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]

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How can we ensure the fidelity of sensor data?

A Case Study

Vehicle detection experiment [Duarte 2004]

• Sensing performance variation– Same sensor at different time and across different sensors

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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

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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]

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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

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Agenda

• Motivation• Preliminaries• 2-Tier Calibration Approach– Local sensing model generation– Optimal system-level calibration

• Evaluation• Conclusion

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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

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• Unconstrained optimization– Efficient solution

Light Spot Detection

• No-calibration approach• Device-level approach– Sensor w/ highest light-distance curve as ground truth

TelosB

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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

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Thank you!

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Light Spot Detection

more effective in low SNR cases