Probabilistic Recovery of Incomplete Sensed Data in IoT

Fekade, Berihun; Maksymyuk, Taras; Kyryk, Maryan; Jo, Minho

doi:10.1109/jiot.2017.2730360

Cited by 91 publications

(44 citation statements)

References 10 publications

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“…Hybrid methods Fekade et al [63] proposed a k-means clustering and Probabilistic Matrix Factorization (PMF) approach to recover missing values. Firstly, k-means clustering is done to divide the data into clusters, and within each cluster, PMF is applied.…”

Section: Singular Value Decompositionmentioning

confidence: 99%

“…They assessed their solution on simulated errors whereby they artificially injected 100 anomalies. For missing data imputation, the Intel Lab dataset has also been used by D' Aniello et al [62] and Fekade et al [63], where the former simulated the missing errors at 5%, 10%, 20%, 30%, 40% and 50% rates and the latter simulated the missing error by making 10% of the total data empty.…”

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confidence: 99%

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Sensor data quality: a systematic review

2020

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Sensor data quality plays a vital role in Internet of Things (IoT) applications as they are rendered useless if the data quality is bad. This systematic review aims to provide an introduction and guide for researchers who are interested in quality-related issues of physical sensor data. The process and results of the systematic review are presented which aims to answer the following research questions: what are the different types of physical sensor data errors, how to quantify or detect those errors, how to correct them and what domains are the solutions in. Out of 6970 literatures obtained from three databases (ACM Digital Library, IEEE Xplore and ScienceDirect) using the search string refined via topic modelling, 57 publications were selected and examined. Results show that the different types of sensor data errors addressed by those papers are mostly missing data and faults e.g. outliers, bias and drift. The most common solutions for error detection are based on principal component analysis (PCA) and artificial neural network (ANN) which accounts for about 40% of all error detection papers found in the study. Similarly, for fault correction, PCA and ANN are among the most common, along with Bayesian Networks. Missing values on the other hand, are mostly imputed using Association Rule Mining. Other techniques include hybrid solutions that combine several data science methods to detect and correct the errors. Through this systematic review, it is found that the methods proposed to solve physical sensor data errors cannot be directly compared due to the non-uniform evaluation process and the high use of non-publicly available datasets. Bayesian data analysis done on the 57 selected publications also suggests that publications using publicly available datasets for method evaluation have higher citation rates.

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Section: Singular Value Decompositionmentioning

confidence: 99%

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confidence: 99%

Sensor data quality: a systematic review

2020

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“…Instead of using the system resource allocation for each cluster, many clusters can simultaneously use the same resource and divide it among themselves. [12], [13] c) The method of optimizing network using ant algorithm optimal routing. This algorithm is used for objective estimation vector, which includes such metrics as delay, distance between jumps, cost, load and reliability.…”

Section: Network Optimization By Analyzing Statisticsmentioning

confidence: 99%

Calculation Functions of SDN Controller for Wireless Backhaul Infrastructure

Skulysh¹,

Globa²

2017

AEI

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Rapidly increasing traffic demands set up much higher requirements for data rates in the upcoming 5G mobile networks. The most promising approach for capacity enhancement is optimal reuse of network resources by deploying a large number of small cells. In order to maintain the efficiency and performance of new heterogeneous infrastructure, new approached are required to ensure optimal traffic transmission with high capacity. In this paper, we systematize mathematical methods for SDN-controller optimization. The paper contains all main computational methods ensuring the smooth operation of network applying SDN technology. Proposed approach allows to increase the efficiency of network resources utilization, provide the fault-tolerance of infrastructure and high bandwidth of the transport network.

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“…Anonymization of missing data streams is very challenging [29]- [31], researchers found three major techniques to treat missingness for anonymization. There are imputation, marginalization and partitioning.…”

Section: Introductionmentioning

confidence: 99%