Compatibility between semantic communication and traditional mobile communication systems remains a significant challenge. Therefore, we propose a cross-layer encrypted semantic communication (CLESC) framework for panoramic video transmission, incorporating feature extraction, encoding, encryption, cyclic redundancy check (CRC), and retransmission processes to achieve compatibility between semantic communication and traditional communication systems. Additionally, we propose an adaptive cross-layer transmission mechanism that dynamically adjusts CRC, channel coding, and retransmission schemes based on the importance of semantic information. This mechanism ensures that important information is prioritized under poor transmission conditions. To verify the aforementioned framework, we design an end-to-end adaptive panoramic video semantic transmission (APVST) network that leverages a deep joint source-channel coding (JSCC) structure and attention mechanism, integrated with a latitude adaptive module that facilitates adaptive semantic feature extraction and variable-length encoding of panoramic videos. Simulation results demonstrate that the proposed CLESC framework effectively achieves compatibility and adaptability between semantic and traditional communication systems, significantly enhancing channel robustness. Compared to traditional and artificial intelligence (AI)-based video source coding transmission schemes, our proposed CLESC achieves superior transmission performance under low signal-to-noise ratio (SNR) conditions.

In order to satisfy the requirements for service transformation and upgrading toward industrial digitization, networking, and intelligence, sixth generation-enabled industrial Internet of Things (IIoT) imposes new requirements on deterministic delay. However, the existing best-effort communication networks increase the uncertainty of transmission, making it difficult for users to ensure deterministic delay performance. In this article, we propose a deterministic delay guarantee strategy (DDGS) under reconfigurable intelligent surface (RIS)-assisted communication networks to ensure network performance in IIoT scenarios. In particular, we utilize stochastic network calculus (SNCs) to derive the probability that the delay falls within a specific time window, characterizing the probabilistic bounds of deterministic delay. Then, we explore the relationship between delay determinacy and wireless resources by jointly optimizing the transmit power, the channel blocklength allocation, and the phase-shift matrix at the RIS to maximize delay determinacy. Based on the interdependence of action choices among users and past experience, this article proposes a performance guarantee parameterized deep Q-network (PG-PDQN) algorithm to solve the complex problem containing a mixture of discrete and continuous action spaces. Simulation results show that the DDGS strategy significantly improves the delay determinacy compared to other strategies, and the PG-PDQN algorithm has good convergence, thus effectively improving the network performance.

Semantic communication (SemCom) is regarded as a promising and revolutionary technology in 6G, aiming to transcend the constraints of “Shannon’s trap” by filtering out redundant information and extracting the core of effective data. Compared to traditional communication paradigms, SemCom offers several notable advantages, such as reducing the burden on data transmission, enhancing network management efficiency, and optimizing resource allocation. Numerous researchers have extensively explored SemCom from various perspectives, including network architecture, theoretical analysis, potential technologies, and future applications. However, as SemCom continues to evolve, a multitude of security and privacy concerns have arisen, posing threats to the confidentiality, integrity, and availability of SemCom systems. This paper presents a comprehensive survey of the technologies that can be utilized to secure SemCom. Firstly, we elaborate on the entire life cycle of SemCom, which includes the model training, model transfer, and semantic information transmission phases. Then, we identify the security and privacy issues that emerge during these three stages. Furthermore, we summarize the techniques available to mitigate these security and privacy threats, including data cleaning, robust learning, defensive strategies against backdoor attacks, adversarial training, differential privacy, cryptography, blockchain technology, model compression, and physical-layer security. Lastly, this paper outlines future research directions to guide researchers in related fields.

In immersive communication, delivering real-time, high-resolution 360-degree panoramic videos imposes extremely high demands on network performance. In this paper, we propose a rate splitting multiple access (RSMA)-enabled adaptive panoramic video semantic transmission (APVST) framework. Specifically, APVST is built based on the deep joint source-channel coding (JSCC) structure and achieves adaptive semantic extraction and variable-length coding of panoramic frames. Additionally, APVST employs an entropy model and a latitude adaptive module to jointly achieve rate control, and utilizes a weight attention module to enhance the panoramic video quality. Given the overlapping field of view (FoV) when users watch panoramic videos, RSMA is integrated into the semantic transmission to further improve system efficiency. Therefore, we introduce an RSMA-enabled semantic stream transmission scheme, and formulate a joint optimization problem for latency and video quality by optimizing power, common rate, and channel bandwidth allocation ratios, aiming to maximize the users’ quality of service (QoS). To address this problem, we develop a deep reinforcement learning (DRL) approach based on the proximal policy optimization (PPO) algorithm, which integrates semantic-level FoV information to effectively adapt to dynamically changing environments. Simulation results indicate that our proposed APVST reduces bandwidth consumption by 20% compared to semantic video transmission schemes and 45% compared to traditional ones. Furthermore, our research validates the effectiveness of RSMA in panoramic video semantic transmission, demonstrating QoS improvements of up to 20% compared to other multiple access schemes.

