sensor nets the business of surveillance

Emerging Sensor Networks Can Make Sense for Your Business

Tech 2003, May 7, 2003Dr. Lisa Ann Osadciw from DREAMSNet –(Development and Research in Evolutionary Algorithms for MultiSensor Smart Networks)http://www.ecs.syr.edu/research/DREAMSNet/

Objective

Awareness of the new emerging sensor networksProvide you with a new understanding of how you may take advantage of these networks to improve your business

OverviewIntroduce Sensor NetworksSensor Network Applications

Problems Solved by Sensor NetworksMinimizing Sensor Network Costs

Business Advantages of Sensor Network TechnologyBusinesses for Sensor Networks TodayResearch Groups for Future Sensor NetworksDREAMSNet

Environmental Sensor Network ConceptLarge Intelligent Defense Sensor NetworksBiometric Security Sensor Networks

• Biometric Technology• Adaptive Multimodal Biometric Fusion Algorithm

Summary, Suggestions, and Questions

Sensor Network System View

Observation Space: nature, people, etc.

Information Space: aircraft detection and location, person identification, etc.

Sensors

Sensor Manager (AI)

Data Fusion Processing

Sensor Status

Controls

Measurements

Current Research Focus Areas

Database

Information Acquisition

Trusted Data Network

Sensors Intelligent Processing

Control and Response

Resource Management

DREAMSNet

Sensor Network Applications

Defense ApplicationsMedical Applications

Patient wears sensors recording or sending dataMonitor patients in the hospital

Weather ApplicationsIndoor Environmental ControlBuilding SecurityMore Applications will Emerge as Sensor and Network Technology Improves and Businesses

Problems Solved through Sensor Networks

distributed sensing observations (e.g. temperature, air flow, movement of objects)Situation assessment based on observationsAssessment accuracy is due to the set of observations rather than any single observationSensors cover a large regionMeasurements must be collected over a significant time duration. For example:

More than one patient must be monitored Whole building must be coveredStructure monitoring (airplanes, bridges, etc.)

Minimize Sensor Network System Cost

Current research area50%-90% of sensor cost lies in the wire so wireless communication reduces cost.Micro-electromechanical systems (MEMS) will allow systems to be embedded into ceiling fans, ceiling tiles, furniture, etc. (use of such will be discussed later). [Berkeley Center for Built Environments]Placement, quantity, and type of sensor

Just Some of the Many Advantages Possible Using Sensor Networks

Monitor manufacturing processes long distance and even over the internet anywhere in the world.Move the monitoring of any business function that is followed by a sensor or software to anywhere in the world.Add remote control to your business.Increase security without adding more keys or passwords. Increase it while removing keys through biometrics.Reduce operation costs through better heating control and more effective business operations.Track employees remotely for security or safety purposes.Improve employee satisfaction by increasing employee comfort as well as reduce illness.



Business Advantages of Sensor Network TechnologyBusinesses Supplying Sensor Networks TodayResearch Groups Improving Future Sensor NetworksDREAMSNet




Businesses

Xsilogy – software and hardware products to integrate with your own existing sensing devices (www.xsilogy.com)

Network Associates, Inc. (NAI) – battlefield network problems, SensIt - cryptography (robustness, weight, communications) (http://www.nai.com/common/media/nai/pdf/NAI-Labs-DSNS-1-5-01.pdf)

Intel – embedded sensors monitoring the well-being of a wide variety of things, which currently focus on grapes for winemaking (http://www.intel.com/labs/features/rs01031.htm)

IBM T.J. Watson Research Center – software, operating systems, protocols, biometrics (http://www.watson.ibm.com/)

Lockheed Martin – networked systems, architectures, defense applications (http://www.lockheedmartin.com/)

Businesses(cont)

Crossbow – licensed by Intel to sell the mote sensors (

www.xbow.com)

Microstrain- sells wireless sensors for strain, temperature, humidity type sensors (http://www.microstrain.com/)

Active Research Groups

Syracuse University – DREAMSNet in CASE(Development and Research in Evolutionary Algorithms for Multisensor Smart Networks)- architectures, algorithms, and sensor control (http://www.ecs.syr.edu/research/DREAMSNet/)

University of California - CITRIS (Center of Information Technology Research in the Interest of Society) – collaboration of groups (graphics.cs.ucdavis.edu/~okreylos/ ResDev/SensorNetworks/ )University of Vermont Wireless Ad-Hoc Research Group –mainly architectures and algorithms ( http://www.emba.uvm.edu/~jfrolik/uvmwan.htm)

