Intelligent fault diagnosis for drilling equipment is often compromised by non-stationary noise and domain discrepancies. To address this, we propose TWE-DANN, a novel framework integrating Wavelet Threshold Denoising (WTD), Empirical Mode Decomposition (EMD), and Domain-Adversarial Neural Networks (DANN). The model features a hybrid filtering architecture to suppress heterogeneous noise and employs adversarial adaptation to align cross-domain feature distributions, ensuring robust performance under complex operating conditions. Experiments on real-world drilling datasets demonstrate the effectiveness of the proposed method. On the hydraulic source-domain dataset, under the most severe composite noise conditions, TWE-DANN enables the model to reach an F1-score of 98.64%. On the source (hydraulic) dataset, the model achieves an F1-score of 91.32%. In the cross-domain adaptation task from hydraulic to pneumatic equipment, TWE-DANN attains an F1-score of 86.98% after only five fine-tuning epochs, while outperforming baseline transfer methods by 3.71%.

ABSTRACT This paper presents a new data clustering technique aimed at enhancing the performance of the trainable path‐cost algorithm and reducing the computational complexity of data clustering models. The proposed method facilitates the discovery of natural groupings and behaviours, which is crucial for effective coordination in complex environments. It identifies natural groupings within a set of features and detects the best clusters with similar behaviour in the data, overcoming the limitations of traditional state‐of‐the‐art methods. The algorithm utilises a density peak clustering method to determine cluster centers and then extracts features from paths passing through these peak points (centers). These features are used to train the support vector machine (SVM) to predict the labels of other points. The proposed algorithm is enhanced using two key concepts: first, it employs Q‐Generalised Extreme Value (Q‐GEV) under power normalisation instead of traditional generalised extreme value distributions, thereby increasing modelling flexibility; second, it utilises the random vector functional link (RVFL) network rather than the SVM, which helps avoid overfitting and improves label prediction accuracy. The effectiveness of the proposed clustering algorithm is evaluated through various experiments, including those on UCI benchmark datasets and real‐world data, demonstrating significant improvements across multiple performance metrics, including F1 measure, Jaccard index, purity, and accuracy, highlighting its capability in accurately identifying paths between similar clusters. Its average F1 measure, Jaccard index, purity, and accuracy is measured 76.87%, 56.29%, 80.29%, and 79.64%, respectively.

Flexible manufacturing systems (FMS), empowered by the Industrial Internet of Things (IIoT), have become a cornerstone of Industry 6.0 by enabling dynamic production adaptation, real-time equipment monitoring, and intelligent scheduling. As these systems increasingly incorporate large language models (LLMs) to support functions such as knowledge querying, decision assistance, and predictive maintenance, ensuring the safety and reliability of human-LLM conversations has become a pressing concern. Specifically, LLMs may generate toxic, biased, or privacy-violating outputs when interacting with sensitive IIoT data and production logic, potentially compromising operational safety. To address this challenge, we propose AugLLMSen, an automated toxicity evaluation framework tailored to the IIoT-driven FMS context. AugLLMSen integrates a question automatic expansion mechanism (Q-Judge) and an output toxicity evaluation model (O-Judge) into a closed-loop pipeline, enabling large-scale assessment of LLM safety across diverse industrial scenarios. Experimental results on open- and closed-source LLMs demonstrate the effectiveness and accuracy of our approach in identifying toxic responses and guiding safe deployment of LLMs in flexible manufacturing environments.

Ensuring the smooth operation of road traffic is a momentous target in Intelligent Transportation Systems, which can be expedited by a secure and reliable Internet of Vehicles (IoV). As prominent carriers of the IoV, intelligent vehicles (IVs), that bear the promising potential for alleviating traffic congestion, have become the core road traffic participants. However, the mixed-traffic environment escalates the risk of IVs, as the discretionary lane change behaviors of nearby human-driven vehicles may result in collisions with IVs, compromising the robust performance of the IoV. Recent studies have utilized advanced deep learning techniques to achieve proactive lane change intention prediction, including Recurrent Neural Networks and Transformer. Although attaining reasonable prediction performance, they adopt the data-driven paradigm, which excessively focuses on learning from data while neglecting the domain knowledge. Against this background, we propose to employ the knowledge-driven paradigm and design KLEP, a knowledge-driven lane change prediction framework. KLEP incorporates driving knowledge into lane change modeling, presenting the top-down hierarchical cognitive process of drivers when performing lane change maneuvers. Extensive experiments conducted on two real-world natural driving datasets demonstrate the effectiveness of KLEP. Compared to state-of-the-art lane change prediction baselines, KLEP consistently outperforms them and achieves average improvements of 6.2-7.1% and 53.0-67.2% on intention classification and intention forecast tasks across different datasets, respectively. We also validate that KLEP has strong interpretability that aligns with real-world physical laws in lane change scenarios and is lightweight enough to fulfill online prediction.

