In Internet of Things (IoT) environments, malicious nodes can not only eavesdrop on legitimate communications but also emit jamming signals, which severely undermine secure and reliable communication among nodes. Reconfigurable Intelligent Surfaces (RIS) have emerged as a lightweight and promising technique to mitigate such threats. However, in typical scenarios that simultaneously involve RIS and multiple nodes, existing Deep Reinforcement Learning (DRL)-based methods often suffer from lengthy training procedures and are vulnerable to adversarial perturbations (AP) introduced by malicious nodes. This paper proposes an offline secure and anti-jamming communication decision framework based on a Temporal Convolutional Network (TCN)-based Twin-Efficiency Diffusion Model (TCN-TEDM). The framework effectively alleviates the tendency of traditional offline RL to yield degraded policies under strong jamming due to adversarial perturbations and state missingness. The model reconstructs perturbed states under a limited compute budget, thereby markedly improving decision stability under jamming. Extensive experimental results demonstrate that the proposed framework can efficiently generalize and transfer existing secure-communication and anti-jamming policies without online interaction, achieving superior stability and performance under state-perturbation and missingness scenarios.

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.

In Agricultural Internet of Things (Ag-IoT), a large number of terminal sensor nodes with limited computational power and energy are widely deployed across various production sites to collect real-time data that support precision agriculture decision-making, which makes it challenging to perform complex computing tasks in real time. This article proposes an offline decision-making framework based on a compositional diffusion model that addresses the limitations of traditional offline deep reinforcement learning (DRL), which often struggles to produce high-quality policies in the presence of malicious-node interference and attacks and with insufficient decision trajectories in agricultural settings. Specifically, the proposed scheme leverages the diffusion model’s reverse generative process to synthesize numerous high-quality augmented samples from a small set of existing decision trajectories, which are then used to train robust policies, mitigating performance degradation in offline DRL due to limited trajectories and adversarial state perturbations introduced by malicious nodes. Experimental results demonstrate that the proposed method improves the quality and generalization of task offloading and resource allocation decisions, exhibiting superior stability and performance even under data sparsity and adversarial attacks.

This paper investigates physical layer security (PLS) in wireless interference networks. Specifically, we consider confidential transmission from a legitimate transmitter (Alice) to a legitimate receiver (Bob), in the presence of non-colluding passive eavesdroppers (Eves), as well as multiple legitimate transceivers. To mitigate interference at legitimate receivers and enhance PLS, artificial noise (AN) aided interference alignment (IA) is explored. However, the conventional leakage minimization (LM) based IA may exhibit confidential signal cancellation phenomenon. We theoretically analyze the cause and then establish a condition under which this phenomenon will occur almost surely. Moreover, we propose a means of avoiding this phenomenon by integrating the max-eigenmode beamforming (MEB) into the traditional LM based IA. By assuming that only statistical channel state informations (CSIs) of Eves and local CSIs of legitimate users are available, we derive a closed form expression for the secrecy outage probability (SOP), and establish a condition under which positive secrecy rate is achievable. To enhance security performance, an SOP constrained secrecy rate maximization (SRM) problem is formulated and an efficient numerical method is developed for the optimal solution. Numerical results demonstrate the effectiveness and the usefulness of the proposed approach.

With the advancement of carbon-neutral and new power system construction, numerous information devices are continuously connected to power distribution systems, gradually breaking the original unobservable state of power distribution systems and making them more vulnerable to false data injection attacks (FDIAs). Contrary to most existing research focusing on the unbalanced network, less attention has been paid to the influence of randomness and fluctuation of distributed photovoltaic (PV) to perform FDIAs in the power distribution system. In this article, the failure mechanism of FDIAs and the improved FDIAs method are proposed simultaneously for the distribution system with a high penetration of distributed PV scenarios. Specifically, based on the reactive power optimization process, the randomness and fluctuation of distributed PV are applied to decrease significantly the stealthiness of the FDIAs. Subsequently, an improved FDIA method, based on time-dependent loss conditional generative adversarial networks, is proposed to enhance the stealth and effectiveness of the attack. Finally, numerical results based on the modified IEEE 33 bus test systems demonstrate the effectiveness of the failure mechanism and the improved FDIAs. Research results can facilitate the execution of countermeasures for distribution systems with a high penetration of distributed PV, posing serious and pressing security concerns in power distribution systems with a high penetration of distributed PV scenarios.

To mitigate multi-user interference at legitimate receivers and assist secure transmission, artificial noise (AN) aided interference alignment (IA) is explored. The feasibility of traditional leakage minimization (LM) based IA is analyzed, including IA equations and rank constraint related to the secret signal transmission. We show that there exist two drawbacks which are not discussed in LM based IA security approaches: 1) the necessary condition for the feasibility of IA equations is loose, and 2) the secret signal cancellation may occur which makes secure transmission infeasible and in fact, there is no systematic analysis of this problem. We analyze the cause and establish a condition under which the secret signal cancellation is inevitable. Then a modified IA approach is proposed by implementing the max-eigenmode beamforming (MEB) for secure transmission. Compared with the traditional LM based IA, a tighter necessary condition can be established, and the secure transmission can be proctected. Numerical results confirm the effectiveness and usefulness of the modified IA approach.

As security issues facing the industrial Internet of Things (IIoT) continue to emerge, industrial organizations are working to further improve the security system. Zero trust (ZT) is seen as the future of industrial security, with a rising voice, but currently, no concrete implementation technique is available. In this article, we start with the requirements of ZT security and attempt to design a ZT technical framework applicable to wireless IIoT. Specifically, a three-step ZT security framework is proposed that builds on the benefits of physical-layer security to enhance ZT in IIoT. Security zone formation is done first, which then facilitates a trusted environment for subsequent device authentication and cryptographic negotiation. By integrating physical-layer security, several promising techniques, including artificial noise, physical fingerprint, and key distribution, are well designed to accomplish the proposed framework. Our analysis reveals that the proposed framework and the designed particular implementation techniques are feasible to enhance ZT security in wireless IIoT.

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.

