For Underwater Internet of Things (UIoT) applications, the accurate and efficient estimation of the direction of arrival (DOA) is fundamental to technologies such as node localization and autonomous underwater vehicle (AUV) node cooperative communication. However, the low signal-to-noise ratio (SNR) and limited energy in underwater environments pose severe challenges to DOA estimation. Furthermore, existing methods typically require a large number of snapshots. To address these issues, this paper proposes the use of an adaptive wavelet denoising model to enhance the quality of underwater acoustic signals. Subsequently, a dual-branch space-time state space network (ST-SSNet) is proposed. This network consists of a time feature extraction branch (TFEB) and a space feature extraction branch (SFEB). The time branch incorporates gating units and time mixing functions into the state space model (SSM) within the Mamba framework to extract temporal features. The spatial branch uses one-dimensional convolutions in different directions and the convolutional block attention module (CBAM) to extract spatial features. Extensive simulation and sea trial experiments demonstrate that ST-SSNet outperforms other deep learning methods in various scenarios, while having lower computational complexity than other methods. Compared to ResNet18, the accuracy improves by 2.14%, and RMSE is reduced by 43.4%.

The Ocean Internet of Things (OIoT) has promoted the development of the ocean devices, which generate a large amount of underwater acoustic data. The transmission of underwater acoustic data requires a large amount of spectrum resources. Aiming at the problems of improving the utilization rate and security of spectrum resources, an underwater acoustic spectrum sensing algorithm based on personalized federated learning (PFL) and RepViG is proposed. A security protection framework is established for underwater acoustic data based on PFL. The common features of each underwater acoustic data are extracted through the meta-model. Each client only needs to fine-tune the parameters of the meta-model based on the local underwater acoustic data to achieve a personalized model. Based on the improved RepViT and the improved ViG, a dual-branch sensing model of RepViG is designed. In the improved RepViT, we employ the Haar wavelet downsampling (HWD) module to retain the low-frequency and high-frequency detail features of the underwater acoustic signal through multi-resolution. And we employ Interactive Convolution Block (ICB) to capture the relationship between local features and global features through multi-scale dynamic convolution kernels. In the improved ViG, we adopt the lightweight Star-Blcok module to reduce feature redundancy and enhance sensing efficiency. Compared with other algorithms, the simulation results show that the detection probability is increased by 7.6%, and the false alarm probability is reduced by 7.5%.

Wireless sensing technology, analyzing signal propagation to sense environments, has advanced in smart homes, health monitoring and security. However, massive data and dynamic communication environments expose limitations in traditional methods: weak security, poor feature extraction, and high environmental dependency. To address these challenges in complex cross-domain scenarios, this paper proposes a collaborative secure framework FL-BLC, which integrates federated learning (FL) and blockchain (BLC), and designs the DB-SE-Yolov8 cross-domain intelligent sensing algorithm. The FL-BLC framework ensures tamper-proof transmission and data privacy by encrypting and hash-verifying locally trained model parameters before batch-writing them to the blockchain. The DB-SE-Yolov8 algorithm employs a dual-branch(DB) design: the upper branch employs average adaptive pooling for global features, while the lower branch employs the Squeeze-and-Excitation (SE) attention mechanism for attention features. A gating mechanism dynamically fuses multi-scale features, reducing complexity and enhancing accuracy across diverse scenarios. Compared with Dual-Attention CSI Network, Environment Independent and Joint Adversarial Domain Adaptation algorithms, DB-SE-Yolov8 significantly improves in-domain and cross-domain sensing performance. For cross-location and cross-orientation scenarios, the sensing accuracy is improved by 7.16% and 8.84%, while sensing efficiency is improved by 10.85% and 11.67%, respectively.

The number of mobile users and applications is rising quickly due to the deployment of fifth generation (5G) communication technology. The prevalence of smart devices and internet of things (IoT) services has made this technology essential to manage the resulting volume of data. However, the increase in mobile communications raises security concerns considering the open and dynamic nature of the industrial internet of things (IIoT) environment. An important research problem is how to exploit the characteristics of wireless channels for safe and reliable information transmission. Therefore, a mobile communication performance analysis and prediction algorithm based on FL-GLP-Net is proposed. First, a mobile security communication system model based on N-Nakagami channels is presented. Then, the non-zero secrecy capacity probability (NSCP) is studied and an exact expression is derived. XGBoost is used to choose the best features based on model performance. A real-time mobile security performance prediction model based on FL-GLP-Net is designed for real-time NSCP prediction. Federated learning (FL), graph attention network (GAT), long short-term memory (LSTM), and pyramid visual converter (PVT) modules are integrated to obtain a model that can deal with the diverse signal features in mobile communication systems. Results are presented to show that the proposed method outperforms other NSCP prediction algorithms. In particular, the mean squared error (MSE) of FL-GLP-Net is 78% better than that of FL-ShuffleNetV2.

