Wei Shinn Ku

Professor

Auburn University

United States

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Wei Shinn Ku is a Professor at Auburn University, located in the United States. His research focuses on advanced topics including multicast routing in wireless sensor networks, cloud security using homomorphic encryption, and traffic prediction through graph neural networks. He has published extensively on issues related to IoT networks, mobile social networks, and task assignment in crowdsourcing environments.

Articles (21)

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2020

Collaborators (3)

NhatHai Phan

New Jersey Institute of Technology

UNITED STATES

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Ruoming Jin

professor

Kent State University

UNITED STATES

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Shubbhi Taneja

Assistant Teaching Professor

Worcester Polytechnic Institute

UNITED STATES

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Wei Shinn Ku

Research Interests

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About

Articles (21)

Secure kNN for Distributed Cloud Environment Using Fully Homomorphic Encryption

An Analysis of Anonymous MQTT for Publish-Subscribe-Based IoT Networks

Attention-Based Spatial-Temporal Graph Neural Network With Long-Term Dependencies for Traffic Speed Prediction

Opportunistic Routing in Drone-Augmented Mobile Social Networks

User Dynamics and Thematic Exploration in r/Depression During the COVID-19 Pandemic: Insights From Overlapping r/SuicideWatch Users

SILP: Enhancing skin lesion classification with spatial interaction and local perception

A convolutional neural network model for accurate short-term leaf area index prediction

Modeling Real-Time Task Assignment for Mobile Crowdsourcing in Opportunistic Networks

Multi-Tree-Based Avoidance Multicast Routing for Wireless Sensor Networks

On Wireless Charging for Mobile Sensors

PedCross: Pedestrian Crossing Prediction for Auto-Driving Bus

Priority-Aware Task Assignment in Opportunistic Network-Based Mobile Crowdsourcing

A secure annuli CAPTCHA system

Barrier Penetration Routing Against Wireless Spy Sensors

Rendezvous Avoidance File Caching for Mobile Social Networks

An RFID Zero-Knowledge Authentication Protocol Based on Quadratic Residues

Towards Wireless Power Transfer in Mobile Social Networks

EDOM: Improving energy efficiency of database operations on multicore servers

Freeway Travel Time Prediction Using Deep Hybrid Model-Taking Sun Yat-Sen Freeway as an Example

Recurrent Learning on PM_{2.5} Prediction Based on Clustered Airbox Dataset

Turbo-GTS: A fast framework of optimizing task throughput for large-scale mobile crowdsourcing

Collaborators (3)

NhatHai Phan

Ruoming Jin

Shubbhi Taneja

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