Fog computing provides a more flexible service environment than cloud computing. The lightweight fog environment is suitable for industrial applications. In order to strengthen service scalability, container virtualization has been proposed and studied in recent years. It is vital to explore the trade-off between service scalability and operating expenses. The paper integrates hypervisor technique with container virtualization, and constructs an integrated virtualization (IV) fog platform for deploying industrial applications based on virtual network function. The paper presents a fuzzy-based real-time auto-scaling (FRAS) mechanism and implements it in the IV fog platform. The FRAS mechanism provides a dynamic, rapid, lightweight and low cost solution to service auto-scaling problem. Experimental results showed that the proposed FRAS mechanism yields a better service scale with lower average delay, error rate and operating expenses compared to other auto-scaling schemes.

Dangerous driving behaviors are diverse and complex. Determining how to analyze the driving behavior of public drivers objectively and accurately has always been a research challenge. This research proposes a macroscopic and dynamic method for evaluating drivers’ dangerous driving degree based on a fuzzy inference system. It also designs fuzzy-macro long short-term memory (LSTM), a variant of LSTM recurrent neural networks, which can predict drivers’ dangerous driving behaviors and risk degree. We elucidate how a macroscopic fuzzy inference dangerous driving behavior system is designed based on various driving behavior factors and the neuron architecture of the fuzzy-macro LSTM network. We collect real driving behavior data of drivers on the road and conduct a series of experimental analyses. Compared with five other commonly used time-series forecasting neural network models, our fuzzy-macro LSTM model performs best in terms of prediction error. Experimental results verify the effectiveness of the proposed method for macroanalysis and prediction of dangerous driving behavior.

The demand for satisfying service requests, effectively allocating computing resources, and providing service on‐demand application continuously increases along with the rapid development of the Internet. Edge computing is used to satisfy the low latency, network connection, and local data processing requirements and to alleviate the workload in the cloud. This paper proposes a gateway‐based edge computing service model to reduce the latency of data transmission and the network bandwidth from and to the cloud. An on‐demand computing resource allocation can be achieved by adjusting the task schedule of the edge gateway via the lightweight virtualization technology, Docker. The edge gateway can also process the service requests in the local network. The proposed edge computing service model not only eliminates the computation burden of the traditional cloud service model but also improves the operation efficiency of the edge computing nodes. This model can also be used for various innovation applications in the cloud‐edge computing environment for 5G and beyond.