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Found 10 papers, 1 papers with code
Securing the Skies: An IRS-Assisted AoI-Aware Secure Multi-UAV System with Efficient Task Offloading
In this paper, we address the intricate challenges of data freshness, and security, especially in the context of eavesdropping and jamming in modern UAV networks.
Reliable and Efficient Data Collection in UAV-based IoT Networks
Internet of Things (IoT) involves sensors for monitoring and wireless networks for efficient communication.
ZEST: Attention-based Zero-Shot Learning for Unseen IoT Device Classification
Recent research works have proposed machine learning models for classifying IoT devices connected to a network.
GFCL: A GRU-based Federated Continual Learning Framework against Data Poisoning Attacks in IoV
In this paper, we propose a Gated Recurrent Unit (GRU)-based federated continual learning (GFCL) anomaly detection framework against Sybil-based data poisoning attacks in IoV.
Adversarial Attacks Against Deep Reinforcement Learning Framework in Internet of Vehicles
As the use of ML in Internet of Vehicles (IoV) continues to advance, adversarial threats and their impact have become an important subject of research worth exploring.
DRLD-SP: A Deep Reinforcement Learning-based Dynamic Service Placement in Edge-Enabled Internet of Vehicles
We propose a Deep Reinforcement Learning-based Dynamic Service Placement (DRLD-SP) framework with the objective of minimizing the maximum edge resource usage and service delay while considering the vehicle's mobility, varying demand, and dynamics in the requests for different types of services.
Machine Learning for Security in Vehicular Networks: A Comprehensive Survey
We classify the ML techniques developed in the literature according to their use in vehicular network applications.
Reinforcement Learning-based Dynamic Service Placement in Vehicular Networks
In our learning framework, for the decision module, we consider two alternative objective functions-minimizing delay and minimizing edge server utilization.
Cost-aware Feature Selection for IoT Device Classification
In this work, we take a more realistic approach, and argue that feature extraction has a cost, and the costs are different for different features.
Machine Learning-based Link Fault Identification and Localization in Complex Networks
In such networks, link faults may result in a link disconnection without immediate replacement or a link reconnection, e. g., a wireless node changes its access point.
