Satellites play a crucial role in Internet of Things (IoT) applications that require precise positioning. Satellite orbit prediction serves as the foundation for providing accurate terminal location services. However, traditional satellite orbit prediction faces challenges like measurement errors, estimation errors, and unmodeled orbit disturbances, leading to low prediction accuracy. To address this issue, this article introduces a groundbreaking satellite digital twin (DT) system based on container technology. This system facilitates real-time mirroring, monitoring, optimization, and control of satellite orbit prediction with low power consumption. Leveraging the advantages of container technology allows for convenient and efficient model updating. Furthermore, a new satellite orbit error prediction model is explored within this system. This model utilizes the seasonal-trend decomposition using locally weighted regression (STL) method and the temporal convolutional network (TCN) algorithm. By decomposing satellite orbit data into multiple components, the proposed model achieves enhanced future orbit Prediction by combining predicted values from each component. Different from existing machine learning (ML) orbit prediction models, our proposed model explores the variation patterns of satellite orbit data from a trend and cycle perspective, rather than relying solely on collecting more data and training larger models to improve prediction accuracy, which makes the novel prediction scheme get good performance while keeping low prediction complexity. Extensive experiments validate the effectiveness of the proposed method using two publicly available satellite orbit datasets (ILRS catalogue and TLE catalogue). The experimental results show that compared with traditional orbit prediction models, the novel DT system has less model update time and occupies less memory. The mean absolute error (MAE) value of the new model is lower than the five ML models in existing researches, which proves that the proposed STL-TCN model has higher prediction accuracy than existing ML orbit prediction models. In addition, we discussed the impact of atmospheric pressure density on the STL-TCN model, and experiments have shown that the correction of different atmospheric pressure density models has a very small impact on the prediction accuracy of the STL-TCN model. Finally, we further investigate the generalization ability of the STL-TCN model for other satellite orbits and future time orbits, and the results show that the novel model has satisfactory generalization ability.

The parallel distributed detection is studied for mobile wireless sensor networks (MWSNs) in the presence of Byzantine attacks in entirely unknown environment or complicated environment from the perspective of the information theory, where we pay most of our attention toward design, analysis, and evaluation of the attack strategy. In particular, the multihop relay network and the harsh wireless communication condition, e.g., the dynamic and entirely unknown channel, are taken into consideration in our configuration. Second, the conditions that the optimal attacking strategy should satisfy is analyzed and developed under different attacking scenarios. Third, the minimum attacking power is developed for the Byzantines to blind the fusion center (FC). Furthermore, the optimal attacking strategies are developed when no prior information of the system is known for the Byzantines. Finally, the traditional four typical attack strategies are evaluated, and we find that the fraction of Byzantines is the only factor that affects the reliable data fusion when the network size and the attacking strategy are fixed. The extensive simulation is conducted to verify our design, analysis, and evaluation of the attack strategy.

Due to the broadcast nature of wireless communication, it is susceptible to eavesdropping by unauthorized users. Artificial noise, as an important physical layer security technique, is different from traditional cryptography-based communication security mechanisms with high computational complexity, and cannot be cracked by the rapidly advancing computing power. The theoretical research on the security of artificial noise-assisted multiple input multiple output (MIMO) communication systems is quite comprehensive. However, since artificial noise is deeply dependent on the accurate instantaneous channel feedback, its practical application effect still lacks further experiment verification in actual MIMO systems. Based on LabVIEW and universal software radio peripheral (USRP) software defined radio technology, this paper presents a hardware simulation platform of artificial noise-assisted MIMO communication systems. The experimental data obtained from this platform demonstrates that artificial noise can effectively enhance system security with a slight influence on the system reliability.

