While physical layer authentication can mitigate wireless channel vulnerabilities, its reliability is often compromised by inherent noise and variability of observed physical layer attributes. As a solution, collaborative physical layer authentication (CPLA) introduces multiple nodes to enhance performance, but incurs additional computational and communication costs for collaborators. Without incentive, desired collaborators may act selfishly and withdraw, and involving unreliable collaborators could degrade performance. Therefore, this paper proposes an incentive mechanism with a new centralized governance approach to coordinate CPLA, engaging reliable collaborators to optimize authentication accuracy. Specifically, we model the interaction between the center and collaborators as a Stackelberg game to establish. To reduce redundant computations in equilibrium solving, we first construct a candidate pool containing potential trainable combinations. Subsequently, we design incentive and training schemes for each candidate combination. Moreover, a quality-driven combination selection scheme is proposed to maximize incentive effectiveness. Based on the candidate pool and strategies, it integrates a deep Q-network as collaborator quality manager and a combination-level evaluation module, and via “filter-then-verify” identifies optimal incentive targets with low complexity while improving authentication accuracy. Simulations demonstrate that the proposed scheme successfully incentivizes selfish collaborators and achieves 99% authentication accuracy in unreliable collaborative environments.

Abstract In the realm of conventional smart distribution grid resource allocation, the prevalent issue resides in its narrow focus on base station capacity, striving to optimize resource allocation for base station communication while disregarding the genuine requirements on the user side. This inadvertently leads to excessive squandering of wireless resources, despite already fulfilling the fundamental service demands of terminal operations. This article, while taking into account the capacity of base stations, introduces an innovative approach by amalgamating the terminal operations concerning data rate, latency, and packet loss rate. Through the construction of a Quality of Experience (QoE) evaluation framework, a scenario is realized within which user experience requirements are ensured by terminals in various practical settings of smart distribution grids, without wireless resources being needlessly dissipated. In this article, the dynamic resource allocation is tackled using Deep Q‐Networks (DQN), while the reward function is formulated based on QoE. The simulation results, which track the accumulation of reward values throughout the entire operational process, provide substantial validation for the effectiveness and practicality of the ultimately formulated dynamic resource allocation scheme.

In light of the decentralized architecture inherent in cell-free massive multiple-input-multiple-output (MIMO)-enabled mobile edge computing networks, a novel computational task offloading scheme, denoted as Joint Security and Energy-based Load Balancing (JSEON), is proposed. Distinguished from existing studies of only combating passive eavesdropper, two different schemes are integrated to combat the eavesdropper for passive eavesdropping in uplink offloaded task transmission and pilot contamination attack (PCA) in downlink post-data transmission. In an endeavor to refine the portrayal of load-balancing effectiveness within access points equipped with independent edge servers (AP-ES), a novel performance metric termed the energy-based load imbalance degree (e-LoBaR) is introduced. Additionally, this study unveils a pioneering security and energy-based load-balancing (SEAGOING) algorithm, which concurrently optimizes the matching relationships between user equipment (UE) and AP-ESs, the task offloading ratio, and the jamming strategy to verify the effectiveness of proposed JSEON scheme, which can enhance security in a more load-balanced manner. Simulation results show that the proposed algorithm effectively mitigates eavesdropping threats in both uplink and downlink transmissions while improving AP-ES load balancing compared to benchmark schemes.

The smart distribution grid (SDG), characterized by large-scale interconnections and strong dependence on information and communication technologies, is highly susceptible to potential security threats, such as spoofing attacks and man-in-the-middle attacks. These threats may lead to the leakage of sensitive user power-expenditure information, even cause great economic damage. Therefore, authentication is of utmost importance in guaranteeing the electrical safety of SDGs. In this paper, we present a distributed physical layer authentication (DPLA) scheme tailored for smart meter authentication. The scheme overcomes the limitations of traditional upper-layer cryptography-based mechanisms, and achieves lightweight continuous authentication in a cooperative manner. To fully exploit the channel information collected by collaborative nodes located in different azimuths, a CNN algorithm is designed for deep feature extraction. Moreover, a situational-aware dynamic weighted voting strategy is introduced to coordinate inconsistent opinions, thereby making unified decisions. Aimed at maximizing the integrated performance gains of DPLA, both long-term reputation and short-term performance are taken into account for node’s weight update. Finally, simulations are carried out. The results demonstrate that our scheme outperforms DPLAs based on static voting strategies with respect to authentication accuracy, anti-disturbance robustness and environmental adaptability; Hence, it caters to the demand for high-quality continuous authentication in SDGs.