Effective energy design and secure communications are critical in the Internet of Things (IoT). A cooperative jamming (CJ) scheme based on deep reinforcement learning (DRL) is proposed for intelligent reflecting surface (IRS) aided secure cooperative network with eavesdroppers. Our goal is to maximize the average secrecy rate, or energy efficiency with secrecy constraints by jointly optimizing the transmit, jamming beamforming matrices, and IRS phase-shift matrix. In order to figure out the challenging nonconvex optimization problem, the deep deterministic policy gradient (DDPG) optimization algorithm is proposed to solve the energy efficiency maximization problem. In order to speed up task processing and reduce service delay, the edge computing server is employed for DRL training to improve computing speed and promote secure communication efficiency. By taking advantages of the features of proximity to end devices and computing resources, the edge devices provide a low latency efficient training support to the legal users and real-time control to the IRS units. By this way, a high security and low energy consumption IoT system is built. Numerical results show that the novel scheme improves the secrecy rate and energy efficiency compared with the benchmark scheme.

We investigate the physical layer security (PLS) in multi-user interference networks. In particular, we consider secure transmission from a legitimate source (Alice) to a legitimate destination (Bob), coexisting with multiple passive eavesdroppers (Eves), as well as multiple legitimate transceivers. By assuming that only statistical channel state informations (CSIs) of Eves and local CSIs of legitimate users are available, we propose an artificial noise (AN) assisted interference alignment (IA) for security enhancement. Unlike traditional IA based security approaches which may result in secret signal cancellation, we design a modified alternating minimization (AM) scheme to overcome this threat, by incorporating the max-eigenmode beamforming (MEB) for secure transmission. Moreover, by partitioning the IA equation into three independent subsets and their combinations, a much tighter necessary condition for IA feasibility is established. We also provide guiding insights into the practical system designs, including useful guidelines for the selection of the dimension of AN. Furthermore, the power allocation ratio between the secret signal and the AN signal is optimized to minimize the secrecy outage probability (SOP), subject to a minimum secrecy rate constraint. Numerical results demonstrate that our design can enhance both quality and security of the secret signal, and thus is suitable and reliable for PLS in interference networks.

The rapid development and continuous innovation of wireless services have led to a boom in the number and types of smart terminals. The huge amount of data that deep learning needs to calculate relies on the continuous improvement of hardware devices to solve. Therefore, the realization of intelligent transportation system(ITS) has become the general trend. The increase in the number of Internet of Things devices and the changes in the location of vehicles in the Internet of Vehicles(IOV) system have put forward higher requirements for the reliability and effectiveness of information transmission and the effective use of spectrum resources. Aiming at the influence and elimination of aggregated interference in intelligent transportation system, an anti-fragile communication algorithm is proposed to improve the reliability of signal transmission. At the same time, the outage probability of energy harvesting and cognitive radio technology enhanced with relay cooperative transmission under cognitive wireless network system with the aggregate interference can be deduced in detail. Finally, the correctness of theoretical analysis and the reliability of the proposed anti-fragile communication algorithms are verified by simulation experiments.

Secure transfer of wireless information is becoming a critical issue in multi-user interference networks. In this paper, we consider secure transmission from a source (Alice) to a legitimate destination (Bob), coexisting with a passive eaves-dropper (Eve) and$K$transceiver pairs. By assuming that only statistical channel state information (CSI) of Eve and local CSIs of legitimate users are known, a secrecy beamforming scheme with artificial noise (AN) is designed for secure transmission, and a modified interference alignment (IA) scheme is proposed for secrecy enhancement. Unlike the conventional AN-aided IA approaches which may lead to private signal cancellation, we propose a novel design modification with security protection. Moreover, a definite connection between improperness and in-feasibility of IA is established, to provide guiding insights on IA requirements. Based on a strict mathematical analysis, we further characterize the impact of transmit power on transceiver design and secrecy performance. Numerical results confirm that our design enables high transmission security with performance guarantee, and thus is suitable and stable for physical layer security (PLS) in multi-user interference networks.

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.

This article introduces a novel physical-layer (PHY-layer) authentication scheme, called transfer learning-based PHY-layer authentication (TL-PHA), aiming to achieve fast online user authentication that is highly desired for latency-sensitive applications such as edge computing. The proposed TL-PHA scheme is characterized by incorporating with a novel convolutional neural network architecture, namely, the triple-pool network (TP-Net), for achieving lightweight and online classification, as well as effective data augmentation methods for generation of data set samples for the network model training. To assess the performance of the proposed scheme, we conducted two sets of experiments, including the one using computer-simulated channel data and the other utilizing real experiment data generated by our wireless testbed. The results demonstrate the superiority of the proposed scheme in terms of authentication accuracy, detection rate, and training complexity compared to all the considered counterparts.

Residing in the proximity of end devices, edge computing (EC) holds great potential to provide low-latency, energy-efficient, and secure services, which has become an essential part of the Industrial Internet of Things (IIoT). To future accelerate task processing and reduce service latency, this work proposes an online incentive-driven task allocation scheme to stimulate collaborative computing among EC servers and IIoT devices. To better serve dynamic and heterogeneous tasks in terms of profiles and importance, EC servers (including neighboring servers) and IIoT devices with available resources can cooperatively process the tasks. Considering the heterogeneity of computing resources in edge servers and industrial IoT devices, we formulate a task allocation problem, which is NP hard. An online incentive-driven task allocation algorithm is proposed to this NP-hard problem, which will optimize task assignment strategies to maximize system utility, promote faster computing, and stimulate collaborative computing. Theoretical analyses show that the online incentive algorithm can satisfy incentive compatibility, individual rationality, computational efficiency, and feasibility. The results demonstrate that the proposed task allocation scheme with collaborative EC achieves superior performance and effectiveness.

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.