During the last few years, due to the COVID-19 pandemic, there has been a significant development of eHealth monitoring systems. However, most of the systems to date have been developed specifically for patient monitoring by nurses, physicians, and specialists. To keep attendants informed about the health status of their patients in the hospitals, we are developing an edge-enabled companion-side eHealth monitoring system for smart hospitals based on the Internet of Things (IoT). In most existing edge-enabled eHealth monitoring systems, the utilized edge devices have limited storage capacity and energy resources, resulting in network outages and loss of data packets. Although these challenges lead to life-threatening problems, much less attention has been paid to these shortcomings in the previous work. Therefore, we first deploy edge-enabled health evaluators to receive the medical signals from the biosensors in each unit of time, and evaluate the health status of the patients. Then, each evaluator generates and stores only one health number instead of caching the data from all sensor nodes, which increases the storage efficiency. We also employ a wireless mobile charger (WMC) to charge the batteries of the health evaluators. Unlike previous work, the different attributes of the WMC are individually optimized to achieve different objectives, resulting in improved network performance and efficiency of the WMC. Experimental results show that the performance of the proposed system is better than other solutions by 99% in cloud storage optimization, 83% in edge storage optimization, 23% in end-to-end delay, and 10% in energy efficiency of edge devices.

The technology of the Internet of Vehicles (IoV) and digital twins (DTs) is driving deeper connectivity between vehicles and road infrastructure. Through the data exchange of IoV and the simulation of DT technology, vehicle driving decisions, traffic management, and road planning are optimized. However, DT models contain a large amount of private vehicle data, causing the risk of privacy leakage. Distributed artificial intelligence (AI) methods, particularly federated learning (FL) algorithms, ensure data security and privacy by sharing data models rather than sharing private data. Current mainstream algorithms use FL and local differential privacy (LDP) or blockchain approaches to protect data security at the cost of lower model accuracy and larger computation time. In the vehicle road cooperation, we designed a three-layer DT-driven personalized privacy-preserving framework, which includes a physical layer, a DT layer, and an application layer. In our proposed framework, to improve the security and performance of DT models, a time-sensitive PLDP-based FL (TimeSenFLDP) mechanism is proposed to achieve different privacy levels of the DT model of vehicles over sharing time steps. Compared with the mainstream algorithm (e.g., DP-SGD), the experiments prove that our proposed algorithm has 18.07%, 16.32%, and 7.5% accuracy improvement in FedAvg, FedProx, and FedDyn, respectively.

The convergence of blockchain and deep learning (DL) drives the intelligence of the Internet of Things (IoT) with security guarantees. However, the soaring resource consumption resulting from blockchain mining and DL model training has overwhelmed the extremely resource-constrained IoT. In this article, we first build a blockchain and DL-empowered cloud–edge orchestrated framework for an extremely resource-constrained IoT environment. To solve the resource bottleneck of this framework, we then propose a Zero-knowledge Proof of Learning (ZPoL) consensus approach to channel the meaningless Proof of Work (PoW) mining energy waste to valuable DL model training, while protecting the DL model privacy. Besides, to encourage resource-constrained IoT devices to perform meaningful DL model mining in our ZPoL consensus, we design a model quality-aware incentive mechanism based on a two-stage Stackelberg game. Moreover, we conduct extensive simulations and experiments to evaluate our proposed ZPoL-based framework. The numerical simulation illustrates that our proposed incentive mechanism could motivate IoT devices to actively join in DL model mining. Compared with the existing blockchain and DL-enabled IoT system, experimental results demonstrate that our proposed ZPoL-based framework could significantly reduce the communication, computation, and storage cost, which is more applicable to a resource-constrained IoT environment.

