Workshop on Sensor Fusion/Data Integration for CBR Defense Salt Lake City, Utah 24-25 September 2003...

Workshop on Sensor Fusion/Data Integration for Workshop on Sensor Fusion/Data Integration for CBR DefenseCBR Defense

Salt Lake City, Utah 24-25 September 2003Salt Lake City, Utah 24-25 September 2003

Atmospheric Modeling, Data Assimilation, Atmospheric Modeling, Data Assimilation, and Sensor Networks for Detection and and Sensor Networks for Detection and

Analysis of Airborne Agent Spread. Analysis of Airborne Agent Spread.

Part II: Sensor Networks and Data Stream Part II: Sensor Networks and Data Stream IntegrationIntegration

Dr. Michael Murphy

Dr. Boris Khattatov

Prof. Leo Franca



CO emitted by factories in Colorado on CO emitted by factories in Colorado on December 7 2000December 7 2000



Wireless Sensor NetworksWireless Sensor Networks

• Low cost per unit.Low cost per unit.• Amenable to “Deploy ‘em and leave ‘em” Amenable to “Deploy ‘em and leave ‘em”

situations” situations” e.g.,e.g., air dropping into a hot zone. air dropping into a hot zone.• Nanotechnology-based sensors are being Nanotechnology-based sensors are being

developed that use far less power than developed that use far less power than traditional sensors.traditional sensors.

• However, sensors can produce signals with However, sensors can produce signals with high signal-to-noise ratios.high signal-to-noise ratios.

• Have sensitivity to biases and bias drifts.Have sensitivity to biases and bias drifts.• Have severe power and communications Have severe power and communications

constraints.constraints.



Sensor-Stream AnalysisSensor-Stream Analysis

• New methods for calculating sensor bias New methods for calculating sensor bias using data assimilation techniques.using data assimilation techniques.

• New programming methods for sensor New programming methods for sensor networks to adapt to communications and networks to adapt to communications and power limitations.power limitations.

• Adding novel methods for “event Adding novel methods for “event triggering” and for summarizing massive triggering” and for summarizing massive data streams.data streams.



Sensor Network ApplicationSensor Network Application

• A contamination event occurs, resulting in a A contamination event occurs, resulting in a developing plume. developing plume.

• A semi-autonomous swarm of actuated A semi-autonomous swarm of actuated sensors is deployed to collect contaminant sensors is deployed to collect contaminant samples.samples.



Wireless Sensor NetsWireless Sensor Nets(from (from Kevin L. Moore, Director

Center for Self-Organizing and Intelligent SystemsUtah State University)



Wireless Sensor NetsWireless Sensor Nets(from (from Kevin L. Moore, Director

Center for Self-Organizing and Intelligent SystemsUtah State University)

Analysis Model



Experiments with Sensor StreamsExperiments with Sensor Streams

We have a simulation of an airborne We have a simulation of an airborne contaminant event and therefore can contaminant event and therefore can simulate sensor readings.simulate sensor readings.



Sensitivity and Selectivity IssuesSensitivity and Selectivity Issues



The Bias problem – Bias DriftsThe Bias problem – Bias Drifts



Solution, use model together with Solution, use model together with Kalman filter to compute biases.Kalman filter to compute biases.

( )t t x x K y HxT T 1( )t t

K B H HB H OT T -1 - ( ) t t t t B B B H HB H O HB

Here, y is the observations from the sensors. Here, y is the observations from the sensors. The operator H relates model observations The operator H relates model observations and biases from the previous time step. and biases from the previous time step.



Solution, use model together with Solution, use model together with Kalman filter to compute biases.Kalman filter to compute biases.

We used this method to successfully We used this method to successfully estimate satellite clock and receiver error. estimate satellite clock and receiver error.



Improving models and integrationImproving models and integration

• Integration of GIS into the plume model – Integration of GIS into the plume model – possibly incorporating terrain features possibly incorporating terrain features and buildings.and buildings.

• At what level should the model be At what level should the model be integrated with the sensor network and integrated with the sensor network and how can they be used?how can they be used?

• Correcting biases, identifying Correcting biases, identifying miscalibrated sensors, detecting miscalibrated sensors, detecting important events…important events…



TinyDB:Acquisitional Query Language TinyDB:Acquisitional Query Language For Sensor Networks (Madden For Sensor Networks (Madden et alet al))

• When should samples for a particular When should samples for a particular query be taken?query be taken?

• What sensor nodes have data relevant to a What sensor nodes have data relevant to a particular query?particular query?

• In what order should samples for this In what order should samples for this query be taken, and how should sampling query be taken, and how should sampling be interleaved with other operations?be interleaved with other operations?

• Is it worth expending computational power Is it worth expending computational power or bandwidth to process and relay a or bandwidth to process and relay a particular sample?particular sample?



