Blake Burns Texas A&M University - Corpus Christi Anne Edmundson University at Buffalo

1. Dr. Longzhuang Li Faculty Mentor Texas A&M University - Corpus Christi Blake Burns Texas A&M 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 databasesand 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 #1 TinyDB #2 TinyDB #3 WSN or TOSSIM

17. Approach (part 5/5) TinyDB GUI JDBC TinyDB Client API DBMS PC side 0 Mote side 0 2 TinyDB query processor 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

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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

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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

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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