Toward an Utra-Low Latency Based Q-learning Assisted 6TiSCH Network in Smart Industries

Abstract

Smart industry is a significant implementation of Internet of Things (IoT). IPv6 over Time Slotted Channel Hopping (6TiSCH) is a new standard that emerges as promising technology for industrial communications. The demanding issues of smart industries include scalability, and latency. Literature works presented a scheduling scheme for 6TiSCH-based industries. However, the prime limitation in those works is that the works are not suitable for practical scenario where emergency situations frequently occur. This paper aims to improve scalability, and latency in 6TiSCHbased smart industries for practical scenario. New Fog-assisted 6TiSCH Q_learning algorithm is designed, and smart communication layer. Scalability and latency is handled by fog computing in smart communication layer. In fog layer, rank based Q-learning algorithm performs offloading to manage energy consumption among fog nodes.