The introduction of information and communication technologies makes network environments increasingly open, leaving smart-grid control systems incredibly vulnerable to malicious attacks. False data injection (FDI) attacks stealthily tamper with measurement data, resulting in erroneous decisions made by the control center that greatly influence the normal operation of the power system. By taking advantage of real-time data acquisition with edge computing, in this article, we propose a scheme based on classification of predicted residuals (CPRs) for the FDI attack detection. The CPR scheme first predicts the acquired measurement data at the edge of the sensing network via developing an accurate prediction model. Followed the novel real-time classification method under the edge devices supporting, it classifies the predicted residuals independent of the false data to enhance the detection accuracy. Through these two steps, the detection rate of FDI attacks is greatly improved. The proposed scheme is validated in a real microgrid testbed. Experimental results show that the CPR scheme performs well in detecting FDI attacks and remains sensitive in injection attack probability and magnitude. The detection scheme even has effectiveness at low injection attack probability and magnitude (5% and 0.018 per thousand, respectively). Furthermore, it also proves that the proposed scheme has applicability in high real-time requirements at the edge of smart grids.

Edge computing (EC) is an essential component of large-scale intelligent manufacturing systems for Industry 4.0, which promises to provide a preprocessing platform for the massive data generated by the terminals and guarantee lower delay and more security compared to directly processing data in cloud computing. Nevertheless, access authentication is a crucial security issue of current EC systems, and, thus, this article presents a solution to enhance the access classification accuracy by exploiting the physical layer information. Our method employs a weighted voting scheme for channel state information based authentication using a single sample which includes sample segmentation, grouping, and weighted voting and finally achieves the fast and low complexity secure-access requirement of the EC system without increasing the individual devices’ sample size and computational complexity. Experimental results utilizing public datasets and field-measured datasets demonstrate that the proposed weighted voting method has higher accuracy and robustness than existing methods.

The Internet of Vehicles (IoV) has always attracted attention as the emerging communication network with the most development potential in the 5G era. However, the performance of IoV under 5G ultra-dense networks is an open issue, especially in practice the outage probability and ergodic capacity of the relay cooperative IoV network under aggregate interference are still unclear. Therefore, an opportunistic Decoding and Forwarding (DF) relay cooperative transmission algorithm was proposed in this paper when the destination node of IoV has aggregated interference. In addition, based on mathematical theoretical knowledge such as numerical analysis, the closed expressions of the outage probability and ergodic capacity of the IoV system under aggregated interference was derived. Finally, simulation experiments verify the effectiveness of the proposed scheme and the correctness of the theoretical analysis, which improves the transmission rate of the system.

Identity-based attacks (IBAs) are one of the most serious threats to wireless networks. Recently, there is an increasing interest in using the received signal strength (RSS) to detect IBAs in wireless networks. However, current schemes tend to generate excessive false alarms in the mobile scenario. In this paper, we propose a stronger Reciprocal Channel Variation-based Identification and classification (RCVIC) scheme for the mobile wireless networks, which exploits the reciprocity of the wireless fading channel and RSS variations naturally incurred by mobility to improve the detection performance. Different from current schemes only detect IBAs, RCVIC scheme conducts a multi-stage detection processes. If the IBAs are detected, RCVIC scheme partitions the received frames into two classes. The frames in the same class should be sent from the same senders, which could benefit the further analysis, such as network forensics, attacker localizing and trajectory analysis, etc. The feasibility of RCVIC are numerically evaluated through theoretical analysis and simulations. It is further validated through experiments using off-the-shelf 802.11 devices under different attacking patterns in real indoor and outdoor mobile scenarios.

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.

Although the full multiple-symbol detection (MSD) for IEEE 802.15.4c multiple phase shift keying (MPSK) receivers gives much better performance than the symbol-by-symbol detection (SBSD), its implementation complexity is extremely heavy. We propose a simple MSD scheme based on two implementation-friendly but powerful strategies. First, we find the best and second-best decisions in each symbol position with the standard SBSD procedure, and the global best decision is frozen. Second, for the remaining symbol positions, only the best and second-best symbol decisions, not all the candidates, are jointly searched by the standard MSD procedure. The simulation results indicate that the packet error rate (PER) performance of the simplified MSD scheme is almost the same as that of the full scheme. In particular, at PER of 1 × 10 − 3 , no more than 0.2 dB performance gap is observed if we just increase the observation window length N to 2. However, the number of decision metrics needed to be calculated is reduced from 256 to 2. Thus, much balance gain between implementation complexity and detection performance is achieved.

Turbo codes are a family of near Shannon limit error correction coding schemes that usually are adopted for wireless data transmission. To reduce the power dissipation of a long-term evolution (LTE) advanced turbo decoder, in this paper, we propose a reverse calculation based low memory turbo decoder architecture by partitioning the trellis diagram and simplifying the max* operator. The designed forward state metrics calculation architecture is merged with two classical decoding schemes. Through field programmable gate array (FPGA) hardware implementation, the state metrics cache (SMC) capacity is reduced by 65%, the power dissipation of the reverse calculation architecture is significantly reduced for all tested clock frequencies, and the decoding performance is not affected as compared with classical decoding schemes. The proposed reverse calculation architecture is an effective technique to achieve better decoding performance for power-constrained applications.

Recently, MIMO (multiple-input-multiple-output) physical layer secure transmission has attracted great attentions. However, current schemes cannot defend against the passive eavesdroppers with arbitrary number of antennas. To address this problem, in this work, we propose a practical physical layer MIMO secure communication scheme (PLSC) to defend against such an eavesdropper with arbitrary number of antennas. In the proposed scheme, the transmitter first independently generates a random binary sequence as the “key bits (KB)” to “encrypt” (XOR) the confidential information. After that, the transmitter sends the “encrypted information” over the wireless channel, along with mapping key bits to the legitimate receiver simultaneously. The key principle lies in that the KB information is coded in the indexes of the activated/non-activated antennas combination of the legitimate user. Then, the legitimate receiver first observes his/her activated antenna indexes to obtain the corresponding key bits. After that, he/she demodulates the “encrypted information” at the activated antennas, and finally “decrypts” (XOR) the confidential information by using the observed key bits. However, due to the uniqueness and independence of MIMO wireless channel, for any other eavesdroppers who suffer an independent channel from legitimate users, we prove that it cannot observe any information about KB from the received signals, regardless of how many antennas it has used. Consequently, without knowledge of KB, it cannot decrypt any information about the confidential information, too. The reliability and security of PLSC are theoretically demonstrated. The simulation and numerical results fully verified the validity and effectiveness of the proposed scheme.

