Target Localization Using Ensemble Support Vector Regression in Wireless Sensor Networks

Kim, Woojin; Park, Jaemann; Kim, H. Jin

doi:10.1109/wcnc.2010.5506589

Cited by 20 publications

(21 citation statements)

References 15 publications

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“…To manage the sensor node resources and collected data, some schemes need to be taken into account to provide efficient data processing or message transfer, especially in large-scale WSNs. In general, there are two types of information processing in WSNs, named distributed processing [12,13,16,17] and centralized processing [14,[18][19][20][21]. For the distributed processing, when the sensor nodes sense and collect data, they will calculate or process the data followed by sending the data to the sink.…”

Section: Related Workmentioning

confidence: 99%

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Modeling of Location Estimation for Object Tracking in WSN

Chu

Lee

Chang

et al. 2013

Journal of Applied Mathematics

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Location estimation for object tracking is one of the important topics in the research of wireless sensor networks (WSNs). Recently, many location estimation or position schemes in WSN have been proposed. In this paper, we will propose the procedure and modeling of location estimation for object tracking in WSN. The designed modeling is a simple scheme without complex processing. We will use Matlab to conduct the simulation and numerical analyses to find the optimal modeling variables. The analyses with different variables will include object moving model, sensing radius, model weighting value , and power-level increasing ratio k of neighboring sensor nodes. For practical consideration, we will also carry out the shadowing model for analysis.

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Section: Related Workmentioning

confidence: 99%

“…The data aggregation technology [18][19][20][21] might be employed before the data reach the sink so as to reduce the amount of data transfer and consequently provide the energy saving. The research in [14] set a data volume threshold for the detected object data in WSNs.…”

Section: Related Workmentioning

confidence: 99%

Modeling of Location Estimation for Object Tracking in WSN

Chu

Lee

Chang

et al. 2013

Journal of Applied Mathematics

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“…One common strategy was to divide the data into subsets, in which sequences of sub-SVM models were trained to capture local information. The final estimation was obtained by combining the outputs of all the submodels [20], [21]. In other work, a series of different kernels was employed to represent the local information during classification [22]- [24].…”

Section: Introductionmentioning

confidence: 99%

A Novel LS-SVM Modeling Method for a Hydraulic Press Forging Process With Multiple Localized Solutions

Fan

Huang

2015

IEEE Trans. Ind. Inf.

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An effective model of the forging process is crucial for the optimal operation and health management of a hydraulic press machine (HPM). Modeling this forging process is difficult, because multiple localized nonlinear solutions and modeling of unknown complex interactions between localized regions are required. In this paper, a novel least squares support vector machine (LS-SVM) method is developed for modeling the forging process. The proposed method integrates the advantages of local LS-SVM modeling and global regularization. Local LS-SVM modeling is performed to capture the local dynamics for each local working region. Global regularization is performed to minimize the global error and improve the global generalization of the local models. These features guarantee continuity and smoothness between the local LS-SVM models and avoid over-fitting of each local LS-SVM model. The algorithm developed here is simple and may easily be added into existing HPM systems. Experiment data from a practical HPM demonstrate the effectiveness of the proposed method. Index Terms-Forging process, least squares support vector machine (LS-SVM), multiple working regions, regularization.Manuscript Xinjiang Lu (M'12) received the B.E. and M.E. degrees in process modeling and control, robust design, integration of design and control from the

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“…Many works focus on using Machine Learning concept for localization in WSNs. In particular, based on SVR, Kim et al [1] developed an Ensemble method. The experiment shows that this estimator is performing in terms of accuracy and robustness.…”

Section: Introductionmentioning

confidence: 99%

Comparative study of learning-based localization algorithms for Wireless Sensor Networks: Support Vector regression, Neural Network and Naïve Bayes

Ahmadi

Bouallègue

2015

2015 International Wireless Communications and Mobile Computing Conference (IWCMC)

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In recent years, there has been a growing interest in localization for wireless sensor networks. Since the complex behavior of such network, various machine learning-based methods are proposed in order to improve localization goals. The objective of this paper is to compare three well known learningbased localization techniques using Received Signal Strength Indicator (RSSI) : the Support Vector regression, Naïve Bayes and Artificial Neural Network. We take into consideration two performance keys : the localization error and the computation complexity.

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Target Localization Using Ensemble Support Vector Regression in Wireless Sensor Networks

Cited by 20 publications

References 15 publications

Modeling of Location Estimation for Object Tracking in WSN

Modeling of Location Estimation for Object Tracking in WSN

A Novel LS-SVM Modeling Method for a Hydraulic Press Forging Process With Multiple Localized Solutions

Comparative study of learning-based localization algorithms for Wireless Sensor Networks: Support Vector regression, Neural Network and Naïve Bayes

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Target Localization Using Ensemble Support Vector Regression in Wireless Sensor Networks

Cited by 20 publications

References 15 publications

Modeling of Location Estimation for Object Tracking in WSN

Modeling of Location Estimation for Object Tracking in WSN

A Novel LS-SVM Modeling Method for a Hydraulic Press Forging Process With Multiple Localized Solutions

Comparative study of learning-based localization algorithms for Wireless Sensor Networks: Support Vector regression, Neural Network and Na&#x00EF;ve Bayes

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Comparative study of learning-based localization algorithms for Wireless Sensor Networks: Support Vector regression, Neural Network and Naïve Bayes