UAV-Enhanced Intelligent Offloading for Internet of Things at the Edge

Abstract

With the explosive growth of diverse Internet of Things (IoT) applications, mobile edge computing (MEC) has been brought to settle the conflict between computation-intensive applications and resource-limited IoT mobile devices (IMDs). Note that the assistance of unmanned aerial vehicles (UAVs) is of great importance in providing reliable connectivity in areas with limited or no available communication infrastructure. To cope with the surging demands for Big Data processing from UAV-aided IoT applications, combining UAV-aided communication and MEC has been envisioned to be a promising paradigm, which gives rise to the so-called UAV-enhanced edge. In consideration of IMDs' limited battery capacity and UAV energy budget, in this article we study the energy reduction problem in UAV-enhanced edge by smartly making offloading decisions, allocating transmitted bits in both uplink and downlink, as well as designing UAV trajectory. This joint optimization problem is formulated as a mix-integer nonconvex optimization problem, and an alternative optimization algorithm based on block coordinate descent and successive convex approximation techniques is proposed as our solution. Extensive numerical results demonstrate that the overall energy consumption for accomplishing the tasks can be effectively reduced by adopting our joint optimization scheme, and the necessity of task offloading, UAV trajectory design, and bit allocation during transmission is validated.