In this article, a scheme to detect both clone and Sybil attacks by using channel-based machine learning is proposed. To identify malicious attacks, channel responses between sensor peers have been explored as a form of fingerprints with spatial and temporal uniqueness. Moreover, the machine-learning-based method is applied to provide a more accurate authentication rate. Specifically, by combining with edge devices, we apply a threshold detection method based on channel differences to provide offline training sample sets with labels for the machine learning algorithm, which avoids manually generating labels. Therefore, our proposed scheme is lightweight for resource constrained industrial wireless devices, since only an online-decision making is required. Extensive simulations and experiments were conducted in real industrial environments. Both results show that the authentication accuracy rate of our strategy with an appropriate threshold can achieve 84% without manual labeling.

The ongoing deployment of 5G network involves the Internet of Things (IoT) as a new technology for the development of mobile communication, where the Internet of Everything (IoE) as the expansion of IoT has catalyzed the explosion of data and can trigger new eras. However, the fundamental and key component of the IoE depends on the computational intelligence (CI), which may be utilized in the sixth generation mobile communication system (6G). The motivation of this article presents the 6G enabled network in box (NIB) architecture as a powerful integrated solution that can support comprehensive network management and operations. The 6G enabled NIB can be used as an alternative method to meet the needs of next-generation mobile networks by dynamically reconfiguring the deployment of network functions, providing a high degree of flexibility for connection services in various situations. Especially the CI technology such as evolutionary computing, neural computing and fuzzy systems utilized as a part of NIB have inherent capabilities to handle various uncertainties, which have unique advantages in processing the variability and diversity of large amounts of data. Finally, CI technology for NIB, which is widely used is also introduced such as distributed computing, fog computing, and mobile edge computing in order to achieve different levels of sustainable computing infrastructure. This article discusses the key technologies, advantages, industrial scenario applications of CI technology as NIB, typical use cases and development trends based on IoE, which provides directional guidance for the development of CI technology as NIB for 6G.

Industrial wireless cyber-physical systems are vulnerable to malicious node attacks, for example, clone node attack. The existing clone detection schemes are either based on upper layer observations or physical layer channel state information. The schemes based on upper layer observations are vulnerable to defamation while the schemes based on channel state information perform better against defamation, but are badly affected by channel conditions. This article applies physical layer reputation and back propagation neural network to clone detection, aiming at improving the detection accuracy. The proposed scheme accumulates the physical layer reputations by channel state information and input them to the neural network. The cloud server performs attack detection by group detection first. If a certain group is classified as attacked, the corresponding edge processor will perform attack tracing to identify the specific clone nodes. During the attack tracing stage, multiple reputations of each node is adopted for a comprehensive inspection. Extensive experiments are conducted on the Universal Software Radio Peripheral platform. The numerical results show that the proposed scheme significantly improves the detection accuracy.

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.

This article studies the confidential transmission performance of an electric vehicle (EV) in heterogeneous network when it communicates with vehicle to grid (V2G). Based on the relay selection strategy that maximizes the signal-to-noise ratio(SNR), the electric vehicle as a legitimate user in this article uses a multi-antenna maximum ratio combining method for signal reception. Among them, a single antenna is configured for the power grid sender, relay nodes and illegal eavesdropping users. The wireless channel adopts Nakagami-m fading channel and the relay adopts decode and forward (DF) method. First, based on the stochastic geometric analysis method, statistical characteristics such as probability density function(PDF) and cumulative distribution function(CDF) of the received SNR are obtained at legitimate users and illegal eavesdropping users, respectively. Then, a functional analysis method is used to derive closed expressions for the secrecy outage probability (SOP) and non-zero security capacity probability in multieavesdropping user systems. Finally, the effects of the system's related parameters on SOP and non-zero security capacity probability are verified through simulations. The simulation results prove the correctness of the theoretical analysis, which can guarantee the privacy and security of electric vehicle users in heterogeneous network.

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.

Though representing a promising approach for personalization, targeting, and recommendation, aggregation of user profiles from multiple social networks will inevitably incur a serious privacy leakage issue. In this paper, we propose a Novel Heterogeneous De-anonymization Scheme (NHDS) aiming at de-anonymizing heterogeneous social networks. NHDS first leverages the network graph structure to significantly reduce the size of candidate set, then exploits user profile information to identify the correct mapping users with a high confidence. Performance evaluation on real-world social network datasets shows that NHDS significantly outperforms the prior schemes. Finally, we perform an empirical study on privacy leakage arising from cross-network aggregation based on four real-world social network datasets. Our findings show that 39.9 percent more information is disclosed through de-anonymization and the de-anonymized ratio is 84 percent. The detailed privacy leakage of user demographics and interests is also examined, which demonstrates the practicality of the identified privacy leakage issue.

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.

In this study, the authors propose a simple and robust near‐optimal single differential coherent detection scheme for IEEE 802.15.4 binary phase shift keying (BPSK) receivers. The detection process is initiated by a bootstrap processor, which is responsible for estimating and compensating the residual carrier frequency offset effect in the sample from the autocorrelator output. In particular, for this bootstrap processor, a simple estimator with only one addition operation is provided. This low complexity benefits from that the high signal‐to‐noise ratio and a trigonometric approximation are combined, i.e. , for simplification of the full estimation scheme. Further, to achieve near optimal performance as well as robustness to carrier frequency offset, the observation space is subdivided into four equi‐angular regions to decrease the estimation error introduced by the involved approximation. The proposed method is validated through detailed physical layer (PHY) simulations according to the IEEE 802.15.4 standard. The simulation results demonstrate that, compared with the full estimator, the proposed algorithm can acquire a fairly accurate estimation, with almost no loss of reliability and robustness, whereas complexity reduction is also achieved.

Cloud computing is an Internet-based computing pattern through which shared resources are provided to devices on-demand. It is an emerging but promising paradigm to integrating mobile devices into cloud computing, and the integration performs in the cloud based hierarchical multi-user data-shared environment. With integrating into cloud computing, security issues such as data confidentiality and user authority may arise in the mobile cloud computing system, and it is concerned as the main constraints to the developments of mobile cloud computing. In order to provide safe and secure operation, a hierarchical access control method using modified hierarchical attribute-based encryption (M-HABE) and a modified three-layer structure is proposed in this paper. In a specific mobile cloud computing model, enormous data which may be from all kinds of mobile devices, such as smart phones, functioned phones and PDAs and so on can be controlled and monitored by the system, and the data can be sensitive to unauthorized third party and constraint to legal users as well. The novel scheme mainly focuses on the data processing, storing and accessing, which is designed to ensure the users with legal authorities to get corresponding classified data and to restrict illegal users and unauthorized legal users get access to the data, which makes it extremely suitable for the mobile cloud computing paradigms.