This article investigates energy-efficient secure communication design with the help of multiple phase-adjustable intelligent reflecting surfaces (IRSs). By creating desirable radiation patterns of wireless environment, the IRSs are used to significantly improve the number of bits securely delivered to the destination per energy consumption in Joule, also known as the secrecy energy efficiency (SEE). By manipulating the discrete reflecting coefficients of multiple IRSs and taking advantage of the active beamforming, this article develops an efficient alternating optimization algorithm based on successive convex approximation and penalty-based techniques to effectively fight against multiple eavesdroppers. Simulation results characterize the graceful tradeoff between conflicting performance metrics, i.e., total power consumption and secrecy rate in the multi-IRS-aided secure communication system, and corroborate that incorporating multiple IRSs is beneficial to both the SEE and secrecy rate enhancement compared to existing related benchmarks.

Abstract This paper proposes a comprehensive study for the physical layer channel based authentication (PLA) designs in mmWave communications, which reveals the principles that how does the sparse properties of mmWave channel can benefit the PLA designs and performance. First, by fully investigating the channel perturbations of mmWave channel caused by the environmental changes within the channel coherence time, the generalized detection designs are investigated for mmWave channels. In general, it shows that the mmWave channel perturbations will seriously degrade the detection performance. To solve this problem, a lightweight but effective PLA scheme is proposed by fully utilizing the sparsity of mmWave channel. The key physical factors that impact the detection performance are fully investigated by conducting thoroughly theoretical analysis and simulations, and the detection spoofing probability of the scheme is show to be higher than 95%, while keeping the probability of false rate below 1%. The study indicates that channel sparsity is with great potential to design lightweight but effective PLA mechanisms for the future mmWave communication systems.

With the increasing demands for sharing confidential information among massive Internet of Things (IoT) devices in 5G and beyond wireless networks, many applications require the common secret key generation for a group of IoT devices. However, most of the existing works on physical layer secret key generation (PLKG) only focus on the pairwise key generation between two users, which is a low efficient and high cost to be extended to the scenarios of group key generation. In this work, we propose a new efficient multiple-input–multiple-output (MIMO) physical layer group secret key generation scheme to reduce the consumption of channel probing and improve the efficiency for group key generation. Different from current schemes, in the proposed scheme, the transmitter randomly generates the group secret key and directly broadcasts the downlink data symbols to the group users. At the receiver end, each group user can efficiently “observe” the common group key through the downlink broadcasting data symbols, while keeping perfect secrecy of the shared group key against eavesdroppers. The performance of reliability, security, and the group key generation rate is fully investigated, which shows the advantages of high efficiency, low consumption, and strong robustness of the proposed scheme. Extensive simulations are conducted to validate the effectiveness of the proposed scheme.

Industrial wireless networks (IWNs) systems require high performance and high security in critical manufacturing processes. However, the wireless environment suffers from low performances and fragile security due to lack of physical connection. In this paper, by taking advantages of reconfigurable intelligent surface (RIS) to enhance the physical layer (PHY) security of IWNs thus to resist eavesdropping attacks, where RIS reflects the incident signal at a certain phase shift to assist the legitimate transmission partners to transfer secret information without eavesdroppers' channel state information(CSI). In this way, the reflection of radio waves is actively controlled to overcome the negative effects of natural wireless propagation. The average secrecy capacity of the legal users is maximized by optimizing the phase shift of the RIS, which provides a cost-effective high security transmission solution for the IWNs. The widely experiments proof the effectiveness of proposed scheme.

In this article, a hierarchical task offloading strategy is presented for delay-tolerant and delay-sensitive missions by integrating edge computing and artificial intelligence into Cybertwin-based network to guarantee user Quality of Experience (QoE), low latency, and ultrareliable services, which are huge challenges to the Internet of Things (IoT) due to diverse application requirements, heterogeneous multidimensional resources, and time-varying network environments. The novel scheme achieves faster task processing, dynamic real-time allocation, and lower overhead by taking advantages of a multiagent deep deterministic policy gradient (MADDPG). Moreover, federated learning is used to train the MADDPG model. Numerical results demonstrate that the proposed algorithm improves system processing efficiency and task completion ratio compared to the benchmark schemes.