The rumors in the healthcare system have the attributes of fast spread and severe social influence. Even worse, it may cause the collapse of medical services and the death of many patients. To prevent its serious impact on society, the target of rumor suppression for the healthcare system is to restrain the spread of rumors (negative opinions) and maximize the spread of antirumors (positive opinions). Therefore, in this article, for the first time, we propose comprehensive learning-based particle swarm optimization with opinion maximization (OM) to address the rumors suppression problem in the healthcare system. We define the rumor suppression problem in the healthcare system based on OM and devise two opinion propagation models. Then, we propose a directed acyclic graph-based objective function to evaluate the opinion propagation and solve this problem using comprehensive learning particle swarm optimization. Experimental results show that our proposed scheme achieves better results for positive opinion propagation in the scenario of rumor suppression in the healthcare system than the baseline algorithms.

Although modern Maritime Transportation Systems (MTS) have been extensively benefited from Internet of Things (IoT) technology, but still the risks and challenges in safety and reliability have increased substantially. The involvement of different maritime parties in the marine transportation flow scheduling and management further escalates these challenges. Thus, we need an IoT-based collaborative processing system that unifies the modular structure and integrates multiple modules involved in MTS. Moreover, the need for a shared and controlled access mechanism that cannot be manipulated or tampered by unauthorized parties is also essential requirement in MTS. Blockchain, as an emerging technology, has become a key tool in data security protection because of its non-tampering and non-forgery characteristics. Keeping in view of this aspect, in this paper, an IoT-based collaborative processing system based on blockchain is proposed for marine transportation flow scheduling and management. In addition, we propose a novel consensus mechanism based on Verifiable Random Function (VRF) and reputation voting to reduce the communication cost in blockchain consensus communication process. The proposed scheme has been validated in a simulated environment and the results illustrate that the scheme has obvious effect in resisting replay attack and camouflage attack. Furthermore, the optimized consensus mechanism improves the security by 8% and the transaction processing speed by 6% on the premise that the communication cost is basically unchanged.

Abstract The conventional technique of leukocyte cell classification involves segmenting the required portion of cells from input image, extracting features of the segmented nuclei, reducing and optimizing these features and then implements the classifier. Thus, designing a good classifier by using such techniques increases the time complexity of the system. In order to resolve such issues, the proposed work implements the deep convolutional neural network (DCNN)‐based models for classifying malignant versus normal WBCs. The proposed system is validated on 108 images of ALL‐IDB 1. Due to limited number of training samples, data augmentation is used to create a similar type of virtual image. In this work, experimentation is carried out for discrimination between normal and infected WBC using DCNN with four different activation functions. By using this method, a set of 6000 samples are generated and used for proper training of the DL model for all activation functions. The performance of each trained model is evaluated in terms of accuracy, recall, precision and F‐measure with the maximum values of 98.1%, 98.3%, 98.3% and 98.3% are achieved, respectively. Finally, it has been concluded that the defined DCNN model and ReLu activation function yield outstanding performance for lymphoblast characterization using microscopic blood images.

The existing Underwater Internet of Things (UIoT) is based on the IP architecture, which is not conducive to the efficient storage and distribution of huge amounts of content generated in underwater. Actively pushing all content to users causes much unnecessary resource consumption in the UIoT. The information-centric networking (ICN) architecture opens new horizons up for these challenges. However, the slowness of underwater propagation speed makes traditional ICN not suitable for UIoT, especially considering about delay time. In this article, we propose an information-centric wireless sensor networking scheme with water-depth-aware content caching (ICWSN-WDA) to solve the above challenges. First, we design a naming scheme and a hybrid communication mode suitable for ICWSN-WDA. The communication mode in underwater we design is divided into push and pull traffic, which balances energy consumption and delay time. Second, we define a push level to decide which water depth the content actively pushes to, finding a suitable junction point of two modes. Third, as the water depth is deeper, it becomes more difficult to replace the sensor battery. To save energy consumption of deep-water sensors, the water-depth-aware caching mechanism is proposed based on water depth, popularity, and senor energy. Our extensive evaluation confirms the effectiveness of our proposed scheme, and it balances energy consumption constraints and latency.