Emerging technologies such as artificial intelligence and big data have made numerous Internet of things (IoT) applications possible. In particular, the Artificial Intelligence of Things (AIoT) has the potential to promote the digitization and intelligent connection of all things. However, the openness and diversity of AIoT makes data information vulnerable to security attacks which can lead to a disruption of mobile communication networks. The complexity of real-time data security events requires accurate prediction of AIoT security performance. In this paper, a secure communication system model based on decode-and-forward (DF) relaying is proposed and its security performance is analyzed. Expressions for the secrecy outage probability (SOP) are derived, and these are used to evaluate the security performance. For this purpose, an intelligent SOP prediction algorithm based on MS-Net is proposed. MobileNet and SqueezeNet networks are used to design an improved lightweight MS-Net model, which is composed of a depth separable convolution block and a fire module in parallel. The fire module is used to reduce the number of parameters in the first branch, and the depth-separable convolution block is employed in the second branch instead of the standard convolution. This can adapt to nonlinear characteristic in the AIoT safety data and reduce energy consumption. Afterwards, the convolutional block attention module(CBAM) attention mechanism is used to improve the model’s ability to capture features. The proposed algorithm provides better AIoT security performance than other algorithms. In particular, the mean squared error (MSE) is 68.1% better than that of RegNet.

Exploring the relative positions is a key issue in many emerging location-aware applications such as autonomous driving and formation control, where there exists no infrastructure to provide the absolute position information. In this paper, we establish a theoretical framework to address the state estimation problems in relative localization networks. In particular, we introduce the relative error for state estimates based on the concept of the equivalent state class, and apply the Fisher information analysis to derive the performance bounds. Then we present how measurement uncertainties influence the performance limits in the relative localization networks with self-measurements, after which our framework is extended to the scenarios with clock asynchronization and temporal cooperation. Finally, the connection between the theoretical foundation and the algorithm design is illustrated to provide insights into the operations in practical relative localization networks.

The rapid expansion of Internet of Vehicles (IoV) networks has facilitated high throughput and reliable vehicular communications. Mobile vehicular networks face the challenges: diversification of network equipment, user mobility, and the broadcast nature of wireless channels, so physical layer security modeling of IoV communication systems has become important. The complexity of wireless communication channels makes real-time prediction of secrecy performance challenging. This paper presents an analysis of secrecy performance for mobile vehicular networks. To ensure data secure transmission, we have employed the decode-and-forward (DF) relaying scheme. The signal-to-noise ratio (SNR) of the effective end-to-end link is employed to obtain the mathematical expression results, which can evaluate the secrecy performance. The theoretical secrecy performance is confirmed via simulation. Then, we design a dense-inception convolution neural network (DI-CNN) model, and propose a DI-CNN-based intelligent prediction algorithm.Transformer, ShuffleNetV2, RegNet and YOLOv5 methods are employed to analyze the performance of DI-CNN algorithm. It is shown that the DI-CNN approach has a prediction accuracy that is 48.8% better than Transformer.

Cooperation among entities within networks for information exchange and measurement is a promising paradigm for high-accuracy positioning in automated vehicles. However, due to imperfect clocks and inefficient wireless protocols, current cooperative positioning techniques have inadequate accuracy and timeliness. This paper presents a novel localization framework for connected automated vehicles (CAVs) capable of achieving high-accuracy relative positioning with high update rates. We design a signal-multiplexing network measuring (SNM) protocol to optimize the measurement update rates and propose new range estimations to achieve high-accuracy ranging against clock errors and mobility. Using range estimations, we develop a relative localization algorithm that leverages intra- and inter-node cooperation with coordinate reference alignment to reconstruct the geometric relationships among the nodes. Performance analyses and simulation results demonstrate that our method achieves high-accuracy positioning with timely updates, ensuring reliability and robustness in asynchronous vehicle localization.