Wireless industrial cyber-physical systems are increasingly popular in critical manufacturing processes. These kinds of systems, besides high performance, require strong security and are constrained by low computational capabilities. Physical layer authentication (PHY-AUC) is a promising solution to meet these requirements. However, the existing threshold-based PHY-AUC methods only perform ideally in stationary scenarios. To improve the performance of PHY-AUC in mobile scenarios, this article proposes a novel threshold-free PHY-AUC method based on machine learning (ML), which replaces the traditional threshold-based decision-making with more adaptive classification based on ML. This article adopts channel matrices estimated by the wireless nodes as the authentication input and investigates the optimal dimension of the channel matrices to further improve the authentication accuracy without increasing too much computational burden. Extensive simulations are conducted based on a real industrial dataset, with the aim of tuning the authentication performance, then further field validations are performed in an industrial factory. The results from both the simulations and validations show that the proposed method significantly improves the authentication accuracy.

Physical layer security of non-orthogonal multiple access (NOMA) systems has attracted great attentions. However, the impact of mobility on physical layer security of NOMA systems has not been well studied. In this paper, to fill this gap, we investigate the impact of random mobility on physical layer security of NOMA systems. Considering scenarios where a base station (BS) or access point (AP) communicates to two random mobile users with a passive eavesdropper in two concentric circles, we study the secrecy performance with combinations of two typical random mobility models: random waypoint (RWP) and random direction (RD). A general analytical framework to numerically calculate the average secrecy rates of NOMA mobile users under steady state is provided. By comparing secrecy performance of mobile users with static users, we find that RWP mobile users can achieve higher average secrecy rates than the users with other mobility combinations. Meanwhile, two types of secrecy fairness for mobile users are fully considered and we propose a novel sum average secrecy rate maximization problem, subject to average power limits and users' QoS (quality of service) requirements. Considering eavesdropper's channel state information (CSI) is unknown to BS, we propose a threshold power allocation strategy to improve the sum average secrecy rate of NOMA mobile users. Extensive numerical simulations are conducted to validate our model and theoretical analysis.

Physical layer authentication based on channel state information is an effective solution to preventing spoofing attacks in wireless communications by comparing the channel impulse responses. Existing theoretical analyses and experiments have proved the feasibility and efficiency in labs or offices. However, the environment of industrial wireless communication is significantly different. This paper applies physical layer authentication based on channel state information to measurements from four different industrial wireless communication scenarios, including indoor, outdoor, moving, and stationary scenarios. The analysis of the results allows to derive meaningful insights on the applicability of such a method to industrial wireless communications.

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.

Wireless physical layer security has attracted great attention in recent years. Although mobility is an intrinsic property of wireless networks, most of the existing works only consider static scenarios and the impact of mobility on wireless physical layer security is not well understood. To fill this gap, in this paper, we investigate physical layer security in the scenario with a random mobile receiver under Rayleigh fading channel. We consider a common scenario where a base station or access point communicates to a random mobile receiver with a passive eavesdropper in a circular region. The secrecy performances under three typical random mobility models are studied: random waypoint model (RWP); random direction model (RD); and border move model. We investigate the secrecy characteristics of the mobile user under steady-state running and provide a general analytical framework for computing the ergodic secrecy capacity. We derive tractable closed-form expressions of positive secrecy capacity probability and secrecy outage probability for RWP and RD mobility users, respectively. By comparing the secrecy performance of mobility with static case, we find that the RWP mobile user can achieve much better secrecy performance than the others. We then investigate the secrecy performance of RWP mobile user with pause time. Furthermore, the two types of secrecy improvement strategies for random mobile users are proposed. In the first strategy, a transmit subcell for RWP users is bounded. We allow the transmitter to communicate with the mobile user only when he is moving within the subcell. In the other strategy, the transmitter is turned on only when the evaluated secrecy performance reaches to a required level. We strike a good trade-off between the secrecy improvements and transmit outage probability. Extensive simulation results validate our theoretical analysis. Finally, we extend our framework to other realistic scenarios, including nodes moving in other shapes of areas, and multiple non-cooperative and cooperative eavesdroppers.

By utilizing the radio channel information to detect spoofing attacks, channel based physical layer (PHY‐layer) enhanced authentication can be exploited in light‐weight securing 5G wireless communications. One major obstacle in the application of the PHY‐layer authentication is its detection rate. In this paper, a novel authentication method is developed to detect spoofing attacks without a special test threshold while a trained model is used to determine whether the user is legal or illegal. Unlike the threshold test PHY‐layer authentication method, the proposed AdaBoost based PHY‐layer authentication algorithm increases the authentication rate with one‐dimensional test statistic feature. In addition, a two‐dimensional test statistic features authentication model is presented for further improvement of detection rate. To evaluate the feasibility of our algorithm, we implement the PHY‐layer spoofing detectors in multiple‐input multiple‐output (MIMO) system over universal software radio peripherals (USRP). Extensive experiences show that the proposed methods yield the high performance without compromising the computing complexity.

Performance analysis is studied in this paper for the wireless transmissions in Internet of Things (IoT) system, where both the direct link and the multihop relaying caching wireless transmission from the source node to the destination node are taken into the consideration. The key feature is the Nakagami channels of the wireless channel from the source node to the destination node, which results in the difficulty of the theoretical analysis over the system performance. To tackle this difficulty, the probability distribution function (PDF) of the received signal‐to‐noise ratio (SNR) at the destination node is derived by exploiting the function and integral properties. Then, the outage probability and bit error rate (BER) of the whole wireless IoT system are derived in the analytical expression without any approximation. Numerical simulations demonstrate the accuracy of the derived theoretical analysis for this system.

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.

