To overcome the problem of low system resource utilization caused by the lack of exploration of environmental changes in Industrial Internet of Things (IIoT) service orchestration, while ensuring Quality of Service (QoS), we propose an Adaptive Orchestration Algorithm of Service Function Chain (AOASFC) Based on Deep Reinforcement Learning (DRL). Our approach in paper integrates joint deployment and routing information to manage system resources, thereby optimizing orchestration strategies. Furthermore, to enhance the capability of exploring environmental changes, we design a curiosity-driven module that evaluates the environmental changes before and after the DRL agent’s decision-making process, generating intrinsic rewards to guide a more comprehensive exploration process. Our approach effectively mitigates the high bias issue caused by updating value function, because we integrate Proximal Policy Optimization (PPO) with Generalized Advantage Estimation (GAE) and perform weighted averaging on multi-step estimates, optimizing temporal difference learning. In performance comparisons, we have compared DeepCoordblue(a centralized DRL orchestrator) and BSP(a greedy heuristic baseline) algorithm, AOASFC demonstrates superior performance in different traffic arrival patterns of SFC deployment scenarios: it not only improves system throughput by 15.35% and 11.64% respectively, but also keeps end-to-end latency below 50ms while significantly enhancing resource utilization.

Medical Mixed Reality (MR) has made significant progress in virtual surgery simulation and tumor teaching. This paper proposes a framework for pulmonary nodule attribute editing based on image feature consistency, achieving spatial alignment of multi-stage case data. To address the limitations of traditional time-image reconstruction, we design an adversarial siamese model architecture capable of synthesizing missing nodule images, completing temporal data, and fine-grained modeling of nodule growth. To tackle challenges such as deformation, background inconsistency, and attribute uncertainty in generated samples, we introduce a Denoising Diffusion Implicit Model (DDIM) and construct an attribute vector space for pathological feature editing. Additionally, we propose a separable image reconstruction strategy to enhance local feature stability. Extensive validation on the lung-specific LIDC-IDRI dataset demonstrates superior performance with SSIM of 97.5${\%}$ and LPIPS of 0.036. To further verify generalization capability, cross-organ testing on the liver-focused LiTS dataset achieves competitive results with SSIM of 85.0${\%}$ and LPIPS of 0.128. These outcomes provide strong technical support for high-fidelity virtual surgery and intelligent tumor teaching platforms.

With the rapid development of the industrial internet, the value of internal data is increasing. Federated learning, which can protect data privacy, is crucial in this context. However, it faces challenges such as device heterogeneity, data heterogeneity, and single point of failure in industrial internet scenarios. To address these, we propose the Blockchain-integrated Decentralized Asynchronous Federated Learning (BDAFL) algorithm. It leverages blockchain and the Raft consensus algorithm to decouple global model updates from a central server, using multiple servers to aggregate partial model parameters and mitigate single-point failure impacts. For device heterogeneity, BDAFL introduces a weighted mechanism based on dynamic waiting times and update frequencies. To handle data heterogeneity, it uses the Earth Mover’s Distance (EMD) to measure data distribution differences and adjusts local model parameter weights accordingly. Experimental results show that BDAFL improves model accuracy by 1.27% on MNIST, 0.99% on CIFAR-10 and 0.63% on a self-bulid bearing fault dataset compared to similar algorithms, and outperforms them in precision, recall, and F1 scores across all classification categories.

Due to the harsh deployment environment of the underwater coustic sensor networks (UASNs), a reliable and energy-saving routing algorithm has always been an important challenge and a hot topic. A Nonuniform clustering (NC) algorithm is designed first in which clusters are generated according to different node densities. Based on NC, the backbone of the underwater acoustic sensor network is formed in UASNs. To guarantee the reliability of data transmission of the backbone network, an NC routing algorithm based on a virtual gravitational potential field (NC_RVGPF) is proposed. This algorithm: 1) establishes a virtual gravitational potential field model to allow data transmission by 3-D underwater nodes; 2) designs the virtual gravitational potential energy by combining the transmission distance between the nodes, the residual energy of the nodes, and other parameters; and 3) selects the path of the highest average potential energy as being the optimal path of data transmission. The simulation results show that compared with the classical routing algorithm, the NC_RVGPF algorithm has higher transmission efficiency, less energy consumption, and can more effectively extend the network’s lifetime.

