UAV-Assisted Multi-Access Edge Computing
▶ Introduction
 

Due to the exponential growth of the internet of things devices (IoTDs), such as metering devices and wearable devices, more computation oriented applications, such as smart farming, face recognition, virtual reality (VR), and augmented reality (AR) are coming up in our life. These IoTDs are low power devices and have limited computation capacity. Therefore, it is difficult for IoTDs to process their data locally. Fortunately, deploying the mobile edge computing (MEC) concept brings the computation resources nearer to the IoTDs and reduces the energy consumption of the IoTDs by offloading their data to the MEC server. However, in some areas, e.g., smart farming in rural areas, disaster rescue operations, and military operation, IoTDs will be far from the MEC services and out of the coverage of the mobile infrastructure. In the cases as mentioned above, it is difficult for the IoTDs to use the MEC services. Unmanned aerial vehicles (UAVs) have been widely deployed due to its high ?exibility, and low cost of deployment. By deploying the MEC-enabled UAV, we can save the deployment cost of the mobile infrastructures and provide the remote MEC services to the IoTDs on demand.

Fig. 1: UAV-Assisted MEC System [1]

▶ Research Issues

 
  • Trajectory optimization
  • Hover time optimization
  • Energy-efficient joint task offloading and deployment
  • Air-to-ground channel modeling


  • ▶ References

     
    1. Madyan Alsenwi, Yan Kyaw Tun, Shashi Raj Pandey, Nway Nway Ei, Choong Seon Hong, "UAV-Assisted Multi-Access Edge Computing System: An Energy-Efficient Resource Management Framework," The International Conference on Information Networking (ICOIN 2020), January 7-10, 2020, Barcelona, Spain


    ▶ Achievements

     
    1. Madyan Alsenwi, Yan Kyaw Tun, Shashi Raj Pandey, Nway Nway Ei, Choong Seon Hong, "UAV-Assisted Multi-Access Edge Computing System: An Energy-Efficient Resource Management Framework," The International Conference on Information Networking (ICOIN 2020), January 7-10, 2020, Barcelona, Spain.
    2. Ki Tae Kim, Yu Min Park, Choong Seon Hong, "Machine Learning Based Edge-Assisted UAV Computation Offloading for Data Analyzing," The International Conference on Information Networking (ICOIN 2020), January 7-10, 2020, Barcelona, Spain.
    3. Nway Nway Ei, Chit Wutyee Zaw, Min Kyung Lee and Choong Seon Hong, "Cell Association in Energy-Constrained Unmanned Aerial Vehicle Communications Under Altitude Consideration," The International Conference on Information Networking (ICOIN 2019), Jan. 9-11, 2019, Kuala Lumpur, Malaysia.
    4. Nway Nway Ei, Choong Seon Hong, "Energy-Efficient User Association and Resource Allocation in MEC-Enabled UAV-Assisted Network," 2020년 한국컴퓨터종합학술대회(KCC 2020), 2020.07.02~04.
    5. KiTae Kim and Choong Seon Hong, "Optimal Task-UAV-Edge Matching for Computation Offloading in UAV-Assisted Mobile Edge Computing," The 20th Asia-Pacific Network Operations and Management Symposium(APNOMS 2019), Sep. 18-21, 2019, Matsue, Japan.
    6. Aunas Manzoor, Do Hyeon Kim, and Choong Seon Hong, "Energy Efficient Resource Allocation in UAV-based Heterogeneous Networks," The 20th Asia-Pacific Network Operations and Management Symposium(APNOMS 2019), Sep. 18-21, 2019, Matsue, Japan.
    7. Nway Nway Ei, Choong Seon Hong, "User-Association for Sum Rate Maximization in Air-Ground Integrated Network", 2019년 한국소프트웨어종합학술대회(KSC 2019), 2019.12.18~12.20.
    8. Nway Nway Ei, Choong Seon Hong, "Coexistence of Aerial and Ground users in UAV-Enabled Wireless Networks", 2019년 한국컴퓨터종합학술대회(KCC 2019), 2019.6.26~6.28.
    9. Md. Shirajum Munir, Sarder Fakhrul Abedin and Choong Seon Hong, "Artificial Intelligence-based Service Aggregation for Mobile-Agent in Edge Computing," The 20th Asia-Pacific Network Operations and Management Symposium(APNOMS 2019), Sep. 18-21, 2019, Matsue, Japan