Recommending missing sensor values

Li, Chung‐Yi; Su, Wei-Lun; McKenzie, Todd G.; Hsu, Fu-Chun; Lin, Shou-De; Hsu, Jane Yung-jen; Gibbons, Phillip B.

doi:10.1109/bigdata.2015.7363779

Cited by 8 publications

(3 citation statements)

References 20 publications

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“…In this respect, temporal correlation is calculated using the time at which each data from a sensor were gathered. Linear interpolation [27], Last Observation Carried Forward (LOCF) [28], autoregressive model [29], and Support Vector Regression (SVR) [30] are commonly used to perform temporal imputation although they can also be used to perform other types of imputations. However, these methods do not handle long temporal gaps efficiently and have a tendency to increase bias.…”

Section: )mentioning

confidence: 99%

Long Gaps Missing IoT Sensors Time Series Data Imputation: A Bayesian Gaussian Approach

et al. 2022

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Missing sensor data is a common problem associated with the Internet of Things (IoT) ecosystems, which affect the accuracy of the associated services such as adequate medical intervention for older adults living at home. This problem is caused by many factors, power down is one of them, communication failure and sensor failure are another two reasons. Multiple missing data imputation methods have been developed to solve this issue. However, irregular temporal missing data locations is challenging to handle, due to lack of knowledge of their occurrence probability and their random temporal location. In this paper, we propose a Bayesian Gaussian Process based imputation technique that accounts for temporal forcing to fill in the missing sensor data. Our approach; Bayesian Gaussian Process (BGaP); can impute missing data efficiently at any missing rate and for any temporal location using prior knowledge gathered on past observations. We illustrated how our approach performs using real data collected from sensors deployed in the residence of 10 older adults over a two-year period. Using our novel approach, we were able to impute all the missing data which allowed us to observe long-term behavior changes that we would not have been able to observe otherwise.INDEX TERMS Missing Sensor Data, Missing Data Imputation, Bayesian Gaussian Process, Long-term Older adults Behavior Monitoring.

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Section: )mentioning

confidence: 99%

Long Gaps Missing IoT Sensors Time Series Data Imputation: A Bayesian Gaussian Approach

et al. 2022

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“…Chung-Yi Li et al [6] proposed a novel imputation method that utilized the recommendation system while doing imputation. The proposed model was evaluated using two sensor datasets.…”

Section: Related Workmentioning

confidence: 99%

Imputing the Missing Values in Iot Using Estcp Model

Mary¹

2017

ijarcs

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:In Internet of Things, occurrence of missing data is inevitable due to its intrinsic characteristics. 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 malfunctions which often resulted in data incompleteness. A robust approach to missing data is an indispensible component of analysis to promote the perfect explanation of research findings.As the data generated by the IoT devices is usually correlated in space 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 correlated proximate missing data imputation model (ESTCP)has considerably improved the accuracy than that of the previously proposed STCP model and the existing single imputation and multiple imputation techniques.

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“…Recently, the intrinsic low-rank property of highdimensional data has been considered. In contrast to many existing approaches that make strong assumption about data, Li et al applies matrix factorization (MF)-based method to recover missing data [14], which learns inter-sensor and intra-sensor correlations by exploiting their latent similarity. Finally, they extend the methods to account for possible correlations among multiple types of sensors.…”

Section: Introductionmentioning

confidence: 99%

Reconstruction of Missing Big Sensor Data

Shao,

Chen

2017

Preprint

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With ubiquitous sensors continuously monitoring and collecting large amounts of information, there is no doubt that this is an era of big data. One of the important sources for scientific big data is the datasets collected by Internet of things (IoT). It's considered that these datesets contain highly useful and valuable information. For an IoT application to analyze big sensor data, it is necessary that the data are clean and lossless. However, due to unreliable wireless link or hardware failure in the nodes, data loss in IoT is very common. To reconstruct the missing big sensor data, firstly, we propose an algorithm based on matrix rank-minimization method. Then, we consider IoT with multiple types of sensor in each node. Accounting for possible correlations among multiple-attribute sensor data, we propose tensor-based methods to estimate missing values. Moreover, effective solutions are proposed using the alternating direction method of multipliers. Finally, we evaluate the approaches using two real sensor datasets with two missing data-patterns, i.e., random missing pattern and consecutive missing pattern. The experiments with real-world sensor data show the effectiveness of the proposed methods.

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Recommending missing sensor values

Cited by 8 publications

References 20 publications

Long Gaps Missing IoT Sensors Time Series Data Imputation: A Bayesian Gaussian Approach

Long Gaps Missing IoT Sensors Time Series Data Imputation: A Bayesian Gaussian Approach

Imputing the Missing Values in Iot Using Estcp Model

Reconstruction of Missing Big Sensor Data

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