Organizing and Querying the Big Sensing Data with Event-Linked Network in the Internet of Things

Sun, Yunchuan; Yan, Hongli; Zhang, Junsheng; Xia, Ye; Wang, Shenling; Bie, Rongfang; Tian, Yingjie

doi:10.1155/2014/218521

Cited by 44 publications

(22 citation statements)

References 28 publications

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“…The sampling system collects 65536-point data continuously and stores them once the sampling is completed (the sampling and storage interval is 9 s), data sampling and storing is repeated without break. To some extent, it is also a kind of "Big Sensing Data" [18], if early alarming can be alerted with downsampled data that will be a good progress. Figure 2 shows original acoustic signal and its power spectrum of the material particles detected by the acoustic sensor (under the normal condition of which the gas velocity is 0.71 m/s, the reaction temperature is 86 ∘ C, and the internal pressure is 1.9 Mpa).…”

Section: Sampling System Of Acousticmentioning

confidence: 99%

Fluidized Bed Agglomeration Diagnosis Based on Wavelet Packet Entropy and Gaussian Test

LinWeiguo

WuShuochen

WuHaiyan

et al. 2016

International Journal of Distributed Sensor Networks

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Aiming at the synthesis process through the ethylene polymerization by virtue of fluidized bed reactors, this paper proposed a different approach for agglomeration monitoring and early alarming in polymerization reaction with Gaussian test of wavelet packet entropies from low-frequency audible acoustic signals. In comparison to high-frequency acoustic emission signals, audible acoustic signals can reduce the signal bandwidth, simplify the signal processing circuit, facilitate the real-time measurement, and fault detection. Compared with the approach based on regression modeling and pattern recognition, this approach can overcome the defects of false alarm and missing alarm caused by inadequate samples and weak generalization capability of diagnosis model and is easy to be implemented. Experiments in a pilot plant have verified the effectiveness of the approach, and earlier warning could be realized compared with the existing approaches on the production apparatus (material level detection and temperature measurement).

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Section: Sampling System Of Acousticmentioning

confidence: 99%

Fluidized Bed Agglomeration Diagnosis Based on Wavelet Packet Entropy and Gaussian Test

LinWeiguo

WuShuochen

WuHaiyan

et al. 2016

International Journal of Distributed Sensor Networks

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“…Our future work also includes extending our proposed method in big data cases in the internet of things or astronomy context [28][29][30]. In these cases, we will collect information from multiple sources with different scale of uncertainty.…”

Section: Future Workmentioning

confidence: 99%

Model-Based Sensitivity Analysis on Aerosol Optical Thickness Prediction

Han

Gao

Cui

2015

International Journal of Distributed Sensor Networks

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Prediction of aerosol optical thickness (AOT) is important to study worldwide climate changes. Researchers have built multiple AOT prediction models. However, few researches were focused on the validation of input attributes for AOT regression. In this paper, we proposed a support vector regression (SVR) model-based sensitivity analysis approach to order 35 MODIS input attributes according to their sensitivity to prediction outputs. Next, the attribute sensitivity orders are used for feature selection in the context of regression by removing insensitive attribute one at a time or by removing attributes whose sensitive orders are larger than number k. The experimental results based on the collocated data between MODIS and AERONET from 2009 to 2011 showed that the top 10 insensitive attributes can be screened to speed up prediction model computation with very little loss of accuracy. The results also suggested that the top sensitive attributes are the most informative attributes, requiring the highest precision for accurate AOT prediction. Thereby, our approach will be valuable for remote sensing scientists or atmospheric scientists to optimize the design precision of top sensitive attributes in scanning equipment like MODIS and therefore improve AOT retrieval accuracy.

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“…However, the restriction method taken by cellular network operator has the following disadvantages: (1) [5][6][7][8][9][10][11]. Sun and Jara have proposed a series of models to organize network data, including a twolayered data management model to organize the data in Internet of Things [8], an event-linked network (ELN) model for the organization of big sensing data [9], an approach to extract events and their internal links from large scale data leveraging predefined event schemas in the Web of Things [10] and a synergetic mechanism for digital library to provide information service in the mobile and cloud computing environment [11]. The differences between content-based and social network approach are: (1) the aim of contentbased approach is to predict whether a specified number is a spam or not; (2) the social network approach always generates a directed graph from logs, then use algorithms to analyze the behavior of mobile users.…”

Section: Related Workmentioning

confidence: 99%

Mining Potential Spammers from Mobile Call Logs

Liu

Chen

2015

International Journal of Distributed Sensor Networks

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With the rapid development of mobile telecommunication, voice call spam has become a growing problem in China. Many mobile phone users have become the victim of spam calls and suffered heavy financial loss. Discovering of call spammers can benefit mobile network operators as well as users. Nowadays, the popular method for the task of mining call spammers has been performed by different applications on smartphones. These applications combine manual and automatic methods to detect spammers. Although the results of these client-based solutions are quite satisfying, it is extremely unfortunate that many people still use feature phones, which can not be equipped with third party applications. In this paper, we propose a server-based solution and take a call log file as an example, to analyze the characteristics of mobile call patterns. A time-based graph model and a simple and effective call log rank (CLRank) algorithm with ranking and classification were proposed to find potential call spammers. Compared with existing methods, our model just uses link information, and thus protects user privacy to the maximum extent. Experimental results show that our proposed model can find spammers from call logs automatically, dynamically, and effectively (with 84.5∼91.8% of accuracy) without any manual interventions.

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Organizing and Querying the Big Sensing Data with Event-Linked Network in the Internet of Things

Cited by 44 publications

References 28 publications

Fluidized Bed Agglomeration Diagnosis Based on Wavelet Packet Entropy and Gaussian Test

Fluidized Bed Agglomeration Diagnosis Based on Wavelet Packet Entropy and Gaussian Test

Model-Based Sensitivity Analysis on Aerosol Optical Thickness Prediction

Mining Potential Spammers from Mobile Call Logs

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