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Collaborative Target Detection in WirelessSensor Networks with Reactive Mobility
Rui Tan 1 Guoliang Xing1 Jianping Wang1
Hing Cheung So2
1Department of Computer ScienceCity University of Hong Kong
2Department of Electronic EngineeringCity University of Hong Kong
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Outline
1. Motivation
2. Preliminaries
3. Problem Formulation
4. Near-optimal Solution
5. Performance Evaluation
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Challenges for Mission-critical SensingApplications
• Stringent QoS requirements• Target detection/tracking, security surveillance• High detection probability• Low false alarm rate• Bounded detection delay
• Unpredictable network dynamics• Coverage holes caused by death of nodes
• Changing physical environments• Different spatial distribution of events
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Exploit Mobility in Target Detection
• Sense better signal by moving sensors closer to targets
• Adapt to the changes of network condition and physicalenvironments
Example: fire detection
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Mobile Sensor Platforms
Robomote @ USC Koala @ NASA GRC PackBot @ iRobot.com
Challenges• Low movement speed (0.1 ∼ 2m/s)
• Increase detection latency
• High manufacturing cost• A small number of mobile sensors available
• High energy consumption• Locomotion consumes much higher power than wireless
communication
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Overview of Our Approach
• Data-fusion based target detection• Explore the collaboration between mobile and static
sensors
• Near-optimal sensor movement scheduling algorithm• Reduce moving distance of sensors• Satisfy QoS requirements:
• Low false alarm rate• High detection probability• Bounded detection delay
• Performance evaluation using real data traces
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Outline
1. Motivation
2. Preliminaries
3. Problem Formulation
4. Near-optimal Solution
5. Performance Evaluation
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Signl Energy Model and Noise Model
00.5
11.5
22.5
33.5
0 0.2 0.4 0.6 0.8 1
Sig
nalE
nerg
y(×10−
2)
1/distance2 (×10−2)
0
2
4
6
8
10
-4 -3 -2 -1 0 1 2 3 4
Occ
urre
nce
Noise energy (×10−4)
• Plotted using real data traces from DARPA SensIT experiments
e(x) =initial target energy
x2 noise ∼ N(µ, σ2)
Measurement = e(x) + noise
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Single-sensor Detection Model
• Local decision of sensor i
=
{
1 if ei ≥ λ0 if ei < λ
• The false alarm rate ofsensor i
P iF = Q
(
λ − µ
σ
)
• The detection probability
P iD = Q
(
λ − µ − e(xi)
σ
)
CCDF: Q(x) = 1 −
R x−∞
φ(t)dt
eiλµ
noise
µ + e(xi)
measurement
• closer to the target, higher PD
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Decision Fusion Model
• System detection decision
Majority Rule:{
1 if more than n/2 sensors decide 10 otherwise
• The system false alarm rate
PF = Q
n2 −
∑ni=1 P i
F√
∑ni=1 P i
F +∑n
i=1(PiF )2
• The system detection probability
PD = Q
n2 −
∑ni=1 P i
D√
∑ni=1 P i
D +∑n
i=1(PiD)2
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Outline
1. Motivation
2. Preliminaries
3. Problem Formulation
4. Near-optimal Solution
5. Performance Evaluation
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Target Detection with Mobile Sensors
• Long distance movement can• quickly deplete the battery of a mobile node• disrupt the network topology
• Problem formulation: minimize the moving distance ofsensors subject to
• PF ≤ α, e.g., 5%• PD ≥ β, e.g., 95%• Average detection delay ≤ D, e.g., 15s
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A Two-phase Detection Approach
target
e1 <λ2
e1+e2 >λ2
terminate!e1 e2
• 1st phase: each sensor makes local decision by e0 ≷ λ1
• If the system decision is 1, the 2nd phase is initiated
• 2nd phase: mobile sensors move and periodically sense• A sensor terminates the detection and decides 1 if
e1 + e2 + · · · + ej ≥ λ2
• Make final detection decision
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Advantages of Reactive Mobility
• Sensors move reacting to positive decision in the 1st phase
• Avoid unnecessary movement by consensus check in the1st phase
• Reduce the probability of movement when the target isabsent
• Terminate moving once enough signal energy is obtained• If a loud target appears, mobile sensors can terminate
movement quickly
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Problem Formulation
Objective: Find the two detection thresholds λ1, λ2 and amovement schedule to minimize the expectedmoving distance:
Pa · PD1 · L1 + (1 − Pa) · PF1 · L0
correct detection false alarm
• Pa: the probability that a target appears• L0(L1): the expected moving distance when
the target is absent (present)
Constraints:• PF1 · PF2 ≤ α• PD1 · PD2 ≥ β
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Outline
1. Motivation
2. Preliminaries
3. Problem Formulation
4. Near-optimal Solution
5. Performance Evaluation
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The Structure of Optimal Solution
• Theorem 1: Total moving distance decreases with the systemdetection probability in the 2nd phase, i.e., PD2
• Linear approximation using the 1st order Taylor expansion
Q−1(PD2) =n2 −
∑ni=1 P i
D2√
∑ni=1 P i
D2 −∑n
i=1(PiD2)
2
≃ − 2√n
n∑
i=1
P iD2 + constant
PD2 increases with∑n
i=1 P iD2 with high probability
• Simplified problem formulation
• Maximize∑n
i=1 P iD2 subject to the constraints:
PF1 · PF2 ≤ α PD1 · PD2 ≥ β
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The Structure of Optimal Solution (Cont.)
• Combination of sensor movement is exponential• Finding maximized
∑ni=1 P i
D2 is exponential
• Theorem 2: In the optimal solution, each mobile sensormove in parallel and consecutively
• Implication•
∑ni=1 P i
D2 can be maximized by DynamicProgramming
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Dynamic Programming: An Example
• Two sensors: A and B
• Budget: two sensor moves
• Suppose:
PAD(0) = 0.40, PA
D(1) = 0.50, PAD(2) = 0.60
PBD (0) = 0.46, PB
D (1) = 0.60, PBD (2) = 0.67
PAD(2) + PB
D(0) = 1.06A
B
PAD(0) + PB
D(2) = 1.07A
B
PAD(1) + PB
D(1) = 1.10A
B
This procedure can be implemented via Dynamic Programming
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Outline
1. Motivation
2. Preliminaries
3. Problem Formulation
4. Near-optimal Solution
5. Performance Evaluation
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Simulation Settings
• Data: public dataset ofDARPA SensIT experiment
• Targets: AmphibiousAssault Vehicles (AAVs)
• Sensors are randomlydeployed in a 50m×50mfield
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Impact of The Number of Mobile Sensors
• Total 12sensors
• 10% to 35%performanceimprovementby 6 mobilesensors 30
40
50
60
70
80
90
100
0.5 1 1.5 2 2.5 3 3.5 4 4.5 5
De
tect
ion
prob
abi
lity
(%)
False alarm rate (%)
static1/2 mobileall mobile
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Conclusions
• Propose a two-phase detection approach• Reactive mobility• Collaboration between static and mobile sensors
• Develop a near-optimal movement scheduling algorithm
• Provide insights into detection system design• Efficient movement schedule of a small number of mobile
sensors significantly boost the detection performance
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Thanks!