Acquisitional Query Language For Acquisitional Query Language For Sensor Networks (Madden)Sensor Networks (Madden)

(Inputs)(Inputs)

• Power-consumption profile such as this one Power-consumption profile such as this one from [Mainwaring], might be given as follows: from [Mainwaring], might be given as follows:

• Topology of the sensor network is usually Topology of the sensor network is usually inferred upon initialization and updatedinferred upon initialization and updated..



Example Acquisitional QueriesExample Acquisitional Queries

• The basic query:The basic query:

SELECT nodeid, light, tempSELECT nodeid, light, temp

FROM sensorsFROM sensors

SAMPLE INTERVAL 1s FOR 10sSAMPLE INTERVAL 1s FOR 10s

• Consumes too much powerConsumes too much power



Better Acquisitional QueriesBetter Acquisitional Queries

• Landmark query: Output a stream of counts Landmark query: Output a stream of counts indicating the number of recent light readings indicating the number of recent light readings that were brighter that the current reading. that were brighter that the current reading.

• Sliding-window queries: Report the average Sliding-window queries: Report the average volume over the last 30 seconds once every 5 volume over the last 30 seconds once every 5 seconds, sampling once per second. seconds, sampling once per second.

• STOP-ON events. Continue reporting light STOP-ON events. Continue reporting light readings every second until the level falls readings every second until the level falls below a certain thresholdbelow a certain threshold



Event-Based Acquisitional QueriesEvent-Based Acquisitional Queries

• Event based queries: Report the average Event based queries: Report the average light and temperature level at sensors light and temperature level at sensors near a bird nest where a bird has just near a bird nest where a bird has just been detected. Every time a bird-detect been detected. Every time a bird-detect event occurs, the query is issued from the event occurs, the query is issued from the detecting node and the average light and detecting node and the average light and temperature are collected from nearby temperature are collected from nearby nodes once every 2 seconds for 30 nodes once every 2 seconds for 30 seconds. seconds.



Improving Acquisitional ProcessingImproving Acquisitional Processing

• TinyDB would benefit from an improved TinyDB would benefit from an improved means of sensor “performance” means of sensor “performance” description (sensitivity, selectivity, description (sensitivity, selectivity, stability, lifetime, ruggedness, etc.) This stability, lifetime, ruggedness, etc.) This would allow for more sophisticated query would allow for more sophisticated query processing. processing.



Sensor Response ExamplesSensor Response Examples



Improving Acquisitional ProcessingImproving Acquisitional Processing

• We’d like to investigate other event-We’d like to investigate other event-triggering algorithms. triggering algorithms.

• Assimilated models diverging from Assimilated models diverging from observationsobservations

• Wavelet techniquesWavelet techniques

• Streaming Singular Value DecompositionStreaming Singular Value Decomposition



Wavelet Analysis For Sensor Streams Wavelet Analysis For Sensor Streams (Mattias)(Mattias)

• Suppose we have a signal S = [2,2,7,9]Suppose we have a signal S = [2,2,7,9]

• We first average the cumulative frequencies, We first average the cumulative frequencies, pairwise to get the new lower-resolution signal pairwise to get the new lower-resolution signal [2,8].[2,8].

• We continue recursively until we get the We continue recursively until we get the average of [5].average of [5].

• To reconstruct the signal, we need To reconstruct the signal, we need detail detail coefficients – coefficients – With Haar Wavelets, we store the With Haar Wavelets, we store the pairwise distances of the original values [0,2] pairwise distances of the original values [0,2] and [6].and [6].



Wavelet Analysis For Sensor StreamsWavelet Analysis For Sensor Streams

• Suppose we have a signal S = [2,2,7,9]Suppose we have a signal S = [2,2,7,9]

• The wavelet transform, W is thus [5,6,0,2].The wavelet transform, W is thus [5,6,0,2].

• We can then reconstruct the signal using:We can then reconstruct the signal using:• S(0) = W(0) – ½ W(1) – ½ W(2)S(0) = W(0) – ½ W(1) – ½ W(2)• S(1) = W(0) – ½ W(1) + ½ W(2)S(1) = W(0) – ½ W(1) + ½ W(2)• S(2) = W(0) + ½ W(1) – ½ W(3)S(2) = W(0) + ½ W(1) – ½ W(3)• S(3) = W(0) + ½ W(1) + ½ W(3)S(3) = W(0) + ½ W(1) + ½ W(3)




• The advantage of using wavelets is that a The advantage of using wavelets is that a large number of detail coefficients are very large number of detail coefficients are very small in magnitude. small in magnitude.

• Truncating these coefficients introduces Truncating these coefficients introduces very small errors in the signal. (JPEG2000 very small errors in the signal. (JPEG2000 image compression works this way.)image compression works this way.)