Center for Embedded Network, UCLA – network security and protocols (http://cens.ucla.edu/)

IrisNet – Carnegie Mellon University – protocols, database, operating system issues (http://www-2.cs.cmu.edu/~srini/)

Active Research Groups

Intel Research Laboratory at Berkeley – mote sensors or tiny wireless sensors slightly larger than a penny collect light, temperature, humidity (http://intel-research.net/berkeley/features/tiny_db.asp)

Sensor Computing Research (SCR) at University of Missouri- Rolla – security application (http://web.umr.edu/~mma882/Sensor.htm)

Wireless Sensor Network Management Protocols (SNMP) – Rutgers University (http://www.cs.rutgers.edu/~bdeb/sensor_networks.html)

DREAMSNet Research Summary

Intelligent Sensor Network ControlMathematics – POSets, Bayesian Belief Networks, Swarm AgentsArchitectures – design software to be as flexible as possible

ProjectsSensor Management for Biometric Security – buildings, any access control problemFace Recognition – improving performanceSensor Management for Intelligent Defense SensorsCommunication Protocols – message routing based on information importance and resources

Intelligence Within Sensor Network

Software architecture designed for flexibilityIsolating the code that is application specific increases the software’s robustness

intelligence Mission Control

System Manager

System Models

sensor data

commands

Performance Estimates

Design Objectives for Sensor Manager

Problem: As intelligent networks of heterogeneous sensors are created, automatically assessing and optimizing global performance for changing mission requirements easily overwhelms processors and operators.Objectives:

Adapt intelligent sensors for changing mission requirements.Design a mathematical framework based on global performance parameters that adapt to mission changes.Solve the resulting complex optimization problem with computational efficiency.

Technical Challenges

Reduce contention for sensor resources.Prevent overwhelming communication network with sensor data.Enhance, not hinder, existing sensor’s operation and adaptability.Automatically compensate within the network for a sensor failure or degradation.

Sensor Networks Discussed in this Talk

Built Environmental Sensor NetworksImprove Occupant’s ComfortReduce Heating and Cooling Costs

Defense Sensor NetworksAccurate Regional SurveillanceReduce Response Time

Biometric Building Security Sensor Networks

Improve Identity Verification AccuracyReduce Cost of Maintaining System

Built Environmental Sensor Network

Wide Variety of Purposes or MissionsChemical/biological agent detectionFire and smoke detectionMonitor occupant movement

Wireless Communication – cost savingsMore Efficient Use of Resources within Building (e.g. electricity, gas, light)

Why Consider Built Indoor Environmental Sensor Networks?

90% of our time is spent here but 30% are uncomfortable*$40 - $250 billion productivity loss due to poor Indoor Environment Quality (IEQ)*40% of total building energy consumption is for environmental control$110 billion annual economic loss due to air pollution in urban areas

* EQS Center Overview by H. Ezzat Khalifa, Syracuse University

Built Environment Sensor Network

Video C am era

Video C am era

R obot Sensor

R obot Sensor

Diagram of Built Environment Network

Defense Sensor Network

Network a heterogeneous set of sensor varying in complexityObjectives

Solve resource contention problemRapid response to changing missionMinimize communication bandwidth contention

Joint Directors of Laboratories: JDL Fusion Levels

JDL 1:

Object Detection and Feature Processing

JDL 2: Situation Assessment

JDL 3:

Threat Assessment

JDL 4:

Sensor Management

Sensor Observation Data

Sensors

DREAMSNet

General Approach

Design a isolated block of software to handle the mission management needs.Design and isolate the interface to existing sensors and other fusion software packages.Optimize performance over separate geographic regions using a very new individual agent based approaches.

Agents can actually physically move through the network where our software existsAgents make decisions concerning sensor operation using models we receive from the sensor manufacturer. (currently Lockheed Martin)

Sensor Management Blocks

Mission Manager

Sensor Manager

Sensor Information

Sensor Models

Assessment Data from Existing System

Any Human Operator Information

Operator

Sensor Status Information

New Segmented Geographical ApproachP2

P3

Normal Surveillance

Support Threat Identification

Surveillance in Harsh Weather

,6

,3 ,2 ,5

,3,1 ,2 ,4

,4,5 ,1

,4

,47 ,22 ,31

System Subfunctions

Pd Time DelayAccuracy

Flexible Performance Measures

Regional Coverage

Low RCS Detection

Track Quality

Pd in weather

Missions

Performance Parameters

Evolutionary Program ApproachesTechnique Date

Published

Probability of Optimality (local optimal)

