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Nearest neighbor imputation using spatial–temporal correlations in wireless sensor networks

YuanYuan Li, Lynne E. Parker
2014 Information Fusion  
Thus, we have developed a novel Nearest Neighbor (NN) imputation method that estimates missing data in WSNs by learning spatial and temporal correlations between sensor nodes.  ...  Since sensor data collected by a WSN is generally correlated in time and space, we illustrate how replacing missing sensor values with spatially and temporally correlated sensor values can significantly  ...  Matt Welsh, of Harvard University, who made the Reventador data from Volcano Tingurahua available to us. We also thank Dr.  ... 
doi:10.1016/j.inffus.2012.08.007 pmid:28435414 pmcid:PMC5396980 fatcat:nnx6ldnd3zgzzoioikswkh7pfa

Temporal and Spatial Nearest Neighbor Values Based Missing Data Imputation in Wireless Sensor Networks

Yulong Deng, Chong Han, Jian Guo, Lijuan Sun
2021 Sensors  
In this paper, the temporal and spatial nearest neighbor values-based missing data imputation (TSNN), a new imputation based on the temporal and spatial nearest neighbor values has been presented.  ...  Data missing is a common problem in wireless sensor networks.  ...  The authors also acknowledge Linguo Li, of Fuyang Normal University, China, who gave us kind help in the data validation. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/s21051782 pmid:33806481 fatcat:6so3r7jroneutmy47ebx4lay2y

Online Missing Data Imputation Using Virtual Temporal Neighbor in Wireless Sensor Networks

Yulong Deng, Chong Han, Jian Guo, Linguo Li, Lijuan Sun, Xingsi Xue
2022 Wireless Communications and Mobile Computing  
A wireless sensor network (WSN) is one of the most typical applications of the Internet of Things (IoT).  ...  Researching in the same way, in this paper, we propose VTN imputation, an online missing data imputation algorithm based on virtual temporal neighbors.  ...  Temporal and spatial nearest neighbor value-based missing data imputation (TSNN) [25] is proposed to make imputation in WSNs by the combination of four spatial and temporal nearest neighbor values, which  ... 
doi:10.1155/2022/4909476 fatcat:d2fxv7njxnbfpkjmzsq4warumu

A New Missing Values Estimation Algorithm in Wireless Sensor Networks Based on Convolution

Feng Liu
2013 Sensors & Transducers  
Nowadays, with the rapid development of Internet of Things (IoT) applications, data missing phenomenon becomes very common in wireless sensor networks.  ...  This problem can greatly and directly threaten the stability and usability of the Internet of things applications which are constructed based on wireless sensor networks.  ...  In the further work [12], a temporal correlation based missing values imputation algorithm which adopt linear interpolation model and a spatial correlation based missing values imputation algorithm which  ... 
doaj:0c241a0cc2cb45fc8f3d11fe1c5b26bd fatcat:n5lbloird5aqlmm7d5kxro3lka

Efficient Spatial Data Recovery Scheme with Error Refinement for Cyber-physical Systems

Naushin Nower, Yasuo Tan, Yuto Lim
2018 International Journal of Computer Applications  
In this scheme, we present an algorithm with Pearson Correlation Coefficient (PCC) to efficiently solve the missing data for both deterministic and stochastic traffic patterns.  ...  In this paper, we proposed a data recovery called Efficient Spatial Data Recovery with an error refinement (ESDR/ER) procedure for CPS to minimize the error estimation and maximize the accuracy of the  ...  Besides that, a future work will focus on examining the real-time recovery using the proposed ESDR/ER scheme.  ... 
doi:10.5120/ijca2018916558 fatcat:e4lmsnokwzdmhnogac74ams64i

Imputing the Missing Values in IoT using FRBIM

2019 International journal of recent technology and engineering  
One such challenge is missing data imputation in Internet of Things.  ...  In this paper, a novel FRBIM (Fuzzy Rule-Based Imputation Model) model is proposed to impute missing data based on the characteristics of IoT data to accomplish high accuracy rate.  ...  These spatial and temporal correlation based imputation models were reviewed to gain insights into the missing data problem in wireless sensor networks and summary of these methods was produced.  ... 
doi:10.35940/ijrte.c5024.098319 fatcat:dsjjykqcinemljxsvokhqua5zy

Missing Data Reconstruction Based on Spectral k-Support Norm Minimization for NB-IoT Data

Luo Xuegang, Lv Junrui, Wang Juan, Xianyong Li
2021 Mathematical Problems in Engineering  
Second, based on the correlations of sensor data and its joint low rank, an algorithm based on the matrix spectral k-support norm minimization with automatic weight is developed.  ...  An effective fraction of data with missing values from various physiochemical sensors in the Internet of Things is still emerging owing to unreliable links and accidental damage.  ...  [9] have described a missing data imputation approach that was combining Fourier and lagged k-nearest neighbor for biomedical time series missing data imputation. e nearest neighbor (NN) imputation  ... 
doi:10.1155/2021/1336900 fatcat:wn3ylalxpvebxlpnjebaook6g4

Multivariate Statistical Approach for Anomaly Detection and Lost Data Recovery in Wireless Sensor Networks

