In-network outlier detection in wireless sensor networks

Branch, Joel W.; Giannella, Chris; Szymanski, Boleslaw K.; Wolff, Ran; Kargupta, Hillol

doi:10.1007/s10115-011-0474-5

Cited by 239 publications

(113 citation statements)

References 65 publications

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“…Experiments show that KPCA performs well in detecting abnormal values and can obtain the abnormal values quickly and effectively. Zhuang et al [10] propose a method of clearing the network outlier values. It is based on the correction of outlier values of wavelet and distance-based DTW (dynamic time warp) outlier.…”

Section: Related Workmentioning

confidence: 99%

“…Although, the paper does not study the specific relationship between the quality indicators and does not explicitly point out the relevance between quality indicators. Ding et al [8] studies the relationship between data quality properties that apply to information systems. However, the quality evaluation property of information systems cannot be used in WSNs, and the paper does not analyze the difference of final results of data cleaning strategies in different orders.…”

Section: Related Workmentioning

confidence: 99%

“…Although the data cleaning strategy might be designed for a given indicator, it may influence another indicator at the same time. For example, the cleaning of missing data mending may lead to uncertain changes in the accuracy measurement of the data when improving the integrity, due to the fact that the related cleaning technologies cannot guarantee data correctness [8]. For abnormal data correction, the data correctness can be improved without changing the measurement of data integrity indicators.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, a proper data cleaning strategy is particularly important to improve the cleaning efficiency and cleaning effect in wireless sensor networks. At present, the issue of data cleaning instruction solutions for information system databases has been studied [8]. This paper studies the impact of relationship between different indicators on the quality assessment during data cleaning.…”

Section: Introductionmentioning

confidence: 99%

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Data quality analysis and cleaning strategy for wireless sensor networks

Cheng

Feng

Shi

et al. 2018

J Wireless Com Network

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The quality of data in wireless sensor networks has a significant impact on decision support, and data cleaning is an effective way to improve data quality. However, if the data cleaning strategies are not correctly designed, it might result in an unsatisfactory cleaning effect with increased system cleaning costs. Initially, data quality evaluation indicators and their measurement methods in wireless sensor networks were introduced. We then explored the impact of relationship between different indicators which are used in the quality assessment. Finally, data cleaning strategy for wireless sensor networks based on the relationship between data quality indicators was proposed by comparing and analyzing data cleaning schemes with different orders. The experimental results showed that the proposed data cleaning strategy can effectively improve data availability and have a better cleaning effect in wireless sensor networks for the same cleaning cost.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Data quality analysis and cleaning strategy for wireless sensor networks

Cheng

Feng

Shi

et al. 2018

J Wireless Com Network

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“…Many foreign scientific research institutions and university laboratories have also begun to invest a lot of time and money on the wireless sensor network hardware circuit design and software system improvements. Branch J. et al [1] studied the detection of outliers in the wireless sensor networks. Fan X. and Song Y.…”

Section: State Of the Artmentioning

confidence: 99%

Mechanical Vibration Monitoring System Based on Wireless Sensor Network

Hongjuan¹,

Xu²,

Xu³

2018

Int. J. Onl. Eng.

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Abstract-In order to solve the problem of mechanical vibration monitoring, a mechanical vibration monitoring system based on wireless sensor network was designed. First, the requirements of the hardware of the wireless rotating mechanical vibration monitoring system were analyzed. The monitoring node and base station node were designed. Then, based on the VisualBasic6.0 development tool, a software for monitoring the vibration of rotating machinery was designed. It had the functions of command control, data waveform display, and network topology display. In the mode of wireless mechanical vibration monitoring, the organization mode of the network, the transmission mode of data and the corresponding packet transmission format were improved. Finally, the reliable transmission of the data was verified. Compared with the traditional cable vibration sensor, the performance of the monitoring system was verified. The results showed that the wireless vibration monitoring system designed in this paper met the requirements for the monitoring of the vibration state of the rotating machinery. Keywords-wireless sensor networks, mechanical vibration monitoring, rotating machinery vibration IntroductionWith the development of science and technology, modern industrial equipment has been upgraded, serialized, refined and automated. This has made outstanding contributions to reducing product cost, protecting environmental energy and improving economic efficiency. At the same time, it puts forward higher requirements for the maintainability of rotating machinery. If a large equipment fails, it will have an impact on the whole production line or even the whole factory, and the economic loss is immeasurable. In order to reduce the impact caused by mechanical faults, more and more members of staff began to study the wired monitoring technology for the vibration of rotating machinery. At present, many wire monitoring equipment for rotating machinery vibration has been successfully developed and adopted by modern industries. With years of experiments and improvements, it can basically meet the monitoring needs of the operating conditions of rotating machinery and equipment in a simple environment. Wired connections have the advantage of simultaneous real-time trans- 126

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Multisensor change detection on the basis of big time‐series data and Dempster‐Shafer theory

Jafari

Qian

et al. 2016

Concurrency and Computation

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Summary With the proliferation of the Internet of Things, numerous sensors are deployed to monitor a phenomenon that in many cases can be modeled by an underlying stochastic process. The goal is to detect change in the process with tolerable false alarm rate. In practice, sensors may have different accuracy and sensitivity range, or they decay along time. As a result, the sensed data will contain uncertainties and sometimes they are conflicting. In this study, we propose a novel framework to take advantage of Dempster‐Shafer theory's capability of representation of uncertainty to detect change and effectively deal with complementary hypotheses. Specifically, Kullback‐Leibler divergence is used as the metric to find the distances between the estimated distribution with the before and after change distributions. Mass functions are calculated on the basis of those distance values for each sensor independently, and Dempster‐Shafer combination rule is applied to combine the mass values among all sensors. In the case of high conflict in various sensor readings, Dezert‐Smarandache combination rule is applied, and the belief, plausibility, and pignistic probability are obtained for decision making. Simulation results using both synthetic data and real data demonstrate the effectiveness of the proposed schemes.

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In-network outlier detection in wireless sensor networks

Cited by 239 publications

References 65 publications

Data quality analysis and cleaning strategy for wireless sensor networks

Data quality analysis and cleaning strategy for wireless sensor networks

Mechanical Vibration Monitoring System Based on Wireless Sensor Network

Multisensor change detection on the basis of big time‐series data and Dempster‐Shafer theory

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