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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
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 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
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
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.
OverviewIntroduce Sensor NetworksSensor Network Applications
Problems Solved by Sensor NetworksMinimizing Sensor Network Costs
Business Advantages of Sensor Network TechnologyBusinesses Supplying Sensor Networks TodayResearch Groups Improving 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
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/)
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
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)
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
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
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
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
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
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
1
0.5
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
3.5
3
2.5
2
1.5
1
0.5
0
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
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
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
Particle Swarm Optimization
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
Accept/Reject Decision
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
e A
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
e A
ccepta
nce R
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
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
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
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5x 10
-3
score
Pro
babi
lity
Den
sity
Fun
ctio
n
Threshold for Sensor 1
-20 -10 0 10 20 30 40 50 60 700
0.001
0.002
0.003
0.004
0.005
0.006
0.007
0.008
0.009
0.01
score
Pro
babi
lity
Den
sity
Fun
ctio
n
Threshold for Sensor 2
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
-40 -20 0 20 40 60 800
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5x 10
-3
score
Pro
babi
lity
Den
sity
Fun
ctio
n
Threshold for Sensor 1
-20 -10 0 10 20 30 40 50 60 700
0.001
0.002
0.003
0.004
0.005
0.006
0.007
0.008
0.009
0.01
score
Pro
babi
lity
Den
sity
Fun
ctio
n
Threshold for Sensor 2
CFA= 1.8 Minima Achieved= 0.0138 Fusion Rule= OR rule
Results and Analysis – Performance of
Particle Swarm Optimization
0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000-1.88
-1.86
-1.84
-1.82
-1.8
-1.78
-1.76
-1.74
-1.72
-1.7 Total Cost Vs Iterations
Iterations-------->
Tot
al C
ost (d
B) ------>
0 100 200 300 400 500 600 700 800 900 1000-2
-1.9
-1.8
-1.7
-1.6
-1.5
-1.4
-1.3 Total Cost Vs Iterations
Iterations-------->
Tot
al C
ost (d
B) ------>
CFA=1.8 CFA=1.9