BINOCULAR: a system monitoring framework

Authors:
Fatih Emekci;Sezai E. Tuna;Divyakant Agrawal;Amr El Abbadi
Affiliations:
University of California Santa Barbara, Santa Barbara, CA;University of California Santa Barbara, Santa Barbara, CA;University of California Santa Barbara, Santa Barbara, CA;University of California Santa Barbara, Santa Barbara, CA
Venue:
DMSN '04 Proceeedings of the 1st international workshop on Data management for sensor networks: in conjunction with VLDB 2004
Year:
2004

Citing 6
Cited 4

Habitat monitoring: application driver for wireless communications technology

SIGCOMM LA '01 Workshop on Data communication in Latin America and the Caribbean
Linear Systems

Linear Systems
Wireless sensor networks for habitat monitoring

WSNA '02 Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications
The cougar approach to in-network query processing in sensor networks

ACM SIGMOD Record
Fjording the Stream: An Architecture for Queries Over Streaming Sensor Data

ICDE '02 Proceedings of the 18th International Conference on Data Engineering
TAG: a Tiny AGgregation service for Ad-Hoc sensor networks

OSDI '02 Proceedings of the 5th symposium on Operating systems design and implementationCopyright restrictions prevent ACM from being able to make the PDFs for this conference available for downloading

An Energy-Efficient Data Collection Framework for Wireless Sensor Networks by Exploiting Spatiotemporal Correlation

IEEE Transactions on Parallel and Distributed Systems
A distributed and self-organizing scheduling algorithm for energy-efficient data aggregation in wireless sensor networks

ACM Transactions on Sensor Networks (TOSN)
Delay-Constrained Optimal Data Aggregation in Hierarchical Wireless Sensor Networks

Mobile Networks and Applications
A two round reporting approach to energy efficient interpolation of sensor fields

SSTD'07 Proceedings of the 10th international conference on Advances in spatial and temporal databases

Quantified Score

Hi-index	0.00

Visualization

Abstract

Recent advances in hardware technology facilitate applications requiring a large number of sensor devices, where each sensor device has computational, storage, and communication capabilities. However these sensors are subject to certain constraints such as limited power, high communication cost, low computation capability, presence of noise in readings and low bandwidth. Since sensor devices are powered by ordinary batteries, power is a limiting resource in sensor networks and power consumption is dominated by communication. In order to reduce power consumption, we propose to use a linear model of temporal, spatial and spatio-temporal correlations among sensor readings. With this model, readings of all sensors can be estimated using the readings of a few sensors by using linear observers and multiple queries can be answered more efficiently. Since a small set of sensors are accessed for query processing, communication is significantly reduced. Furthermore, the proposed technique can also be beneficial at filtering out the noise which directly affects the accuracy of query results.