1
Blake Burns
Texas A&M University
-
Corpus Christi
Anne Edmundson
University at Buffalo
Dr. Longzhuang Li
Faculty Mentor
Texas A&M University
-
Corpus Christi
2
2
Overview
Abstract
Background
Objective
Red Tide
Importance of our Research
Approach
Project Implementation Details
Challenges
Future Works
References
3
3
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
4
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
5
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
5
6
6
Objective
Our goal
to create a
uniform interface
to access to
multiple autonomous heterogeneous structured
data sources that will help to predict red tide
7
7
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.
8
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.
9
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
10
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
11
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
12
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.
13
13
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.
14
14
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.
15
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.
16
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
0
4
0
1
5
2
6
3
7
JDBC
Mote side
PC side
8
18
18
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
19
19
The Interface
(part 1/3)
Main GUI Interface
Select which type of querying to do
20
20
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).
21
21
22
22
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
23
23
24
24
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).
25
25
26
27
28
28
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.
29
29
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
30
30
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
31
31
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.
32
Acknowledgments
Dr. Dulal Kar
Dr. Longzhuang Li
Dr. Ahmed Mahdy
Huy Tran
Bhanu Kamapantula
Tinara Hendrix and Ashley Munoz
National Science Foundation
33
33
Questions?
- Home
- Browse
- All Categories
- AI and Robotics
- Biotechnology
- Clean Technology
- Data Management
- Electronics - Devices
- Enterprise Applications
- Entertainment
- Internet and Web Development
- Management
- Mechanics
- Mobile - Wireless
- Networking and Communications
- Oil and Offshore
- Security
- Semiconductor
- Servers
- Software and s/w Development
- Storage
- Telecommunications
- Urban and Civil
- Community
- Upload
Comments 0
Log in to post a comment