Deep Reinforcement Learning Based Flow Aware-QoS Provisioning in SD-IoT for Precision Agriculture

Abstract

To meet the demands of modern technologies such as 5G, big data, edge computing, precision, and sustainable agriculture, the combination of Internet-of-Things (IoT) with software-defined networking (SDN) known as SD-IoT is suggested to automate the network by leveraging the programmable and centralized SDN interfaces. The previous literature has suggested quality-of-service (QoS) aware flow processing using manual strategies or heuristic algorithms, however, these schemes proposed with white-box approaches do not provide effective results as the network scales or dynamic changes are happening. This article proposes a novel QoS provision strategy using deep reinforcement learning (DRL) to calculate the optimal routes autonomously for SD-IoT traffic. To satisfy the different demands of flows in the SD-IoT network the flows are divided into two types. Hence, based on their service demand the routes are generated for them as per service request. The scenario is explained with precision agriculture based on SD-IoT and results are compared with benchmark strategies. A real internet topology is used for the evaluation of results. The results indicated that the proposed method gives improvements for QoS such as delay, throughput, packet loss rate, and jitter compared with benchmark models.