Energy-Aware Data Collection in Sensor Networks: A Localized Selective Sampling Approach
Mr. Bugra Gedik
Thursday, January 12, 2006
1:30PM - CSB-232
Abstract
One of the most prominent and comprehensive ways of data collection in
sensor networks is to periodically extract raw sensor readings. This way
of data collection enables complex analysis of data, which may not be
possible with in-network aggregation or query processing. However, this
flexibility in data analysis comes at the cost of power consumption. In
this talk, I describe selective sampling for energy-efficient periodic
data collection in sensor networks. The main idea behind selective
sampling is to use a dynamically changing subset of nodes as samplers
such that the sensor readings of sampler nodes are directly collected,
whereas the values of non-sampler nodes are predicted through the use of
probabilistic models that are locally and periodically constructed in an
in-network manner. Selective sampling can be effectively used to
increase the network lifetime while keeping quality of the collected
data high, in scenarios where either the spatial density of the network
deployment is superfluous relative to the required spatial resolution
for data analysis or certain amount of data quality can be traded off in
order to decrease the overall power consumption of the network. A unique
feature of our selective sampling mechanisms is the use of localized
schemes, as opposed to the protocols requiring global information, to
select and dynamically refine the subset of sensor nodes serving as
samplers and the model based value prediction for non-sampler nodes.
Such runtime adaptations create a data collection schedule which is
self-optimizing in response to changes in energy levels of nodes and
environmental dynamics.
Short Bio
Bugra Gedik is currently a Ph.D. candidate in the College of Computing
at the Georgia Institute of Technology. Prior to that, he received a
B.S. degree from the Computer Engineering and Information Science
department of Bilkent University, Turkey. As a member of the DiSL group
at the College of Computing, he has been conducting research on various
aspects of distributed data intensive systems, including peer-to-peer
computing, mobile data management and location-based services, and
sensor network computing. His research emphasis is on developing
system-level architectures and techniques to address scalability
problems in distributed information monitoring services. He is expected
to graduate with a Ph.D. degree in Spring 2006. His thesis is titled
"Scaling Continuous Query Services for Future Computing Platforms and
Applications".
Bugra has been the primary investigator of three projects in DiSL
research group, namely PeerCQ, MobiEyes, and SensorCQ. His research in
these projects resulted in numerous publications that have appeared in
various international conferences and journals on distributed systems
and databases. He was a PC member for the 22nd IEEE International
Conference on Data Engineering (ICDE 2006) and is the recipient of the
best paper award at the 23rd IEEE International Conference on
Distributed Computing Systems (ICDCS 2003). He has also been a
collaborator with IBM T.J. Watson Research Labs and holds or applied for
a number of patents on his work at IBM.
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