Recommendation is widely used to deal with information overloading by suggesting items based on historical information of users. One of the most popular recommendation techniques is matrix factorization (MF), in which the preferences of users are estimated by dot products of their real latent factors between users and items. Although MF can achieve high recommendation accuracy, it suffers from efficiency issues when making preferences ranking in real space. Hash retrieval technique can be applied to recommender systems to speed up preferences ranking. Due to the existence of discrete constraints in learning hash codes, it is possible to exploit a two-stage learning procedure according to most existing methods. This two-stage procedure consists of relaxed optimization by discarding discrete constraints and subsequent binary quantization. However, existing methods have not been able to well handle the change of dot product arising from quantization. To this end, we propose a dot-product based preference preserved hashing method, which quantizes both norm and cosine similarity in dot product respectively. We also design an algorithm to optimize the bit length for norm quantization. Based on the evaluation to several datasets, the proposed framework shows consistent superiority to the competing baselines even though only using shorter binary code.

In this paper, we investigate cooperative security in wireless networks, where a source (Alice) intends to transmit a confidential message to a legitimate destination (Bob), with the help of a cooperative node (Charlie), in the presence of multiple independent eavesdroppers (Eves). In particular, cooperative jamming (CJ) is explored to enhance secure communication between Alice and Bob. We provide a transmit design to maximize the secrecy rate, subject to a secrecy outage probability (SOP) constraint. Specifically, we establish a condition under which positive secrecy rate can be guaranteed. In addition to secrecy rate performance, we also pay attention to the secure energy efficiency, defined as the ratio of secrecy rate to total power consumption. Numerical results validate the effectiveness and energy efficiency of our proposed CJ scheme.

After 4G network becomes a global commercial success, the researchers are now looking at the future 5G technologies, both in standardization bodies and in research projects. Researchers find that the security architecture used in 4G can not satisfy the needs of 5G. Some researchers proposed a kind of unified network security architecture which we find it inflexible. In this paper, we will propose a more flexible hierarchical security architecture which can be adjusted to the needs of the application scenarios of 5G-eMBB, mMTC and URLLC. Under this architecture, we will introduce a cross-layer light weight authentication scheme and a novel physical layer security assisted encryption scheme which is based on both the key streams and the channel information in f-OFDM system.

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.

For current MIMO physical layer security, most of the existing works study how to increase the secrecy capacity. However, the unconditional secure transmission fully achieving the secrecy capacity is difficult to realize, because the theoretical capacity can be achieved only by a wiretap code with infinite blocklength [1]. In most cases, the long codelength secure code is needed to approach the secrecy capacity [14]. Even though the short blocklengh code is prospective for timely and lower burden communication networks, not much exploration has been done to fulfil the unconditional security by a short length wiretap code. In this work, we investigate a lightweight strategy to achieve unconditional secrecy for the practical MIMO Beamforming artifacial noise (MIMO-BAN) physical layer security system under short blocklength wiretap code. Firstly, we analyse the modulated impact on MIMO-BAN secrecy capacity. Then we investigate the achievable secrecy performance for MIMO-BAN physical layer secure system under practical short blocklengh secure code. Based on this, we propose a light wight secrecy strategy, which could guarantee required quality of the main channel while keeping eavesdropper's BER approaching to 0.5. The simulation results verify our analytical performance predictions and demonstrate its feasibility.

An asymmetric zero correlation zone (A-ZCZ) sequence set consists of multiple ZCZ sequence subsets and it is a generalized version of conventional zero correlation zone (C-ZCZ) sequence set. In this paper, a novel construction of A-ZCZ sequence sets is presented by interleaving a given CZCZ sequence set. One of the most important properties of the proposed A-ZCZ sequence set is the cross-correlation function between arbitrary sequences belonging to different sequence subsets, which has quite a large zero-cross-correlation zone (ZCCZ). The optimal A- ZCZ sequence set can be obtained by choosing an appropriate shift sequence and optimal C-ZCZ sequence set.

Many approaches to build a wiretap channel (WTC) by multi‐input multi‐output system have been introduced. Different from those approaches, the authors propose a feedback method combined with the low‐density parity‐check (LDPC) codes for building the WTC I (WTC‐I) to achieve unconditional security under single‐input single‐output system by the soft decision decoding. The novel approach establishes the WTC‐I on both the binary symmetric channel and binary input additive white Gaussian noise channel. In order to keep the eavesdropper be fully ignorant about the secret information, randomness is added to the feedback signals from the destination by taking advantage of feedback. In addition, the message to be sent is encoded by the LDPC codes such that it can be correctly decoded by a legitimate receiver. Furthermore, the secret information transmission capacity can be improved by the soft decision decoding.

Recommender systems have been widely used to deal with information overload, by suggesting relevant items that match users' personal interest. One of the most popular recommendation techniques is matrix factorization (MF). The inner products of learned latent factors between users and items can estimate users' preferences for items with high accuracy, but the preferences ranking is time consuming. Thus, hashing-based fast search technologies were exploited in recommender systems. However, most previous approaches consist of two stages: continuous latent factor learning and binary quantization, but they didn't well deal with the change of inner product arising from quantization. To this end, in this paper, we propose a constraint free preference preserving hashing method, which quantizes both norm and similarity in dot product. We also design an algorithm to optimize the bit length for norm quantization. The performance of our method is evaluated on three real world datasets. The results confirm that the proposed model can improve recommendation performance by 11%-15%, as compared with the state-of-the-art hashing approaches.

In this paper, we consider physical layer security in wireless communication networks in which a source (Alice) intends to send a confidential message to a legitimate destination (Bob) with the help of a cooperative jammer (CJ), in the presence of a passive eavesdropper (Eve). Assuming that only statistical channel state information (CSI) of Eve is available, artificial-noise (AN) assisted beamforming and cooperative jamming are designed. The goal is to maximize the secrecy rate, subject to a constraint on secrecy outage probability. Numerical results validate the effectiveness of our scheme. Moreover, a higher secure energy efficiency (EE) can be achieved as compared with other schemes without a cooperative jammer.

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.

