  Fog nodes in the vicinity of IoT devices are promising to provision low
latency services by offloading tasks from IoT devices to them. Mobile IoT is
composed by mobile IoT devices such as vehicles, wearable devices and
smartphones. Owing to the time-varying channel conditions, traffic loads and
computing loads, it is challenging to improve the quality of service (QoS) of
mobile IoT devices. As task delay consists of both the transmission delay and
computing delay, we investigate the resource allocation (i.e., including both
radio resource and computation resource) in both the wireless channel and fog
node to minimize the delay of all tasks while their QoS constraints are
satisfied. We formulate the resource allocation problem into an integer
non-linear problem, where both the radio resource and computation resource are
taken into account. As IoT tasks are dynamic, the resource allocation for
different tasks are coupled with each other and the future information is
impractical to be obtained. Therefore, we design an on-line reinforcement
learning algorithm to make the sub-optimal decision in real time based on the
system's experience replay data. The performance of the designed algorithm has
been demonstrated by extensive simulation results.
