1109/DCOSS.2011.5982164

Distributed localization and clustering using data correlation and the Occam's razor principle

Pankaj K. Agarwal, Alon Efrat, Chris Gniady, Joseph S.B. Mitchell, Valentin Polishchuk, Girishkumar R. Sabhnani

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Scopus citations

Abstract

We present a distributed algorithm for computing a combined solution to three problems in sensor networks: localization, clustering, and sensor suspension. Assuming that initially only a rough approximation of the sensor positions is known, we show how one can use sensor measurements to refine the set of possible sensor locations, to group the sensors into clusters with linearly correlated measurements, and to decide which sensors may suspend transmission without jeopardizing the consistency of the collected data. Our algorithm applies the "Occam's razor principle" by computing a "simplest" explanation for the data gathered from the network. We also present centralized algorithms, as well as efficient heuristics.

Original language	English (US)
Title of host publication	2011 International Conference on Distributed Computing in Sensor Systems and Workshops, DCOSS'11
DOIs	https://doi.org/10.1109/DCOSS.2011.5982164
State	Published - 2011
Event	7th IEEE International Conference on Distributed Computing in Sensor Systems, DCOSS'11 - Barcelona, Spain Duration: Jun 27 2011 → Jun 29 2011

Publication series

Name	2011 International Conference on Distributed Computing in Sensor Systems and Workshops, DCOSS'11

Other

Other	7th IEEE International Conference on Distributed Computing in Sensor Systems, DCOSS'11
Country/Territory	Spain
City	Barcelona
Period	6/27/11 → 6/29/11

ASJC Scopus subject areas

Computer Networks and Communications
Control and Systems Engineering

Access to Document

10.1109/DCOSS.2011.5982164

Cite this

Agarwal, P. K., Efrat, A., Gniady, C., Mitchell, J. S. B., Polishchuk, V., & Sabhnani, G. R. (2011). Distributed localization and clustering using data correlation and the Occam's razor principle. In 2011 International Conference on Distributed Computing in Sensor Systems and Workshops, DCOSS'11 Article 5982164 (2011 International Conference on Distributed Computing in Sensor Systems and Workshops, DCOSS'11). https://doi.org/10.1109/DCOSS.2011.5982164

Distributed localization and clustering using data correlation and the Occam's razor principle. / Agarwal, Pankaj K.; Efrat, Alon ; Gniady, Chris et al.
2011 International Conference on Distributed Computing in Sensor Systems and Workshops, DCOSS'11. 2011. 5982164 (2011 International Conference on Distributed Computing in Sensor Systems and Workshops, DCOSS'11).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Agarwal, PK, Efrat, A , Gniady, C, Mitchell, JSB, Polishchuk, V & Sabhnani, GR 2011, Distributed localization and clustering using data correlation and the Occam's razor principle. in 2011 International Conference on Distributed Computing in Sensor Systems and Workshops, DCOSS'11., 5982164, 2011 International Conference on Distributed Computing in Sensor Systems and Workshops, DCOSS'11, 7th IEEE International Conference on Distributed Computing in Sensor Systems, DCOSS'11, Barcelona, Spain, 6/27/11. https://doi.org/10.1109/DCOSS.2011.5982164

Agarwal PK, Efrat A , Gniady C, Mitchell JSB, Polishchuk V, Sabhnani GR. Distributed localization and clustering using data correlation and the Occam's razor principle. In 2011 International Conference on Distributed Computing in Sensor Systems and Workshops, DCOSS'11. 2011. 5982164. (2011 International Conference on Distributed Computing in Sensor Systems and Workshops, DCOSS'11). doi: 10.1109/DCOSS.2011.5982164

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abstract = "We present a distributed algorithm for computing a combined solution to three problems in sensor networks: localization, clustering, and sensor suspension. Assuming that initially only a rough approximation of the sensor positions is known, we show how one can use sensor measurements to refine the set of possible sensor locations, to group the sensors into clusters with linearly correlated measurements, and to decide which sensors may suspend transmission without jeopardizing the consistency of the collected data. Our algorithm applies the {"}Occam's razor principle{"} by computing a {"}simplest{"} explanation for the data gathered from the network. We also present centralized algorithms, as well as efficient heuristics.",

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AU - Agarwal, Pankaj K.

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AB - We present a distributed algorithm for computing a combined solution to three problems in sensor networks: localization, clustering, and sensor suspension. Assuming that initially only a rough approximation of the sensor positions is known, we show how one can use sensor measurements to refine the set of possible sensor locations, to group the sensors into clusters with linearly correlated measurements, and to decide which sensors may suspend transmission without jeopardizing the consistency of the collected data. Our algorithm applies the "Occam's razor principle" by computing a "simplest" explanation for the data gathered from the network. We also present centralized algorithms, as well as efficient heuristics.

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Distributed localization and clustering using data correlation and the Occam's razor principle

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