As the communication needs in the smart distribution grid continue to rise, using existing resources to meet this growing demand poses a significant challenge. This paper researches on spectrum allocation strategies utilizing cognitive radio technology. We consider a model containing strong time-sensitive and regular communication service requirements such as distribution terminal communication services, which can be seen as a user with primary data (PD) and weak time-sensitive services such as power quality monitoring, which can be seen as a user with secondary data (SD). To fit the diversity of services in smart distribution grids, we formulate an optimization problem with two indicators, including the sum of SD transmission rates and the maximum latency of them. Then, we analyze the two convex sub-problems and utilize convex optimization methods to obtain the optimal power and frequency bandwidth allocation for the users with SD. The simulation results indicate that, when the available transmission power of SD is low, Maximization of Transmission Sum Rate (MTSR) achieves lower maximum transmit time. Conversely, when the available transmission power is high, the performance of Minimization of the Maximum Latency (MML) is better, compared with MTSR.

Abstract Nowadays, the digital development of marine ranching requires a communication system with wide coverage, high transmission rate and stable communication links. It is known that fixed‐wing unmanned aerial vehicles (UAVs) have great advantages in long‐range applications. They have the potential to serve as low‐altitude communication platforms for maritime communication. In this study, with a developmental perspective, considering the intense growth of marine terminals in the future, a new clustering algorithm applied to cluster nonorthogonal multiple access (C‐NOMA) is proposed and its advantages are investigated. In addition, considering the limited energy of marine terminals, combining the wireless power communication (WPC) technology for the UAV to charge terminals, the charging and communication time are optimized with the Lagrange multiplier method and the bisection search method. After completing the above optimization content of charging and communication, combined with the optimization results, it is found the trajectory that maximizes the energy efficiency of the UAV with the convex optimization technique. Experimental results show that the proposed clustering algorithm has good throughput performance, better fairness and lower algorithm complexity, and the proposed trajectory optimization scheme has better energy efficiency.

Physical-layer security (PLS) has become an intriguing technology to address eavesdropping issues in Internet of Things (IoT) systems owing to its low complexity and latency. As wireless communication technology and computing capabilities advance by leaps and bounds, eavesdroppers have enhanced eavesdropping capabilities. They can analyse the environment and move to find better locations for eavesdropping. To mitigate the significant impact of multiple intelligent eavesdroppers extremely close to the device-constrained transmitters in the uplink multiple-input-multiple-output IoT systems, we exploit time-domain artificial noise (AN) in the PLS design and formulate a game problem against the intelligent eavesdroppers. First, we derive the optimal closed-form solution for eavesdroppers and propose a low complexity difference of the convex (LCDC) algorithm to obtain the optimal strategy of the legitimate users and the access point equipped with a zero-forcing receiver. For general linear receivers, we propose a successive convex approximation (SCA) algorithm for the game problem. Simulations are conducted to verify the convergence and effectiveness of our proposed algorithms. The system secrecy performance of the time-domain AN is much better than that of the frequency-domain AN with single-antenna transmitters.

Physical layer security has attracted significant attention in the field of wireless communications. The application of artificial noise can reduce the eavesdropping ability of illegal eavesdroppers without affecting legitimate users. However, most current physical layer security schemes only consider static scenarios and do not account for mobility. Some schemes analyze security performance in mobile scenarios with friendly jammers but do not consider the handoff and cooperation of friendly jammers due to mobility. To address this challenge, we propose a scheme for soft-handoff-based cooperative jamming (CJSH) in mobility scenarios. Initially, we consider a common scenario where a base station communicates with a legitimate mobile user, alongside a mobile passive eavesdropper and multiple friendly jammers emitting artificial noise in the circular area covered by the base station’s signal. Next, We measure the connection outage probability (COP) and secrecy outage probability (SOP) under the influence of multiple friendly jammers in the proposed scheme. We also design two corresponding thresholds for jammers to join and exit. To balance security and energy consumption, we define the Power Average Security Gain (PASG) as a measure of system performance. Finally, we provide numerical simulation results to verify the rationality of the proposed handoff scheme, demonstrating its effective improvement of the system’s security performance and power utilization.

