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Xiaoyan Hong
Researcher at University of Alabama
Publications - 149
Citations - 6826
Xiaoyan Hong is an academic researcher from University of Alabama. The author has contributed to research in topics: Wireless ad hoc network & Optimized Link State Routing Protocol. The author has an hindex of 33, co-authored 149 publications receiving 6650 citations. Previous affiliations of Xiaoyan Hong include University of California & Rice University.
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Proceedings ArticleDOI
On-Demand Anonymous Routing with Distance Vector Protecting Traffic Privacy in Wireless Multi-hop Networks
TL;DR: This paper proposes two anonymous routing algorithms, called randomized routing algorithm and probabilistic penalty-based routing algorithm, both of which aim to differentiate routing paths to the same destination enhancing anonymity of the network traffic.
Journal ArticleDOI
X-Region: A framework for location privacy preservation in mobile peer-to-peer networks
TL;DR: In this paper, the authors proposed x-region as a solution to preserve the location privacy in a mobile peer-to-peer P2P environment where no trust relationships are assumed amongst mobile users, and the main idea is to allow users to share a blurred region known as X-region instead of their exact locations so that one cannot distinguish any user from others inside the region.
Posted Content
Parallel Closed-Loop Connected Vehicle Simulator for Large-Scale Transportation Network Management: Challenges, Issues, and Solution Approaches
TL;DR: In this article, the authors present a conceptual model of an Integrated Distributed Connected Vehicle Simulator (IDCVS) that can emulate real-time traffic in a large metro area by incorporating hardware-in-the-loop simulation together with the closed-loop coupling of SUMO and OMNET++.
Proceedings ArticleDOI
Modeling Ad-hoc rushing attack in a negligibility-based security framework
TL;DR: This paper proposes an RP (n-runs) complexity class with a global virtual god oracle (GVG) to model a general class of network protocols, and presents an asymptotic invariant for scalable networks: a polynomial-time network algorithm that ensures negligible probability of security failure at each step would stay in the state of ensuring negligible probabilities of security fail globally.
Proceedings ArticleDOI
RL-Sketch: Scaling Reinforcement Learning for Adaptive and Automate Anomaly Detection in Network Data Streams
TL;DR: RL-Sketch is proposed, a adaptive sketch using reinforcement learning in detecting heavy flows that predicts potential heavy flows based on the statistics of network traffic, to achieve both high accuracy and scalability with minor memory.