The Symbiotic Internet of Things (IoT) represents a collaborative framework wherein edge devices and AI models coordinate resource utilization and interact via 6G connectivity to optimize operational performance and system efficiency. Given the interdependent nature of this symbiotic relationship—where the performance and efficiency of each participant are significantly enhanced by the others—accurate traffic prediction becomes crucial. Despite the ability of existing deep learning models to model spatial-temporal dependencies, they face challenges in feature engineering and natural language feature fusion, particularly in few-shot learning scenarios. The recent advancement of pre-trained LLMs has demonstrated superior performance in time series analysis through their language comprehension and generalization capabilities. This work presents an innovative Multi-level Spatial-Temporal Joint Large Language Model (MSTJLLM) designed for traffic forecasting. The model incorporates multi-level embeddings of information flow, integrating historical features, dynamic spatial-temporal features, and prompt text features to capture complex dependencies. This approach aids LLMs in better understanding information flow through prompt text. Fine-tuning strategies enable the LLM to maintain language comprehension while enhancing spatial-temporal prediction accuracy. Tests on three real-world information flowsets demonstrate MSTJLLM’s robust prediction capability in both data-rich and data-scarce scenarios.

With the rapid development of low-end maritime devices and shipborne sensors, traditional single-modal recognition can no longer meet the demand for high accuracy in maritime environment. As a result, multimodal learning, which integrates data from different sensors, has gradually become the main approach for maritime target recognition. However, due to variations in the environments and platforms where data is collected, the quantity of useful information provided by each modality differs, and certain modalities may even introduce noise. This discrepancy adversely affects the performance of multimodal fusion recognition. However, most existing multimodal maritime recognition methods overlook these differences, which constrains the performance of the recognition models. To address this concern, we propose a multi-modal contribution evaluation fusion network (MCEF-NET) to achieve efficiency-enhanced fusion for multimodal maritime target recognition. In this model, a Feature Filter Module (FFM) is introduced to effectively suppress irrelevant information, mitigate distribution discrepancies between modalities, and enhance the robustness of multimodal feature extraction. Furthermore, we design a Contribution-Rating Fusion (CRF) mechanism that dynamically allocates fusion weights according to the contribution of each modality’s features, thereby minimizing the influence of low-value modalities on the final fusion performance. The MCEF-NET was evaluated on the publicly available VAIS maritime infrared-visible multimodal dataset, exhibiting superior accuracy and computational efficiency compared to existing state-of-the-art methods.

As Industry 4.0 continues to evolve, the integration of cyber-physical systems (CPS) into modern engineering systems marks a significant paradigm shift that not only enhances operational efficiency but also opens up new possibilities for innovation and improved quality of life across various sectors. In intricate communication environments, the precise identification of device identities within CPS holds significant importance for augmenting reliability and robustness. For this purpose, we introduce a robust method for specific emitter identification that utilizes a deep residual shrinkage network, aimed at enhancing the model's ability to accurately recognize emitters, even when operating under conditions of low signal-to-noise ratios. This is an end-to-end recognition method that reduces the dependence on expert knowledge. Through the utilization of specially crafted subnetworks, adaptive thresholding is employed to enable each in-phase and quadrature (IQ) signal to possess its unique set of thresholds. The proposed approach reduces noise influence on the model by incorporating a soft threshold within the nonlinear transformation layer of the deep architecture. Experimental results using real-world data demonstrate that this method surpasses the performance of commonly utilized state-of-the-art specific emitter identification models.

Tracking continuous objects in the ocean, such as oil spills, is a significant challenge, and one that underwater acoustic sensor networks (UASNs) are well suited to address. While prevalent approaches often employ complex, multiphase processes to delineate the boundaries of these objects, enabling effective monitoring, they may not be ideally suited for time-sensitive situations, where a rapid response often outweighs the need for extreme precision. To address this gap, we introduce a streamlined boundary tracking algorithm, the centroid computation of feasible region for continuous object tracking (CCFR-COT), intended for unmanned surface vessel (USV)-assisted UASNs. This algorithm utilizes a fundamental full binary tree-structured network configuration along with two of its variations, designed to handle cases where continuous objects disperse in either free space or two semi-infinite spaces. The chosen network configuration aids in the selection of boundary mapping cells, thereby sketching a coarse-grained feasible region that envelops the actual boundary. Sequentially visiting these cells, the USV systematically constructs and deconstructs convex hulls of sensor nodes to progressively reduce location uncertainty within the feasible region. Only the centroid of the final feasible region in each boundary mapping cell is output for boundary fitting. Both experimental and simulated results validate the streamlined design of the CCFR-COT, which ensures timely tracking results and provides a balance between rapid response and tracking accuracy.

