Database Integration using Heterogeneous Sources in Wireless Sensor Networks - PowerPoint PPT Presentation

Loading...

PPT – Database Integration using Heterogeneous Sources in Wireless Sensor Networks PowerPoint presentation | free to download - id: 68322f-MTcyN



Loading


The Adobe Flash plugin is needed to view this content

Get the plugin now

View by Category
About This Presentation
Title:

Database Integration using Heterogeneous Sources in Wireless Sensor Networks

Description:

Dr. Longzhuang Li Faculty Mentor Texas A&M University - Corpus Christi Blake Burns Texas A&M University - Corpus Christi Anne Edmundson University at Buffalo * – PowerPoint PPT presentation

Number of Views:5
Avg rating:3.0/5.0
Date added: 24 October 2019
Slides: 34
Provided by: AnnieEd
Category:

less

Write a Comment
User Comments (0)
Transcript and Presenter's Notes

Title: Database Integration using Heterogeneous Sources in Wireless Sensor Networks


1
Dr. Longzhuang Li Faculty Mentor Texas AM
University - Corpus Christi
Blake Burns Texas AM University - Corpus
Christi Anne Edmundson University at Buffalo
1
2
Overview
  • Abstract
  • Background
  • Objective
  • Red Tide
  • Importance of our Research
  • Approach
  • Project Implementation Details
  • Challenges
  • Future Works
  • References

2
2
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
  • wind
  • precipitation
  • sun light intensity
  • chlorophyll concentration
  • dissolved oxygen
  • dissolved nitrogen and phosphorus
  • chemical oxygen demand
  • temperature
  • salinity
  • pH
  • water transparency
  • tidal currents

3
3
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

4
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
5
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

6
6
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.

7
7
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.

8
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

9
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

10
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

11
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.

12
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.

13
13
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.

14
14
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.

15
16
Approach (part 4/5)
?
?
End Users
Applications
Global Uniform Interface
TinyDB 3
TinyDB 2
TinyDB 1
WSN or TOSSIM
17
Approach (part 5/5)
TinyDB GUI
JDBC
TinyDB Client API
DBMS
PC side
0
Mote side
0
TinyDB query processor
2
1
3
8
4
5
6
Sensor network
7
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

18
18
19
The Interface (part 1/3)
  • Main GUI Interface
  • Select which type of querying to do

19
19
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).

20
20
21
21
21
22
If there were attributes selected then the below
window appears displaying the data from the
database for the selected attributes
If no attributes were selected the window below
appears
22
22
23
23
23
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).

24
24
25
25
25
26
26
27
27
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.

28
28
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

29
29
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

30
30
31
References
  • S. Chawathe et al. The TSIMMIS Approach to
    Mediation Data Models and Languages. In Proc.
    10th 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 5th 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.

31
31
32
Acknowledgments
  • Dr. Dulal Kar
  • Dr. Longzhuang Li
  • Dr. Ahmed Mahdy
  • Huy Tran
  • Bhanu Kamapantula
  • Tinara Hendrix and Ashley Munoz
  • National Science Foundation

32
33

Questions?
33
33
About PowerShow.com