Large Industrial Networks Research Articles

Deep Learning for the Industrial Internet of Things (IIoT): A Comprehensive Survey of Techniques, Implementation Frameworks, Potential Applications, and Future Directions.

The Industrial Internet of Things (IIoT) refers to the use of smart sensors, actuators, fast communication protocols, and efficient cybersecurity mechanisms to improve industrial processes and applications. In large industrial networks, smart devices generate large amounts of data, and thus IIoT frameworks require intelligent, robust techniques for big data analysis. Artificial intelligence (AI) and deep learning (DL) techniques produce promising results in IIoT networks due to their intelligent learning and processing capabilities. This survey article assesses the potential of DL in IIoT applications and presents a brief architecture of IIoT with key enabling technologies. Several well-known DL algorithms are then discussed along with their theoretical backgrounds and several software and hardware frameworks for DL implementations. Potential deployments of DL techniques in IIoT applications are briefly discussed. Finally, this survey highlights significant challenges and future directions for future research endeavors.

Abstract interpretation of distributed network control planes

The control plane of most computer networks runs distributed routing protocols that determine if and how traffic is forwarded. Errors in the configuration of network control planes frequently knock down critical online services, leading to economic damage for service providers and significant hardship for users. Validation via ahead-of-time simulation can help find configuration errors but such techniques are expensive or even intractable for large industrial networks. We explore the use of abstract interpretation to address this fundamental scaling challenge and find that the right abstractions can reduce the asymptotic complexity of network simulation. Based on this observation, we build a tool called ShapeShifter for reachability analysis. On a suite of 127 production networks from a large cloud provider, ShapeShifter provides an asymptotic improvement in runtime and memory over the state-of-the-art simulator. These gains come with a minimal loss in precision. Our abstract analysis accurately predicts reachability for all destinations for 95% of the networks and for most destinations for the remaining 5%. We also find that abstract interpretation of network control planes not only speeds up existing analyses but also facilitates new kinds of analyses. We illustrate this advantage through a new destination "hijacking" analysis for the border gateway protocol (BGP), the globally-deployed routing protocol.

Design of Energy-Saving Control Software Algorithms in Large Industrial Network

In the operation process of large industrial control network, with conventional fuzzy PID control algorithm for industrial control networks energy-saving control, excessive industrial networks will aggravate machine wear of the single network, thereby reduce the effect of energy-saving for industrial network. This paper presents an approach for industrial network energy-saving control based on non-uniform data production rate. According to the relationship between the network load and loss, the loss model of industrial control network can be established. Adaptive linear genetic method is utilized to control industrial control network energy-saving load, so as to achieve energy-saving control of industrial control network. Experimental results show that the algorithm can effectively improve the energy-saving efficiency of industrial control network, and achieve satisfactory results.

Implementation of a controller for a static VAr compensator in large industrial networks

This paper deals with the development of a control algorithm for reactive-power compensation, flicker compensation and voltage regulation in large, industrial networks. The concept of compensation with a static-var compensator (SVC) and control-algorithm verification with realistic simulation examples using an actual industrial-network model are presented. In this paper a new control algorithm for control SVC devices is proposed. With this controller the SVC devices can operate in three modes: reactive-power compensation, flicker-level compensation or voltage regulation. The advantages of the proposed controller compared to other known regulators are the better flicker-level compensation and cheaper installation, because it requires only one measurement location. The data obtained with the simulations showed that the proposed controller can be successfully used for the control SVC devices for the reduction of flicker levels, the compensation of reactive power and for the regulation of voltage levels in large industrial networks.