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Aidin Ferdowsi
Researcher at Virginia Tech
Publications - 43
Citations - 1483
Aidin Ferdowsi is an academic researcher from Virginia Tech. The author has contributed to research in topics: Intelligent transportation system & Reinforcement learning. The author has an hindex of 18, co-authored 42 publications receiving 933 citations.
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Journal ArticleDOI
Machine Learning for Wireless Connectivity and Security of Cellular-Connected UAVs
TL;DR: In this paper, the authors expose the wireless and security challenges that arise in the context of UAV-based delivery systems, UAVbased real-time multimedia streaming, and UAVenabled intelligent transportation systems.
Journal ArticleDOI
Deep Learning for Reliable Mobile Edge Analytics in Intelligent Transportation Systems: An Overview
TL;DR: In this article, an edge analytics architecture for intelligent transportation systems (ITSs) is introduced in which data is processed at the vehicle or roadside smart sensor level to overcome the ITS's latency and reliability challenges.
Journal ArticleDOI
Deep Learning for Signal Authentication and Security in Massive Internet-of-Things Systems
Aidin Ferdowsi,Walid Saad +1 more
TL;DR: In this article, a novel watermarking algorithm is proposed for dynamic authentication of IoT signals to detect cyber-attacks, which enables the IoT devices (IoTDs) to extract a set of stochastic features from their generated signal and dynamically watermark these features into the signal.
Proceedings ArticleDOI
Generative Adversarial Networks for Distributed Intrusion Detection in the Internet of Things
Aidin Ferdowsi,Walid Saad +1 more
TL;DR: A distributed generative adversarial network (GAN) is proposed to provide a fully distributed IDS for the IoT so as to detect anomalous behavior without reliance on any centralized controller and it is shown analytically that the proposed distributed GAN has higher accuracy of detecting intrusion.
Proceedings ArticleDOI
Deep Reinforcement Learning for Minimizing Age-of-Information in UAV-Assisted Networks
TL;DR: In this article, a UAV-assisted wireless network is studied, in which energy-constrained ground nodes are deployed to observe different physical processes, and a deep reinforcement learning (RL) algorithm is proposed to obtain the optimal policy that minimizes the weighted sum-AoI, referred to as the age-optimal policy.