Multiobject tracking (MOT) technology can be deployed to the Internet of Things (IoT) devices to enhance the security and reliability of some video analysis applications, such as video surveillance and intelligent security system. However, since the IoT devices with limited computing capacity and storage, most existing MOT methods are difficult to deploy to IoT devices and exhibit poor tracking robustness in scenes with frequent occlusions, severe crowded, and scale variations. To alleviate the aforementioned issues, we propose a regression-based online MOT method. First, an object-aware embedding extraction module (OAEM) is designed to extract preliminary discriminative embedding of the object in the current frame. Then, an embedding aggregation module (EAM) is proposed to obtain high-quality aggregated embedding in temporal. Finally, the temporal embedding combined with the preliminary embedding extracted from the current frame to obtain a refined embedding feature for subsequent position prediction and association. Importantly, we achieve a beneficial interaction between embedding extraction, position prediction and association task. The proposed method does not suffer from significant memory consumption. Therefore, our method is a potential solution for intelligent video analysis on IoT devices. To evaluate the proposed method, we have conducted numerous experiments on the MOT16, MOT17, and MOT20 benchmark data sets. Results demonstrate that the proposed method can provide a more robust tracking performance compared to other optimal methods.

High-accuracy position information is essential for the emerging applications of Internet of Things, where relative localization is often more pertinent in many cooperative tasks. In this paper, we propose a distributed relative localization scheme for large-scale 3D networks where the relative position relationships of the entire network as well as the subnetwork are concerned. In particular, we first design the prioritized geometry merging procedure with merging confidence evaluation of the geometry pairs for the entire network localization. Then we specifically extend this priority-based methodology for the algorithm design of subnetwork-aimed localization. Numerical results demonstrate that the proposed schemes significantly outperform existing algorithms.

With the emerging paradigm of Internet of Things, high-accuracy localization has been an ever-present key issue, and relative position information is getting growing attention in cooperative tasks. In this paper, we propose an efficient relative localization algorithm for three-dimensional anchor-free networks with limited communication range. Specifically, we first design the condition number-based geometry selection criteria. Then we develop a scheme to establish a well-conditioned reference coordinate system for the network. Furthermore, an iterative relative localization algorithm is proposed, in which the reliability of the agents is evaluated for dynamic virtual anchor extension. The numerical results validate that the performance gain of the proposed relative localization scheme over existing algorithms is significant.

The explosive growth of Internet of Vehicle (IoV) applications has made information security a significant issue. Mobile IoV users are dynamic and the communication environment is very complex, which makes it very difficult to guarantee real-time secrecy communication performance. Thus, a reliable and effective evaluation and prediction of secrecy performance is critical. In this article, we have derived novel expressions for secrecy performance. A grey wolf optimization generalized regression (GWO-GR) algorithm is proposed to predict the secrecy performance and carry out the secrecy performance assessment. A generalized regression (GR) neural network is designed. Out of the input and output layers, the proposed GR network has a pattern layer and a summation layer, which can obtain a global convergence of network results. To further optimize the GR network, the grey wolf optimization algorithm is used to obtain the best spread factor for it, which can accelerate its rapid convergence. Through the simulated numerical results, we can obtain: 1) the proposed GWO-GR prediction algorithm is shown to provide better performance prediction results than other machine-learning-based methods; 2) in particular, the prediction accuracy is improved by 17.7%; and 3) the execution time has an 88.9% reduction.

The integration of artificial intelligence and Internet of Things (IoT) has promoted the rapid development of artificial IoT (AIoT) networks. A wide range of AIoT applications have generated a great deal of data. The fifth-generation (5G) mobile communication has powerful data processing capabilities, and it is a key technology to enable AIoT big data processing. The explosive growth of the 5G users has made information security in AIoT networks a significant issue. Real-time security evaluation in AIoT networks is difficult due to user mobility and dynamic wireless environments. Thus, the evaluation and prediction of secrecy performance is a very critical research. In this article, new expressions for the nonzero secrecy capacity probability (NSCP) are derived to evaluate the mobile collaborative secrecy performance. An improved convolutional neural network (CNN) model, named as SI-CNN in this article, is proposed to predict the NSCP performance. The SI-CNN model combines the SqueezeNet and InceptionNet, and it has four convolution layers, which all adopt the same convolution model. For the first two layers, they employ a 2 × 1 convolution and a three-branch convolution, which not only increase the number of channels but also extract more features. For the last two layers, they employ the same structure, but different convolution kernels. The proposed SI-CNN prediction algorithm is shown to provide better NSCP performance prediction than other state-of-the-art methods. In particular, compared with wavelet neural network, the prediction precision of SI-CNN is improved by 26.8%.