Recently, there has been a growing interest in Semantic Communication (SemCom) frameworks that aim to enhance intelligent communications by exploiting the intended meaning of transmitted information. In this context, some researchers have introduced federated learning (FL) to train semantic models effectively and efficiently, while keeping private data on the respective devices. However, publicly sharing model updates during co-training in SemCom can potentially lead to the privacy leakage. To address this issue, this paper conducts membership inference attacks (MIA) against FL-based SemCom co-training processes. Through experiments, we discover instances of privacy leakage, with the rate of leakage varying as the models converge during training. Based on these findings, we propose the Adaptive Privacy Budget-based Differential Privacy (APB-DP) method for secure and effective semantic model training. APB-DP utilizes differential privacy (DP) to safeguard against MIA by introducing artificial noise during the training process, while also dynamically adapting the privacy budget (i.e., the level of noise) as the models converge. This ensures that the privacy protection remains effective throughout the training process. On the other hand, APB-DP takes into account the impact of wireless channels to prevent unnecessary interference. Simulation results show that APB-DP significantly reduces privacy leakage rate by 13% compared to FL-based SemCom. Additionally, it reduces performance loss rate by 71% compared to the state-of-the-art DP-based model training scheme known as NbAFL.

Deep neural network (DNN) is a promising technology for Internet of Visual Things (IoVT) devices to extrct their visual information from unstructured data. However, it is hard to deploy a complete DNN model at resource-constrained IoVT devices to fulfill their latency, energy, and inference accuracy demands. Exploiting the reachable and available computing resources of IoVT devices and mobile-edge computing (MEC) servers, we propose an edge-device collaborative DNN inference framework to empower resource-constrained IoVT devices to perform DNN-based inference. Especially, the DNN model partition separates the DNN model into two parts, which are deployed on both the IoVT devices and multiaccess MEC server for performing inference collaboratively. The DNN early exit and computation resource allocation are employed to accelerate the DNN inference while guaranteeing the inference accuracy. Moreover, a metric to measure the inference performance of average latency and accuracy (IPLA) is designed. Joint multiuser DNN partitioning, early exit point selection, and computation resource allocation are optimized to maximize the tradeoff performance of inference latency and accuracy. We model the optimized problem as an Markov decision process and propose a deep deterministic policy gradient-based edge-device collaborative DNN inference algorithm to solve the problem of huge state space and high-dimensional continuous actions. Experiments are conducted with the Alexnet model on the data set of CIFAR-10 and Resnet-50 model on the data set of ImageNet. Simulation results verify that the proposed algorithm speeds up the overall inference execution of IoVT devices while guaranteeing inference accuracy.

This article proposes a secrecy and energy-efficient device-edge co-inference scheme for resource-constrained Artificial Intelligence of Things (AIoT) devices with physical layer security assistance. Our approach leverages split learning, where the AIoT device executes the initial part of the AI model, and the mobile edge computing server (MECs) computes the remainder, reducing energy consumption (EC) and inference delay. We measure secrecy capacity under the finite blocklength regime to address the vulnerability of intermediate feature data (IFD) to eavesdropping over wireless channels and its short block length characteristics. The objective is to minimize the average EC of the device-edge co-inference by jointly optimizing deep neural network (DNN) model partitioning and resource allocation. We formulate a distributed reinforcement learning-based joint DNN model partitioning and resource allocation (DRPA) algorithm, which uses knowledge-based reinforcement learning for optimal DNN partitioning and a convex optimization approach for resource allocation. Simulation results demonstrate that the DRPA algorithm achieves near-optimal performance, closely matching the results of exhaustive search methods.

The future 6G systems are expected to achieve intelligent connection and interaction between various heterogeneous terminals, increasing the fragility for spoofing attacks. Due to the high security and energy efficiency, Physical Layer Authentication (PLA) has been regarded as a powerful method to verify the identity of devices. Nevertheless, due to the inaccurate identifying fingerprints caused by the imperfect estimation and variations of the limited fingerprints, most of the state-of-the-art PLA schemes have low reliability and robustness in low Signal-Noise Ratio (SNR) environments. Besides, most PLA schemes rely on the prior knowledge of attackers to establish authentication models, thus reducing the feasibility of actual communications. To address the first challenge, we propose a multi-attacker detection architecture based on multi-dimensional fingerprints, which can provide more robust identifiable spatial attributes for devices by using fingerprints observed by receivers in multi-locations. Upon the designed detection architecture, to tackle the second issue, we propose four clustering-based PLA schemes without requiring their training fingerprint sets. Considering that the aforementioned schemes can divide fingerprints from different transmitters into several disjoint clusters but can not precisely identify forged fingerprints, we further propose the graph learning-based PLA approaches with only a few labeled fingerprints. The simulation results on real industrial outdoor and indoor datasets demonstrate the superiority of the designed detection system in Adjusted Mutual Information (AMI) and authentication accurate rate (AucRate) over the single observation-based PLA schemes.

