A5086839388- Cloud Computing and Resource Management
- Metaheuristic Optimization Algorithms Research
- Advanced Multi-Objective Optimization Algorithms
- IoT and Edge/Fog Computing
- UAV Applications and Optimization
- Distributed and Parallel Computing Systems
- Blockchain Technology Applications and Security
- Smart Agriculture and AI
- Antenna Design and Analysis
- Metamaterials and Metasurfaces Applications
- Robotic Path Planning Algorithms
- Distributed Control Multi-Agent Systems
- Grey System Theory Applications
- Energy Efficiency and Management
- Optimal Experimental Design Methods
- Advanced Antenna and Metasurface Technologies
- Advanced Neural Network Applications
- Advanced Data Storage Technologies
- Simulation Techniques and Applications
- Evolutionary Algorithms and Applications
- IoT-based Smart Home Systems
Southeast University
2022-2024
State Key Laboratory of Millimeter Waves
2024
The rapid proliferation of Internet Things (IoT) ground devices (GDs) has created an unprecedented demand for computing resources and real-time data-processing capabilities. Integrating unmanned aerial vehicles (UAVs) into Mobile Edge Computing (MEC) emerges as a promising solution to bring computation storage closer the data sources. However, UAV heterogeneity time window constraints task execution pose significant challenge. This paper addresses multiple routing problem in MEC...
The rapid development of the Internet Vehicles (IoV) and intelligent transportation systems has led to increased demand for real-time data processing computation in vehicular networks. To address these needs, this paper proposes a task offloading framework UAV-assisted Vehicular Edge Computing (VEC) systems, which considers high mobility vehicles limited coverage computational capacities drones. We introduce Mobility-Aware Task Offloading (MAVTO) algorithm, designed optimize decisions,...
Serverless functions (SFs) and on-demand virtual machines (VMs) are common cloud resources for scientific workflow applications, which widespread in many fields. SFs paid by actual running time with higher unit costs resource utilization than VMs billing units. Generally, each application is executed on a limited budget. In this article, we study the challenging scheduling problem budget to minimize makespan hybridization of BCWS (Budget Constrained Workflow Scheduling) algorithm proposed....
Many-objective optimization problems (MaOPs), are the most difficult to solve when it comes multiobjective issues (MOPs). MaOPs provide formidable challenges current evolutionary methods such as selection operators, computational cost, visualization of high-dimensional trade-off front. Removal reductant objectives from original objective set, known reduction, is one significant approaches for MaOPs, which can tackle with more than 15 made feasible by its ability greatly overcome existing...