۱- Introduction

۲- System model

۳- Q-learning-based dynamic spectrum access

۴- Numerical tesults

۵- Conclusions

References

Abstract

In recent years, Industrial Internet of Things (IIoT) has attracted growing attention from both academia and industry. Meanwhile, when traditional wireless sensor networks are applied to complex industrial field with high requirements for real time and robustness, how to design an efficient and practical cross-layer transmission mechanism needs to be fully investigated. In this paper, we propose a Q-learning-based dynamic spectrum access method for IIoT by introducing cognitive self-learning technical solution to solve the difficulty of distributed and ordered self-accessing for unlicensed terminals. We first devise a simplified MAC access protocol for unlicensed users to use single available channel. Then, a Qlearning-based multi-channels access scheme is raised for the unlicensed users migrating to other lower cells. The channel with most Q value will be considered to be selected. Every mobile terminals store and update their own channel lists due to distributed network mode and non-perfect sensing ability. Numerical results are provided to evaluate the performances of our proposed method on dynamic spectrum access in IIoT. Our proposed method outperforms the traditional simplified accessing methods without self-learning capability on channel usage rate and conflict probability.

Introduction

In the context of Industry 4.0, Industrial Internet of Things (IIoT) provides new driving force for the development of high efficient, low-energy, flexible and smart factories, by introducing sensing capability, cloud computing, intelligent robotics and wireless sensor networks into modern industrial environment [1–۴]. An inevitable tendency toward global mobile networks that combines artificial intelligent, automation, warehousing systems and production facilities in the shape of Cyber-Physical Systems as well as cognitive IIoT emerges [5–۷]. In the process of continuously sensing industrial field, exchanging control information, self-learning and adapting dynamic networks, deciding and performing transmission strategy, plenty of challenges need to be addressed. Many techniques including intelligent algorithms, deep learning, cognitive radio have been applied to enhance the robustness, accuracy and efficiency of the IIoT [8– ۱۱]. In particular, many technical solutions which have been adopted in wireless sensor networks can be adapted to IIoT environment after being revised according to the corresponding new characteristics of IIoT [12–۱۷]. In [12], a wireless sensor networks based on safe navigation scheme for micro flying robots in the IIoT has been raised to detect the static and dynamic obstacles in indoor environment. In [13], a three-stage multi-view stacking ensemble machine learning model based on hierarchical time series feature extraction methods were designed to resolve the anomaly detection problem in IIoT. In [14], a multi-level DDoS mitigation framework were devised to defend against DDoS attacks for IIoT, which includes the edge computing level, fog computing level, and cloud computing level. In [15], the authors developed an IIoT based solution to ensure a real-time connection between products and assembly lines. The raised dynamic cycle time setting method considered the varying complexity of the product on the basis of the real-time information offered by sensor nodes and indoor positioning systems.