In order to truly promote the further development of the Industrial Internet of Things (IIoT), terminal authentication of the IIoT is essential. Physical-layer authentication (PLA) has recently attracted much attention for its high security and lightweight. Nevertheless, most existing PLA schemes in conjunction only the observation of a single receiver will lead to low-reliability and low-robustness of authentication, especially in hostile time-varying wireless channels. To tackle this issue, we developed a multiobservation-multichannel-attribute (MOMCA) based multiuser authentication architecture, which considers both the observations of multireceivers and multiple channel attributes of each observation to enhance wireless security. Specifically, the proposed architecture can provide additional spatial recognition characteristics for multiusers. To better fit the channel features of multiobservations, we proposed two gradient boosting optimization-based schemes. One uses Taylor expansion to approximate objective functions and adds the regularization term to avoid overfitting issues. The other can obtain higher authentication performance by sampling the signal data with small gradient characteristics. The simulations on real industrial indoor and outdoor datasets verify the superiority of the proposed schemes in authentication accuracy over six baseline authentication schemes.

With the continuous development of artificial intelligence (AI) technology, AI applications will play an increasingly important role in the sixth generation (6G) networks. At the same time, the emergence of technologies such as cloud computing has led to a growing number of AI models being applied in the Internet-of-Things (IoT). However, increasing sizes of AI models cause heavy burden on networks. In this paper, a multipath transmission scheme for the model slices based on the network function virtualization (NFV) is proposed. First, an optimization problem is formulated to decide the storage nodes for the model slices and the routing. With the physical network resource constraints, the problem is formulated as a mixed integer linear programming (MILP) to minimize the transmission cost. Second, a heuristic algorithm based on the steiner tree problem is designed to solve the optimization problem. Finally, based on the transfer learning method we get one generic slice and two specific slices from VGG16 for simulation. The results show when the destination nodes number and the network size are large, the transmission scheme for model slices has better performance in bandwidth utilization.

Recently, learning (DL)-based physical-layer authentication (PLA) has attracted much attention since artificial neural networks (ANNs) can be built to extract useful features from complex wireless environments, thus achieving high authentication performance and lightweight deployment in mobile edge computing (MEC)-Industrial Internet of Things (IIoT) scenario. However, the low latency characteristic of MEC makes it impossible to have much time to obtain sufficient signals for training the authentication system, which will cause over-fitting issues and deteriorate the authentication performance. Data augmentation is an effective method to address this problem. However, existing PLA with data augmentation can not generate representative and high-quality samples, consequently lacking generality in the actual identity authentication. To tackle this problem, a novel channel impulse response (CIR)-based multiuser authentication named latent perturbed neural networks (LPNNs) is proposed in this article, aiming at achieving high authentication performance even when trained a few data. Instead of relying on the generation of synthetic samples, the proposed LPNN adds Gaussian noise in the smooth latent space to avoid underdetermined and poor generalization, which has better interpretability. Specifically, to obtain a better understanding than a black box that connects input CIRs to authentication results, we defined Fingerprint Library and provided post-hoc explanations to answer the following question: which library examples explain the authentication results issued for a given CIR sample? Moreover, the simulations under the static and dynamic IIoT scenarios verify the superiority in authentication accuracy of the proposed LPNN over vanilla deep neural network (DNN) and convolutional neural network (CNN).

Recently, physical-layer authentication (PLA) has attracted much attention since it takes advantage of the channel randomness nature of transmission media to achieve communication confidentiality and authentication. In the complex environment, such as the Industrial Internet of Things (IIoT), machine learning (ML) is widely employed with PLA to extract and analyze complex channel characteristics for identity authentication. However, most PLA schemes for IIoT require attackers’ prior channel information, leading to severe performance degradation when the source of the received signals is unknown in the training stage. Thus, a channel impulse response (CIR)-based PLA scheme named “hierarchical variational autoencoder (HVAE)” for IIoT is proposed in this article, aiming at achieving high authentication performance without knowing attackers’ prior channel information even when trained on a few data in the complex environment. HVAE consists of an autoencoder (AE) module for CIR characteristics extraction and a variational AE (VAE) module for improving the representation ability of the CIR characteristic and outputting the authentication results. Besides, a new objective function is constructed in which both the single-peak and the double-peak Gaussian distributions are taken into consideration in the VAE module. Moreover, the simulations are conducted under the static and mobile IIoT scenario, which verify the superiority of the proposed HVAE over three comparison PLA schemes even with a few training data.

