Sensor nets the business of surveillance
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Sensor nets the business of surveillance Sensor nets the business of surveillance Presentation Transcript

  • Emerging Sensor Networks Can Make Sense for Your Business Tech 2003, May 7, 2003 Dr. Lisa Ann Osadciw from DREAMSNet – ( D evelopment and R esearch in E volutionary A lgorithms for M ultiSensor S mart N etworks) http://www.ecs.syr.edu/research/DREAMSNet/
  • Objective Awareness of the new emerging sensor networks Provide you with a new understanding of how you may take advantage of these networks to improve your business
  • Overview Introduce Sensor Networks Sensor Network Applications Problems Solved by Sensor Networks Minimizing Sensor Network Costs Business Advantages of Sensor Network Technology Businesses for Sensor Networks Today Research Groups for Future Sensor Networks DREAMSNet Environmental Sensor Network Concept Large Intelligent Defense Sensor Networks Biometric Security Sensor Networks Biometric Technology Adaptive Multimodal Biometric Fusion Algorithm Summary , Suggestions, and Questions View slide
  • 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 View slide
  • Current Research Focus Areas DREAMSNet Database Information Acquisition Trusted Data Network Sensors Intelligent Processing Control and Response Resource Management
  • Overview Introduce Sensor Networks Sensor Network Applications Problems Solved by Sensor Networks Minimizing Sensor Network Costs Business Advantages of Sensor Network Technology Businesses for Sensor Networks Today Research Groups for Future Sensor Networks DREAMSNet Environmental Sensor Network Concept Large Intelligent Defense Sensor Networks Biometric Security Sensor Networks Biometric Technology Adaptive Multimodal Biometric Fusion Algorithm Summary , Suggestions, and Questions
  • Sensor Network Applications Defense Applications Medical Applications Patient wears sensors recording or sending data Monitor patients in the hospital Weather Applications Indoor Environmental Control Building Security More 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 observations Assessment accuracy is due to the set of observations rather than any single observation Sensors cover a large region Measurements must be collected over a significant time duration. For example: More than one patient must be monitored Whole building must be covered Structure monitoring (airplanes, bridges, etc.)
  • Minimize Sensor Network System Cost Current research area 50%-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
  • Overview Introduce Sensor Networks Sensor Network Applications Problems Solved by Sensor Networks Minimizing Sensor Network Costs Business Advantages of Sensor Network Technology Businesses for Sensor Networks Today Research Groups for Future Sensor Networks DREAMSNet Environmental Sensor Network Concept Large Intelligent Defense Sensor Networks Biometric 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.
  • Overview Introduce Sensor Networks Sensor Network Applications Problems Solved by Sensor Networks Minimizing Sensor Network Costs Business Advantages of Sensor Network Technology Businesses Supplying Sensor Networks Today Research Groups Improving Future Sensor Networks DREAMSNet Environmental Sensor Network Concept Large Intelligent Defense Sensor Networks Biometric 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/)
  • Overview Introduce Sensor Networks Sensor Network Applications Problems Solved by Sensor Networks Minimizing Sensor Network Costs Business Advantages of Sensor Network Technology Businesses for Sensor Networks Today Research Groups for Future Sensor Networks DREAMSNet Environmental Sensor Network Concept Large Intelligent Defense Sensor Networks Biometric 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)
  • Overview Introduce Sensor Networks Sensor Network Applications Problems Solved by Sensor Networks Minimizing Sensor Network Costs Business Advantages of Sensor Network Technology Businesses for Sensor Networks Today Research Groups for Future Sensor Networks DREAMSNet Environmental Sensor Network Concept Large Intelligent Defense Sensor Networks Biometric Security Sensor Networks Biometric Technology Adaptive Multimodal Biometric Fusion Algorithm Summary , Suggestions, and Questions
  • DREAMSNet Research Summary Intelligent Sensor Network Control Mathematics – POSets, Bayesian Belief Networks, Swarm Agents Architectures – design software to be as flexible as possible Projects Sensor Management for Biometric Security – buildings, any access control problem Face Recognition – improving performance Sensor Management for Intelligent Defense Sensors Communication Protocols – message routing based on information importance and resources
  • Intelligence Within Sensor Network Software architecture designed for flexibility Isolating the code that is application specific increases the software’s robustness