Energy harvesting (EH) is a promising and critical technology to mitigate the dilemma between the limited battery capacity and the increasing energy consumption in the Internet of everything. However, the current EH system suffers from energy-information cross threats, facing the overlapping vulnerability of energy deprivation and private information leakage. Although some existing works touch on the security of energy and information in EH, they treat these two issues independently, without collaborative and intelligent protection cross the energy side and information side. To address the aforementioned challenge, this article proposes a joint protection framework of energy security and information privacy for EH with an incentive federated learning approach. First, we design a federated-learning-based malicious energy user detection method according to energy status and behaviors to provide energy security protection. Second, a differential-privacy-empowered information preservation scheme is devised, where sensitive information is perturbed and protected by the customized demand-based noise. Third, a noncooperative-game-enabled incentive mechanism is established to encourage EH nodes to participate in the joint energy-information protection system. The proposed incentive mechanism derives the optimal energy-information security strategy for EH nodes and achieve a tradeoff between the protection of energy security and information privacy. Evaluation results have verified the effectiveness of our proposed joint protection mechanism.

Cloud services provide decentralized and pervasive access for resources to reduce the complex infrastructure requirements of the user. In decentralized service access, the implication of security is tedious to match the user requirements. Therefore, cloud services incorporate cybersecurity measures for administering standard resource access to users. In this paper, a shared two-way security model (STSM) is proposed to provide adaptable service security for the end-users. In this security model, a cooperative closed access session for information sharing between the cloud and end-user is designed with the help of cybersecurity features. This closed access provides less complex authentication for users and data that is capable of matching the verifications of the cloud services. A deep belief learning algorithm is used to differentiate the cooperative and non-cooperative secure sessions between the users and the cloud to ensure closed access throughout the data sharing time. The output of the belief network decides the actual session time between the user and the cloud, improving the span of the sharing session. Besides, the proposed model reduces false alarm, communication failures, under controlled complexity.

Satellite communication system is expected to play a vital role for realizing various remote Internet-of-Things (IoT) applications in sixth-generation vision. Due to unique characteristics of satellite environment, one of the main challenges in this system is to accommodate massive random access (RA) requests of IoT devices while minimizing their energy consumptions. In this article, we focus on the reliable design and detection of RA preamble to effectively enhance the access efficiency in high-dynamic low-earth-orbit (LEO) scenarios. To avoid additional signaling overhead and detection process, a long preamble sequence is constructed by concatenating the conjugated and circularly shifted replicas of a single root Zadoff-Chu (ZC) sequence in RA procedure. Moreover, we propose a novel impulse-like timing metric based on length-alterable differential cross-correlation (LDCC), that is immune to carrier frequency offset (CFO) and capable of mitigating the impact of noise on timing estimation. Statistical analysis of the proposed metric reveals that increasing correlation length can obviously promote the output signal-to-noise power ratio, and the first-path detection threshold is independent of noise statistics. Simulation results in different LEO scenarios validate the robustness of the proposed method to severe channel distortion, and show that our method can achieve significant performance enhancement in terms of timing estimation accuracy, success probability of first access, and mean normalized access energy, compared with the existing RA methods.

One of the most promising application areas of the industrial Internet of Things (IIoT) is vehicular ad hoc networks (VANETs). VANETs are largely used by intelligent transportation systems to provide smart and safe road transport. To reduce the network burden, software-defined networks (SDNs) act as a remote controller. Motivated by the need for greener IIoT solutions, this article proposes an energy-efficient end-to-end security solution for software-defined vehicular networks (SDVNs). Besides, SDN's flexible network management, network performance, and energy-efficient end-to-end security scheme plays a significant role in providing green IIoT services. Thus, the proposed SDVN provides lightweight end-to-end security. The end-to-end security objective is handled in two levels: 1) in roadside unit (RSU)-based group authentication scheme, each vehicle in the RSU range receives a group ID-key pair for secure communication; and 2) in private collaborative intrusion detection system (p-CIDS), the SDVN detects the potential intrusions inside the VANET architecture using collaborative learning that guarantees privacy through a fusion of differential privacy and homomorphic encryption schemes. The SDVN is simulated in NS2 and MATLAB, and results show increased energy efficiency with lower communication and storage overhead than existing frameworks. In addition, the p-CIDS detects the intruder with an accuracy of 96.81% in the SDVN.