Toward Developing Fog Decision Making on the Transmission Rate of Various IoT Devices Based on Reinforcement Learning

In recent years, the focus on reducing the delay and the cost of transferring data to the cloud has led to data processing near end devices. Therefore, fog computing has emerged as a powerful complement to the cloud to handle the large data volume belonging to the Internet of Things (IoT) and the requirements of communications. Over time, because of the increasing number of IoT devices, managing them by a fog node has become more complicated. The problem addressed in this study is the transmission rate of various IoT devices to a fog node in order to prevent delays in emergency cases. We formulate the decision making problem of a fog node by using a reinforcement learning approach in a smart city as an example of a smart environment and then develop a Qlearning algorithm to achieve efficient decisions for IoT transmission rates to the fog node. Although to the best of our knowledge, thus far, there has been no research with this objective, in this study two more approaches, random-based and greedy-based, are simulated to show that our method performs considerably better (over 99.8 percent) than these algorithms.