  • 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 Networks Improve Occupant’s Comfort Reduce Heating and Cooling Costs Defense Sensor Networks Accurate Regional Surveillance Reduce Response Time Biometric Building Security Sensor Networks Improve Identity Verification Accuracy Reduce Cost of Maintaining System
  • Overview Introduce Sensor Networks Sensor Network Applications Problems Solved by Sensor Networks Minimizing Sensor Network Costs Business Advantages of Sensor Network Technology Businesses for Sensor Networks Today Research Groups for Future Sensor Networks DREAMSNet Environmental Sensor Network Concept Large Intelligent Defense Sensor Networks Biometric Security Sensor Networks Biometric Technology Adaptive Multimodal Biometric Fusion Algorithm Summary , Suggestions, and Questions
  • Built Environmental Sensor Network Wide Variety of Purposes or Missions Chemical/biological agent detection Fire and smoke detection Monitor occupant movement Wireless Communication – cost savings More 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
  • Diagram of Built Environment Network
  • Overview Introduce Sensor Networks Sensor Network Applications Problems Solved by Sensor Networks Minimizing Sensor Network Costs Business Advantages of Sensor Network Technology Businesses for Sensor Networks Today Research Groups for Future Sensor Networks DREAMSNet Environmental Sensor Network Concept Large Intelligent Defense Sensor Networks Biometric 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 complexity Objectives Solve resource contention problem Rapid response to changing mission Minimize 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 exists Agents 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 Approach P 2 P 3
  • 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 Delay Accuracy Flexible Performance Measures Regional Coverage Low RCS Detection Track Quality Pd in weather Missions Performance Parameters
  • Evolutionary Program Approaches * Technique 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 P global V Sensor Selections Check Performance in the Volume 3D Space Sensor Parameter Choices
  • Example Performance Evaluation
  • Choice 1: Boost Performance to More Uniformly Cover Region
  • Choice 2: Sensor Manager Moves Sensors
  • Overview Introduce Sensor Networks Sensor Network Applications Problems Solved by Sensor Networks Minimizing Sensor Network Costs Business Advantages of Sensor Network Technology Businesses for Sensor Networks Today Research Groups for Future Sensor Networks DREAMSNet Environmental Sensor Network Concept Large Intelligent Defense Sensor Networks Biometric Security Sensor Networks Biometric Technology Adaptive Multimodal Biometric Fusion Algorithm Summary , Suggestions, and Questions
  • Biometric Security Network Control access Flexible and change security levels in real-time Improved accuracy over single biometric modality Minimize transaction time Serve all users (universal)
  • Biometric Terms Universality – everyone is processed with equal ease and accuracy False Rejection Rate (F RR ) – rate at which the system does not allow access to someone who should be allowed, genuine user. False Acceptance Rate (F AR ) – rate at which the system accidentally allows someone in who should be restricted. Accuracy – how well the system does on both F RR and F AR 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 u 1 u 2 u 3 u g
  • LAN WAN ENROLLMENT SUBSYSTEM AUTHENTICATION SUBSYSTEM SECURITY FIREWALL READER STATION
  • LAN WAN ENROLLMENT SUBSYSTEM AUTHENTICATION SUBSYSTEM SECURITY FIREWALL READER STATION
  • LAN WAN ENROLLMENT SUBSYSTEM AUTHENTICATION SUBSYSTEM SECURITY FIREWALL READER STATION FUSION ACCEPT REJECT FUTURE BIOMETRICS
  • 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 2 2 N 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 C FA Sensor Models Output the best solution To Fusion Processor
  • Example Performance Improvement Using Multimodal Biometrics 1 Sensor F AR = 0.000001% F RR = NA% 2 Sensors F AR = 0.000001% F RR = 62% 3 Sensors F AR = 0.000001% F RR = 85% Improvement F AR = 0.000001% F RR = 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 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=3 40% savings over fixed rate of n=12, m=3
  • Overview Introduce Sensor Networks Sensor Network Applications Problems Solved by Sensor Networks Minimizing Sensor Network Costs Business Advantages of Sensor Network Technology Businesses for Sensor Networks Today Research Groups for Future Sensor Networks DREAMSNet Environmental Sensor Network Concept Large Intelligent Defense Sensor Networks Biometric 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 C FA = 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 C FA = 1.8 Minima Achieved= 0.0138 Fusion Rule= OR rule
  • Results and Analysis – Performance of Particle Swarm Optimization C FA =1.8 C FA =1.9