DeepMDP: A Novel Deep-Learning-Based Missing Data Prediction Protocol for IoT

Kök, İbrahim; Özdemir, Suat

doi:10.1109/jiot.2020.3003922

Cited by 36 publications

(7 citation statements)

References 64 publications

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“…Results show that all models have high accuracy. As IoT missing data mainly occur due to sensor failures or network problems-which fits the MCAR category [19]-many works in the literature start from this premise to impute missing data [18][19][20].…”

Section: Related Workmentioning

confidence: 99%

“…They assumed that a record has three attributes and predicts the missing value using the other two attributes received in the same timestamp. In [19], Kök et al, used the same models as those proposed by [17], but they implemented them in edge and Cloud devices. Their goal was to have a low delay and efficient use of the network's bandwidth.…”

Section: Related Workmentioning

confidence: 99%

“…Consequently, the IoT gateway sends complete records to the application server. To the best of our knowledge, there is no other work in the literature that proposes imputation techniques considering resource-constrained devices [10,[14][15][16][17][18][19][20][21][22]. As a result, most related work does not analyze their proposals' execution time and memory footprint.…”

Section: Introductionmentioning

confidence: 99%

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Missing Data Imputation in Internet of Things Gateways

2021

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In an Internet of Things (IoT) environment, sensors collect and send data to application servers through IoT gateways. However, these data may be missing values due to networking problems or sensor malfunction, which reduces applications’ reliability. This work proposes a mechanism to predict and impute missing data in IoT gateways to achieve greater autonomy at the network edge. These gateways typically have limited computing resources. Therefore, the missing data imputation methods must be simple and provide good results. Thus, this work presents two regression models based on neural networks to impute missing data in IoT gateways. In addition to the prediction quality, we analyzed both the execution time and the amount of memory used. We validated our models using six years of weather data from Rio de Janeiro, varying the missing data percentages. The results show that the neural network regression models perform better than the other imputation methods analyzed, based on the averages and repetition of previous values, for all missing data percentages. In addition, the neural network models present a short execution time and need less than 140 KiB of memory, which allows them to run on IoT gateways.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Missing Data Imputation in Internet of Things Gateways

2021

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“…Kök and Özdemir and Chen et al used unsupervised deep learning based on deep belief networks to perform a time series analysis to achieve this. The mechanism is divided into data preprocessing and model training phases to provide predictions for both short‐term load cases and long‐term load cases, allowing a comprehensive understanding of the system's state at the time of submission to the computational task 33,34 . However, model parameters for deep learning usually involve many parameters, and therefore, model training for deep learning is very time‐consuming.…”

Section: Introductionmentioning

confidence: 99%

Task‐oriented resource prediction and adjustment algorithm for space‐based information network

Wang,

Liu,

Chen

et al. 2023

Satell Commun Network

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SummaryThe tasks of a space‐based information network are complex and diverse, but the resources of a space‐based environment are minimal. The existing methods are challenging to match task demand to resource supply accurately. Aiming at the problem of accurate prediction from task to resource, we propose a resource prediction adjustment strategy. First, we propose a multidimensional resource prediction algorithm based on improved particle swarm optimization and back propagation (IPSO‐BP) neural network. The improved PSO is used to optimize the weight and threshold of BP neural network to make up for the defects that BP neural network is easy to fall into local minimum and the predicted output value is not unique. Second, to meet the quality of service (QoS) of tasks, we propose a density‐based performance evaluation algorithm (DPEA) to adjust resources. This method uses the idea of local sensitive hash to select the evaluation subset for the configuration task, then dynamically selects the nearest neighbors of the configuration task, and uses the idea of weighted average to evaluate the QoS performance index of the configuration task. Simulation results show that the proposed resource prediction and adjustment strategy effectively reduces the scheduling time overhead and improves resource utilization.

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“…In real-world scenarios, missing information is an inevitable issue due to many reasons such as deficiencies of data collection, errors in data storage and equipment failure [1][2][3]. Generally, missing data is the absence of certain information from a dataset and it may undermine data integrity and reduce the dependability of analysis results.…”

Section: Introductionmentioning

confidence: 99%

Joint clustering and missing value imputation for incomplete data via fuzzy modeling and alternate optimization

Wang

Pang

Zhang

et al. 2023

Preprint

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Since missing information is an ordinary phenomenon in actual scenarios that increases the difficulty of data analysis, missing value imputation has attracted ever-growing attention in recent years, by exploiting data modeling. Particularly, missing information in engineering design and optimization is a challenging topic. In this work, an exquisite missing value imputation method based on Takagi-Sugeno (TS) fuzzy modeling is proposed, which first divides incomplete dataset by clustering into several fuzzy subsets and finally establishes global model with different regression models on each subset. Specifically, to improve the clustering performance of practical incomplete issues, we incorporate the measurement of common and uncommon observed feature subspace information of data instances into the fuzzy c-means (FCM) framework. Moreover, to better improve the model accuracy, feature selection is introduced to each fuzzy rule and update model parameters and imputations by co-training. The experiments reported both on UCI and tunnel boring machine (TBM) operation datasets demonstrate the proposed method can accurately accomplish missing value imputation of incomplete data.

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DeepMDP: A Novel Deep-Learning-Based Missing Data Prediction Protocol for IoT

Cited by 36 publications

References 64 publications

Missing Data Imputation in Internet of Things Gateways

Missing Data Imputation in Internet of Things Gateways

Task‐oriented resource prediction and adjustment algorithm for space‐based information network

Joint clustering and missing value imputation for incomplete data via fuzzy modeling and alternate optimization

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