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Capacity- and energy-aware activation of sensor nodes for area phenomenon reproduction using wireless network transport

Capacity- and energy-aware activation of sensor nodes for area phenomenon reproduction using... Capacity- and Energy-Aware Activation of Sensor Nodes for Area Phenomenon Reproduction Using Wireless Network Transport XIAOLONG HUANG and IZHAK RUBIN, University of California, Los Angeles We consider a sensor network involving sensors placed in specific locations. An area phenomenon is detected and tracked by activated sensors. The area phenomenon is modeled to consist of K spatially distributed point phenomena. The activated sensors collect data samples characterizing the parameters of the involved point phenomena. They compress observed data readings and transport them to a processing center. The center processes the received data to derive estimates of the point phenomena's parameters. Our sensing stochastic process models account for distance-dependent observation noise perturbations as well as noise correlations. At the processing center, sample mean calculations are used to derive the estimates of the underlying area phenomenon's parameters. We develop computationally efficient algorithms to determine the specific set of sensors for activation under capacity and energy resource constraints so that a sufficiently low reproduction distortion level is attained. We derive lower bounds on the realizable levels of the distortion measure. Using illustrative cases, we demonstrate one of our algorithms to yield distortion levels that are very close to the lower bound, while http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Sensor Networks (TOSN) Association for Computing Machinery

Capacity- and energy-aware activation of sensor nodes for area phenomenon reproduction using wireless network transport

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2013 by ACM Inc.
ISSN
1550-4859
DOI
10.1145/2489253.2489269
Publisher site
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Abstract

Capacity- and Energy-Aware Activation of Sensor Nodes for Area Phenomenon Reproduction Using Wireless Network Transport XIAOLONG HUANG and IZHAK RUBIN, University of California, Los Angeles We consider a sensor network involving sensors placed in specific locations. An area phenomenon is detected and tracked by activated sensors. The area phenomenon is modeled to consist of K spatially distributed point phenomena. The activated sensors collect data samples characterizing the parameters of the involved point phenomena. They compress observed data readings and transport them to a processing center. The center processes the received data to derive estimates of the point phenomena's parameters. Our sensing stochastic process models account for distance-dependent observation noise perturbations as well as noise correlations. At the processing center, sample mean calculations are used to derive the estimates of the underlying area phenomenon's parameters. We develop computationally efficient algorithms to determine the specific set of sensors for activation under capacity and energy resource constraints so that a sufficiently low reproduction distortion level is attained. We derive lower bounds on the realizable levels of the distortion measure. Using illustrative cases, we demonstrate one of our algorithms to yield distortion levels that are very close to the lower bound, while

Journal

ACM Transactions on Sensor Networks (TOSN) – Association for Computing Machinery

Published: Jul 1, 2013

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