An asymmetric zero‐correlation zone (A‐ZCZ) sequence pair set is a ZCZ sequence pair set consisting of multiple ZCZ sequence pair subsets. Two arbitrary sequence pairs which belong to different subsets should have a large zero cross‐correlation zone (ZCCZ) in the A‐ZCZ sequence pair set. Additionally, each subset is a typical ZCZ sequence pair set. The proposed A‐ZCZ sequence pair sets can be generated based on interleaved technique from perfect sequence or perfect sequence pair of length P = Nmk with N ≥ 1, m ≥ 1, k > 1. The new A‐ZCZ sequence pair sets are applied to quasi‐synchronous code‐division multiple‐access (QS‐CDMA) systems and it can hypothetically achieve larger ZCCZ than typical ZCZ sequence pair sets, as well as eliminating both multiple access and multipath interference in the QS‐CDMA system.

Peer-to-peer (P2P) systems and online social network (OSN) both have achieved tremendous success. Recent studies suggest that the cooperation of P2P and OSN can achieve better efficiency and security. Unfortunately, novel security problems are emerging as the mutual cooperation and dependence contributes to forming the interdependent networks which are more vulnerable for malicious attack as well as rumor propagation. In this paper, we examined the security environment for P2P and OSN, respectively, and analyzed the security problem derived from the cooperation and interdependence of two networks. The spreader-ignorant-recaller-stifler (SICR) is leveraged to model the rumor spreading in the interdependent networks. In order to enhance the security, we proposed two security schemes named authentication intervening and splitting target and their performance summaries indicate to be effective, simple, and potentially transformative way to guarantee the security for interdependent networks of P2P and OSN.

Cloud computing is an Internet-based computing paradigm which may share resources to provide on-demand services to devices. Integrating smart grid into cloud computing is emerging and promising, and this integration may execute in the cloud based hierarchical multi-user data-shared environment, where security issues such as data confidentiality and manager authority may arise in the smart grid system. In order to provide safe and secure grid operations, a hierarchical access control method using modified hierarchical attribute-based encryption (M-HABE) and a modified three layers structure are proposed in this paper. In power grids, the system can be controlled and monitored by the enormous sensitive data which may be from sensor nodes, smart measurement units and soon on. The novel scheme mainly focus on the data process, storage and access to ensure the managers with legal authorities to get corresponding sensitive data and restrict illegal managers and unauthorized legal managers from accessing the data.

This study proposes an approach for achieving strong communication security based on explosive fountain code with coset pre‐coding, where both main and wire‐tap channels are memoryless binary erasure channels. Coset pre‐coding is used to prevent the eavesdroppers from intercepting the confidential information from the leaked bits. Further, an explosive fountain code is designed to ensure the reliability and low information leakage. By this way, the proposed approach can keep strong security and reliability when the erasure probability of the main channel is slightly lower than that of the wire‐tap channel. Extensive simulations are conducted to verify the correctness and effectiveness of the approach.

In the implementation of turbo‐like decoder, the size of state metrics cache (SMC) has a predominant impact on the core area and the overall power dissipation. Different from previous reported decoding schemes, in the proposed decoding schemes, a compressing module and a regeneration module are added to the decoder. The compressing module sorts the forward state metrics from the minimum to the maximum, by which an index sequence and the corresponding increase metrics are calculated, and subsequently are stored in the SMC. In the regeneration module, the forward state metrics are estimated with the index sequence and the increase metrics that accessed from the SMC. With the cost of dummy calculation that is performed by the compressing and the regeneration modules, two decoding schemes are proposed. For an eight‐state turbo codes, the linear and the nonlinear estimation based decoding schemes reduce the SMC size by 62.5% and 57.5%, respectively. The bit error rate (BER) simulation is performed for both binary turbo code and duo binary convolutional turbo code, and shows BER of the linear estimation‐based scheme is superior to that of the enhanced max‐log‐MAP (the maximum a posteriori probability) algorithm, whereas BER of the non‐linear estimation‐based decoding scheme is very close to that of the near optimal decoding scheme.

In this study, a modified channel‐independent weighted bit flipping (CIWBF) decoding algorithm is proposed for low‐density‐parity‐check codes. This modification, based on the ‘additive offset term adjustment’ in the reliability of the check sum, can also be applied to the reliability ratio weighted bit flipping algorithm. Simulation results show that the performance of the author's improved modified CIWBF algorithm is better than that of CIWBF algorithm about 0.45 dB at bit‐error rate of 10 −5 over an additive white Gaussian noise channel without much complexity increase.

The explosion of Internet traffic calls for quality of service (QoS)-guaranteed packet transmission in wavelength division multiplexing (WDM) networks with high energy and bandwidth efficiency. Conventional routing and wavelength assignment (RWA) algorithms focus on circuit switching, which does not well meet this requirement due to the bursty nature of IP traffic. Based on a novel traffic matrix decomposition technique, we study the joint design of traffic scheduling and routing in a reconfigurable WDM optical network to improve energy and bandwidth efficiency. Specifically, every node in the network is equipped with a set of parallel tunable lasers, each with a reconfiguration overhead. A dynamic matrix is adopted to model the traffic among the nodes and is decomposed into a set of transmission configurations (i.e., traffic scheduling). The configurations are then fulfilled by tuning the parallel lasers and routing the scheduled traffic under the topology constraint, to achieve loss-free packet transmissions with bounded delay (i.e., QoS guarantee). We reveal that a tradeoff exists between the packet delay and the required number of tunable lasers. The latter is then minimized under a given packet delay to save energy. As far as we know, this is the first work to adopt traffic matrix decomposition in WDM networks to save energy. The proposed framework is validated by extensive simulation studies.

Cloud services based on data center networks (DCNs) require a transmission infrastructure with high-capacity, low-latency, low-cost and high-availability, which can be offered by survivable optical networks. DCN placement is a fundamental issue in supporting cloud services in optical networks. It concerns not only the cost of providing cloud services, but also the service availability against failures via proper service replicas. In this paper, we jointly optimize DCN placement with service routing and protection to minimize the network cost, while ensuring fast protection of all services against any single link failure or service failure at a particular DCN. An ILP (Integer Linear Program) is first formulated to achieve optimal joint design. It integrates p-cycle (preconfigured protection cycle) for fast protection against a single link failure, and DCN replicas and fast service rerouting against a service failure. To make the design more scalable, a two-step heuristic is then proposed for large-size network scenarios. The first step separately solves the DCN placement and service routing problem in the failure-free scenario, and the second step takes fast service protection into account. The proposed design is validated by extensive numerical experiments.