Reachability (global optimal)

Computation Time

Complexity

Traveling Salesman Problem (classic NP complete)

1930 5(worst)

1(best)

5(worst)

5(worst)

Genetic Algorithm

1975 1(best)

5(worst)

2 2

Simulated Annealing

1983 2 4 3 3

Particle Swarm Optimization

1995 3 3 1(best)

1(best)

Ant System 1995 4 2 4 4

*

Illustration of Optimization Process with Swarms

Sensor 1 Sensor 2 Sensor 3

Pglobal

V

Sensor Selections

Check Performance in the Volume

3D Space

Sensor Parameter Choices

Example Performance Evaluation

Plot of Pd Data

longitude (deg)

latit

ude

(deg

)

0 0.5 1 1.5 2 2.5 3

3

2.5

2

1.5

1

0.5

0

Choice 1: Boost Performance to More Uniformly Cover Region

Plot of Pd Data

longitude (deg)

latit

ude

(deg

)

0 0.5 1 1.5 2 2.5 3

3

2.5

2

1.5

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0

Choice 2: Sensor Manager Moves Sensors

Plot of Pd Data

longitude (deg)

latit

ude

(deg

)

0 0.5 1 1.5 2 2.5 3 3.5 4

4

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Biometric Security Network

Control accessFlexible and change security levels in real-timeImproved accuracy over single biometric modalityMinimize transaction timeServe all users (universal)

Biometric Terms Universality – everyone is processed with equal ease and accuracyFalse Rejection Rate (FRR) – rate at which the system does not allow access to someone who should be allowed, genuine user.False Acceptance Rate (FAR) – rate at which the system accidentally allows someone in who should be restricted.Accuracy – how well the system does on both FRR and FAR

Failure to Enroll – the system does not accept the initial biometric data for the individual.Failure to Acquire – the initial set of data was accepted but the individual is unsuccessful in producing biometric data later. This may be due to a cold for voice recognition, injured hand for hand geometry or fingerprints, etc.

Why Choose Biometric Security?

Improves authentication by automatically using physiological or behavioral characteristics to verify identity.Eliminates the problem of forgotten identifiers required for knowledge-based identity schemes.

password problems account for between 40% to 80% of all IT help desk calls. (Forrester Research, Inc. - http://www.forrester.com) resetting forgotten or compromised passwords cost as much as $340/user/year. (GartnerGroup - http://www.gartnergroup.com)

Increases the difficulty and cost of forging the required identifiers.

“While fooling biometrics is extremely difficult, people can buy false driver's licenses and green cards for $50 in many American towns bordering Mexico. Most college campuses are awash in false, paper-based ID cards used to purchase drinks.” (The Limits of Privacy by Amitai Etzioni)

Eliminates the risk of guessing identifiers to gain access.

Bayesian Decision Fusion

Each sensor decides to accept or reject a individual prior to fusion

A global decision at the fusion center FACE VOICE HAND

FUSION

u1 u2 u3

ug

LAN

WANENROLLMENTSUBSYSTEM

AUTHENTICATIONSUBSYSTEM

SECURITYFIREWALL

READERSTATION

LAN

WANENROLLMENTSUBSYSTEM

AUTHENTICATIONSUBSYSTEM

SECURITYFIREWALL

READERSTATION

FUSION

ACCEPTREJECT

FUTUREBIOMETRICS

Current Technical Challenges Being Addressed

Design a self-adapting biometric fusion processor.Addresses universality problem.Reduces failure to acquire with backup biometric data.

Improve face recognition.Easier and less invasive biometric.Face recognition already exists through photo IDs, which can be used of individuals that are not enrolled.

Advance smart card technology to increase communication distance.

Ease of use.Individuals maintain their own biometric data set.Improves logistics with data already with the individual.

Functional Diagram of ABMF Algorithm

Bayesian Decision Fusion

Biometric Sensor 1

Biometric Sensor 2

Biometric Sensor N


22N Possible

fusion rules for N sensors

Costs for False Acceptance and False Rejection Optimum

Fusion Rule

Cost Manager

User Constraint

Security State

Accept/Reject

Accept/Reject Decision



Characteristics of Resulting Adaptive Multimodal Biometric Fusion (AMBF)

Improves accuracy performance of authentication.Reduces the problem of a biometric identifier not being truly universal.Reduces problems enrolling individuals.Increases the system’s tolerance to any temporary problems in collecting the biometric identifier.Provides the system with inherent adaptability to changing security needs.

