Deep Reinforcement Learning for the management of Software-Defined Networks in Smart Farming

The Internet of Things and the millions of devices that generate and collect data through sensors to send it to the Cloud are part of the life of users in many contexts, including smart farming and precision agriculture scenarios. This volume of data is stored and processed in the Cloud, with the purpose of obtaining knowledge and valuable information for organizations. Edge Computing has emerged to reduce the costs associated with transferring, processing and storing data from IoT environments in the Cloud. This paradigm allows data to be pre-processed at the edge of the network before they are sent to the Cloud, obtaining shorter response times and maintaining service even during communication breakdowns between the IoT and Cloud layers. Furthermore, there is a increasing trend to shared physical network resources among diverse user entities through Software-Defined Networks and Network Function Virtualization with the aim to reduce costs. In this sense, smart mechanisms are required to optimize virtual dataflows in the networks, as Deep Reinforcement Learning techniques. This paper proposes a Double Deep-Q Learning approach to manage virtual dataflows in SDN/NFV using an Edge-IoT architecture, formerly applied in smart farming and Industry 4.0 scenarios.