In 3GPP LTE-A, Coordinated Multi-Point (CoMP) is adopted to enhance the transmission rates of edge users. To maximize the total downlink throughput of all edge users, it is crucial to properly determine the set of simultaneously served users in each physical resource block (PRB) and the cooperative base stations (BSs) for each scheduled user, as well as the transmit power of the BSs. Based on the reference signal receiving power (RSRP) of each edge user, we first propose two simple and integrated transmission scheduling algorithms, one distributed and the other centralized, to choose cell-edge users and cooperative BSs in each PRB. With the scheduling results, the classic Water-Filling (WF) algorithm is carried out over all PRBs at each BS to get an initial single cell power allocation. To take the interference among different cooperative BS sets into account, we further formulate a non-cooperative power allocation game to adjust the initial power allocation for interference coordination, where the initial power allocation provides the strategy space of the game for each BS. This increases the total downlink throughput of edge users over all BSs. We prove that the game has a unique Nash Equilibrium (NE), and design an algorithm to find the NE. Performance gain is then demonstrated through extensive simulation studies.

Water‐Filling (WF) is widely applied in power allocation in multichannel wireless communications. By mathematically linearising the optimal WF expression, the authors observe an intrinsic parallel‐shift property of WF, based on which a fast and efficient WF algorithm is proposed. Compared with the conventional WF algorithms, it greatly simplifies WF execution by removing the Lagrange multiplier (or water‐level) searching process. The authors further apply the proposed parallel‐shift WF to solve the power allocation problem in orthogonal frequency division multiplexing (OFDM)‐based underlay cognitive radios (CRs), with the objective of maximising the secondary user's throughput over all OFDM sub‐channels under the transmit power and the interference constraints. To this end, the existing algorithm adopts an iterative binary searching process to find the solution, where the conventional WF algorithm with Lagrange multiplier searching is invoked in each iteration. In contrast, the authors propose a new power allocation algorithm to remove the iterative binary searching process. It runs the simplified parallel‐shift WF only once, and then directly calculates the final solution using a power adjustment process (with the parallel‐shift property as the underlying enabling mechanism). Numerical results show that both the proposed parallel‐shift WF and the OFDM‐based CR power allocation algorithms can run multiple times faster than the existing counterparts, and the gap on the running time increases with the total number of OFDM sub‐channels in the system.

The study introduces the novel message authentication schemes for the smart meter system, where the symmetric cryptography‐based physical layer‐assisted message authentication (PLAA) scheme and the public key infrastructure‐ based PLAA scheme are introduced. The proposed schemes integrate the conventional message authentication schemes and the physical layer authentication mechanisms by taking advantage of temporal and spatial uniqueness in physical layer channel responses, aiming to achieve fast authentication while minimising the packet transmission overhead. The authors also verify their claims through extensive analysis and simulation via comparing with proposed PLAA scheme with traditional upper layer authentication schemes. The proposed novel schemes yield the lower time delay for authenticating each message, which can satisfy the requirement of the real‐time control over the smart grid.

Multi-user transmission, enabled through spatial multiplexing, is a promising technique in wireless systems by sending information to more than one user on a common resource block. Although efficient, precise channel information is needed in order to minimize the possible interferences. Motivated by its importance, the study investigates interference management in multi-user downlink systems under heterogeneous feedback of channel state information to the transmitter (CSIT). In the considered scenario, the base station (BS) can obtain different levels of channel realization information from the users under multi-user transmission. In particular we focus on a generic scenario with two user equipments (UEs), one (UE-1) being able to provide a perfect CSIT to the BS and the other (UE-2) unable to, where an effective transmission strategy at the BS is proposed with the premise of mitigating interference at UE-1. Unfortunately the absence of CSIT for UE-2 pushes it in interference-limited regime. To this end, two approaches are considered in the study: one by developing additional spatial diversity, and the other via advanced receiver design at UE-2. We investigate both of the options and show that the rate of UE-2 gets significantly improved in both cases. However hardware and RF design constraints may make the advanced receiver design solution preferable over the spatial diversity solution. We then put this heterogeneous CSIT feedback system in the context of long term evolution (LTE) of third generation partnership project (3GPP) where interference cannot be nulled out even for UE-1 due to the feedback of low level quantized CSIT and the restriction of the use of a very limited set of beamformers. The paper demonstrates that the advanced interference-aware receiver design can bring significant gains in the considered scenario, thereby underlining the necessity of intelligent receiver-processing in modern wireless systems.

The wireless networks lack a physical boundary due to the broadcasting nature of wireless transmissions. The security has become a critical concern in the physical layer of wireless networks. In this work, we present a cross-layer security scheme for STBC system. By introducing a distort signal set the sender randomly flip-flops between the distort signal set and the orthogonal code set to confuse the attacker. The physical-layer security is enhanced as a result. In the proposed scheme the physical-layer may rely on upper-layer encryption techniques for security, which results in a cross-layer security scheme.

The paper introduces a novel message authentication framework over broadcast channels, where a symmetric cryptography-based physical layer assisted message authentication (PLAA) scheme is introduced in wireless networks. The proposed framework integrate the conventional message authentication schemes and the physical layer authentication mechanisms by taking advantage of temporal and spatial uniqueness in physical layer channel responses, aiming to achieving fast authentication while minimising the packet transmission overhead. Our claims through extensive analysis and simulation will be verified via comparing with public key infrastructure-based PLAA scheme and traditional upper layer authentication schemes.

In this paper, we introduce a novel framework for physical layer assisted message authentication (PAA) under public key infrastructure (PKI) in vehicular communication networks. The proposed framework takes advantage of temporal and spatial uniqueness in physical layer channel responses for each transmission pair, in which a trust between two vehicles can be maintained by comparing the current estimated channel response and the previous estimated channel response. We will show that the proposed message authentication framework can achieve extremely high efficiency and minimal authentication delay without compromising the security requirements, which is further verified through both analysis and simulation.