In the context of the industrial Internet of Things (IIoT), communication devices are typically mobile, increasing the complexity and diversity of channels due to metal device occlusion. A crucial aspect of this intricate environment is the development of an authentication scheme based on physical layer channel characteristics. One approach to achieving this is through deep learning, which is a hot topic in physical layer authentication. However, designing a network that is suitable for channel classification tasks and establishing a reasonable training procedure that leads to high authentication accuracy can be challenging. To address the physical layer authentication of mobile devices in IIoT, we implement ResNet to extract channel features of Channel State Information (CSI) from different transmitters and classify them at the network output layer, enabling authentication decisions based on classification results. To improve accuracy and speed up network convergence, we utilize the exponentially averaging data augmentation algorithm and parameter-based transfer learning strategy during the training procedure. Simulation results demonstrate that multi-user physical layer authentication based on ResNet can achieve higher authentication accuracy as the number of network layers increases. The data augmentation and transfer learning are proved to improve the authentication accuracy. Numerical results on NIST industrial datasets reveal that the authentication scheme based on ResNet50 can achieve 99.64% authentication accuracy in scenarios with four users present, which is 32.68% higher than existing algorithm.

Collaborative physical layer authentication (CPLA) is a promising alternative, addressing common single-point failure issues in centralized authentication systems through its unique architecture. However, the necessary involvement of multiple parties increases the risk to collaborative systems, particularly from hostile cooperators, significantly impacting the performance of CPLA. In existing CPLA approaches, the most common strategy to combat malicious cooperators attacks is to select the best collaborative combination. This strategy achieves the customization goal by excluding hostile-minded devices. However, processing a non-fixed search space typically demands a substantial investment of time and resources. As a remedy, we propose a decision-level-based CPLA scheme with a weighted voting mechanism. Our scheme aims to implement streamlined and effective dynamic management of cooperators to ensure that multi-directional information provides positive effects on authentication. Specifically, we conduct a two-stage performance appraisal of all cooperators. To measure the trustworthiness of cooperators, an impression-driven reliability evaluation scheme is developed. We analyze the riskiness of individual cooperators to prevent centers from falling into cognitive blind spots. Finally, we validate the feasibility of the scheme. The results demonstrate that, in a scenario where 50% of participants are malicious, our approach achieves an accuracy improvement of 2.96% to 3% compared to other dynamic weighted voting schemes. The robustness and stability of the proposed CPLA scheme outperform the benchmark schemes.

Collaborative physical layer authentication (CPLA), which leverages spatial diversity, holds promise for enhancing the performance of feature-based physical layer authentication. However, some existing CPLA schemes simply aggregate the local information of collaborators to make final judgments and rarely consider the involvement of malicious collaborators. In this paper, we propose an impression-weighted based local decision aggregation scheme for detecting spoofing attacks in the presence of malicious collaborators. Specifically, the authenticator continually evaluates the authentication capabilities of collaborators by verifying the accuracy of local decisions and then synthesizes their long-term capabilities into impressions using a fuzzy membership function. These impression values will be dynamically updated upon completion of each authentication task. Moreover, a reinforcement learning scheme is employed to find the optimal threshold for authentication in a dynamic environment. Simulation results validate the high robustness and effectiveness of our proposed approach, guaranteeing the CPLA system's reliable operation.

The emergence of edge computing (EC) and artificial intelligence (AI) is driving the rapid growth of industrial internet of things (IIoT). However, few works comprehensively consider the impact of resource reallocation and number of reallocation on the system delay in dynamic industrial scenarios with time-varying geographic location characteristics. This paper takes the dynamic resource reallocation problem between the physical layer and edge layer within a time-varying factory scenario into account, proposes a reallocation-decision variable and reduces the computational stress on edge nodes caused by frequent reallocation. An optimization problem with the objective of minimizing the system average delay is established and a proximal policy optimization (PPO) based dynamic resource reallocation (P-DRR) algorithm is proposed for the problem solving. Experimental results show that P-DRR algorithm can effectively reduce average delay compared to the baseline algorithms without causing large computational pressure on edge nodes.

Machine learning (ML)-based physical layer authentication (PLA) has attracted much attention since neural networks can be constructed to identify channel characteristics in complex wireless environments. This enables high-authentication performance and lightweight deployment in the Internet of Things (IoTs). Due to the booming growth of IoT connections, the workload of the central authenticator increases significantly. As a result, resource-constrained terminals are unable to independently handle the computationally complex ML task. