Mobilityassisted Spatiotemporal Detection in Wireless Sensor Networks

1
Mobility-assisted Spatiotemporal Detection in
Wireless Sensor Networks

• Guoliang Xing1 JianpingWang1 Ke Shen3 Qingfeng
  Huang2 Xiaohua Jia1 Hing Cheung So1
• 1City University of Hong Kong
• 2Palo Alto Research Center (PARC) Inc.
• 3Michigan State University

2
Outline

• Motivation
• Problem formulation
• Optimal movement scheduling
• Simulations
• Conclusion

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Mission-critical Target Detection

• Stringent Spatiotemporal QoS requirements
• High detection probability of any target, e.g.,
  90
• Low false alarm rate, e.g., 5
• Bounded detection delay, e.g., 20s
• Network and environmental dynamics
• Death of nodes (battery depletions, attacks)
• Changing noise levels and target profiles

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State of the Art

• Over-provisioning of sensing capability
• Careful advance network planning
• Dense node deployment
• Incremental redeployments
• High (re)-deployment cost in order to deal with
  network and environmental dynamics

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Mobility-assisted Target Detection

• Mobile sensors collaborate with static sensors in
  target detection
• Achieve higher signal-to-noise ratios by moving
  closer to possible targets
• Reconfigure sensor coverage dynamically

target
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Mobile Sensor Platforms
PackBot _at_ iRobot.com
Koala _at_ NASA
Robomote _at_ USC

• Limitations
• Low movement speed (0.11 m/s)
• High power consumption (60 W for PackBot)

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Overview of Our Approach

• Data-fusion-based detection model for
  collaboration between mobile and static sensors
• Optimal sensor movement scheduling algorithm
• Minimizes the moving distance of sensors
• Meets spatiotemporal QoS requirements high
  detection probability, low false alarm rate, and
  bounded detection delay
• Simulations based on real data traces of target
  detection

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Outline

• Motivation
• Problem formulation
• Optimal movement scheduling
• Simulations
• Conclusion

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Signal and Noise Models
Plotted based on real acoustic sensor data traces
in military vehicle detection

• Target's acoustic energy decays quadratically
  with distance
• Noise energy follows the Normal distribution
• Sensor reading decayed target energy noise
  energy

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Fusion-based Detection Model
sensor reading distribution
noise energy distribution
sensor reading distribution

• All readings in a cluster are summed and compared
  with a threshold ?

False alarm rate PF 1-Xn(n ?) Detection
prob. PD 1 Xn(n? - S W(di)) Xn CDF of
Chi-squre distribution W(di) Energy measurement
of sensor di from target
energy
detection threshold
false alarm rate
detection probability
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A Two-phase Detection Scheme

• First phase static detection
• All sensors send readings to cluster head
• Cluster head makes a detection decision, if
  positive, starts the 2nd phase
• Second phase movement scheduling
• Mobile sensors move toward the possible target
  according to a movement schedule
• Cluster head makes the final detection decision

• First phase static detection
• All sensors send readings to cluster head
• Cluster head makes a detection decision, if
  positive, starts the 2nd phase
• Second phase movement scheduling
• Mobile sensors move toward the possible target
  according to a movement schedule
• Cluster head makes the final detection decision

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Problem Formulation
M1
Example movement schedule (sensors are assumed to
move at steps) M1 t0 - one step, t3 - two
steps M2 t1 - one step, t2 - one step M3 t1
- two steps, t2 - one step
M2
target
M3

• Find two detection thresholds and a movement
  schedule
• Minimizes the expected moving distance of sensors
  
• Detection prob. a, false alarm rate ß,
  detection delay T

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Problem Formulation Contd.

• Y target appearance probability
• S movement schedule
• S total number of steps
• Steps(?1, ?2, S) expected total num of steps
• Find ?1, ?2, and schedule S to minimize
• Constraints
• PD1PD2 a
• PF1PF2 ß
• Moving distance of any sensor in schedule S T x
  speed

Steps(?1, ?2, S) YPD1 (1-Y)PF1) x S
the probability that sensors move
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Outline

• Motivation
• Problem formulation
• Optimal movement scheduling
• Simulations
• Conclusion

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Structure of Optimal Solution

• For two schedules S and S', if S S' and
  E(S) E(S'), we can find ?1, ?2, ?1', ?2', such
  that
• Steps(?1, ?2, S) Steps(?1', ?2', S')
• E(S) is the total energy measured by sensors
• Implications
• Detection thresholds can be found if a schedule
  is given
• Optimal schedule maximizes the sum of energy
  readings

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Examples of Optimal Schedules

• Assume that all sensors can move one step every
  second, and detection delay is T seconds
• Case 1 only one step allowed
• Opt schedule move B/C one step at time zero
• Case 2 two steps allowed
• Schedule I move B and C one step at time zero
• Schedule II move A two steps at time zero

sample T-1 second
sample T-2 second
All move combinations must be considered to find
the optimal schedule!
B
A
C
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Finding the Optimal Schedule

• If a sensor moves in the 2nd phase, it moves
  continuously before a stop
• Num of move combinations is limited
• Dynamic programming algorithm
• E(j,h) total energy measured by sensor 1j with
  total num of h steps
• e(hj) total energy measured by sensor j

E(j,h) max E(j-1,h-hj) e(hj)
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Simulations

• Public dataset of detecting military vehicles
  Duarte04
• Target and noise energy models are estimated from
  training data set
• Sensors are randomly deployed in a 5050m2 field

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Performance Results
Detection Prob. ()
Average Moving Steps
False alarm rate ()
Requested Detection Prob. ()
Total 6 sensors are deployed
MD-random1 randomly choose one sensor and for
next step MD-random2 randomly choose one sensor
and moves to the target
Mobility improves detection prob. by 2040!
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Conclusion

• Proposed a two-phase target detection model based
  on data fusion
• Developed an optimal sensor movement scheduling
  algorithm
• Minimizes the expected moving distance of sensors
• Meets spatiotemporal QoS requirements
• Conducted simulations based on real data traces
  of target detection