Fog Assisted 6TiSCH Tri-Layer Network Architecture for Adaptive Scheduling and Energy-Efficient Offloading Using Rank-Based Q-Learning in Smart Industries

The smart industry is a significant realization of the internet of things. IPv6 over time-slotted channel hopping (6TiSCH) is the new standard that has emerged as a promising technology for industrial communications. The demanding issues of smart industries include scalability, latency, and energy efficiency. Existing studies have presented scheduling schemes for 6TiSCH-based industries. However, these works are not suitable for practical scenarios where emergencies frequently occur. Hence, this study aims to improve scalability, latency, and energy efficiency in 6TiSCH-based smart industries for practical scenarios. A new fog assisted 6TiSCH Tri-Layer architecture is designed with a network layer, smart communication layer, and cloud layer. Scalability and latency are realized by fog computing in the smart communication layer. The rough set-based root selection (R2S) algorithm improves the network layer’s energy efficiency by constructing an R2S and destination-oriented direct acyclic graph. Emergency cognizant distributed adaptive scheduling minimizes the latency for data transmission through fuzzy Bayesian learning-based parent selection and multiobjective gravitational search algorithm-based channel selection. In the fog layer, the rank-based Q-learning algorithm performs offloading to manage the energy consumption among fog nodes. Industrial data are stored in the cloud layer where they can be accessed by end users. The performance is evaluated by modeling the tri-layer 6TiSCH network in network simulator-3.26. Experiments show that the proposed work achieves satisfactory results in terms of energy consumption, latency, end-to-end delay, response time, and transmission efficiency.