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A spatial-temporal imputation technique for classification with missing data in a wireless sensor network

YuanYuan Li, L.E. Parker
2008 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems  
We then estimate missing inputs by using a new spatial-temporal imputation technique. We have evaluated this approach through experiments on both real sensor data and artificially generated data.  ...  We have developed a novel method to estimate missing observations in wireless sensor networks. We use a hierarchical unsupervised fuzzy ART neural network to represent the data cluster prototypes.  ...  We also thank Michael Bailey for help with implementing the fuzzy ART algorithm, the operator control program and integrating the Deluge system.  ... 
doi:10.1109/iros.2008.4650774 dblp:conf/iros/LiP08 fatcat:s3i3vonuhjfptgsuxa4emtgeve

Classification with missing data in a wireless sensor network

YuanYuan Li, Lynne E. Parker
2008 IEEE SoutheastCon 2008  
We have developed a novel method to estimate missing observations in wireless sensor networks.  ...  We then estimate missing inputs by a spatial-temporal imputation technique.  ...  We also thank Michael Bailey for help with implementing the fuzzy ART algorithm, the operator control program and integrating the Deluge system.  ... 
doi:10.1109/secon.2008.4494352 fatcat:grzqyzwjwfgo3ddc37wijx22cu

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

YuanYuan Li, Lynne E. Parker
2014 Information Fusion  
Missing data is common in Wireless Sensor Networks (WSNs), especially with multi-hop communications.  ...  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.  ...  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


I. Priya Stella Mary
2017 International Journal of Advanced Research in Computer Science  
and time, in this paper it is demonstrated experimentally that substituting missing sensor values with spatially and temporally correlated sensor readings using thenovel extended spatial and temporal  ...  This missing data phenomenon occurs due to a variety of reasons such as uneven network communication, synchronization difficulties, untrustworthy sensor devices, environmental aspects and other device  ...  [11] developed a new imputation technique to deal with missing data problem in wireless sensor networks.  ... 
doi:10.26483/ijarcs.v8i9.5145 fatcat:blrgu7ztsjf7dmhdhk5ks4vtbi

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.  ...  Acknowledgments e authors would like to thank the reviewers for their comments, which helped to improve the paper. is work is partly supported by the National Natural Science Foundation of China under  ... 
doi:10.1155/2022/4909476 fatcat:d2fxv7njxnbfpkjmzsq4warumu

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

Yulong Deng, Chong Han, Jian Guo, Lijuan Sun
2021 Sensors  
Data missing is a common problem in wireless sensor networks.  ...  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.  ...  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

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.  ...  The convolution theory, which is usually used in the area of signal and image processing, can also be a practical and efficient way to estimate the missing sensor data.  ...  [7] proposed an imputation technique for context data missing.  ... 
doaj:0c241a0cc2cb45fc8f3d11fe1c5b26bd fatcat:n5lbloird5aqlmm7d5kxro3lka

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 addresses the data processing techniques such as data denoising, data outlier detection, missing data imputation and data aggregation.  ...  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  ...  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

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  
However, missing values are very common in the IoT for a variety of reasons, which results in the fact that the experimental data are incomplete.  ...  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

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.  ...  The authors in [14] introduce a data mining methodology based on exploiting spatial-temporal relationships among sensors in WSNs for missing data imputation.  ... 
doi:10.1155/2015/672124 fatcat:gc226fx3rndffbjp6jshoev6ky

From Data Acquisition to Data Fusion: A Comprehensive Review and a Roadmap for the Identification of Activities of Daily Living Using Mobile Devices

Ivan Pires, Nuno Garcia, Nuno Pombo, Francisco Flórez-Revuelta
2016 Sensors  
Jointly with this classification, the suitability of the use of these sensors in mobile systems for the recognition of ADLs is also evaluated.  ...  This paper focuses on the research on the state of the art for sensor fusion techniques, applied to the sensors embedded in mobile devices, as a means to help identify the mobile device user's daily activities  ...  The authors would also like to acknowledge the contribution of the COST Action IC1303-AAPELE -Architectures, Algorithms and Protocols for Enhanced Living Environments.  ... 
doi:10.3390/s16020184 pmid:26848664 pmcid:PMC4801561 fatcat:ushwthv6xrcgbi7lc2sejwgtje

Networkmetrics: multivariate big data analysis in the context of the internet

José Camacho, Roberto Magán-Carrión, Pedro García-Teodoro, James J. Treinen
2016 Journal of Chemometrics  
Building on this parallelism, we review four classes of problems in networking: estimation, anomaly detection, optimization and classification.  ...  Problems in the Internet, or in general in networking, are not very different from chemometric problems.  ...  Missing Data Imputation Missing data imputation is another relevant estimation problem. In the context of networking, missing data imputation is of especial interest in sensor networks.  ... 
doi:10.1002/cem.2829 fatcat:nrplayn4ffdrvdmiornicvxfny

A Method for Sensor-Based Activity Recognition in Missing Data Scenario

Tahera Hossain, Md. Atiqur Rahman Ahad, Sozo Inoue
2020 Sensors  
For the missing data pattern, we considered data to be missing in a random pattern, which is a realistic missing pattern for sensor data collection.  ...  Learning with missing data reinforces the model to regulate missing data during the classification of various activities that have missing data in the test module.  ...  On the other hand, for real-life situation in wireless sensor network, the missing data are not of a higher rate.  ... 
doi:10.3390/s20143811 pmid:32650486 pmcid:PMC7412080 fatcat:xoy64i5a5rfqzez3cni3pdjgsq

SemImput: Bridging Semantic Imputation with Deep Learning for Complex Human Activity Recognition

Muhammad Asif Razzaq, Ian Cleland, Chris Nugent, Sungyoung Lee
2020 Sensors  
In this study, we propose a semantic imputation framework to improve the quality of sensor data using ontology-based semantic similarity learning.  ...  We found that our semantic imputed datasets improved the classification accuracy with 95.78% as a higher one thus proving the effectiveness and robustness of learned models.  ...  Abbreviations The following abbreviations are used in this manuscript:  ... 
doi:10.3390/s20102771 pmid:32414064 pmcid:PMC7294435 fatcat:ldfkwu3iezhlxm7gnbifx2zp4e

Tampered Data Recovery in WSNs through Dynamic PCA and Variable Routing Strategies

Roberto Magán-Carrión, Fernando Pulido-Pulido, José Camacho, Pedro García-Teodoro
2013 Journal of Communications  
This paper introduces a tolerance approach to fight against data modification attacks in WSNs, which is based on a missing data imputation scheme.  ...  Wireless sensor networks (WSNs) are highly sensible to data integrity attacks, which have an important impact on a number of relevant deployments and services.  ...  In reference [13] , the authors provide a data mining-based technique addressing the missing data imputation problem in mobile sensor networks.  ... 
doi:10.12720/jcm.8.11.738-750 fatcat:v4negwn6ifbptj3r5lp43riguy
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