The cooperative physical layer security scheme can enhance the communication security of industrial wireless nodes that are computing and energy limitation, which provides the lightweight security for future industrial wireless networks (IWNs). By aiding an edge computing device, the optimal cooperative jamming (CJ) node can be selected, and employed to collaboratively transmit jamming signals to maximumly weaken the quality of the wiretap channel. For overcoming the drawback of the existing work that only studies the physical layer security in static scenes, a practical scenario where legitimate user and eavesdropper move randomly is considered in this article. The ergodic secrecy capacity with CJ in random mobile scenes is derived. An edge computing device is employed to make intelligent selection of an optimal cooperative node. Test and verification are carried out in the industrial factory. The test results show that the novel scheme can improve the secrecy of random mobile users in the IWNs.

Physical-layer security (PLS) in Internet of Things (IoT) has attracted great attentions recently. Although mobility is an intrinsic property of IoT networks, most of the existing works only investigate the secure transmission design for static users. To fill this gap, this article specifically investigates the secrecy throughput maximization problems for the mobile IoT user under two typical mobility models: 1) random waypoint model (RWP) and 2) random direction model (RD). The insights about how the mobility patterns, and security–reliability requirements affect the mobile user’s secrecy throughput are revealed. First, in order to establish the relationship between security and reliability of the mobile user, a general analytic framework is provided to derive the closed-form expressions of transmit secrecy outage probability (TSOP) for the mobile user. Second, two transmission schemes are proposed to maximize the secrecy throughput of the mobile user by ensuring a certain level of transmit probability (TP) and TSOP requirements. The numerical and simulation results verify the validity and effectiveness of the proposed schemes, and indicate that by adopting appropriate mobility pattern, the user’s secrecy throughput can be improved, and the constraint on its moving region can be largely reduced. Those properties are lightweight and feasible to enhance security for many mobile IoT scenarios.

Unlike most of the upper layer authentication mechanisms, the physical (PHY) layer authentication takes advantages of channel impulse response from wireless propagation to identify transmitted packages with low-resource consumption, and machine learning methods are effective ways to improve its implementation. However, the training of the machine-learning-based PHY-layer authentication requires a large number of training samples, which makes the training process time consuming and computationally resource intensive. In this article, we propose a data augmented multiuser PHY-layer authentication scheme to enhance the security of mobile-edge computing system, an emergent architecture in the Internet of Things (IoT). Three data augmentation algorithms are proposed to speed up the establishment of the authentication model and improve the authentication success rate. By combining the deep neural network with data augmentation methods, the performance of the proposed multiuser PHY-layer authentication scheme is improved and the training speed is accelerated, even with fewer training samples. Extensive simulations are conducted under the real industry IoT environment and the figures illustrate the effectiveness of our approach.

User mobility is a major feature of wireless networks. At present, physical layer security research rarely considers the impact of user mobility on the physical layer security performance in the network. In this study, the authors propose a random mobile user physical layer security model and study the impact of random waypoint movement users on the security performance of the physical layer in wireless networks, in which an artificial noise security scheme under the multi‐antenna transmission model is considered. They derive the closed expression of positive secrecy capacity probability and secrecy outage probability under this model. The results prove that system security is enhanced because of the users’ mobility.

The energy‐constrained devices such as the mobile terminals and nodes of the Internet of Things make lightweight security schemes an urgent need. The traditional identity authentication techniques can provide protection for the user's privacy and information to a certain extent, but they suffer from heavy cost. Non‐cryptographic authentication mechanisms based on the physical layer characteristics are new techniques, which have a higher security level. The recognition technique of radio transmitter based on radio‐frequency fingerprint ( RFF ) is one of the non‐cryptographic authentication techniques. The authors propose a lightweight one‐time password (OTP) authentication scheme based on RFF (RFF‐OTP), which is a novel cross‐layer secure authentication scheme and can provide mutual authentication between the mobile terminal and server, by combining RFF recognition algorithm with a hash encryption algorithm. By theoretical analysis and Syverson and van Oorschot logic verification, they prove that the RFF‐OTP scheme is simple, efficient, flexible and independent of trusted‐party while it also can resist the cloning attack and satisfy the anonymity compared with the OTP authentication scheme. Besides, it only requires the password to log into the system in the authors’ scheme in comparison to the OTP scheme that needs both ID and password for the same purpose.

