Missing data problem in the monitoring system: A review
J Du, M Hu, W Zhang - IEEE Sensors Journal, 2020 - ieeexplore.ieee.org
Missing data is a common phenomenon in sensor networks, especially in the large-scale
monitoring system. It can be affected by various kinds of reasons. Moreover, incomplete data …
monitoring system. It can be affected by various kinds of reasons. Moreover, incomplete data …
A novel reinforced dynamic graph convolutional network model with data imputation for network-wide traffic flow prediction
Traffic data missing issues due to unpredictable equipment failure, extreme weather, and
other reasons have brought great challenges to traffic flow prediction modeling. In this …
other reasons have brought great challenges to traffic flow prediction modeling. In this …
GraphSAGE-based traffic speed forecasting for segment network with sparse data
Forecasting of traffic conditions plays a significant role in smart traffic management systems.
With the prevalent use of massive vehicle trajectory data, agencies inevitably encounter …
With the prevalent use of massive vehicle trajectory data, agencies inevitably encounter …
Machine learning-based imputation soft computing approach for large missing scale and non-reference data imputation
Missing data is a common problem in real-world data sets and it is amongst the most
complex topics in computer science and many other research domains. The common ways …
complex topics in computer science and many other research domains. The common ways …
An intelligent scheme for big data recovery in Internet of Things based on multi-attribute assistance and extremely randomized trees
Due to the inherent characteristics of sensor nodes in Internet of Things, such as constrained
energy, data redundancy, limited communication range and computing capabilities, the data …
energy, data redundancy, limited communication range and computing capabilities, the data …
Enhanced data imputation framework for bridge health monitoring using Wasserstein generative adversarial networks with gradient penalty
The availability of complete data is essential for accurately assessing structural stability and
condition in structural health monitoring (SHM) systems. Unfortunately, data missing is a …
condition in structural health monitoring (SHM) systems. Unfortunately, data missing is a …
A deep learning based data recovery approach for missing and erroneous data of iot nodes
P Vedavalli, D Ch - Sensors, 2022 - mdpi.com
Internet of things (IoT) nodes are deployed in large-scale automated monitoring applications
to capture the massive amount of data from various locations in a time-series manner. The …
to capture the massive amount of data from various locations in a time-series manner. The …
Identification of Traffic Flow Spatio-Temporal Patterns and Their Associated Weather Factors: A Case Study in the Terminal Airspace of Hong Kong
In this paper, a data-driven framework aimed at investigating how weather factors affect the
spatio-temporal patterns of air traffic flow in the terminal maneuvering area (TMA) is …
spatio-temporal patterns of air traffic flow in the terminal maneuvering area (TMA) is …
Energy-aware deep reinforcement learning scheduling for sensors correlated in time and space
Millions of battery-powered sensors deployed for monitoring purposes in a multitude of
scenarios, eg, agriculture, smart cities, industry, etc., require energy-efficient solutions to …
scenarios, eg, agriculture, smart cities, industry, etc., require energy-efficient solutions to …
Effective data management strategy and RDD weight cache replacement strategy in Spark
K Jiang, S Du, F Zhao, Y Huang, C Li, Y Luo - Computer Communications, 2022 - Elsevier
With the dramatic increase in internet users and their demand for real-time network
performance, Spark has distributed computing environment has emerged. It is widely used …
performance, Spark has distributed computing environment has emerged. It is widely used …