System-level Calibration for Fusion- based Wireless Sensor Networks Rui Tan 1 Guoliang Xing 1 Zhaohui Yuan 2 Xue Liu 3 Jianguo Yao 4 1 Michigan State University, USA 2 Wuhan University, P.R. China 3 University of Nebraska-Lincoln, USA 4 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 UVa UWisc 29 Palms, CA, [Duarte 2004] 2 / 18

3 / 18 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 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 6 / 18 distance reading local sensing model distance reading 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: y i = s i + n i Signal decay Gaussian noise S: target source energy d i : distance from target r i : reference distance k i : decay factor Acoustic noise [Duarte 2004] 8 / 18

Linear Calibration Calibrated reading = a i  y i – a i : calibration coefficient Goal: system-level sensing model S’: common source energy r: common reference distance k: common decay factor 9 / 18

Data Fusion Model System detection Detection decision is probabilistic – False alarm rate – Detection probability 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 μ i and variance σ i Estimate decay model via linearization Linear fitting on real data unknown target energy } local model: {μ i, σ i, k i, b i } 12 / 18 – Estimate s i from multiple samples Q(x): Q-function of normal dist.

System-level Calibration Problem Given local models {μ i, σ i, k i, b i | i=1, …, N}, find 1) common signal decay model {S’, k, r} 2) calibration coefficients {a 1, a 2, …, a N } maximize detection prob. s.t. false alarm rate ≤ α Sensing performance of calibrated network is optimized! 13 / 18

Opt System-level Calibration 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% improvement10-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

18 Thank you!

Light Spot Detection more effective in low SNR cases 19