Internet of Things (IoT) is becoming an emerging paradigm to achieve ubiquitous connectivity, via massive deployment of physical objects, such as sensors, controllers, and actuators. However, concerns on the IoT security are raised due to the wireless broadcasting nature and the energy constraint of the physical objects. In this paper, we study secure downlink transmission from a controller to an actuator, with the help of a cooperative jammer to fight against multiple passive and noncolluding eavesdroppers. In addition to artificial noise aided secrecy beamforming for secure transmission, cooperative jamming (CJ) is explored to further enhance physical layer security. In particular, we provide a secrecy enhancing transmit design to minimize the secrecy outage probability (SOP), subject to a minimum requirement on the secrecy rate. Based on a strict mathematical analysis, we further characterize the impacts of the main channel quality and the minimum secrecy rate on transmit designs. Numerical results confirm that our design can enhance both security (in terms of SOP) and power efficiency as compared with the approach without CJ.

This paper focuses on the security risks in the access authentication of Internet of Things, to provide an optimized classification algorithm of radio frequency fingerprinting (RFF). The novel method is based on coherent integration, multiresolution analysis, and Gaussian support vector machine (SVM). First, we proposed a de-noising algorithm for RFF as the present performance of RFF is seriously affected by the noise. The optimized coherent integration first developed in this paper effectively improves the signal-to-noise ratio (SNR) of the waveform without increasing the number of required signals, by which a de-noising processing is performed and the identification accuracy is improved. Then a wavelet-based multiresolution analysis is applied to extract feature points in the waveform that has passed the de-noising optimizer, because the less sample points are needed for the SVM classification processing, which reduces the computational complexity of SVM compared to the classical SVM classification methods where massive sample points are necessary. Extensive experiments are performed. Simulation result shows that the optimized classification algorithm achieves a high accuracy (exceed 99%) at a relatively low SNR (0 dB), which is the best result compared with other existing methods.

This paper presents a multi‐time channel prediction system based on backpropagation (BP) neural network with multi‐hidden layers, which can predict channel information effectively and benefit for massive MIMO performance, power control, and artificial noise physical layer security scheme design. Meanwhile, an early stopping strategy to avoid the overfitting of BP neural network is introduced. By comparing the predicted normalized mean square error (NMSE), the simulation results show that the performances of the proposed scheme are extremely improved. Moreover, a sparse channel sample construction method is proposed, which saves system resources effectively without weakening performances.

In this paper, we study artificial noise (AN) assisted beamforming secure transmission system with imperfect main channel state information (CSI) between a friendly cooperative jammer (Oscar) and an authorized receiver (Bob). We deduce out the maximum secrecy rate of secure system and the corresponding optimal power allocation ratio between information signal and AN under the constraint of secrecy outage probability. For realistic security communication system, we use advanced back propagation neural network (BPNN) for channel estimation. The numerical results show that estimation error results in a decrease in secrecy rate, but BPNN channel estimator still can guarantee secure transmission. Analytical derivations and numerical simulations are presented to validate the correctness of obtained expressions.

This study investigates the framework of associating multiple‐input–multiple‐output (MIMO) beamforming with secure code to achieve unconditional secure communications in the wireless passive eavesdropping environment. The schemes are based on a two‐step method under Wyner's wiretap channel model. First, with MIMO transmit beamforming, one can utilise the spatial degree of freedom to cripple eavesdroppers’ interceptions even when he does not know the eavesdropper's channel state information. Consequently, by taking the threshold characteristics of the secure code, the legitimate receivers will continue to extend an average bit error rate advantage over eavesdroppers when they share similar conditions (background noise power and channel gains). By this way, the proposed system could achieve almost zero information obtained by the eavesdroppers while still keeping rather lower error transmissions for the main channel. A profound theoretical analysis for the MIMO advantage channel and the exact closed‐form expressions of secrecy outage probability for the secure code joint system are presented. The authors launch extensive experiments to verify the proposed security systems and demonstrate its feasibility and implement ability.