In industrial Internet of Things, the combination of specific emitter identification (SEI) and key authenticated technologies can effectively resist spoofing attacks and improve system security. However, most existing SEI approaches extract features based on real valued operations and only work in static scenario. This motivated us to develop a novel SEI method tasked with: exploiting the high potential model for SEI based on the inphase/quadrature (I/Q) signal that is represented by complex number, and realizing rapid reconstruction of the model in the face of dynamic scenarios. To this end, in this article, we introduce a two-stage cross-domain identification model. First, a complex-valued separate residual network (CVSRN) with novel separate residual modules is proposed as the pretrained model. The CVSRN can automatically extract effective inherent features directly from raw signals in an end-to-end manner, which favors complex-valued signals that are found in two distinct signal paths. Second, three transfer strategies are proposed to achieve rapid construction of the target SEI model. They leverage the knowledge learned from the pretrained CVSRN to facilitate the recognition of a new but similar emitters. We benchmark our proposed approach against four state-of-the-art SEI methods on real-world data and exhibit that it is not only competitive but also able to cope with complex dynamic scenarios.

Proactive caching in 6 G cloud-edge collaboration scenarios, intelligently and periodically updating the cached contents, can either alleviate the traffic congestion of backhaul link and edge cooperative link or bring multimedia services to mobile users. To further improve the network performance of 6 G cloud-edge, we consider the issue of multi-objective joint optimization,i.e., maximizing edge hit ratio while minimizing content access latency and traffic cost. To solve this complex problem, we focus on the distributed deep reinforcement learning (DRL)-based method for proactive caching, including content prediction and content decision-making. Specifically, since the prior information of user requests is seldom available practically in the current time period, a novel method named temporal convolution sequence network (TCSN) based on the temporal convolution network (TCN) and attention model is used to improve the accuracy of content prediction. Furthermore, according to the value of content prediction, the distributional deep Q network (DDQN) seeks to build a distribution model on returns to optimize the policy of content decision-making. The generative adversarial network (GAN) is adapted in a distributed fashion, emphasizing learning the data distribution and generating compelling data across multiple nodes. In addition, the prioritized experience replay (PER) is helpful to learn from the mosteffectivesample. So we propose a multivariate fusion algorithm called PG-DDQN. Finally, faced with such a complex scenario, a distributed learning architecture,i.e., multi-agent learning architecture is efficiently used to learn DRL-based methods in a manner of centralized training and distributed inference. The experiments prove that our proposal achieves satisfactory performance in terms of edge hit ratio, traffic cost and content access latency.

Specific emitter identification (SEI) is a technology that extracts subtle features from signals sent by emitters to identify different individuals. It can effectively improve the security of the Industrial Internet of Things (IIoT) by acting on the physical layer of the internet. Recent research on SEI has focused on deep learning (DL) models that can automatically learn effective inherent emitter features from raw signals. Nevertheless, training popular DL models is computationally expensive because of the numerous hyperparameters and nonscalable structures. This limits the application of DL-based SEI models in certain practical IIoT scenarios. To address this concern, we propose an adaptive broad learning (ABL) method to build a lightweight SEI model. In the proposed model, the raw signal samples are mapped to feature nodes, and the emitters are denoted as the output nodes. The hidden nodes are directly connected to the output nodes by a broad network. Through this flat structure, the size and calculation amount of the model can be effectively reduced. To further economize the computational cost, we designed an adaptive node expansion strategy for rapidly obtaining the optimal hyperparameters of the models. The results of experiments on real-world data prove the superiority of ABL over popular state-of-the-art DL-based SEI models.