AMBF – The Particle Swarm Optimizer

Random Initialization of Particles

Velocity and Position Updates

Cost Evaluation

Save the best solution so far

Update Particles Memory

i<n

PSO parameters

CFA

Sensor Models

Output the best solution

To Fusion Processor

Example Performance Improvement Using Multimodal Biometrics

10-8

10-6

10-4

10-2

100

102

30

40

50

60

70

80

90

100

% False Acceptance Rate

% G

enuin

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ccepta

nce R

ate

SINGLE BIOMETRIC SENSOR PERFORMANCE

FACEVOICEHAND

10-8

10-6

10-4

10-2

100

102

30

40

50

60

70

80

90

100

% False Acceptance Rate

% G

enuin

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ccepta

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ate

PERFORMANCE COMPARISON 1 vs 2 vs 3

Single-VoiceTwo with AND fusionThree with only AND

1 Sensor

FAR= 0.000001%

FRR= NA%

2 Sensors

FAR= 0.000001%

FRR= 62%

3 Sensors

FAR= 0.000001%

FRR= 85%

Improvement

FAR= 0.000001%

FRR= 23%

Biometric Technology Market

Finger-scan commands 50% of non-AFIS (Automated Fingerprint Identification System) biometric revenue.

Facial-scan follows with 15.4% of the non-AFIS market.

Why focus on improving face recognition?

Inexpensive biometric sensors.More universally accepted biometric.A simple driver’s license may be used.Current techniques have poor performance.Currently, this is a modality that is easy to spoof.

EM Based Eigenfaces Example

Multiple Poses Are Better

Illustration of Approach – More information in EM Eigenfaces

Eigen faces – darker and more blurred

Our EM Eigen faces –a more interesting image

Performance Improvements with Single Image

Slight accuracy improvement Larger time improvement

Improved Accuracy for 5 Training Faces – EM Eigenfaces contain more information

0 5 10 15 20 25 30 35 4010

20

30

40

50

60

70

80

90Experiment :2 - 5 training faces for 40 subjects

Features

Acc

ura

cy %

Snap Shot PCTEM PCT

0 5 10 15 20 25 30 35 4015

20

25

30

35

40

45

Features

Tim

e Snap Shot PCTEM PCT

Experiment :2 - 5 training faces for 40 subjects

Very slight accuracy improvement

Significant time improvement – 11 sec for 20 features

Reducing Energy Expended by Smart Card

Adapt error control to the link quality and available power.Forward Error Control (FEC) Scheme with varying Reed-Solomon code rate.53% savings over fixed rate of n=6, m=340% savings over fixed rate of n=12, m=3

Summary

Built Environmental Sensor Networks need to be tailored for the needs of the business. Much interaction required between customer and designer.Multimodal Biometrics required to support user diversity in biometric identifiers as well as changing physical characteristics over time.There are more similarities between the smaller networks and larger networks than expected.

Suggestions

This is an explosive area of research now so it’s difficult to keep up with. “Sensor Networks” covers a large number of topics so make sure during discussions that both parties are discussing the same thing.Businesses will be the first to define new applications so contact between researchers and potential customers is essential.

Questions???

Dr. Lisa Osadciw DREAMSNet

277 Link Hall Syracuse University Syracuse, NY –13244

Visit us at : www.ecs.syr.edu/research/dreamsnet

Results and Analysis – Experimental Settings

Gaussian models for sensors is assumed

Swarm parameters

Results and Analysis – Experiment I

-40 -20 0 20 40 60 800

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Threshold for Sensor 1

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CFA= 1.9 Minima Achieved = 0.0102 Fusion Rule = AND rule

Imposter Distribution

Genuine Distribution

Region of False Rejection Region of False Acceptance

Results and Analysis – Experiment II

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CFA= 1.8 Minima Achieved= 0.0138 Fusion Rule= OR rule

Results and Analysis – Performance of


0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000-1.88

-1.86

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-1.82

-1.8

-1.78

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-1.7 Total Cost Vs Iterations

Iterations-------->

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ost (d

B) ------>

0 100 200 300 400 500 600 700 800 900 1000-2

-1.9

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Iterations-------->

Tot

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ost (d

B) ------>

CFA=1.8 CFA=1.9

sensor nets the business of surveillance

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