The global healthcare industry and artificial intelligence have promoted the development of the diversified intelligent healthcare applications. Internet of Things (IoT) will play an important role in meeting the high throughput requirements of diversified intelligent healthcare applications. However, the mobile IoT-enabled healthcare networks are diverse and open, the healthcare big data transmission is vulnerable to a potential attack, which can cause network outages and serious healthcare security issues. To process the complex healthcare security event in real time, security performance prediction is critical for mobile IoT-enabled healthcare networks. In this article, we first analyze the security performance, and derive the novel expressions for the security performance in a closed form. Then, to analyze the security performance in real time, a security performance intelligent prediction algorithm is proposed. An improved convolutional neural network (CNN) model is designed, which combines the four-layer convolution and a four-branch inception block, and can adopt different convolution kernels in the same layer. The four-branch inception block can increase the width of the CNN while reducing the parameters. The improved CNN model can not only increases the width of the CNN, extract different sizes of healthcare data features, but also increases the adaptability to the nonlinear healthcare big data. Compared with different methods, the proposed intelligent algorithm can obtain better security performance prediction. In particular, for prediction precision, the proposed intelligent algorithm is increased by 20%.

Industry 5.0 has developed in full swing, and accelerated the process of the sixth-generation (6G) mobile communication. Physical layer security is important for complex 6G mobile communication networks. To process active complex events in 6G mobile cooperative networks, predicting secrecy performance in time is essential for the mobile communication quality evaluation. Using amplify-and-forward (AF) relaying, we propose a transmit antenna selection (TAS) based secrecy scheme in this article. To analyze the security of 6G mobile cooperative networks, signal-to-noise ratio of the end-to-end link is used to derive the novel expressions for secrecy outage probability (SOP). The theoretical results are confirmed by simulation results. Then, we use SOP as the important merit to evaluate the secrecy performance, and set up the dataset. To achieve secrecy performance prediction, a convolutional neural network (CNN) based SOP prediction algorithm is proposed. The designed CNN model has five convolution layers, which all use the same convolution in the padding and do not change data size. For this improved CNN structure, we adopt the idea of SqueezeNet, which belongs to the lightweight CNN. The improved CNN model can greatly reduce the parameters and network complexity on the premise of ensuring the prediction accuracy. We also examine the following state-of-the-art techniques, first, Elman, second, InceptionNet, third, deep neural network (DNN), and fourth, support vector machine methods. The proposed CNN algorithm can achieve better SOP prediction results than other existing methods. In particular, compared with DNN method, the prediction accuracy is increased by 66.7%.

Recently, recommender systems are applied to provide personalized recomendation for healthcare wearables. However, due to the sparsity problem, traditional recommendation algorithms are difficult to achieve desired performance. Considering that consumers often buy and rate other types of items on E-commerce platforms, we can leverage significant information in the auxiliary domains to improve the recommendation performance of healthcare wearables, which can be regarded as cross-domain recommendation. However, traditional cross-domain recommendation model cannot fully represent user's characteristics and fail to consider the leaks of original auxiliary domain ratings during the information transfer process. To overcome the two shortcomings, this paper proposes a Privacy-Preserving Cross-Domain Healthcare Wearables Recommendation algorithm (PPCDHWRec). Firstly, user's characteristics are divided into domain-dependent features and domain-independent features, which complement each other and fully depict the user's characteristics. Secondly, inspired by the latent factor model, we factorize the original rating information of each auxiliary domain by Funk-SVD and Orthogonal Nonnegative Matrix Tri-Factorization (ONMTF) model, to obtain user's domain-dependent and domain-independent features, respectively. Finally, the Factorization Machine algorithm is used to fuse the obtained user's features with the target domain information to provide the recommendation results. By hiding the item latent factors obtained in the factorization process, PPCDHWRec ensures that the original information cannot be inferred from the transferred user hidden vector. Hence, PPCDHWRec is a privacy-preserving recommendation model. Experiments on two groups of auxiliary domains, having high and low correlations with target domain, show the effectiveness of PPCDHWRec.

With the help of the new generation information technology, the Internet of Vehicle (IoV) networks have become widespread. IoV can improve the automatic driving ability, and provide users with intelligence, comfort, safety, energy saving and efficiency traffic services. However, the IoV networks face serious challenges due to the complex wireless environment. The vehicles cannot obtain the real-time traffic condition and early warning information, which leads to the decrease of link quality and the failure of information transmission. To evaluate the communication quality of IoV networks, the outage probability (OP) is commonly employed as a metric. This paper considers mobile IoV networks, and investigates communication quality prediction. Novel OP expressions are derived, which can analyze the OP performance. Then, to predict OP in real time, an intelligent OP prediction approach with an Elman model is proposed. This is evaluated with data generated using the OP expressions. In terms of computational complexity and prediction accuracy, the results obtained show that the Elman-based approach provides better forecasting effect than other methods. For prediction accuracy, the proposed Elman approach is increased by 84.6%. For computational complexity, the execution time is reduced by 79.9%.

