Task Offloading Optimization for UAV-Assisted Fog-Enabled Internet of Things Networks

Abstract

Recently, unmanned aerial vehicles (UAVs) have been considered as an efficient way to provide enhanced coverage or relaying services to Internet of Things devices (IDs) in wireless systems with limited or no infrastructure. In this article, a UAVs-assisted fog-enabled Internet of Things (IoT) network is studied, in which moving UAVs are equipped with computing capabilities to offer task offloading opportunities to IDs. Besides, there are two types of IDs, namely, requested-IDs (R-IDs), which has task offloading requirement, and free-IDs (F-IDs), which could offload tasks for R-IDs with idle computation resources. Two offloading links are considered: 1) the device-to-device (D2D) link and 2) the ground-to-air (G2A) link, which are responsible for both the uplink and downlink offloading procedure. To minimize the total network overhead, we jointly optimize the UAV trajectory, transmission power, and computation offload radios, while satisfying Quality-of-Service (QoS) requirements of R-IDs. The optimization problem is nonconvex, and the UAV-assisted task offloading optimization algorithm is proposed to obtain the local optimal solutions, which decomposes the original problem into two parallel subproblems and solved alternately. Finally, simulation results demonstrate that the proposed algorithm could achieve superior performance in terms of the network overhead compared with algorithms in the literature.