With rapid development of computing technologies, large amount of data is gathered from various terminals or industrial devices, however data reliability and trust is a very important issue to be considered especially when fraud or dishonest data is integrated in the system, moreover, misusing of the gathered data may lead serious problems, upon this problem, in this paper, we proposed a blockchain-based trusted data management scheme (called BlockTDM) in edge computing, in which we proposed a flexible and configurable blockchain architecture that including mutual authentication protocol, flexible consensus and smart contract, block and transaction data management, blockchain nodes management and deployment. In the proposed BlockTDM scheme, we designed user-defined sensitive data encryption approach before the data stores in blockchain system, and we designed conditional access and decryption query of the protected blockchain data and transactions from blockchain system, and the BlockTDM can still support matrix-based multichannel data segment and isolation for detailed sensitive data protection or privacy protection. Finally, we evaluated the proposed BlockTDM scheme security, availability and efficiency with large amount of experiments. Analysis and evaluation manifest that the proposed BlockTDM provides a general, flexible and configurable blockchain-based paradigm for trusted and tamper-resistant data management, which is suitable for various application scenes with high level security and creditability.

Rapid advancements of sixth-generation (6G) network and Internet of Everything (IoE) supports numerous emerging services and application. Increasing mobile internet traffic and services, on the other hand, presented a number of challenges that could not be addressed with the current network design. The cybertwin is equipped with a variety of capabilities, including communication assistants, network data loggers, and digital asset owners, to address these difficulties. While spectrum resources are limited, effective resource management and sharing are essential in achieving these requirements. With this motivation, this article presents a new metaheuristic with blockchain based resource allocation technique (MWBA-RAT) for cybertwin driven 6G on IoE environment. The incorporation of the blockchain in 6G enables the network to monitor, manage, and share resources effectively. The proposed MWBA-RAT technique designs a new quasi-oppositional search and rescue optimization (QO-SRO) algorithm for the optimal resource allocation process and this shows the novelty of the work. The QO-SRO algorithm involves the integration of the quasi oppositional based learning concept with the traditional SRO algorithm to improve its convergence rate. A wide range of experiments are performed to highlight the enhanced outcomes of the MWBA-RAT technique.

Surface detection of small defects plays a vital role in manufacturing and has attracted broad interest. It remains challenging primarily due to the small size of the defect relative to the large surface and the rare occurrence of defects. To address this problem, in this article we propose a novel machine vision approach for automatically identifying the tiny flaws that may appear in a single image. First, the presented defect exaggeration approach produces both the flawless image and the corresponding exaggerated version of the defect by taking the variations in the image as regularization terms. Second, a generative adversarial network (GAN) in conjunction with a convolutional neural network (CNN) is proposed to guarantee the accuracy of tiny surface defect detection by producing exaggerated defect image samples. Furthermore, the limited dataset of the training samples for defect detection is enlarged by exploiting the GAN technique with the variation exaggerated images. To evaluate the performance of our proposed method, we conduct comparison experiments between the state-of-the-art techniques with and without the proposed algorithm as well as comparison experiments between the state-of-the-art techniques and our method. The experimental results on different types of surface image samples demonstrate that the proposed method can significantly improve the performance of the state-of-the-art approaches while achieving a defect detection accuracy of 99.2%.

Wireless Sensor Network (WSN) is an essential technology for the Internet-of-Things (IoT) and intelligence-based applications. In the case of Intelligent Transportation Systems (ITS), the WSNs play an important role in safety and efficient traffic management. Therefore, there is enormous demand for energy efficient WSNs for dynamic resource allocation in vehicles and infrastructures. This work presents a Multi-Input Multi-Output (MIMO) technique model in WSNs, which addresses the Cluster Head (CH) recognition issue for MIMO sensor networks by using Back Propagation Neural Network (BPNN). The conventional CH identification suffers from a lack of location identification due to the dynamic and real-time environment. Thus, to obtain more precise positioning accuracy, the proposed work uses BPNN combined with a distributed gradient drop technique to calculate the position of the unknown CH. This reduces the distance estimation error, and the particle swarm optimization technique is further used to obtain the optimal weight and threshold of the network. The work is validated by using mathematical analysis, simulations, and comparison with existing techniques. The proposed model shows a better performance in terms of energy consumption, error rate, and computation time.

The fifth industrial revolution (Industry 5.0) integrates humans and machines to satisfy the increasing customization demands of the manufacturing complexity using an optimized robotized manufacturing process. Industry 5.0 make use of collaborative robots (cobots) for optimizing productivity and ensuring safety. At the same time, unmanned aerial vehicles (UAVs) are predicted to be the main part of industry 5.0 in the forthcoming days. Regardless of high mobility and energy-limited UAVs for wireless communication as significant advantages, different issues are also existing in the UAV networks, such as security, reliability, etc. Several research works have focused on resolving security issues in UAV communication to support safety-critical applications. With this motivation, this article presents an artificial intelligence-based UAV-borne secure communication with classification (AIUAV-SCC) framework for industry 5.0 environment. The proposed AIUAV-SCC model involves two major phases namely image steganography-based secure communication and deep learning (DL)-based classification. At the initial stage, a new image steganography technique with multilevel discrete wavelet transformation, quantum bacterial colony optimization based optimal pixel selection, and encryption processes take place. Next, in the second stage, the Bayesian optimization (BO)-based SqueezeNet model is applied for the classification of securely received UAV images where the parameters in the SqueezeNet method are optimally tuned by the utilize of the BO technique. To validate the performance of the presented model, extensive simulations are applied using the UC Merced dataset (UCM) aerial dataset and the outcomes are investigated under several dimensions. The outcomes make sure the goodness of the presented model on test UCM aerial dataset over the compared methods.