Abstract
Driven by the growing popularity of mobile applications, such as the Internet of Things (IoT), fog computing has been envisioned as a promising approach to enhance the computation capability of mobile devices and reduce the energy consumption. In this article, we aim to investigate the dynamic computation offloading problem in the IoT fog system under the fast time-varying wireless channel conditions. Our work differs from the existing work, which is based on the assumption that the channel-state information can be perfectly obtained by the offloading agent (e.g., the IoT device). In reality, due to hardware limitation, short sensing time, and network connectivity issues in IoT fog systems, it is difficult for the IoT device to have the perfect knowledge of a dynamic channel environment. Therefore, in this article, we propose a partially observable offloading scheme to enable the IoT device to make the optimal offloading decision with imperfect channel-state information. The optimization problem is formulated as a partially observable Markov decision process (POMDP) formulation, with the objective of minimizing the IoT device's energy consumption while meeting its requirement on task processing delay. To find the optimal offloading solution, an offline algorithm based on the deep recurrent $Q$ -network (DRQN) is developed. Finally, extensive simulation experiments are performed to evaluate the effectiveness of the proposed offloading scheme.
Published Version
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