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Blake BurnsTexas A&M University - Corpus Christi

Anne EdmundsonUniversity at Buffalo

Dr. Longzhuang LiFaculty MentorTexas A&M University - Corpus Christi

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OverviewAbstractBackgroundObjectiveRed TideImportance of our ResearchApproachProject Implementation DetailsChallengesFuture WorksReferences

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AbstractUsing 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

demandtemperaturesalinitypHwater transparencytidal currents

windprecipitationsun light intensitychlorophyll

concentrationdissolved oxygendissolved nitrogen

and phosphorus

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Background (part 1/2)

Wireless Sensor NetworksConsists of spatially distributed autonomous sensors to

cooperatively monitor physical or environmental conditions, such as temperature, sound, vibration, pressure, motion or pollutants.

TinyOSOperating system for wireless embedded sensor

networksMinimizes code size because of memory constraints

TinyDBQuery processing system used on network of TinyOS

sensorsGiven a specific query, TinyDB collects data from sensor

nodesTOSSIM

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 dataWireless Sensor Network

applications:Environmental

Marine monitoringLandslide detection

MedicalMonitor vital signs

MilitarySmart UniformsEvent monitoring for enemy detection 5

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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)

ConsequencesDisturbs marine ecosystemAffects fishes, oysters, mussels and whelksSignificant because humans consume them

Existing approachSatellite imagerySatellites only see ocean surfaceWeather prevents frequent coverageClouds and fog obscure visible and infrared

dataExpensive for environmental monitoring

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Red Tide (part 3/4)

Why wireless sensor networks?Real-time monitoringCollects surface and sub-surface informationNot too expensiveCapable of remote monitoring in any

environment

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Red Tide (part 4/4)

Predicting red tideMeasure temperature, dissolved oxygen

content, salinityAlgae absorb oxygen so low levels of oxygen

show possible red tidesVariations in temperature are observedMeasure 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 APIDBMS

Sensor network

Approach (part 5/5)

TinyDB query processor

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4

0

1

5

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JDBC

Mote side

PC side

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Project Implementation Details

Three separate main componentsAttribute specific queryMap 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 InterfaceSelect which type of querying to do

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The Interface (part 2/3)

Attribute specific querySelect 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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ChallengesTook 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 dataEven still it will only generate one custom

attribute per run through.

TinyDB would not store dataWe 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 interfaceGather data from TinyDBs as well as other

databasesSelect 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 internetAllows for easier access

Deploy application onto a smart phoneInformation 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. 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.

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Acknowledgments

Dr. Dulal KarDr. Longzhuang LiDr. Ahmed MahdyHuy TranBhanu KamapantulaTinara Hendrix and Ashley MunozNational Science Foundation

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Questions?