The Bioterrorism Sensor Location Problem - PowerPoint PPT Presentation

About This Presentation
Title:

The Bioterrorism Sensor Location Problem

Description:

... nodes corresponding to sensors red, nodes corresponding to sensor messages blue) ... Jay Spingarn, DefenseThreat Reduction Agency. Fred Steinberg, MITRE Corp. ... – PowerPoint PPT presentation

Number of Views:20
Avg rating:3.0/5.0
Slides: 65
Provided by: dimacsR
Category:

less

Transcript and Presenter's Notes

Title: The Bioterrorism Sensor Location Problem


1
The Bioterrorism Sensor Location Problem
2
  • Early warning is critical
  • This is a crucial factor underlying governments
    plans to place networks of sensors/detectors to
    warn of a bioterrorist attack

The BASIS System
3
Locating Sensors is not Easy
  • Networks of sensors are expensive
  • Ways to locate them to maximize coverage and
    expedite an alarm are not easy to determine
  • Approaches that improve upon existing, ad hoc
    location methods could save countless lives in
    the case of an attack and also money in capital
    and operational costs.

4
Two Fundamental Problems
  • Sensor Location Problem (SLP)
  • Choose an appropriate mix of sensors
  • decide where to locate them for best protection
    and early warning

5
Two Fundamental Problems
  • Pattern Interpretation Problem (PIP) When
    sensors set off an alarm, help public health
    decision makers decide
  • Has an attack taken place?
  • What additional monitoring is needed?
  • What was its extent and location?
  • What is an appropriate response?

6
Two Fundamental Problems
  • The SLP and PIP are ripe for
  • Precise formulation
  • Mathematical modeling
  • Algorithmic analysis
  • Applications of powerful new statistical methods

7
Two Fundamental Problems
  • Relevant tools include
  • Network design
  • Network analysis
  • Location theory
  • Reliability theory
  • Data mining
  • Fluid dynamic modeling
  • Source-to-dose modeling
  • Time series analysis

8
Types of Sensors
  • There are many types of sensors.

Portal shield
Dry filtration unt (portable)
Bioparticulate counter/detector
9
The SLP
  • Sensor technology is changing rapidly
  • Sensors come with a variety of characteristics
  • A good sensor location methodology should not be
    dependent upon particular sensor technologies.

10
The SLP What is a Measure of Success of a
Solution?
  • A modeling problem.
  • Needs to be made precise.
  • Many possible formulations.

11
The SLP What is a Measure of Success of a
Solution?
  • Identify and ameliorate false alarms.
  • Defending against a worst case attack or an
    average case attack.
  • Minimize time to first alarm? (Worst case?
    (Average case?)
  • Maximize coverage of the area.
  • Minimize geographical area not covered
  • Minimize size of population not covered
  • Minimize probability of missing an attack

12
The SLP What is a Measure of Success of a
Solution?
  • Cost Given a mix of available sensors and a
    fixed budget, what mix will best accomplish our
    other goals?

13
The SLP What is a Measure of Success of a
Solution?
  • Its hard to separate the goals.
  • Even a small number of sensors might detect an
    attack if there is no constraint on time to
    alarm.
  • Without budgetary restrictions, a lot more can be
    accomplished.

14
Modeling Issues Types of Sensors
  • Sensor technology is changing rapidly.
  • Methods we develop should not be dependent upon
    todays technology.

Much of present technology depends upon hand-held
rapid PCR assay together with software for BW
agent identification
15
Modeling Issues Types of Sensors
  • Sensors differ as to
  • Response
  • Accuracy and reliability
  • Stationarity vs. mobility
  • Constraints on their location
  • Cost
  • How sensor data is reported

16
Reporting of Sensor Data
  • Do humans physically examine collection devices?

17
Reporting of Sensor Data
  • Is the data electronically reported?
  • Reporting at discrete times?
  • Reporting continuously?

