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" />
Wireless Sensor Network
Paper Submission
"K-Nearest Neighbor Based Missing Data Estimation Algorithm in Wireless Sensor Networks"
written by Liqiang Pan, Jianzhong Li,
published by Wireless Sensor Network, Vol.2 No.2, 2010
has been cited by the following article(s):
  • Google Scholar
  • CrossRef
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[2] Correlation Analysis and Statistical Characterization of Heterogeneous Sensor Data in Environmental Sensor Networks
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[4] Missing value imputation using a novel grey based fuzzy c-means, mutual information based feature selection, and regression model
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[6] iDEG: Integrated Data and Energy Gathering Framework for Practical Wireless Sensor Networks Using Compressive Sensing
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[7] Thirty Years of Machine Learning: The Road to Pareto-Optimal Next-Generation Wireless Networks
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[8] Privacy-aware Controllable Compressed Data Publishing against Sparse Estimation Attack in Internet of Things
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[9] Increasing The Precision Of Noise Source Detection System using KNN Method
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[10] An RNN-based Delay-guaranteed Monitoring Framework in Underwater Wireless Sensor Networks
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[11] Machine Learning Algorithms and Fault Detection for Improved Belief Function Based Decision Fusion in Wireless Sensor Networks
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[12] Profiling-Based Classification Algorithms for Security Applications in Internet of Things
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[13] PCI-MDR: Missing Data Recovery in Wireless Sensor Networks using Partial Canonical Identity Matrix
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[14] Privacy-aware data publishing against sparse estimation attack
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[15] Deep Learning of Virtual Marine Sensors
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[16] Multi-attribute Missing Data Reconstruction Based on Adaptive Weighted Nuclear Norm Minimization in IoT
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[17] Reconstruction of Missing Big Sensor Data
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[18] An Agricultural Sensor Data Recovering Method Based on Matrix Completion Theory
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[19] Missing value estimation for microarray data through cluster analysis
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[20] Predicting Missing Values in Wireless Sensor Network using Spatial-Temporal Correlation
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[21] Missing Data Estimation Algorithm Based on Temporal Correlation in Wireless Sensor Networks
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[22] Interpolating the Missing Values for Multi-Dimensional Spatial-Temporal Sensor Data: A Tensor SVD Approach
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[23] A new regularized recursive dynamic factor analysis with variable forgetting factor for wireless sensor networks with missing data
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[24] Recover Missing Sensor Data with Iterative Imputing Network
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[25] kNN ensembles with penalized DTW for multivariate time series imputation
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[26] 无线传感器网络中基于灰色关联度的丢失数据估算算法
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[27] ST-MVL: Filling Missing Values in Geo-sensory Time Series Data
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[28] Missing data: On criteria to evaluate imputation methods
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[29] Восстановление пропущенных значений в разнородных шкалах с большим числом пропусков
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[30] A benchmark for spatial and temporal correlation based data prediction in wireless sensor networks
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[31] 基于属性相关性的无线传感网络缺失数据估计方法
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[32] Coverage Aware Scheduling in Wireless Sensor Networks: An Optimal Placement Approach
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[33] 基于属性相关性的无线传感网络缺失值估计方法
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[34] Recommending missing sensor values
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[35] Using temporal correlation and time series to detect missing activity-driven sensor events
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[36] An Optimal Linear Predictive Model for Missing Data Estimation in Wireless Sensor Network
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[37] 核电厂环境辐射监测传感器网络中缺失值的粒子群算法-最小二乘支持向量机估计算法
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[38] Determining missing values in dimension incomplete databases using spatial-temporal correlation techniques
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[39] No-sense: Sense with dormant sensors
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[40] A GS-MPSO-WKNN method for missing data imputation in wireless sensor networks monitoring manufacturing conditions
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[41] CIAM: An adaptive 2-in-1 missing data estimation algorithm in wireless sensor networks
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[42] A Hybrid Approach for Improving the Data Quality of Mobile Phone Sensing
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[43] An Estimation Model of Missing Data for Smart Phone Sensing
??????????? ???, 2013
[44] Model-based Transmission Reduction and Virtual Sensing in Wireless Sensor Networks
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[45] 农业物联网应用发展研究
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[46] 스마트폰 센싱을 위한 손실 데이터 추정 모델
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[47] 物联网信息感知与交互技术
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[48] 基于 SVM 的核电站环境辐射监测网络中传感器节点缺失值估计算法
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[49] Dispersion–based prediction framework for estimating missing values in wireless sensor networks
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[50] Collaborative estimation of environmental parameters
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[51] LSSVM based missing data imputation in nuclear power plant's environmental radiation monitor sensor network
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