Roberto Magán-Carrión, José Camacho, Pedro García-Teodoro
2015 International Journal of Distributed Sensor Networks  
Data loss due to integrity attacks or malfunction constitutes a principal concern in wireless sensor networks (WSNs).  ...  We also introduce a novel data arrangement method to exploit the spatial correlation among the sensors in a more efficient manner.  ...  Their missing data NN algorithm will use the nearest neighbors found in -tree traversal to impute the lost sensor value.  ... 
doi:10.1155/2015/672124 fatcat:gc226fx3rndffbjp6jshoev6ky

Balancing Lifetime and Classification Accuracy of Wireless Sensor Networks [article]

Kush R. Varshney, Peter M. van de Ven
2012 arXiv   pre-print
Wireless sensor networks are composed of distributed sensors that can be used for signal detection or classification.  ...  A specific such algorithm is Fisher discriminant analysis (FDA), the classification accuracy of which has been previously studied in the context of wireless sensor networks.  ...  Another direction for future research is to model temporal correlation in the sensor measurements in addition to spatial correlation.  ... 
arXiv:1208.2278v1 fatcat:b7hcjwocijh2dd2tqe2fmo4epm

Balancing lifetime and classification accuracy of wireless sensor networks

Kush R. Varshney, Peter M. van de Ven
2013 Proceedings of the fourteenth ACM international symposium on Mobile ad hoc networking and computing - MobiHoc '13  
Wireless sensor networks are composed of distributed sensors that can be used for signal detection or classification.  ...  A specific learning algorithm is Fisher discriminant analysis (FDA), the classification accuracy of which has been previously studied in the context of wireless sensor networks.  ...  Another direction for future research is to model temporal correlation in the sensor measurements in addition to spatial correlation.  ... 
doi:10.1145/2491288.2491289 dblp:conf/mobihoc/VarshneyV13 fatcat:xlk657qwzfgabhfrl6fzbl52kq

An Overview of IoT Sensor Data Processing, Fusion, and Analysis Techniques

Rajalakshmi Krishnamurthi, Adarsh Kumar, Dhanalekshmi Gopinathan, Anand Nayyar, Basit Qureshi
2020 Sensors  
This paper also aims to address data analysis integration with emerging technologies, such as cloud computing, fog computing and edge computing, towards various challenges in IoT sensor network and sensor  ...  In the recent era of the Internet of Things, the dominant role of sensors and the Internet provides a solution to a wide variety of real-life problems.  ...  The authors in [28] suggested a novel method of nearest neighbor imputation to impute missing values based on the spatial and temporal correlations between sensor nodes.  ... 
doi:10.3390/s20216076 pmid:33114594 fatcat:7chsqdqulzfejczzkwvmvr63dq

DNN-MVL: DNN-Multi-View-Learning-Based Recover Block Missing Data in a Dam Safety Monitoring System

Yingchi Mao, Jianhua Zhang, Hai Qi, Longbao Wang
2019 Sensors  
The successive missing data takes the side effects on the accuracy of real-time monitoring as well as the performance on the data analysis in the wireless sensor networks.  ...  DNN-MVL mainly considers five views: global spatial view, global temporal view, local spatial view, local temporal view, and semantic view.  ...  Author Contributions: All four authors have equally contributed to the work presented in this paper.  ... 
doi:10.3390/s19132895 fatcat:6h6tao3kbvarfcvswl6hxhcrgu

Missing Data Estimation for Traffic Volume by Searching an Optimum Closed Cut in Urban Networks

Shangbo Wang, Guoqiang Mao
2018 IEEE transactions on intelligent transportation systems (Print)  
To fully exploit the spatial-temporal correlation and road topological information in urban traffic network, we propose an Optimum Closed Cut (OCC) based spatio-temporal imputation technique, which is  ...  OCC based estimator can provide more accurate imputation results compared to NHA (Nearest Historical Average) and correlative k-NN (k-Nearest Neighbors) methods.  ...  The proposed technique utilizes both the road topological information and the spatio-temporal correlation among road traffic for imputation, while using a minimal number of sensor measurements.  ... 
doi:10.1109/tits.2018.2801808 fatcat:chuva6rlnjfmjjc2t44du6j4ca

Missing Value Imputation Based on Gaussian Mixture Model for the Internet of Things

Xiaobo Yan, Weiqing Xiong, Liang Hu, Feng Wang, Kuo Zhao
2015 Mathematical Problems in Engineering  
Nowadays, the IoT has been used widely and commonly by a variety of domains, such as transportation and logistics domain and healthcare domain.  ...  This paper, for the characteristics of the data itself and the features of missing data in IoT, divides the missing data into three types and defines three corresponding missing value imputation problems  ...  In [17] , researchers propose a novel nearest neighbor (NN) imputation algorithm to estimate missing values in wireless sensor network by learning spatial-temporal correlation between wireless sensor  ... 
doi:10.1155/2015/548605 fatcat:x5hdxmhzq5f7lo3avqq27q2j64

Recover Missing Sensor Data with Iterative Imputing Network [article]

Jingguang Zhou, Zili Huang
2017 arXiv   pre-print
In contrast, our model captures the latent complex temporal dynamics by summarizing each observation's context with a novel Iterative Imputing Network, thus significantly outperforms previous work on the  ...  Sensor data has been playing an important role in machine learning tasks, complementary to the human-annotated data that is usually rather costly.  ...  Some studies include the spatial cue into the missing data recovery. (Pan and Li 2010) presented a K-nearest neighbor method for jointly spatial and temporal data imputation.  ... 
arXiv:1711.07878v1 fatcat:mwcos65ojvg4bh2zf4rmllqxsm
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