Database Integration using Heterogeneous Sources in Wireless ...

flangeeasyMobile - Wireless

Nov 21, 2013 (3 years and 6 months ago)

61 views

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Blake Burns

Texas A&M University
-

Corpus Christi

Anne Edmundson

University at Buffalo

Dr. Longzhuang Li

Faculty Mentor

Texas A&M University
-

Corpus Christi


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Overview


Abstract


Background


Objective


Red Tide


Importance of our Research


Approach


Project Implementation Details


Challenges


Future Works


References


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Abstract


Using marine wireless sensor networks to collect
meaningful data for future analysis in predicting
the presence of red tides.



Selected attributes for data collection:



chemical oxygen demand


temperature


salinity


pH


water transparency


tidal currents



wind


precipitation


sun light intensity


chlorophyll concentration


dissolved oxygen


dissolved nitrogen and
phosphorus


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Background
(part 1/2)


Wireless Sensor Networks


Consists of spatially distributed autonomous sensors to
cooperatively monitor physical or environmental conditions, such
as temperature, sound, vibration, pressure, motion or pollutants.


TinyOS


Operating system for wireless embedded sensor networks


Minimizes code size because of memory constraints


TinyDB


Query processing system used on network of TinyOS sensors


Given a specific query, TinyDB collects data from sensor nodes


TOSSIM


Simulates a complete TinyOS sensor network

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Background
(part 2/2)



Wireless Sensor Network purposes:


Equipped with capabilities to measure/change environment


Sense, process, and communicate data


Wireless Sensor Network applications:


Environmental


Marine monitoring


Landslide detection


Medical


Monitor vital signs


Military


Smart Uniforms


Event monitoring for enemy detection


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Objective



Our goal

to create a
uniform interface

to access to
multiple autonomous heterogeneous structured
data sources that will help to predict red tide

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Objective Details


We are creating an interface that will forward a
query to multiple databases

and provide results in
a uniform manner for the specified information
regarding red tides



There are multiple ways a user may define their
query: by attribute, by date, or by node.


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Red Tide
(part 1/4)


What is red tide?


Red tide is a naturally
-
occurring,
higher
-
than
-
normal concentration
of the microscopic algae
Karenia
brevis.


This organism produces a toxin that
paralyzes fish causes them to
suffocate. When red tide algae
reproduce in dense concentrations
they are visible as discolored
patches of ocean water, often
reddish in color.

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Red Tide
(part 2/4)


Consequences


Disturbs marine ecosystem


Affects fishes, oysters, mussels and whelks


Significant because humans consume them


Existing approach


Satellite imagery


Satellites only see ocean surface


Weather prevents frequent coverage


Clouds and fog obscure visible and infrared data


Expensive for environmental monitoring



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Red Tide
(part 3/4)


Why wireless sensor networks?


Real
-
time monitoring


Collects surface and sub
-
surface information


Not too expensive


Capable of remote monitoring in any environment


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Red Tide
(part 4/4)


Predicting red tide


Measure temperature, dissolved oxygen content, salinity


Algae absorb oxygen so low levels of oxygen show
possible red tides


Variations in temperature are observed


Measure chlorophyll which is the indicator of red tide
algae


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Importance of our Research


Our research will reduce the difficulty of
processing the coastal data through our uniform
interface that can access all the data related to the
coastal systems.



This will also help in detecting the presence of red
tide and predicting future red tides.

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Approach
(part 1/5)


Due to limited resources, these attributes were
simulated using TOSSIM on TinyOS.



TinyDB was utilized to filter, and aggregate data from
wireless sensor nodes.



Restrictions with TOSSIM only allow one attribute to
be simulated in each network; therefore, 12 networks
were simulated and three TinyDBs were used, each
holding data from four networks.

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Approach
(part 2/5)


This was only possible because each network had
data in common with the other networks: a node
ID.



Using the given framework for TinyDB, an
application was created that allowed the user these
capabilities.

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Approach
(part 3/5)


This interface acts as the communication between
the user and the wireless sensor network.



This implementation provides a user with a
flexible means to gather information from
multiple marine wireless sensor networks.

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End Users





Applications

Global Uniform


Interface

TinyDB

#3

TinyDB

#2

TinyDB

#1

Approach
(part 4/5)

WSN or
TOSSIM

TinyDB GUI

TinyDB Client API

DBMS

Sensor network

Approach
(part 5/5)

TinyDB query

processor

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0

1

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3

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JDBC

Mote side

PC side

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Project Implementation Details



Three separate main components



Attribute specific query


Map feature to query by specific node


[NYI] date range querying


Java based Graphical User Interfaces



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The Interface
(part 1/3)


Main GUI Interface


Select which type of querying to do




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The Interface
(part 2/3)


Attribute specific query


Select any number of attributes and get all
available data for those attributes



The following slides are some screenshots of the
attribute specific query in action using simulated
data (not accurate).



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If no
attributes
were selected
the window
below appears

If there were attributes selected
then the below window appears
displaying the data from the
database for the selected
attributes

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The Interface
(part 3/3)


Node specific query
(Map Interface)


Allows the user to select a node from a map to
query and retrieves the selected nodes data.



The following slides are of the node specific query
in action on simulated data (not accurate).



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Challenges


Took far too long to get to implementation


First 4
-
5 weeks only reading and software tweaking of
TOSSIM and TinyDB.



TOSSIM would not generate custom data


Even still it will only generate one custom attribute
per run through.



TinyDB would not store data


We had to modify the main program to store the data
in a file.





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Future Work
(part 1/2)


Incorporate time as a factor in submitting a query


Morning, afternoon, evening options



Increase flexibility of interface


Gather data from TinyDBs as well as other databases


Select multiple nodes from the map



Present the data in a more organized and logical
manner


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Future Work
(part 2/2)


Make the application available via the internet


Allows for easier access



Deploy application onto a smart phone


Information is always available to the user and more
accessible



Eventually deploy sensor nodes to collect data and
use the application for these nodes


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References


S. Chawathe et al.
The TSIMMIS Approach to Mediation: Data Models and
Languages.

In
Proc. 10
th

Meeting of the Information Processing Society of Japan,
1994.


Ibrahim, R. Kronsteiner, and G. Kotsis.
A Semantic Solution for Data Integration
in Mixed Sensor Networks.

Computer Communications
, 28(2005) 1564
-
1574.



A. Zafeiropoulos, N. Konstantinou, S. Arkoulis, D. Spanos, and N. Mitrou.
A
Semantic
-
based Architecture for Sensor Data Fusion.

In
the Second International
Conference on Mobile Ubiquitous Computing, Systems, Services, and
Technologies
, 2008.



S. Mihaylov, M. Jacob, Z. Ives, and S. Guha.
A Substrate for In
-
network Sensor
Data Integration.

In
the 5
th

Workshop on Data Management for Sensor Networks
,
2008.



I. Botan, Y. Cho, etc
. Design and Implementation of the Maxstream Federated
Stream Processing Architecture.

ETH Zurich, Technical Report, June 2009.



N. Tatbul.
Streaming Data Integration: Challenge and Opportunities.

In
the
Second International Workshop on New Trends in Information Integration

(NTII),
March 2010.


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Acknowledgments


Dr. Dulal Kar


Dr. Longzhuang Li


Dr. Ahmed Mahdy


Huy Tran


Bhanu Kamapantula


Tinara Hendrix and Ashley Munoz


National Science Foundation


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Questions?