18
Other Relevant Modeling Issues
  • Probability of Release at Different Locations
  • Geography
  • Buildings
  • Weather
  • Population Distribution

19
Algorithmic Approaches I Greedy Algorithms
20
Greedy Algorithms
  • Find the most important location first and locate
    a sensor there.
  • Find second-most important location.
  • Etc.
  • Builds on earlier work at IDA (Grotte, Platt)
  • Steepest ascent approach.
  • No guarantee of optimality.
  • In practice, peak of objective function is rather
    flat, so not hard to get close to optima.

21
Algorithmic Approaches II Variants of Classic
Location and Clustering Methods
22
Algorithmic Approaches II Variants of Classic
Location and Clustering Methods
  • Location theory locate facilities (sensors) to
    be used by users located in a region.
  • Cluster analysis Given points in a metric space,
    partition them into groups or clusters so points
    within clusters are relatively close.
  • Clusters correspond to points covered by a
    facility (sensor).

23
Variants of Classic Location and Clustering
Methods
  • k-median clustering Given k sensors, place them
    so each point in the city is within x feet of a
    sensor.
  • Complications More dimensions location affects
    sensitivity, wind strength enters, sensors have
    different characteristics, etc.
  • This higher-dimensional k-median clustering
    problem is hard! Best-known algorithms are due to
    Rafail Ostrovsky.

24
Variants of Classic Location and Clustering
Methods
  • Further complications make this even more
    challenging
  • Different costs of different sensors
  • Restrictions on where we can place different
    sensors
  • Is it better to have every point within x feet of
    some sensor or every point within y feet of at
    least three sensors (y gt x)?
  • Approximation methods due to Chuzhoy,
    Ostrovsky, and Rabani and to Guha, Tardos, and
    Shmoys are relevant.

25
Algorithmic Approaches III Variants of Highway
Sensor Network Algorithms
26
Variants of Highway Sensor Network Algorithms
  • Sensors located along highways and nearby
    pathways measure atmospheric and road conditions.
  • Muthukrishnan, et al. have developed very
    efficient algorithms for sensor location.
  • Based on bichromatic clustering and
    bichromatic facility location (color nodes
    corresponding to sensors red, nodes corresponding
    to sensor messages blue)

27
Variants of Highway Sensor Network Algorithms
  • These algorithms apply to situations with many
    more sensors than the bioterrorism sensor
    location problem.
  • As BT sensor technology changes, we can envision
    a myriad of miniature sensors distributed around
    a city, making this work all the more relevant.

28
Algorithmic Approaches IV Variants of Air
Pollution Monitoring Models
29
Variants of Air Pollution Monitoring Models
  • Long history of using models to locate air
    pollution monitors.
  • MENTOR Modeling Environment for Total Risk
    developed by team at Rutgers and R.W.J. Medical
    School (Panos Georgopoulos, Paul Lioy) at Center
    for Exposure and Risk Modeling

30
Variants of Air Pollution Monitoring Models
  • MENTOR builds on
  • personal exposures
  • Source-to-dose modeling
  • Environmental conditions
  • Weather
  • MENTOR is a powerful computational tool.
  • However, the models it uses are not nearly as
    large or as complex as those traditionally used
    in nuclear weapons research at Los Alamos and
    elsewhere.

31
Variants of Air Pollution Monitoring Models
  • Combine air pollution monitor placement modeling
    tools like MENTOR with iteration/simulation
    tools.
  • Piecewise heuristic approach developed by Tom
    Boucher, David Coit, E. Elsayed
  • Based on initial simulation results, divide
    problem into subproblems and repeat local
    optimization algorithms
  • Method recently applied to counter-terrorism
    applications by Pate-Cornell.

32
Algorithmic Approaches V Building on Equipment
Placing Algorithms
33
Building on Equipment Placing Algorithms
  • The Node Placement Problem is problem of
    determining locations or nodes to install certain
    types of networking equipment.
  • Coverage and cost are a major consideration.
  • Researchers at Telcordia Technologies have
    studied variations of this problem arising from
    broadband access technologies.

