Speaker: Muhammad Asim (西木), PhD Computer Science and Technology, CSU.
Report Time: 2 021年 11 月 05日 morning 8: 30- 9: 30
Report location: Conference Room 313, Computer Building
Introduction of Topics:
With the development of mobile communication technology and the popularization of user equipment (UEs), a considerable number of latency and resource-intensive applications are emerging, such as face recognition, virtual reality, and online games. However, due to limitations that exist at UEs, such as battery life and limited computation resources, it is very challenging to execute these tasks locally. To tackle the above-mentioned challenges, mobile edge computing (MEC) has been proposed which can provide services close to UEs. However, MEC still has some limitations, for example, the locations of edge servers are usually fixed and cannot be adjusted according to user requirements. In addition, in large-scale natural disasters, the existing terrestrial communication networks could be destroyed, in which case it would be difficult for MEC to provide timely services. To handle the above-mentioned challenges in MEC, UAV has been introduced, which is considered as a prominent technology to enhance the capabilities of MEC. However, in the overcrowded region, the communication signals between UEs and UAVs may be blocked by obstacles, such as high buildings and trees. Recently, intelligent reflecting surface (IRS) has been integrated to enhance the communication between UEs and UAVs and become a very hot research topic.
As a class of nature-inspired computational approaches, computational intelligence (CI) exhibits great potential in addressing complex optimization problems, which has attracted much attention from both academia and industry. Specifically, CI gained significant attention and popularization in solving optimization problems in MEC systems.
Topic of Report 1: A Variable-Length Trajectory Planning Algorithm for Multi-Unmanned Aerial Vehicle-Enabled MEC System.
Summary of Report 1:
This report focuses on a multi-unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) system, aiming to minimize the overall energy consumption of the system by designing the optimal trajectories of multiple UAVs. This problem is difficult to address because when planning the trajectories, we need to consider not only the order of stop points (SPs), but also their deployment (including the number and locations) and the association between UAVs and SPs. A novel CI technique is proposed to solve the above-mentioned optimization problem in the studied MEC system.
Topic of Report 2: Joint Optimization of Trajectory and passive beamforming in IRS-aided UAV-Assisted Mobile Edge Computing Systems.
Summary of Report 2:
This report presents a multi-intelligent reflecting surface (IRS)- aided multi-unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system aiming to minimize the overall cost of the system via jointly optimizing the trajectories of UAVs and phase shifts of IRSs. The problem is very complex, as it is mixed-variables, NP-hard, non-convex, and non-linear. Therefore, it is challenging to be solved by conventional optimization-based solutions. An algorithm is proposed to solve the above-mentioned optimization problem in the studied MEC system.
Brief introduction of the speaker:
Muhammad Asim received the M.S. degree in Mathematics from University of Peshawar, Peshawar, Pakistan in 2013 and the M.Phil. degree in Mathematics from Kohat University of Science & Technology, Kohat, Pakistan in 2016. He is currently pursuing the Ph.D. degree in Computer Science and Technology, Central South University, Changsha, China. He has published several ESCI/SCI/JCR papers in high-quality journals, such as IEEE Transactions, ISA Transactions, Soft Computing, etc. His current research interests include computational intelligence techniques, cloud computing, and edge computing.