System-level calibration for data fusion in wireless sensor networks

Frequent sensor calibration is essential in sensor networks with low-cost sensors. We exploit the fact that temporally and spatially close measurements of different sensors measuring the same phenomenon are similar. Hence, when calibrating a sensor, we adjust its calibration parameters to minimize the differences between co-located measurements of previously calibrated sensors. In turn, freshly calibrated sensors can now be used to calibrate other sensors in the network, referred to as multi-hop calibration.We are the first to study multi-hop calibration with respect to a reference signal (micro-calibration) in detail. We show that ordinary least squares regression-commonly used to calibrate noisy sensors-suffers from significant error accumulation over multiple hops. In this paper, we propose a novel multi-hop calibration algorithm using geometric mean regression, which (i) highly reduces error propagation in the network, (ii) distinctly outperforms ordinary least squares in the multi-hop scenario, and (iii) requires considerably fewer ground truth measurements compared to existing network calibration algorithms. The proposed algorithm is especially valuable when calibrating large networks of heterogeneous sensors with different noise characteristics. We provide theoretical justifications for our claims. Then, we conduct a detailed analysis with artificial data to study calibration accuracy under various settings and to identify different error sources. Finally, we use our algorithm to accurately calibrate 13 million temperature, ground ozone (O3), and carbon monoxide (CO) measurements gathered by our mobile air pollution monitoring network.

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“…The existing literature on sensor calibration can be classified into micro-and macro-calibration approaches [46,48].…”

Section: Sensor Calibrationmentioning

confidence: 99%

“…Macro-calibration. Macro-calibration algorithms try to maximize the similarity among the measurements of all sensor nodes in the network [4,7,16,31,46]. In macro-calibration, the main goal is not to adjust sensor calibration according to a reference signal but to achieve a high similarity between sensors in the network.…”

Section: Sensor Calibrationmentioning

confidence: 99%

Reducing multi-hop calibration errors in large-scale mobile sensor networks

Saukh

Hasenfratz

Thiele

2015

Proceedings of the 14th International Conference on Information Processing in Sensor Networks

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“…The energy of many physical signals attenuates with the distance from the signal source [13]. Suppose sensor i is i d meters away from the target that emits a physical signal with the source energy of 0 S .…”

Section: A Signal Attenuation Modelmentioning

confidence: 99%

A Fusion Algorithm for Target Detection in Distributed Sensor Networks

Mei

2014

2014 International Conference on Computational Intelligence and Communication Networks

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In this article, we propose a target detection method in wireless sensor networks based on distributed data fusion. Firstly, we introduce a tree topology. It is different from the conventional tree topology, the sensors in our topology are assigned with weights which are proportional to the received Signal to Noise Ratio (SNR), and are arranged orderly. So the Fusion Center (FC) achieves the highest SNR. Secondly, we propose a fusion algorithm which takes the channel noise into consideration on the base of this topology. The sensor makes a decision relying to its two children nodes and its own observation. At last, we prove that the probability of detection is optimized (maximized) for a given pre specified small probability of false alarm. Our simulation results show the effectiveness of our algorithm by comparing with other detection approaches and analyzing the performance of our algorithm.

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“…; that is, when = 1, there is 1 ( 1 = 1) to satisfy formula (14) in Theorem 8, and 1 satisfies the Lyapunov condition. According to [28],Ŝum( 1 ) = ∑ 1 =1 1 meets the application conditions of central limit theorem; that is,Ŝum( 1 ) obeys normal distribution.…”

Section: Theorem 8 the Groups Of Sequence Of Random Variablesmentioning

confidence: 99%

Research on Reliability-Oriented Data Fusaggregation Algorithm in Large-Scale Probabilistic Wireless Sensor Networks

Peng

Zhang

et al. 2014

International Journal of Distributed Sensor Networks

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A lot of facts show that many researches just place emphasis on data aggregation or data fusion, which is not beneficial to analyze the sensed data thoroughly and will lead to the aggregation results' not being used fully; worse yet, the actual networks are always existed with lossy links; many now available aggregation algorithms are based on ideal network models and not any further analysis and fusion about aggregation results are done. Thus, we propose the concept of data fusaggregation so as to support processing sensed data while transmitting in large-scale probabilistic wireless sensor networks and propose a reliability-oriented data fusaggregation algorithm (RODFA) to assist users to get the monitoring information from the monitored geographic environment and measure the reliability of the information they get. RODFA also facilitates network administrator to improve the system sensing performance for large-scale probabilistic WSNs. In RODFA, the parameter , which could reflect the reliability of aggregation result intuitively, is defined and calculated and it plays an important part in helping users to process aggregation result further. In our experiment, the validity of RODFA is verified by our simulation results, and the influence of network sizes and network performances on data fusaggregation is analyzed.

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System-level calibration for data fusion in wireless sensor networks

Cited by 35 publications

References 30 publications

Reducing multi-hop calibration errors in large-scale mobile sensor networks

Reducing multi-hop calibration errors in large-scale mobile sensor networks

A Fusion Algorithm for Target Detection in Distributed Sensor Networks

Research on Reliability-Oriented Data Fusaggregation Algorithm in Large-Scale Probabilistic Wireless Sensor Networks

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