The continuously increasing number of connected smart devices has led to the emergence of a crucial fault detection challenge to the Internet of Things (IoT). In this study, we aim to identify a method for the effective detection of faults in IoT devices. An IoT network model is first established, and a data edge verification mechanism based on blockchain is proposed; the blockchain is used to ensure that the data cannot be tampered with, and their accuracy is verified using the edge. Finally, a data set accuracy weighted random forest based on particle swarm optimization is proposed. The simulation results demonstrate that the proposed detection algorithm is both effective and efficient.

In this paper, a high-availability data collection scheme based on multiple autonomous underwater vehicles (AUVs) (HAMA) is proposed to improve the performance of the sensor network and guarantee the high availability of the data collection service. Multi-AUVs move in the network and their trajectory is predefined. The nodes near the trajectory of an AUV directly send their data to the AUV while the others transmit data to nodes that are closer to the trajectory. Malfunction discovery and repair mechanisms are applied to ensure that the network operates appropriately when an AUV fails to communicate with the nodes while collecting data. Compared with existing methods, the proposed HAMA method increases the packet delivery ratio and the network lifetime.

Wireless sensor networks (WSNs) are an important core of the Industrial Internet of Things (IIoT). Wireless rechargeable sensor networks (WRSNs) are sensor networks that are charged by mobile chargers (MCs), and can achieve self-sufficiency. Therefore, the development of WRSNs has begun to attract widespread attention in recent years. Most of the existing energy replenishment algorithms for MCs use one or more MCs to serve the whole network in WRSNs. However, a single MC is not suitable for large-scale network environments, and multiple MCs make the network cost too high. Thus, this paper proposes a collaborative charging algorithm based on network density clustering (CCA-NDC) in WRSNs. This algorithm uses the mean-shift algorithm based on density to cluster, and then the mother wireless charger vehicle (MWCV) carries multiple sub wireless charger vehicles (SWCVs) to charge the nodes in each cluster by using a gradient descent optimization algorithm. The experimental results confirm that the proposed algorithm can effectively replenish the energy of the network and make the network more stable.

Energy constraint is a critical issue in the development of wireless sensor networks (WSNs) because sensor nodes are generally powered by batteries. Recently, wireless rechargeable sensor networks (WRSNs), which introduce wireless mobile chargers (MCs) to replenish energy for nodes, have been proposed to resolve the root cause of energy limitations in WSNs. However, existing wireless charging algorithms cannot fully leverage the mobility of MCs because unity between the energy replenishment process and mobile data collection has yet to be realized. Thus, in this paper, a joint energy replenishment and data collection algorithm for WRSNs is proposed. In this algorithm, the network is divided into multiple clusters based on a K-means algorithm. Two MCs visit the anchor point in each cluster by moving along the shortest Hamiltonian cycle in opposite directions. The positions of anchor points are calculated by the base station (BS) based on the energy distribution in each cluster. A spare MC is assigned to the network in case either of the two MCs depletes its energy before reaching the BS. After the two MCs' current tours are over, a semi-Markov model is proposed for energy prediction so anchor points can be updated in the next round. Simulation results demonstrate the semi-Markov-based energy prediction model is highly precise, and the proposed algorithm can replenish energy for network energy effectively.

The application-layer distributed denial of service (AL-DDoS) attack makes a great threat against cyberspace security. The attack detection is an important part of the security protection, which provides effective support for defense system through the rapid and accurate identification of attacks. According to the attacker’s different URL of the Web service, the AL-DDoS attack is divided into three categories, including a random URL attack and a fixed and a traverse one. In order to realize identification of attacks, a mapping matrix of the joint entropy vector is constructed. By defining and computing the value of EUPI and jEIPU, a visual coordinate discrimination diagram of entropy vector is proposed, which also realizes data dimension reduction from N to two. In terms of boundary discrimination and the region where the entropy vectors fall in, the class of AL-DDoS attack can be distinguished. Through the study of training data set and classification, the results show that the novel algorithm can effectively distinguish the web server DDoS attack from normal burst traffic.