WSN Summer Project Demo Scenario

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WSN Summer ProjectWSN Summer Project Demo Scenario Demo Scenario

<Draft version><Draft version>

Skövde 2008Skövde 2008

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Shows features (legend)Shows features (legend)

Real-time (RT)Real-time (RT)

Fusion (Fus)Fusion (Fus)

Database, Replication (DB)Database, Replication (DB)

Scalability, ViFuR (VF)Scalability, ViFuR (VF)

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Fire fighting scenario or Fire fighting scenario or Battlefield game?Battlefield game?

The scenario described can be mapped to The scenario described can be mapped to alternative real scenarios – ”imagine!”alternative real scenarios – ”imagine!”

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Scenario featuresScenario features

Persons move in a watch area. They are light-sensed as ’red’ or Persons move in a watch area. They are light-sensed as ’red’ or ’blue’ people. Their locations are reported into a database, from ’blue’ people. Their locations are reported into a database, from where their tracks can be extracted and also predicted by using where their tracks can be extracted and also predicted by using fusion (e.g. Kalman filters). Multiple red people may be separated by fusion (e.g. Kalman filters). Multiple red people may be separated by their expected track (assumed being not too close).their expected track (assumed being not too close).Persons act in the environment by clapping (single/double clap Persons act in the environment by clapping (single/double clap signature) , and those events are reported, with signatures, to the signature) , and those events are reported, with signatures, to the connected database node.connected database node.Disjoint sensor sub nets report their area to separate database Disjoint sensor sub nets report their area to separate database nodes. People tracking and event localization is done at a fusion nodes. People tracking and event localization is done at a fusion node, where database replication provides data for the entire area. node, where database replication provides data for the entire area. Tracks and actions are visualized at multiple client nodes, which Tracks and actions are visualized at multiple client nodes, which have replicas of the visualization data (segment) of the database.have replicas of the visualization data (segment) of the database.

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Features of the ScenarioFeatures of the Scenario

Sensors: Tracking by using visual features (color), event detection and Sensors: Tracking by using visual features (color), event detection and localization by using acoustic features (sound signatures)localization by using acoustic features (sound signatures)Events are localized by using sound timestamps from multiple sensors Events are localized by using sound timestamps from multiple sensors (RT, Fus)(RT, Fus)Action signatures by single or double claps. This emulates processing Action signatures by single or double claps. This emulates processing for signatures (RT, Fus)for signatures (RT, Fus)Tracks + localized events are visualized on multiple displays (DB, Rep)Tracks + localized events are visualized on multiple displays (DB, Rep)Tracking by using the logged visual trace, short term database time Tracking by using the logged visual trace, short term database time series, multiple red people may be distinguished by their physical move series, multiple red people may be distinguished by their physical move limitations (DB, Fus)limitations (DB, Fus)Data from regions of sensors (can’t communicate in between) are Data from regions of sensors (can’t communicate in between) are replicated (VF, DB)replicated (VF, DB)

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Sensor setupSensor setup

Acoustic: Room has 5 MICAz+MTS310, Acoustic: Room has 5 MICAz+MTS310, using the microphone (sound level)using the microphone (sound level)

Visual: Multiple TelosB, using the light Visual: Multiple TelosB, using the light sensors with ”directed sight” (tube) and sensors with ”directed sight” (tube) and colored light sensing (filter) (or distinct colored light sensing (filter) (or distinct gray scale differences). gray scale differences). ””Colored jackets” worn by people?Colored jackets” worn by people?

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Sensor placementSensor placementS S

S S

S

L L L

L L L

L L L

L L L

LL S= Colored light sensor = Sound sensor

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Technical needsTechnical needs

Move-events + classification from sensorsMove-events + classification from sensorsAction-events + signature from sensorsAction-events + signature from sensorsFeed of sensor data + (re-)configuration. Feed of sensor data + (re-)configuration. Support request for 1) - single sensor input 2) - periodic sensor input Support request for 1) - single sensor input 2) - periodic sensor input 3) - sensor input (single or periodic) in response to event (including 3) - sensor input (single or periodic) in response to event (including timer), all sensor input includes a vector of all sensor readings in the timer), all sensor input includes a vector of all sensor readings in the sensor node sensor node (should we prepare for the possibility of requesting a subset, or (should we prepare for the possibility of requesting a subset, or adding additional sensors?)adding additional sensors?)(should we prepare for the need to buffer multiple readings and (should we prepare for the need to buffer multiple readings and send as single message to save bandwidth clutter?)send as single message to save bandwidth clutter?)Database replication, single master, but extendable for PRiDeDatabase replication, single master, but extendable for PRiDe

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Scenario, decision on Friday 11thScenario, decision on Friday 11th

Consider alternative scenario descriptionsConsider alternative scenario descriptions

Using the same technical features to Using the same technical features to describe alternative scenarios:describe alternative scenarios:

Map application to demo: ”Imagine!”Map application to demo: ”Imagine!”

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Impl:Mote communicationImpl:Mote communication

1.1. Tmote Connect approachTmote Connect approach

2.2. TelosB gateway TelosB gateway Tablet USB Tablet USB

3.3. Tinymote gateway Tinymote gateway Tablet SD slot Tablet SD slot

4.4. Bluetooth gateway Bluetooth gateway Tablet (port?) Tablet (port?)

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Impl:DatabaseImpl:Database

1.1. Store sensor data in local BDB databaseStore sensor data in local BDB database

2.2. Visualize sensor dataVisualize sensor data

3.3. Replicate sensor data to other nodesReplicate sensor data to other nodes

4.4. Visualization sensor data at other DB Visualization sensor data at other DB nodenode