[HTML][HTML] An overview of IoT sensor data processing, fusion, and analysis techniques
In the recent era of the Internet of Things, the dominant role of sensors and the Internet
provides a solution to a wide variety of real-life problems. Such applications include smart …
provides a solution to a wide variety of real-life problems. Such applications include smart …
[HTML][HTML] Deep learning for network traffic monitoring and analysis (NTMA): A survey
Modern communication systems and networks, eg, Internet of Things (IoT) and cellular
networks, generate a massive and heterogeneous amount of traffic data. In such networks …
networks, generate a massive and heterogeneous amount of traffic data. In such networks …
[HTML][HTML] A comparative study on online machine learning techniques for network traffic streams analysis
Modern networks generate a massive amount of traffic data streams. Analyzing this data is
essential for various purposes, such as network resources management and cyber-security …
essential for various purposes, such as network resources management and cyber-security …
From statistical‐to machine learning‐based network traffic prediction
I Lohrasbinasab, A Shahraki… - Transactions on …, 2022 - Wiley Online Library
Nowadays, due to the exponential and continuous expansion of new paradigms such as
Internet of Things (IoT), Internet of Vehicles (IoV) and 6G, the world is witnessing a …
Internet of Things (IoT), Internet of Vehicles (IoV) and 6G, the world is witnessing a …
[HTML][HTML] DCServCG: A data-centric service code generation using deep learning
Z Alizadehsani, H Ghaemi, A Shahraki… - … Applications of Artificial …, 2023 - Elsevier
Modern software development paradigms, including Service-Oriented Architecture (SOA),
tend to make use of available services eg, web service Application Programming Interfaces …
tend to make use of available services eg, web service Application Programming Interfaces …
Fault diagnosis using data fusion with ensemble deep learning technique in IIoT
S Venkatasubramanian, S Raja… - Mathematical …, 2022 - Wiley Online Library
Detecting the breakdown of industrial IoT devices is a major challenge. Despite these
challenges, real‐time sensor data from the industrial internet of things (IIoT) present several …
challenges, real‐time sensor data from the industrial internet of things (IIoT) present several …
[HTML][HTML] Deep Reinforcement Learning for QoS provisioning at the MAC layer: A Survey
Abstract Quality of Service (QoS) provisioning is based on various network management
techniques including resource management and medium access control (MAC). Various …
techniques including resource management and medium access control (MAC). Various …
[HTML][HTML] TONTA: Trend-based online network traffic analysis in ad-hoc IoT networks
Abstract Internet of Things (IoT) refers to a system of interconnected heterogeneous smart
devices communicating without human intervention. A significant portion of existing IoT …
devices communicating without human intervention. A significant portion of existing IoT …
An Integration of Genetic Feature Selector, Histogram-Based Outlier Score, and Deep Learning for Wind Turbine Power Prediction
During the last decades, the importance of clean energy resources is being increased. Wind
is one of the most significant clean energy resources. Forecasting the output power of wind …
is one of the most significant clean energy resources. Forecasting the output power of wind …
NODSTAC: novel outlier detection technique based on spatial, temporal and attribute correlations on IoT Bigdata
MV Brahmam, S Gopikrishnan - The Computer Journal, 2024 - academic.oup.com
An outlier in the Internet of Things is an immediate change in data induced by a significant
difference in the atmosphere (Event) or sensor malfunction (Error). Outliers in the data cause …
difference in the atmosphere (Event) or sensor malfunction (Error). Outliers in the data cause …