34
The Broadband Access Node Placement Problem
  • There are inherent range limitations that drive
    placement.
  • E.g. customer for DSL service must be within xx
    feet of an assigned multiplexer.
  • Multiplexer sensor.
  • Problem solved using dynamic programming
    algorithms.
  • (Tamra Carpenter, Martin Eiger,David Shallcross,
    Paul Seymour)

35
The Broadband Access Node Placement Problem
Complications
  • Restrictions on types of equipment that can be
    placed at a given node.
  • Constraints on how far a signal from a given
    piece of equipment can travel.
  • Cost and profit maximization considerations.
  • Relevance of work on general integer programming,
    the knapsack cover problem, and local access
    network expansion problems.

36
The Pattern Interpretation Problem
37
The Pattern Interpretation Problem
  • It will be up to the Decision Maker to decide how
    to respond to an alarm from the sensor network.

38
The Pattern Interpretation Problem
  • Little has been done to develop analytical models
    for rapid evaluation of a positive alarm or
    pattern of alarms from a sensor network.
  • How can this pattern be used to minimize false
    alarms?
  • Given an alarm, what other surveillance measures
    can be used to confirm an attack, locate areas of
    major threat, and guide public health
    interventions?

39
The Pattern Interpretation Problem (PIP)
  • Close connection to the SLP.
  • How we interpret a pattern of alarms will affect
    how we place the sensors.
  • The same simulation models used to place the
    sensors can help us in tracing back from an alarm
    to a triggering attack.

40
Approaching the PIP Minimizing False Alarms
41
Approaching the PIP Minimizing False Alarms
  • One approach Redundancy. Require two or more
    sensors to make a detection before an alarm is
    considered confirmed.

42
Approaching the PIP Minimizing False Alarms
  • Portal Shield requires two positives for the
    same agent during a specific time period.
  • Redundancy II Place two or more sensors at or
    near the same location. Require two proximate
    sensors to give off an alarm before we consider
    it confirmed.
  • Redundancy drawbacks cost, delay in confirming
    an alarm.

43
Approaching the PIP Using Decision Rules
  • Existing sensors come with a sensitivity level
    specified and sound an alarm when the number of
    particles collected is sufficiently high above
    threshold.

44
Approaching the PIP Using Decision Rules
  • Alternative decision rule alarm if two sensors
    reach 90 of threshold, three reach 75 of
    threshold, etc.
  • One approach use clustering algorithms for
    sounding an alarm based on a given distribution
    of clusters of sensors reaching a percentage of
    threshold.

45
Approaching the PIP Using Decision Rules
  • When sensors are to be used jointly, the rules
    for tuning each sensor should be optimized to
    take advantage of the fact that each is part of a
    network.
  • The optimal tuning depends on the decision rule
    applied to reach an overall decision given the
    sensor inputs.

46
Approaching the PIP Using Decision Rules
  • Prior work along these lines in missile detection
    (Cherikh and Kantor)

47
Approaching the PIP Using Decision Rules
  • Most work has concentrated on the case of
    stochastic independence of information available
    at two sensors clearly violated in BT sensor
    location problems.
  • Even with stochastic independence, finding
    optimal decision rules is nontrivial.
  • Recent promising approaches of Paul Kantor study
    fusion of multiple methods for monitoring message
    streams.

48
Approaching the PIP Spatio-Temporal Mining of
Sensor Data
49
Approaching the PIP Spatio-Temporal Mining of
Sensor Data
  • Sensors provide observations of the state of the
    world localized in space and time.
  • Finding trends in data from individual sensors
    time series data mining.
  • PIP detecting general correlations in multiple
    time series of observations.
  • This has been studied in statistics, database
    theory, knowledge discovery, data mining.
  • Complications proximity relationships based on
    geography complex chronological effects.

50
Approaching the PIP Spatio-Temporal Mining of
Sensor Data
  • Sensor technology is evolving rapidly.
  • It makes sense to consider idealized settings
    where data are collected continuously and
    communicated instantly.
  • Then, modern methods of spatio-temporal data
    mining due to Muthukrishnan and others are
    relevant.