• We can approximate the original data We can approximate the original data distribution efficiently by keeping only the distribution efficiently by keeping only the most significant coefficients.most significant coefficients.



Wavelet Analysis For Sensor StreamsWavelet Analysis For Sensor StreamsHistogramsHistograms




• Streaming wavelet coefficients are easy to Streaming wavelet coefficients are easy to compute.compute.

• Thus, we can track changes in significant Thus, we can track changes in significant wavelet coefficients very efficiently. wavelet coefficients very efficiently.

• This gives rise to event-detection methods. This gives rise to event-detection methods. Identifies spikes in the data. Identifies spikes in the data.

• Can also be used to support “aggregate” and Can also be used to support “aggregate” and “point” queries when searching in history.“point” queries when searching in history.



Streaming SVDStreaming SVD



Streaming SVDStreaming SVD

• Singular Value Decomposition has enjoyed a Singular Value Decomposition has enjoyed a wide-variety of data analysis applications for wide-variety of data analysis applications for static data sets (face recognition software, static data sets (face recognition software, search engines, pattern recognition.)search engines, pattern recognition.)

• In a plume-tracking scenario, small In a plume-tracking scenario, small eigenvalues would suggest that sensors are eigenvalues would suggest that sensors are not well-placed.not well-placed.

• There are new and promising algorithms for There are new and promising algorithms for computing SVD over data streams [M. Brand computing SVD over data streams [M. Brand 2003] [S. Guha 2003]. 2003] [S. Guha 2003].



Dual Space Algorithms for Plume Dual Space Algorithms for Plume trackingtracking

Guibas Guibas et alet al..

•The transformation between points and lines in the primal space The transformation between points and lines in the primal space and lines and points in the dual space.and lines and points in the dual space.




•A primal and dual space representation for a set of points and a A primal and dual space representation for a set of points and a line.line.




•A trace of a shadow passing over a sensor network. Far fewer A trace of a shadow passing over a sensor network. Far fewer sensors are used to track it. sensors are used to track it.




• The dual-space approach seems useful for The dual-space approach seems useful for Plume-tracking applications in sensor networks Plume-tracking applications in sensor networks but needs improvements.but needs improvements.

•It only works for shadow-detection and convex It only works for shadow-detection and convex plumes at the moment.plumes at the moment.

•We would like to investigate its performance We would like to investigate its performance with a model-based approach for activating with a model-based approach for activating sensors using TinyDB query framework.sensors using TinyDB query framework.



GPS Issues in Wireless Sensor NetworksGPS Issues in Wireless Sensor Networks

• For mobile or air-dropped sensors, knowing For mobile or air-dropped sensors, knowing

positions is crucial. positions is crucial. • However, a GPS chip on-board a fixed However, a GPS chip on-board a fixed

sensor can be helpful with synchronization sensor can be helpful with synchronization issues in communications protocols. issues in communications protocols. (Spread-spectrum in particular.)(Spread-spectrum in particular.)

• Low cost: $3.50/unit when purchased in Low cost: $3.50/unit when purchased in volume.volume.

• Currently unclear what’s the best way to Currently unclear what’s the best way to integrate GPS and maximize its benefits.integrate GPS and maximize its benefits.



Low-Power Communications ProtocolsLow-Power Communications Protocols

• Most sensor network protocols are designed Most sensor network protocols are designed so that the sensors do not require a so that the sensors do not require a controlling base station to function (e.g., controlling base station to function (e.g., directed diffusion).directed diffusion).

• In the case where base stations are fixed In the case where base stations are fixed and reliable, communications protocols and reliable, communications protocols could be improved by harnessing the base could be improved by harnessing the base station’s extra power and computational station’s extra power and computational ability to optimize routing, while at the ability to optimize routing, while at the same time remaining autonomous, if same time remaining autonomous, if necessary.necessary.



ConclusionsConclusions• TinyDB needs more robust methods for TinyDB needs more robust methods for

incorporating models into its reporting incorporating models into its reporting rules.rules.

• Query optimization is required for sensors Query optimization is required for sensors with complicated propertieswith complicated properties

• Low-power plume-tracking algorithms for Low-power plume-tracking algorithms for sensor networks need refinementsensor networks need refinement

• Streaming wavelet analysis and SVD could Streaming wavelet analysis and SVD could offer improved decision supportoffer improved decision support

• How to integrate GPS into sensor nodes?How to integrate GPS into sensor nodes?

Workshop on Sensor Fusion/Data Integration for CBR Defense Salt Lake City, Utah 24-25 September 2003...

Documents

Transcript of Workshop on Sensor Fusion/Data Integration for CBR Defense Salt Lake City, Utah 24-25 September 2003...