Delay-aware Resource Allocation in Fog-assisted IoT Networks Through Reinforcement Learning
This work addresses quality of service improvement for mobile IoT devices, such as vehicles and wearables, by optimizing resource allocation in dynamic environments, representing an incremental advancement in fog computing.
The paper tackles the challenge of minimizing task delay in mobile IoT networks by jointly allocating radio and computation resources between IoT devices and fog nodes, using an online reinforcement learning algorithm that achieves sub-optimal decisions in real-time.
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.