industrial internet of things
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2022 ◽  
Vol 22 (3) ◽  
pp. 1-22
Yi Liu ◽  
Ruihui Zhao ◽  
Jiawen Kang ◽  
Abdulsalam Yassine ◽  
Dusit Niyato ◽  

Federated Edge Learning (FEL) allows edge nodes to train a global deep learning model collaboratively for edge computing in the Industrial Internet of Things (IIoT), which significantly promotes the development of Industrial 4.0. However, FEL faces two critical challenges: communication overhead and data privacy. FEL suffers from expensive communication overhead when training large-scale multi-node models. Furthermore, due to the vulnerability of FEL to gradient leakage and label-flipping attacks, the training process of the global model is easily compromised by adversaries. To address these challenges, we propose a communication-efficient and privacy-enhanced asynchronous FEL framework for edge computing in IIoT. First, we introduce an asynchronous model update scheme to reduce the computation time that edge nodes wait for global model aggregation. Second, we propose an asynchronous local differential privacy mechanism, which improves communication efficiency and mitigates gradient leakage attacks by adding well-designed noise to the gradients of edge nodes. Third, we design a cloud-side malicious node detection mechanism to detect malicious nodes by testing the local model quality. Such a mechanism can avoid malicious nodes participating in training to mitigate label-flipping attacks. Extensive experimental studies on two real-world datasets demonstrate that the proposed framework can not only improve communication efficiency but also mitigate malicious attacks while its accuracy is comparable to traditional FEL frameworks.

2022 ◽  
Vol 2022 ◽  
pp. 1-14
Zhenzhong Zhang ◽  
Wei Sun ◽  
Yanliang Yu

With the vigorous development of the Internet of Things, the Internet, cloud computing, and mobile terminals, edge computing has emerged as a new type of Internet of Things technology, which is one of the important components of the Industrial Internet of Things. In the face of large-scale data processing and calculations, traditional cloud computing is facing tremendous pressure, and the demand for new low-latency computing technologies is imminent. As a supplementary expansion of cloud computing technology, mobile edge computing will sink the computing power from the previous cloud to a network edge node. Through the mutual cooperation between computing nodes, the number of nodes that can be calculated is more, the types are more comprehensive, and the computing range is even greater. Broadly, it makes up for the shortcomings of cloud computing technology. Although edge computing technology has many advantages and has certain research and application results, how to allocate a large number of computing tasks and computing resources to computing nodes and how to schedule computing tasks at edge nodes are still challenges for edge computing. In view of the problems encountered by edge computing technology in resource allocation and task scheduling, this paper designs a dynamic task scheduling strategy for edge computing with delay-aware characteristics, which realizes the reasonable utilization of computing resources and is required for edge computing systems. This paper proposes a resource allocation scheme combined with the simulated annealing algorithm, which minimizes the overall performance loss of the system while keeping the system low delay. Finally, it is verified through experiments that the task scheduling and resource allocation methods proposed in this paper can significantly reduce the response delay of the application.

Sensors ◽  
2022 ◽  
Vol 22 (1) ◽  
pp. 325
Andrea Vaclavova ◽  
Peter Strelec ◽  
Tibor Horak ◽  
Michal Kebisek ◽  
Pavol Tanuska ◽  

Today, Industrial Internet of Things (IIoT) devices are very often used to collect manufacturing process data. The integration of industrial data is increasingly being promoted by the Open Platform Communications United Architecture (OPC UA). However, available IIoT devices are limited by the features they provide; therefore, we decided to design an IIoT device taking advantage of the benefits arising from OPC UA. The design procedure was based on the creation of sequences of steps resulting in a workflow that was transformed into a finite state machine (FSM) model. The FSM model was transformed into an OPC UA object, which was implemented in the proposed IIoT. The OPC UA object makes it possible to monitor events and provide important information based on a client’s criteria. The result was the design and implementation of an IIoT device that provides improved monitoring and data acquisition, enabling improved control of the manufacturing process.

2022 ◽  
pp. 209-241
Günther Schuh ◽  
Matthias Jarke ◽  
Andreas Gützlaff ◽  
István Koren ◽  
Tim Janke ◽  

2022 ◽  
Vol 181 ◽  
pp. 116-131
Saddam Hussain ◽  
Syed Sajid Ullah ◽  
Ihsan Ali ◽  
Jiafeng Xie ◽  
Venkata N. Inukollu

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