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Toplam kayıt 15, listelenen: 1-10
The impact of error control schemes on lifetime of energy harvesting wireless sensor networks in industrial environments
(ELSEVIER, RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS, 2020)
Due to the harsh channel conditions of the industrial environments, the data transmission over the wireless channel suffers from erroneous packets. The energy consumption of error control schemes is of great importance for ...
Analysis of compressive sensing and energy harvesting for wireless multimedia sensor networks
(ELSEVIER, RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS, 2020)
One of the main concerns of Wireless Multimedia Sensor Networks (WMSNs) is the huge data size causing the higher energy consumption in transmission. The high energy consumption is a critical problem for lifetime of network ...
CBI4.0: A cross-layer approach for big data gathering for active monitoring and maintenance in the manufacturing industry 4.0
(ELSEVIERRADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS, 2021)
Industry 4.0 (I4.0) defines a new paradigm to produce high-quality products at the low cost by reacting quickly and effectively to changing demands in the highly volatile global markets. In Industry 4.0, the adoption of ...
A reliable and secure multi-path routing strategy for underwater acoustic sensor networks
(ELSEVIER, 2022)
Underwater Acoustic Sensor Networks (UASNs) have nowadays become an attractive topic in scientific studies
and commercial applications. An important challenge in UASN’s design is the limited network lifetime and ...
Deep learning approaches for vehicle type classification with 3-D magnetic sensor
(ELSEVIER, 2022)
In the Intelligent Transportation Systems, it is crucial to determine the type of vehicles to improve traffic
management, increase human comfort, and enable future development of transport infrastructures. This paper
presents ...
Machine learning approaches for underwater sensor network parameter prediction
(ELSEVIER, 2023)
Underwater Acoustic Sensor Networks (UASNs) have recently attracted scientists due to its wide range of real -world applications. However, there are design challenges in UASNs, such as limited network lifetime and low ...
A review of on-device machine learning for IoT: An energy perspective
(ELSEVIER, 2024)
Recently, there has been a substantial interest in on-device Machine Learning (ML) models to provide intelligence for the Internet of Things (IoT) applications such as image classification, human activity recognition, ...
An efficient network intrusion detection approach based on logistic regression model and parallel artificial bee colony algorithm
(ELSEVIER, 2024)
In recent years, the widespread use of the Internet has created many issues, especially in the area of cybersecurity. It is critical to detect intrusions in network traffic, and researchers have developed network intrusion ...
A deep neural network approach with hyper-parameter optimization for vehicle type classification using 3-D magnetic sensor
(ELSEVIER, 2023)
The identification of vehicle types plays a critical role in Intelligent Transportation Systems. In this study, battery-operated, easy-to-install, low-cost 3-D magnetic traffic sensors have been developed for vehicle type ...
Big Data acquired by Internet of Things-enabled industrial multichannel wireless sensors networks for active monitoring and control in the smart grid Industry 4.0
(ELSEVIERRADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS, 2021)
Smart Grid Industry 4.0 (SGI4.0) defines a new paradigm to provide high-quality electricity at a low cost by reacting quickly and effectively to changing energy demands in the highly volatile global markets. However, in ...