A5003622027- IoT and Edge/Fog Computing
- Cloud Computing and Resource Management
- UAV Applications and Optimization
- Caching and Content Delivery
- Advanced Wireless Communication Technologies
- Distributed and Parallel Computing Systems
- Distributed Control Multi-Agent Systems
- Energy Harvesting in Wireless Networks
- Age of Information Optimization
- Visual Attention and Saliency Detection
- Face and Expression Recognition
- Software-Defined Networks and 5G
- EEG and Brain-Computer Interfaces
- Neural dynamics and brain function
- Advanced Memory and Neural Computing
- Advanced Computing and Algorithms
- Privacy-Preserving Technologies in Data
- Complex Network Analysis Techniques
- Blockchain Technology Applications and Security
- Vehicular Ad Hoc Networks (VANETs)
- Data Stream Mining Techniques
- Innovation in Digital Healthcare Systems
- Cloud Data Security Solutions
- Advanced Vision and Imaging
- Wireless Communication Security Techniques
Guizhou University
2023-2025
Yanshan University
2021-2023
Wuhan University of Technology
2017-2020
The distribution of the labeled data can greatly affect performance a semi-supervised learning (SSL) model. Most existing SSL models select randomly and equally allocate labeling quota among classes, leading to considerable unstableness degeneration performance. This study unsupervisedly constructs forest that forms another metric space, based on which it is convenient define fuzzy membership function characterize central divergent samples both types with Xor logic. thus be allocated...
Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) has largely extended the border and capacity of artificial intelligence things (AIoT) by providing a key element for enabling flexible distributed data inputs, capacity, high mobility. To enhance privacy AIoT applications, federated learning (FL) is becoming potential solution to perform training tasks locally on IoT devices. However, with limited onboard resources battery each UAV node, optimization required achieve...
The collaborative edge and cloud computing system has emerged as a promising solution to fulfill the unprecedented high requirements of 5G application scenarios. Due vendor variations, it is often difficult manage hardware facilities in such system. Moreover, amount data generated tasks requested by end devices are increasing exponentially, which introduces storage computation bottlenecks. To address these issues, novel systematic framework called software-defined (SD-ECC) designed...
The edge-cloud computing and network slicing have emerged as promising solutions to fulfill the diversity of IoT applications enabled by 5G beyond. However, systems are composed various hardware facilities, leading difficulties in control management. With slicing, underlying resource sharing among multiple slice users is allowed, potential attacks formulation processes malicious usage slices that may result inefficient utilization system. To address aforementioned security issue, we first...
Network slicing is a promising approach supporting various Internet of Everything applications with diverse and differential requirements. To implement flexible efficient network slices, unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) deployed to leverage ground segments, which provides multi-dimensional resources involving hardware devices facilities (e.g., UAVs, servers, devices). Such complex hierarchical structure requires an framework orchestrate control slices while...
Unmanned aerial vehicle (UAV)-enabled edge computing is emerging as a potential enabler for Artificial Intelligence of Things (AIoT) in the forthcoming sixth-generation (6G) communication networks. With use flexible UAVs, massive sensing data gathered and processed promptly without considering geographical locations. Deep neural networks (DNNs) are becoming driving force to extract valuable information from data. However, lightweight servers installed on UAVs not able meet extremely high...
Distributed artificial intelligence (AI) is becoming an efficient approach to fulfill the high and diverse requirements for future vehicular networks. However, distributed tasks generated by vehicles often require resources. A customized resource provision scheme required improve utilization of multi-dimensional In this work, a slice selection-based online offloading (SSOO) algorithm proposed in First, response time energy consumption are reduced processing locally on vehicles. Then,...
Digital twin (DT) is becoming a promising solution for vehicular networks to improve the interoperability of distributed autonomous driving systems. Mobile edge computing (MEC) has been introduced provide low-latency services DT-enabled at network. However, it hard obtain dynamic network topology moving vehicles by ground-based MEC system, which may deteriorate service quality DT synchronization. In this paper, we propose novel unmanned aerial vehicle (UAV)-assisted synchronization framework...