Adaptive Calibration for Fusion-based Wireless Sensor Networks

Tan, Rui; Xing, Guoliang; Li, Xue; Yao, Jianguo; Yuan, Zhaohui

doi:10.1109/infcom.2010.5462036

Cited by 30 publications

(16 citation statements)

References 25 publications

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“…The experiment settings are listed in Table 1. We note that a similar experiment methodology is employed for studying the sensing performance of WSNs in [10,19]. The sensors measure the light intensity every 250 milliseconds.…”

Section: Experiments Methodology and Settingsmentioning

confidence: 99%

“…In our earlier work [18,22], we study the impact of data fusion on spatial and temporal coverage of large-scale sensor networks. In [19], we calibrate the parameters of data fusion such that a sensor network adapts to various dynamics. In this paper, we aim to calibrate the sensor measurements such that the sensors have an identical sensing model that optimizes the system detection performance.…”

Section: Related Workmentioning

confidence: 99%

“…Suppose there are N member sensors in a cluster, the sum of measure- cluster head decides H 1 ; otherwise, it decides H 0 . Such a basic data fusion model has been widely employed in previous work [3,19].…”

Section: Multi-sensor Data Fusion Modelmentioning

confidence: 99%

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System-Level Calibration for Fusion-Based Wireless Sensor Networks

Tan

Xing

Yuan

et al. 2010

2010 31st IEEE Real-Time Systems Symposium

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Wireless sensor networks are typically composed of lowcost sensors that are deeply integrated in physical environments. As a result, the sensing performance of a wireless sensor network is inevitably undermined by biases in imperfect sensor hardware and the noises in data measurements. Although a variety of calibration methods have been proposed to address these issues, they often adopt the devicelevel approach that becomes intractable for moderate-to large-scale networks. In this paper, we propose a two-tier system-level calibration approach for a class of sensor networks that employ data fusion to improve the sensing performance. In the first tier of our calibration approach, each sensor learns its local sensing model from noisy measurements using an online algorithm and only transmits a few model parameters. In the second tier, sensors' local sensing models are then calibrated to a common system sensing model. Our approach fairly distributes computation overhead among sensors and significantly reduces the communication overhead of calibration. Based on this approach, we develop an optimal model calibration scheme that maximizes the target detection probability of a sensor network under bounded false alarm rate. Our approach is evaluated by both experiments on a testbed of TelosB motes and extensive simulations based on data traces collected in a real vehicle detection experiment. The results demonstrate that our system-level calibration approach can significantly boost the detection performance of sensor networks in the scenarios with low signal-to-noise ratios.

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Section: Experiments Methodology and Settingsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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System-Level Calibration for Fusion-Based Wireless Sensor Networks

Tan

Xing

Yuan

et al. 2010

2010 31st IEEE Real-Time Systems Symposium

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“…Sensing diversity encompasses the sensing capability differences between different sensors of the same modality as well as among different modalities. The causes of sensing diversity can be linked to the in-situ reality of a specific deployment [6] as well as hardware differences [7], especially in the case of cheap off-the-shelf motes.…”

Section: Introductionmentioning

confidence: 99%

“…Other works attempt to overcome sensing diversity by correcting for the differences in readings from different sensors [7]. Instead, we take advantage of such sensing differences to provide sensing This work was supported in part by NSF grants ECCS-0901437, CNS-0916994, and CNS-0954039.…”

Section: Introductionmentioning

confidence: 99%

Exploiting sensing diversity for confident sensing in wireless sensor networks

Keally

Zhou

Xing

et al. 2011

2011 Proceedings IEEE INFOCOM

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Abstract-Wireless sensor networks for human health monitoring, military surveillance, and disaster warning all have stringent accuracy requirements for detecting or classifying events while maximizing system lifetime. We define meeting such user accuracy requirements as confident sensing. To perform confident sensing and reduce energy, we must address sensing diversity: sensing capability differences among heterogeneous and homogeneous sensors in a specific deployment. We are among the first to explore the impact of sensing diversity on sensor collaboration, exploit diversity for sensing confidence, and apply diversity exploitation for confident sensing coverage. We show that our diversity-exploiting confident coverage problem is NPhard for any specific deployment and present a practical solution, Wolfpack. Through a distributed and iterative sensor collaboration approach, Wolfpack maximizes a specific deployment's capability to meet user detection requirements and save energy by powering off unneeded nodes. Using real vehicle detection trace data, we demonstrate that Wolfpack provides confident event detection coverage for 30% more detection locations, using 20% less energy than a state of the art approach.

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Magnetic Field Measurement for Power Distribution Systems

2019

Magnetic Field Measurement With Applications to Modern Power Grids

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Adaptive Calibration for Fusion-based Wireless Sensor Networks

Cited by 30 publications

References 25 publications

System-Level Calibration for Fusion-Based Wireless Sensor Networks

System-Level Calibration for Fusion-Based Wireless Sensor Networks

Exploiting sensing diversity for confident sensing in wireless sensor networks

Magnetic Field Measurement for Power Distribution Systems

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