In recent years, the increasing number of industrial infrastructure security incidents around the world has drawn public attention to industrial control networks (ICNs) security issues. Fault diagnosis of industrial devices is an indispensable part of the security system in ICNs. The mainstream fault diagnosis models rely on long-term training and massive fault data, which results in the inability to update the model effectively and timely when the environment changes. Thus, some researchers focus on developing cross-domain industrial fault diagnosis methods. However, they usually presume that the samples of the target and source domains share the same fault mode sets, and existing prior knowledge concerning the label spaces of these two domains. These are difficult to satisfy in actual ICNs. To respond to these challenges, we develop atransferability-measured adversarial adaptation network(TAAN) to identify unknown classes without prior knowledge. It embeds the hybrid transferability estimation into an adversarial domain adaptive network to weigh the contribution of each sample. In this way, TAAN can properly classify samples in a public label space by selectively aligning source and target samples with high transferability. The experimental results obtained using two diagnosis datasets prove that the developed TAAN can achieve satisfactory diagnostic accuracy by effectively bridging the distribution discrepancy under various working conditions.

Due to the flammability, explosiveness and toxicity of continuous objects (e.g., chemical gas, oil spill, radioactive waste) in the petrochemical and nuclear industries, boundary tracking of continuous objects is a critical issue for industrial wireless sensor networks (IWSNs). In this article, we propose a continuous object boundary tracking algorithm for IWSNs – which fully exploits the collective intelligence and machine learning capability within the sensor nodes. The proposed algorithm first determines an upper bound of the event region covered by the continuous objects. A binary tree-based partition is performed within the event region, obtaining a coarse-grained boundary area mapping. To study the irregularity of continuous objects in detail, the boundary tracking problem is then transformed into a binary classification problem; ahierarchical soft margin support vector machinetraining strategy is designed to address the binary classification problem in a distributed fashion. Simulation results demonstrate that the proposed algorithm shows a reduction in the number of nodes required for boundary tracking by at least 50 percent. Without additional fault-tolerant mechanisms, the proposed algorithm is inherently robust to false sensor readings, even for high ratios of faulty nodes ($\approx 9\%$).

The diffusion of toxic gas, biochemical material, and radio-active contamination – known as continuous objects – endangers the safe production of the petrochemical and nuclear industries. To mitigate these well known hazards, the new paradigm ofindustrial wireless sensor networks(IWSNs) shows great potential in monitoring evolving hazardous phenomena in unfriendly industrial fields. In order to prolong the lifetime of these networks, existing research focuses on energy-efficient boundary nodes selection. However, sensor state cannot be scheduled proactively, due to the difficulty in predicting the spatiotemporal evolution of diffusive hazards. In this article, we propose amotion behavior learning predictive tracking(MBLPT) algorithm for continuous objects in IWSNs. Considering the relatively unpredictable patterns exhibited by continuous objects, the MBLPT uses a data-driven approach for motion state recognition, and then utilizesBayesian model averaging(BMA) for future boundary prediction. The prediction of the MBLPT provides the knowledge for establishing a wake-up zone, in which standby nodes are activated in advance to participate in tracking the upcoming boundary. Simulation results demonstrate that the MBLPB achieves superior energy efficiency while keeping effective tracking accuracy.

With the development of the industrial Internet of Things and the advancements in wireless sensor networking technologies, the smart grid based on edge computing now is regarded as being essential for real-time monitoring and automatic control of the electricity generation and distribution. In this article, we propose a highly efficient energy control framework supported by edge computing to reduce energy waste and increase the benefit for industrial users. To this end, battery energy storage systems (BESSs) are currently being employed to store energy for stability of supply and quality of power. The optimal load patterns and corresponding energy storage capacities of the BESSs can be obtained through the framework, according to the energy market and the historical load data of industrial users. However, computing these requires considering the tradeoff between equipment cost, time-of-use electricity price, running expenses, and other related factors, which would be an NP-hard problem. To address this challenge, we also propose an adaptive mixed differential evolution algorithm with a novel mutation strategy. Experiments on real-world data demonstrate the effectiveness of the proposed algorithm and framework.