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Now showing items 51-57 of 57
RL-Assisted Energy-Aware User-Edge Association for IoT-based Hierarchical Federated Learning
(2022 , Conference Paper)
The extremely heavy global reliance on IoT devices is causing enormous amounts of data to be gathered and shared in IoT networks. Such data need to efficiently be used in training and deploying of powerful artificially ...
On the Modeling of Reliability in Extreme Edge Computing Systems
(
IEEE
, 2022 , Conference Paper)
Extreme edge computing (EEC) refers to the end-most part of edge computing wherein computational tasks and edge services are deployed only on extreme edge devices (EEDs). EEDs are consumer or user-owned devices that offer ...
Region of Interest Optimization for Delay-sensitive Telemedicine Applications
(
IEEE
, 2022 , Conference Paper)
Telemedicine is a rising technology that is gaining a lot of interest in the recent decades. Several applications of telemedicine are delay-sensitive and need to be operated in real-time. One of which is surgical tele-mentoring ...
Optimal Resource Management for Hierarchical Federated Learning over HetNets with Wireless Energy Transfer
(
Institute of Electrical and Electronics Engineers Inc.
, 2023 , Article)
Remote monitoring systems analyze the environment dynamics in different smart industrial applications, such as occupational health and safety, and environmental monitoring. Specifically, in industrial Internet of Things ...
Secure Wireless Sensor Networks for Anti-Jamming Strategy Based on Game Theory
(
IEEE
, 2023 , Conference Paper)
The Wireless Sensor Networks (WSN) are designed to remotely monitor and control specific physical or environmental conditions. However, due to the open nature of WSN, many threats and attacks may arise by malicious users ...
Machine Learning Techniques for Network Anomaly Detection: A Survey
(
IEEE
, 2020 , Conference Paper)
Nowadays, distributed data processing in cloud computing has gained increasing attention from many researchers. The intense transfer of data has made the network an attractive and vulnerable target for attackers to exploit ...
Hybrid Machine Learning for Network Anomaly Intrusion Detection
(
IEEE
, 2020 , Conference Paper)
In this paper, a hybrid approach of combing two machine learning algorithms is proposed to detect the different possible attacks by performing effective feature selection and classification. This system uses Random Forest ...