Digital twin (DT) is a virtual representation of physical entities or processes that enables real-time monitoring, analysis, and optimization in the field of intelligent manufacturing. By simulating and optimizing production processes, DT technology could predict and prevent equipment failures, and enhance the efficiency and quality of industrial parts production. However, effectively deploying DTs into Digital Twin Empowered Edge Network (DITEN) in complex Industrial Internet of Things (IIoT) environments remains a significant challenge. Particularly in scenarios with numerous physical entities within IIoT, optimizing the deployment of DTs on edge nodes to minimize interaction latency with physical entities, as well as reducing workload and energy consumption on edge nodes, becomes crucial. To address this challenge, this paper first designs a Bi-Layer DT architecture for IIoT. Furthermore, an Intrinsic Curiosity Module-based Multi-Agent Proximal Policy Optimization algorithm (ICM-MAPPO) is proposed to solve the optimal deployment problem for DTs in DITEN-Empowered IIoT. Numerical experiments validate the effectiveness of the ICM-MAPPO algorithm in minimizing deployment latency and interaction latency while achieving load balance and reducing energy consumption.

Efficient image segmentation algorithms are critical in computer vision, as they maintain high processing speeds while handling large amounts of data and providing practical solutions in resource-limited environments. While existing classical segmentation methods achieve good results, their real-time performance can be further improved. To address this issue, we propose an efficient segmentation network (ESNet) to capture extensive contextual information and improve segmentation fineness, which are two main issues in semantic segmentation, while ensuring real-time capability. Then, we first introduce an efficient context module (ECM) that effectively enlarges the effective receptive field (ERF) of the model and improves its performance in integrating contextual information. Subsequently, a skip connection with a simple feature fusion module (SFFM) is designed to provide rich detail information, thereby improving segmentation fineness. The efficacy of ESNet was evaluated on the PASCAL VOC2012 semantic segmentation dataset against several state-of-the-art methods. ESNet achieves 71.4 FPS at a resolution of 1024×2048 on a single NVIDIA GeForce RTX 3090 GPU and achieves 62.42% mIoU at a resolution of 320×480 with an extremely simple training recipe, striking a fine balance between segmentation accuracy and inference speed.

The design and performance analysis of underwater acoustic sensor networks (UASNs) have attracted intensive attention. In this study, we present a theoretical framework to evaluate the performances of UASNs. In contrast to existing methods, we applied a new communication protocol model to characterize the channel interference by taking into account the unique characteristics of underwater acoustic channels, namely, the concurrent transmission opportunities owing to the large propagation delays of acoustic signals. Using the communication protocol model, we investigated the collision regions of a communication pair and then derived a closed-form expression for the successful packet transmission probability. Moreover, we considered practical networks where each node is equipped with a limited data buffer to store its generated packets. Using collision analysis and a stationary buffer distribution, we derived the closed-form expression of system performance in terms of the throughput and the packet queue delay. Finally, we validated the accuracy of our analysis via extensive simulation results. The impacts of various network parameters on the system performance are also evaluated.

Nowadays, video surveillance is widespread used to achieve security life in the construction of smart city. As a result, prevalence of video surveillance equipments and technologies enables pedestrian identification and tracking to be research hotspots in the field of computer vision, whose development and application are of great significance to the construction of a good social security environment. However, pedestrian identification and tracking in monitoring scenarios still have problems of low recognition accuracy and high model complexity, and computer vision based adaptive recognition methods still have a large room for improvement and development. To address this issue, real-time pedestrian identification and tracking within adaptive collaboration edge computing environment is investigated in this paper. Within the paradigm of edge computing, the Raspberry Pi 3B+ acting as the edge computing node is adopted to handle the related issues of pedestrian identification and tracking. The combination of HOG (Histogram of Oriented Gradient) and SVM (Support Vector Machine) is investigated to achieve pedestrian identification, where, HOG is employed as the feature descriptor and SVM is employed as the classification algorithm. Furthermore, the pixel-based visual tracking algorithm is investigated to achieve effective and uninterrupted pedestrian tracking. By implementing a prototype on Raspberry Pi 3B+ using OpenCV libraries, the experimental results finally have been shown to demonstrate the efficiency of the investigations.

Intelligent transportation is an important guarantee for the safety and efficiency of urban transportation in smart cities, and regular road pavement inspection is the focus of road and bridge maintenance in intelligent transportation. Cracks in concrete pavement are the most common type of pavement damage and the earliest sign of pavement deterioration. However, existing crack detection algorithms suffer from incomplete crack detection and are easily disturbed by pseudo-cracks such as water spots and leaves. To address the above problems, this paper proposes a convolutional neural network (CNN) method that introduces a gradient module and an attention mechanism. The method adopts a CNN model based on the VGG-16 structure as the main body of the network structure, and optimally adjusts the network structure by incorporating a gradient layer and a self-attention mechanism, accelerating the convergence speed of network training and the global information learning ability. A negative sample dataset with pseudo-cracks, such as leaves, water spots and branches was constructed, and comparative experimental analysis was conducted in terms of both visual judgment and objective indicators. The experimental results show that after the introduction of the gradient layer and the self-attentive mechanism, not only the convergence speed of the network training is faster, but also the cracks in the concrete pavement images can be segmented more completely and accurately.

With the development and application of Internet of Things (IoT) technology, IoT has been widely used in agriculture, industry, and urban construction. In the process of building Smart cities, setting up small-scale weather stations based on IoT technology can effectively monitor certain special environments where large weather stations cannot accurately assess and predict short-term extreme weather events. As the summer heat approaches, the number of heatstroke cases is continuously rising. The Wet Bulb Globe Temperature (WBGT) index, which is closely related to heatstroke, provides a simple method for evaluating the thermal work environment and thermal load of workers in hot conditions. In this paper, through adjusting the model structure and moving window, and optimizing the predictive model parameters, an improved Long Short Term Memory (LSTM) algorithm is proposed to forecast WBGT values at future time points: five minutes, thirty minutes, and sixty minutes ahead. The paper also conducts a simple analysis of the daily average performance of WBGT in relation to air humidity. Additionally, it compares dual-input models that include air humidity as input. Through a comparison of four prediction performance evaluation metrics, simple-input LSTM models demonstrate lower error.