A Systematic Mapping Study of UAV-Enabled Mobile Edge Computing for Task Offloading

Abstract

Utilizing Unmanned Aerial Vehicles (UAVs) as flying edge nodes to support task offloading from terminal devices has recently attracted significant research attention. However, the literature lacks a systematic perspective on this emerging topic. The goals are to understand the volume and trends of research, identify use case scenarios and proposed architectures, classify the core topics addressed, explore group techniques explored, recognize task types considered, and summarize open issues needing further work. Publications are mapped by type and source from 2019 to 2023 to assess the maturity and activity level in this field over time. Various use case scenarios for UAV-enabled Mobile Edge Computing (MEC) task offloading are identified and categorized, and different proposed architectures for offloading between UAV-MEC platforms are summarized. Techniques for offloading decision-making and performance enhancement are grouped to identify popular and less explored methods. The literature is also mapped based on the types of tasks considered for offloading to UAV-enabled MEC platforms to recognize the focus areas. Open issues that are briefly discussed across papers but require additional research are summarized on basis of the gaps identified. This systematic perspective consolidates existing research in an organized manner to guide future works and establish a coherent taxonomy to organize future studies and reviews. Overall, mapping trends helps characterize research maturity, guiding its continued development.

Keywords

• Unmanned aerial vehicle (UAV)
• mobile edge computing (MEC)
• task offloading
• systematic mapping study (SMS)