5 citations · 15 across the 13 of their papers we have counts for
13 papers
Rethinking On-Device LLM Reasoning: Why Analogical Mapping Outperforms Abstract Thinking for IoT DDoS Detection
William Pan, Guiran Liu, Binrong Zhu +4
The rapid expansion of IoT deployments has intensified cybersecurity threats, notably Distributed Denial of Service (DDoS) attacks, characterized by increasingly sophisticated patt…
Efficient Phishing URL Detection Using Graph-based Machine Learning and Loopy Belief Propagation
Wenye Guo, Qun Wang, Hao Yue +2
The proliferation of mobile devices and online interactions have been threatened by different cyberattacks, where phishing attacks and malicious Uniform Resource Locators (URLs) po…
Exploring Communication Technologies, Standards, and Challenges in Electrified Vehicle Charging
Xiang Ma, Yuan Zhou, Hanwen Zhang +4
As public awareness of environmental protection continues to grow, the trend of integrating more electric vehicles (EVs) into the transportation sector is rising. Unlike convention…
CSMAAFL: Client Scheduling and Model Aggregation in Asynchronous Federated Learning
Xiang Ma, Qun Wang, Haijian Sun +2
Asynchronous federated learning aims to solve the straggler problem in heterogeneous environments, i.e., clients have small computational capacities that could cause aggregation de…
Energy Efficiency and Delay Tradeoff in an MEC-Enabled Mobile IoT Network
Han Hu, Weiwei Song, Qun Wang +2
Mobile Edge Computing (MEC) has recently emerged as a promising technology in the 5G era. It is deemed an effective paradigm to support computation-intensive and delay critical app…
When Machine Learning Meets Spectrum Sharing Security: Methodologies and Challenges
Qun Wang, Haijian Sun, Rose Qingyang Hu +1
The exponential growth of internet connected systems has generated numerous challenges, such as spectrum shortage issues, which require efficient spectrum sharing (SS) solutions. C…
Energy-Efficient Design for IRS-Assisted MEC Networks with NOMA
Qun Wang, Fuhui Zhou, Han Hu +1
Energy-efficient design is of crucial importance in wireless internet of things (IoT) networks. In order to serve massive users while achieving an energy-efficient operation, an in…
User Scheduling for Federated Learning Through Over-the-Air Computation
Xiang Ma, Haijian Sun, Qun Wang +1
A new machine learning (ML) technique termed as federated learning (FL) aims to preserve data at the edge devices and to only exchange ML model parameters in the learning process.…
Mobility-Aware Offloading and Resource Allocation in MEC-Enabled IoT Networks
Han Hu, Weiwei Song, Qun Wang +2
Mobile edge computing (MEC)-enabled Internet of Things (IoT) networks have been deemed a promising paradigm to support massive energy-constrained and computation-limited IoT device…
Secure and Energy-Efficient Offloading and Resource Allocation in a NOMA-Based MEC Network
Qun Wang, Han Hu, Haijian Sun +1
Energy efficiency and security are two critical issues for mobile edge computing (MEC) networks. With stochastic task arrivals, time-varying dynamic environment, and passive existi…
Energy Efficient Robust Beamforming and Cooperative Jamming Design for IRS-Assisted MISO Networks
Qun Wang, Fuhui Zhou, Rose Qingyang Hu +1
Energy-efficient design and secure communications are of crucial importance in wireless communication networks. However, the energy efficiency achieved by using physical layer secu…
Towards Green Mobile Edge Computing Offloading Systems with Security Enhancement
Haijian Sun, Qun Wang, Xiang Ma +2
Mobile edge computing (MEC) is an emerging communication scheme that aims at reducing latency. In this paper, we investigate a green MEC system under the existence of an eavesdropp…
Energy-Efficient Beamforming and Cooperative Jamming in IRS-Assisted MISO Networks
Qun Wang, Fuhui Zhou, Rose Qingyang Hu +1
The energy-efficient (EE) design problem is very important to design a good performance network. Besides this, the security of communication is also important. Based on the new IRS…