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14216 A Distributed and Self-Organizing Scheduling Algorithm for Energy-Efficient Data Aggregation in Wireless Sensor Networks
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Chatterjea, S. and Nieberg, T. and Meratnia, N. and Havinga, P.J.M. (2008) A Distributed and Self-Organizing Scheduling Algorithm for Energy-Efficient Data Aggregation in Wireless Sensor Networks. ACM Transactions on Sensor Networks, 4 (4). pp. 1-41. ISSN 1550-4859 *** ISI Impact 1,448 ***

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Official URL: http://dx.doi.org/10.1145/1387663.1387666

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Abstract

Wireless sensor networks (WSNs) are increasingly being used to monitor various parameters in a
wide range of environmental monitoring applications. In many instances, environmental scientists
are interested in collecting raw data using long-running queries injected into a WSN for analyzing
at a later stage, rather than injecting snap-shot queries containing data-reducing operators (e.g.,
MIN, MAX, AVG) that aggregate data. Collection of raw data poses a challenge to WSNs as very
large amounts of data need to be transported through the network. This not only leads to high
levels of energy consumption and thus diminished network lifetime but also results in poor data
quality as much of the data may be lost due to the limited bandwidth of present-day sensor nodes.
We alleviate this problem by allowing certain nodes in the network to aggregate data by taking advantage
of spatial and temporal correlations of various physical parameters and thus eliminating
the transmission of redundant data. In this article we present a distributed scheduling algorithm
that decides when a particular node should perform this novel type of aggregation. The scheduling
algorithm autonomously reassigns schedules when changes in network topology, due to failing or
newly added nodes, are detected. Such changes in topology are detected using cross-layer information
from the underlying MAC layer. We first present the theoretical performance bounds of our
algorithm. We then present simulation results, which indicate a reduction in message transmissions
of up to 85% and an increase in network lifetime of up to 92% when compared to collecting
raw data. Our algorithm is also capable of completely eliminating dropped messages caused by
buffer overflow.

Item Type:Article
Research Group:EWI-PS: Pervasive Systems
Research Program:CTIT-WiSe: Wireless and Sensor Systems
Research Project:e-SENSE: Capturing Ambient Intelligence for Mobile Communications through Wireless Sensor Networks, SENSEI: Integrating the PhySical with the Digital World of the Network of the Future
ID Code:14216
Status:Published
Deposited On:20 November 2008
Refereed:Yes
International:Yes
ISI Impact Factor:1,448
More Information:statisticsmetis

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