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System-level Calibration for Fusion-based Wireless Sensor Networks
Rui Tan1 Guoliang Xing1 Zhaohui Yuan2
Xue Liu3 Jianguo Yao4
1 Michigan State University, USA2 Wuhan University, P.R. China
3 University of Nebraska-Lincoln, USA4 Northwestern Polytechnical University, P.R. China
Surveillance Sensor Networks
• Large-scale, dense sensing• Limited resources (power, computation…)• Uncertain data quality– Hardware biases, random noises, complex deployment terrain
VilgilNet @ UVahttp://www.cs.virginia.edu/wsn/vigilnet
SensIT @ UWisc29 Palms, CA, [Duarte 2004]
2 / 18
A Case Study
Vehicle detection experiment [Duarte 2004]
• Sensing performance variation– Same sensor at different time and across different sensors
4 / 18
up to 100% bias
Existing Solutions• Collaborative signal processing, e.g., data fusion• Can mitigate impact of noise• Assume identical sensing model: [Clouqueur04TOC], [Sheng03IPSN],
[Niu04FUSION], [Wang07IPSN], [Li04ICPPW], ....
• Device-level calibration– Intractable for large-scale networks – Can’t handle post-deployment factors
• System-level calibration– Centralized solutions, many samples and transmissions– Assume simplistic signal processing algorithms 5 / 18
A 2-Tier Calibration Approach
cluster head
local model parameters
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distance
read
ing
local sensing modeldistance
read
ing
system sensing model
• Local sensing model generation– Only need limited groundtruth infomation (e.g.,positions)
• System-level calibration– Only need few local sensing model parameters
Agenda
• Motivation• Preliminaries– Sensor measurement model– Data fusion model
• 2-Tier Calibration Approach• Evaluation• Conclusion
7 / 18
Sensor Measurement Model• Reading of sensor i: yi = si + ni
• Signal decay
• Gaussian noise
ikii
i rdS
s)/(
),(~ 2iii Nn
• S: target source energy• di: distance from target• ri: reference distance• ki: decay factor
Acoustic noise [Duarte 2004]
8 / 18
Linear Calibration
• Calibrated reading = ai yi
– ai: calibration coefficient
• Goal: system-level sensing model
ird
SrdS
a ki
kii
i i
)/('
)/(
S’: common source energyr: common reference distancek: common decay factor
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Data Fusion Model• System detection
• Detection decision is probabilistic– False alarm rate– Detection probability
'1' decide threshold,if i
iy
10 / 18
Agenda
• Motivation• Preliminaries• 2-Tier Calibration Approach– Local sensing model generation– Optimal system-level calibration
• Evaluation• Conclusion
11 / 18
Local Model Generation• Estimate noise mean μi and variance σi
• Estimate decay model via linearization
)ln()ln(ln ikiiii rSdks
Linear fitting on real data
unknown target energy95.1ik
} 58.3)ln( ikii rSb
local model: {μi, σi, ki, bi}
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– Estimate si from multiple samples
) exceeds of ratio(1iiiiii yyQys
Q(x): Q-function of normal dist.
System-level Calibration Problem
Given local models {μi, σi, ki, bi | i=1, …, N}, find1) common signal decay model {S’, k, r}2) calibration coefficients {a1, a2, …, aN}
maximize detection prob.s.t. false alarm rate ≤ α
Sensing performance of calibrated network is optimized!
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Opt System-level Calibration
)exp(
)2exp(maxarg
222
ikk
ii
kkii ii
i
ki
bda
db
dk
i
i
14 / 18
• Unconstrained optimization– Efficient solution
Light Spot Detection
• No-calibration approach• Device-level approach– Sensor w/ highest light-distance curve as ground truth
TelosB
15 / 18
Detect light spot on LCD
bias~45%
Trace-driven Simulations• Data traces collected from 23 acoustic sensors
in vehicle detection experiment [Duarte 2004]
16 / 18~50% improvement 10-fold reduction
Conclusions• 2-Tier system calibration approach– Local model generation: low comput. overhead– System-level calibration: low comm. overhead
• Optimal system-level calibration– Maximize system detection performance
• Extensive evaluation
17 / 18
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