54 citations · 70 across the 17 of their papers we have counts for
17 papers
Symbiotic PBFT Consensus: Cognitive Backscatter Communications-enabled Wireless PBFT Consensus
Haoxiang Luo, Qianqian Zhang, Hongfang Yu +2
Wireless blockchain networks have played an important role in many network scenarios, among which wireless Practical Byzantine Fault Tolerance (PBFT) consensus is regarded as one o…
ESIA: An Efficient and Stable Identity Authentication for Internet of Vehicles
Haoxiang Luo, Jin Zhang, Xinling Li +4
Decentralized, tamper-proof blockchain is regarded as a solution to a challenging authentication issue in the Internet of Vehicles (IoVs). However, the consensus time and communica…
Maximize the Long-term Average Revenue of Network Slice Provider via Admission Control Among Heterogeneous Slices
Miao Dai, Gang Sun, Hongfang Yu +1
Network slicing endows 5G/B5G with differentiated and customized capabilities to cope with the proliferation of diversified services, whereas limited physical network resources may…
ESCM: An Efficient and Secure Communication Mechanism for UAV Networks
Haoxiang Luo, Yifan Wu, Gang Sun +2
UAV (unmanned aerial vehicle) is rapidly gaining traction in various human activities and has become an integral component of the satellite-air-ground-sea (SAGS) integrated network…
Performance Analysis and Comparison of Non-ideal Wireless PBFT and RAFT Consensus Networks in 6G Communications
Haoxiang Luo, Xiangyue Yang, Hongfang Yu +3
Due to advantages in security and privacy, blockchain is considered a key enabling technology to support 6G communications. Practical Byzantine Fault Tolerance (PBFT) and RAFT are…
Performance Analysis of Non-ideal Wireless PBFT Networks with mmWave and Terahertz Signals
Haoxiang Luo, Xiangyue Yang, Hongfang Yu +3
Due to advantages in security and privacy, blockchain is considered a key enabling technology to support 6G communications. Practical Byzantine Fault Tolerance (PBFT) is seen as th…
HFedMS: Heterogeneous Federated Learning with Memorable Data Semantics in Industrial Metaverse
Shenglai Zeng, Zonghang Li, Hongfang Yu +4
Federated Learning (FL), as a rapidly evolving privacy-preserving collaborative machine learning paradigm, is a promising approach to enable edge intelligence in the emerging Indus…
SD-AETO: Service Deployment Enabled Adaptive Edge Task Offloading in MEC
Liangjun Song, Gang Sun, Hongfang Yu +1
In recent years, edge computing, as an important pillar for future networks, has been developed rapidly. Task offloading is a key part of edge computing that can provide computing…
PerFED-GAN: Personalized Federated Learning via Generative Adversarial Networks
Xingjian Cao, Gang Sun, Hongfang Yu +1
Federated learning is gaining popularity as a distributed machine learning method that can be used to deploy AI-dependent IoT applications while protecting client data privacy and…
SNPSFuzzer: A Fast Greybox Fuzzer for Stateful Network Protocols using Snapshots
Junqiang Li, Senyi Li, Gang Sun +2
Greybox fuzzing has been widely used in stateless programs and has achieved great success. However, most state-of-the-art greybox fuzzers generally have the problems of slow speed…
Data Heterogeneity-Robust Federated Learning via Group Client Selection in Industrial IoT
Zonghang Li, Yihong He, Hongfang Yu +4
Nowadays, the industrial Internet of Things (IIoT) has played an integral role in Industry 4.0 and produced massive amounts of data for industrial intelligence. These data locate o…
Cross-Silo Heterogeneous Model Federated Multitask Learning
Xingjian Cao, Zonghang Li, Gang Sun +2
Federated learning (FL) is a machine learning technique that enables participants to collaboratively train high-quality models without exchanging their private data. Participants u…
PSACCF: Prioritized Online Slice Admission Control Considering Fairness in 5G/B5G Networks
Miao Dai, Long Luo, Jing Ren +2
5G/B5G is envisioned to support various services with the assistance of network slices, each slice instance asks for adequate resources to provide the pre-negotiated service qualit…
Heterogeneous Federated Learning via Grouped Sequential-to-Parallel Training
Shenglai Zeng, Zonghang Li, Hongfang Yu +4
Federated learning (FL) is a rapidly growing privacy-preserving collaborative machine learning paradigm. In practical FL applications, local data from each data silo reflect local…
Compacting Deep Neural Networks for Internet of Things: Methods and Applications
Ke Zhang, Hanbo Ying, Hong-Ning Dai +4
Deep Neural Networks (DNNs) have shown great success in completing complex tasks. However, DNNs inevitably bring high computational cost and storage consumption due to the complexi…
Communication-efficient Decentralized Machine Learning over Heterogeneous Networks
Pan Zhou, Qian Lin, Dumitrel Loghin +3
In the last few years, distributed machine learning has been usually executed over heterogeneous networks such as a local area network within a multi-tenant cluster or a wide area…
Bus Trajectory-Based Street-Centric Routing for Message Delivery in Urban Vehicular Ad hoc Networks
Gang Sun, Yijing Zhang, Dan Liao +3
This paper focuses on the routing algorithm for the communications between vehicles and places in urban VANET. As one of the basic transportation facilities in an urban setting, bu…