In the field of transportation, the Internet of Vehicles (IoV) is an important component of the Internet of Things. The vehicle-to-vehicle communication is particularly challenging in mobile IoV networks because they are operated in complex and highly variable environments. The mobile IoV transmission interruption level can be evaluated by the outage probability (OP) performance. If the OP performance can be analyzed and predicted accurately, the Quality of Service (QoS) in the mobile IoV networks can be improved. However, the analysis and prediction of mobile IoV transmission channels is very challenging because they are highly dynamic. In this article, the analysis and prediction of the OP performance for mobile IoV networks are investigated. A hybrid decode-amplify-forward (HDAF) relaying scheme with transmit antenna selection (TAS) is considered. The exact OP expressions are derived in a closed form, and the analytical results are verified. To realize the real-time analysis of the OP performance, an intelligent OP prediction algorithm based on the improved cuckoo search (ICS) is presented. The proposed algorithm is compared with different methods and the results show that it has a better OP prediction performance. The prediction accuracy of ICS-BP can be increased by 51.8% compared with the existing algorithms.

With the increasingly mature sensor technology and the increasing popularity of broadband network, “the Internet-of-Everything” era is coming, and the mobile Internet of Things (IoT) is booming around the world. However, the mobile IoT communication networks face serious challenges, which are caused by the complex and variable communication environments. The mobile IoT applications can produce large-scale data, which will consume substantial energy. The transmit antenna selection (TAS) and cooperative communication schemes are commonly used to reduce the complexity and the energy consumption, which directly impact the performance of mobile IoT networks. To evaluate the performance of mobile IoT networks, it is important to analyze outage probability (OP) performance. In this article, we investigate the OP performance analysis of mobile IoT communication networks and propose an OP intelligent prediction algorithm based on an improved convolutional neural network (CNN). First, the mobile OP performance is analyzed by combining the TAS and decode-and-forward cooperative schemes, and the exact OP expressions are derived. Then, an improved CNN is designed to avoid the loss of important information, which contains the input layer, three-convolution layer, one fully connected layer, and output layer. The proposed CNN-based prediction approach is compared with the radial basis function (RBF), generalized regression (GR), Elman, and extreme learning machine (ELM) methods. The simulation results validate that the proposed CNN prediction approach can achieve a better prediction effect than RBF, Elman, GR, and ELM methods. For the CNN approach, it has a 44% increase in the prediction accuracy.

Industrial applications can produce significant amounts of data that require low delay and high data rate communications. Multiple-input-multiple-output filter bank multicarrier (MIMO-FBMC) communications employing offset quadrature amplitude modulation has been proposed for industrial big data due to its reliability and high spectrum efficiency. One of the difficulties in implementing a MIMO-FBMC system is accurate channel estimation (CE). The main factor affecting the CE performance is intrinsic imaginary interference, and the conventional preamble-based CE is not effective in this case. Thus, in this article, a low-complexity sparse adaptive CE scheme is proposed that is based on a dynamic threshold. This reduces the number of inner product calculations by considering only the columns of the measurement matrix greater than the threshold. Simulation results are presented that show that the proposed scheme is better than other well-known methods in terms of computational complexity and CE accuracy.

A filter bank multicarrier (FBMC) with offset quadrature amplitude modulation (OQAM) (FBMC/OQAM) is considered to be one of the physical layer technologies in future communication systems, and it is also a wireless transmission technology that supports the applications of Internet of Things (IoT). However, efficient channel parameter estimation is one of the difficulties in realization of highly available FBMC systems. In this paper, the Bayesian compressive sensing (BCS) channel estimation approach for FBMC/OQAM systems is investigated and the performance in a multiple-input multiple-output (MIMO) scenario is also analyzed. An iterative fast Bayesian matching pursuit algorithm is proposed for high channel estimation. Bayesian channel estimation is first presented by exploring the prior statistical information of a sparse channel model. It is indicated that the BCS channel estimation scheme can effectively estimate the channel impulse response. Then, a modified FBMP algorithm is proposed by optimizing the iterative termination conditions. The simulation results indicate that the proposed method provides better mean square error (MSE) and bit error rate (BER) performance than conventional compressive sensing methods.