Benefiting from the real-time processing ability of edge computing, computing tasks requested by smart devices in the IoT are offloaded to edge computing devices (ECDs) for implementation. However, ECDs are often overloaded or underloaded with disproportionate resource requests. In addition, during the process of task offloading, the transmitted information is vulnerable, which can result in data incompleteness. In view of this challenge, a blockchain-enabled computation offloading method, named BeCome, is proposed in this paper. Blockchain technology is employed in edge computing to ensure data integrity. Then, the nondominated sorting genetic algorithm III (NSGA-III) is adopted to generate strategies for balanced resource allocation. Furthermore, simple additive weighting (SAW) and multicriteria decision-making (MCDM) are utilized to identify the optimal offloading strategy. Finally, performance evaluations of BeCome are given through simulation experiments.

The intelligent detection process for industrial anomalies employs artificial intelligence methods to classify images that deviate from a normal appearance. Traditional convolutional neural network (CNN)-based anomaly detection algorithms mainly use the network to restructure abnormal areas and detect anomalies by calculating the errors between the original image and reconstructed image. However, the traditional CNNs struggle to extract global context information, resulting in poor anomaly detection performance. Thus, a masked Swin Transformer Unet (MSTUnet) for anomaly detection is proposed. To solve the problem of insufficient abnormal samples in the training phase, an anomaly simulation and mask strategy is first applied on anomaly-free samples to generate a simulated anomaly and, then, the Swin Transformer's powerful global learning ability is used to inpaint the masked area. Finally, a convolution-based Unet network is used for end-to-end anomaly detection. Experimental results on industrial dataset MVTec AD show that MSTUnet achieves superior anomaly detection and localization performance.

With the potential of implementing computing-intensive applications, edge computing is combined with digital twinning (DT)-empowered Internet of vehicles (IoV) to enhance intelligent transportation capabilities. By updating digital twins of vehicles and offloading services to edge computing devices (ECDs), the insufficiency in vehicles’ computational resources can be complemented. However, owing to the computational intensity of DT-empowered IoV, ECD would overload under excessive service requests, which deteriorates the quality of service (QoS). To address this problem, in this article, a multiuser offloading system is analyzed, where the QoS is reflected through the response time of services. Then, a service offloading (SOL) method with deep reinforcement learning, is proposed for DT-empowered IoV in edge computing. To obtain optimized offloading decisions, SOL leverages deep Q-network (DQN), which combines the value function approximation of deep learning and reinforcement learning. Eventually, experiments with comparative methods indicate that SOL is effective and adaptable in diverse environments.

Currently, edge computing, emerging as a burgeoning paradigm, is powerful to handle real-time resource provision for the IoT applications. However, due to the spatial distribution of the geographically sparse IoT devices and the resource limitations of the edge computing units (ECUs), the resource utilization of corresponding edge servers is relatively insufficient, and the execution performance is ineffective to some extent. Meanwhile, privacy leakage including personal information, location and media data, etc., during the transmission process from IoTs devices to edge servers severely restricts the application of ECUs in IoT. To address these challenges, a two-phase offloading optimization strategy is put forward for joint optimization of offloading utility and privacy in edge computing enabled IoT. Technically, a utility-aware task offloading method, named UTO, is devised first to obtain the goal of maximizing the resource utilization of ECUs and minimizing the implementation time cost. Then a joint optimization method, named JOM, for utility and privacy trade-offs is designed to balance the privacy preservation and execution performance. Eventually, experimental evaluations are designed to illustrate the efficiency and reliability of UTO and JOM.