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

Applied Mathematics and Statistics

Stony Brook University

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

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
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

Conference

Conference	7th IEEE International Conference on Distributed Computing in Sensor Systems, DCOSS'11
Country/Territory	Spain
City	Barcelona
Period	06/27/11 → 06/29/11

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

@inproceedings{5e3eb6953fef4ceca81b75ea2647973e,

title = "Distributed localization and clustering using data correlation and the Occam's razor principle",

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.",

author = "Agarwal, \{Pankaj K.\} and Alon Efrat and Chris Gniady and Mitchell, \{Joseph S.B.\} and Valentin Polishchuk and Sabhnani, \{Girishkumar R.\}",

year = "2011",

doi = "10.1109/DCOSS.2011.5982164",

language = "English",

isbn = "9781457705137",

series = "2011 International Conference on Distributed Computing in Sensor Systems and Workshops, DCOSS'11",

booktitle = "2011 International Conference on Distributed Computing in Sensor Systems and Workshops, DCOSS'11",

note = "7th IEEE International Conference on Distributed Computing in Sensor Systems, DCOSS'11 ; Conference date: 27-06-2011 Through 29-06-2011",

}

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, 06/27/11. 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 › peer-review

TY - GEN

T1 - Distributed localization and clustering using data correlation and the Occam's razor principle

AU - Agarwal, Pankaj K.

AU - Efrat, Alon

AU - Gniady, Chris

AU - Mitchell, Joseph S.B.

AU - Polishchuk, Valentin

AU - Sabhnani, Girishkumar R.

PY - 2011

Y1 - 2011

N2 - 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.

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.

UR - https://www.scopus.com/pages/publications/80052480055

U2 - 10.1109/DCOSS.2011.5982164

DO - 10.1109/DCOSS.2011.5982164

M3 - Conference contribution

SN - 9781457705137

T3 - 2011 International Conference on Distributed Computing in Sensor Systems and Workshops, DCOSS'11

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

T2 - 7th IEEE International Conference on Distributed Computing in Sensor Systems, DCOSS'11

Y2 - 27 June 2011 through 29 June 2011

ER -

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

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

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