51
Approaching the PIP Spatio-Temporal Mining of
Sensor Data
  • Work on Cellular networks and IP networks is
    relevant.

52
Approaching the PIP Spatio-Temporal Mining of
Sensor Data
  • There is relevant work of Muthukrishnan on
    cellular and IP networks
  • Time-of-day effects in traffic calls across the
    country
  • Geographic patterns in users mobility
  • Correlations between IP router time series data.
  • New challenges heterogeneous capabilities of
    nodes in telecommunications, most nodes have
    similar capabilities.

53
Approaching the PIP Spatio-Temporal Mining of
Sensor Data
  • Promising Statistical Methods
  • Still in idealized setting continuous sensor
    data collection.
  • Building on the Bayesian approach to modeling
    spatio-temporal data.

Thomas Bayes 1702-1761
54
Approaching the PIP Spatio-Temporal Mining of
Sensor Data
  • Promising Statistical Methods
  • Bayesian approaches take advantage of recent
    dramatic advances in simulation technology
    (Markov chain Monte Carlo)
  • Limitations of existing methods dependence on
    batch analysis arrival of new data means start
    from scratch.

55
Approaching the PIP Spatio-Temporal Mining of
Sensor Data
  • Promising Statistical Methods
  • There is need for online or sequential
    methods that update models as data comes in.
  • Relevance of recent work of Ridgeway and Madigan
    on sequential Monte Carlo methods using particle
    filters

56
Approaching the PIP Spatio-Temporal Mining of
Sensor Data
  • Additional Promising Statistical Methods
  • Methods for visualizing the data will help human
    decision makers.
  • Methods of statistical process control are
    relevant to finding the most effective ways to
    aggregate data across sensors to detect
    anomalies.

57
Approaching the PIP Triggering Other Methods of
Surveillance
  • One type of BT surveillance cannot be considered
    in isolation.
  • Relevant work in talks of Madigan/Rolka, Pagano,
    and Zelicoff
  • Question How can the pattern of sensor warnings
    guide other biosurveilance methods?

58
Approaching the PIP Triggering Other Methods of
Surveillance
  • Increased syndromic surveillance?
  • Change threshold for alarm in syndromic
    surveillance?
  • Increased attention to E.R. visits in a certain
    region?

59
Approaching the PIP Triggering Other Methods of
Surveillance
  • Decreased threshold for alarm from subway worker
    absenteeism levels?

60
Approaching the PIP Triggering Other Methods of
Surveillance
  • If there is an initial alarm, each sensor may be
    read more often.
  • How do we pick the sensors to read more
    frequently?
  • This is adaptive biosensor engagement.
  • Methods of bichromatic combinatorial optimization
    may be relevant.
  • As for the SLP, sensors get one color, sensor
    messages another.
  • Relevance of work of Muthukrishnan.

61
There are Remarkably Many Challenges from this
One Problem!
62
Thanks to DIMACS SLP/PIP Team
  • Benjamin Avi-Itzhak
  • Thomas Boucher
  • Tamra Carpenter
  • David Coit
  • Elsayed Elsayed
  • Panos Georgopoulos
  • Mel Janowitz
  • Paul Kantor
  • Howard Karloff
  • Jon Kettenring
  • Paul Lioy
  • David Madigan
  • S. Muthukrishnan
  • Rafail Ostrovsky
  • Michael Rothkopf
  • Yehuda Vardi

63
Thanks also to
  • Jeff Grotte, Institute for Defense Analyses
  • Farzad Mostashari, NYC Dept. of Health
  • Dennis Nash, NYC Dept.of Health
  • Nathan Platt, Institute for Defense Analyses
  • Al Rhodes, Defense Threat Reduction Agency
  • Jay Spingarn, DefenseThreat Reduction Agency
  • Fred Steinberg, MITRE Corp.

64
(No Transcript)
Write a Comment
User Comments (0)
About PowerShow.com