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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.

Due to the absence of routing initiation, the routing protocol requires a secure message transition. The key downside is that there are many current routing protocols. The big downside is the inability of the node to give a message when the attackers are routing. The key attack in the proposed routing model is Distributed Denial of Service (DDOS). The Protected Geographic Routing Protocol (SGRP) is the assured routing carried out in the proposed work. The Protected Geographic Routing Protocol (SGRP) will improve the efficiency of the transmission method by choosing a specific source node. The paper suggested that the Protected Spatial Routing Protocol (PSRP) would recognize and isolate such threats. Several modeling time estimation studies have been carried out to analyze the simulation time and the efficiency of the proposed routing technique. The proposed routing technique demonstrates the performance by calculating the Packets Delivery Ratio(PDR) and Energy consumption. The Routing protocol is used in many applications such as the Industrial Internet of Things (IoT)

Over the past decade, the number of Internet of Things (IoT) devices increased tremendously. In particular, the Internet of Medical Things (IoMT) and the Industrial Internet of Things (IIoT) expanded dramatically. Resource restrictions on IoT devices and the insufficiency of software security solutions raise the need for smart Hardware-Assisted Security (HAS) solutions. These solutions target one or more of the three C’s of IoT devices: Communication, Control, and Computation. Communication is an essential technology in the development of IoT. Bluetooth is a widely-used wireless communication protocol in small portable devices due to its low energy consumption and high transfer rates. In this work, we propose a supervisory framework to monitor and verify the operation of a Bluetooth system-on-chip (SoC) in real-time. To verify the operation of the Bluetooth SoC, we classify its transmission state in real-time to ensure a secure connection. Our overall classification accuracy is measured as 98.7%. We study both power supply current (IVDD) and RF domains to maximize the classification performance and minimize the overhead of our proposed supervisory system.

Accelerating the innovation and development of the Industrial Internet Platform is inevitably necessary for the integration of new-generation information technology and the manufacturing industry. It is also the key point for promoting the construction of manufacturing power and network power. In this paper, based on the comprehensive analysis of the relevant problems of China’s Industrial Internet Platform development monitoring, the development index of the Industrial Internet Platform is designed. Taking a typical domestic Industrial Internet Platform as an example, the development index of the Industrial Internet Platform in 2018, 2019, and 2020 are comprehensively calculated in this paper. The results show that China’s Industrial Internet Platform is rapidly growing in many aspects, such as industrial equipment cloud and industrial APP. There is a large space for improving the industrial knowledge accumulation reuse and the application promotion of small/medium-sized enterprises. The results in this paper can provide scientific suggestions and practical references for the government, enterprises, scientific research institutions, which is of great significance in promoting the healthiness and sustainability of the Industrial Internet Platform.

Abstract Mobile edge computing (MEC) has been considered as a key enabler for the industrial internet of things (IIoT) to cope with the ever-increasing communication and computing demands of nodes. In consideration of the limited power and resource of the IIoT nodes, it is necessary to design cost-effective data sharing mechanisms for MEC-enabled wireless industrial communication networks. In this article, we propose the probabilistic cooperative coded forwarding (PCCF) scheme based on network coding (NC) to minimize the required transmission number in both the data source and relay nodes. The data packets are encoded sparsely in a systematic coding framework so that the decoding process at the receivers can be more efficient. The relationship between the forwarding and coding parameters of the proposed scheme and the successful decoding probability are comprehensively analyzed and the approximations are numerically verified. Throughout the analysis, we find the optimal sparsity of network coding vectors and also the existence of minimum transmission numbers.

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.

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.