Traditional communication systems treat messages’ semantic aspects and meaning as irrelevant to communication, revealing its limitations in the era of artificial intelligence (AI), such as communication efficiency and intent-sharing among different entities. Through broadening the scope of the traditional communication system and the AI-based encoding techniques, in this manuscript, we present a novel semantic communication system, which involves the essential semantic information exploration, transmission and recovery for more efficient communications. Compared to other state-of-the-art semantic communication-related works, our proposed semantic communication system is characterized by the “flow of the intelligence” via the propagation of the model. Besides, the concept of semantic slice-models (SeSM) is proposed to enable flexible model-resembling under the different requirements of the model performance, channel situation and transmission goals. Specifically, a layer-based semantic communication system for images (LSCI) is built on the simulation platform to demonstrate the feasibility of the proposed system and a novel semantic metric called semantic service quality (SS) is proposed to evaluate the semantic communication systems. We evaluate the proposed system on Cityscapes and Open Images datasets, resulting in averaged 10% and 2% bit rate reduction over JPEG and JPEG2000, respectively. In comparison to LDPC, the proposed channel coding scheme can averagely save 2dB and 5dB in AWGN channel and Rayleigh fading channel, respectively.

Blockchain-based dynamic spectrum sharing (DSS) is viewed as a robust measure to address the management and allocation of spectrum resources for Internet of Things (IoT) ecosystems in a secure and auditable manner. However, existing blockchain empowered DSS systems commonly assume the perfect communications among the nodes without any interference and throughput constraints, which will mislead the spectrum owner into making suboptimal decisions, especially under the time-varying and unstable channel situation in IoT. To overcome the influence of the imperfect channel situations on DSS, we propose a novel reputation mechanism-based blockchain empowered DSS system in this paper. By considering both the historical transaction successful rate and the communication throughput and the real-time channel situations, the reputation mechanism enables the spectrum owner to make the appropriate decisions even under the occurrence of the deep decay. Furthermore, the spectrum trading process is formulated as a Stackelberg game to encourage the participation of the spectrum owner and the requester. A pricing scheme is proposed by maximizing the utilities of all nodes jointly. Simulation results confirm that our proposed scheme is superior to existing works in revenue utility.

In the era of the Internet of Things (IoT), reinforcement learning (RL)-based techniques are promising candidates to handle the intrusion response through the interaction between the IoT device and its environment. Given a large number of devices in the IoT network, wireless software-defined networking (W-SDN) is widely agreed to be introduced to facilitate network management, such as launching a new intrusion response scheme (IRS) on massive IoT devices. To guarantee the scalability and security of IoT services on the extended devices in the W-SDN-based network, a distributed RL-based IRS is proposed in this article, called game-theoretic actor–critic-based IRS (GTAC-IRS). GTAC-IRS employs a game-theoretic-based response selection matrix, aiming at reducing training time and facilitating the convergence of the response scheme. GTAC-IRS constructs a well-designed state representation of observed environment status, a low-dimension response matrix, and a simplified response selection policy to lower the complexity of the algorithms. Simulation results reveal that benefiting from local environment observation, GTAC-IRS achieves effective intrusion response without sophisticated feature engineering. Instead of the “warm-start” training adopted in conventional RL-based IRS, the low-dimension response matrix in GTAC-IRS can significantly improve the convergence speed. Thus, GTAC-IRS outperforms other popular IRSs in terms of the response time under the circumstance of the node’s behavior changing or malicious nodes ratio changing.

As a typical Internet-of-Things (IoT) scenario, massive machine-type communications (mMTC) services are expected to grow exponentially and create a multibillion-dollar industry spanning a broad range of vertical sectors. In literature, wireless software-defined network (SDN) is viewed as a promising approach to facilitate the degree of reconfigurability on extended sets of mMTC devices via centralized software updates. However, most of the current anomaly detection scheme (ADS) in SDN suffers from the high risk of overwhelming of the controller as well as excessive energy consumption if directly applied in the network with enormous devices. To address the scalability issues in centralized ADS, we propose a localized ADS scheme, called scalable and energy-efficient anomaly detection scheme (SEE-ADS), comprising of a detection activation module, a lightweight predetection module, a heavyweight anomaly detection module, and a dynamic strategy selection module. Through the cooperation among these modules, the proposed ADS is capable of detecting attacks dynamically and effectively without the risk of energy depletion via discontinuous activation of the heavyweight detection. The lower complexity is fulfilled by developing a localized and adaptive heavyweight detection module, called a localized evolving semisupervised learning-based anomaly detection scheme (LESLA). Besides, the proposed scheme makes full use of feedback from the previous heavyweight activation and the indication of predetection on each packet. The simulation results show that the proposed scheme greatly reduces the overall energy consumption over heavyweight detection. Furthermore, the proposed scheme shows higher sensitivity on abnormal packets and similar false alarm compared with the literature work.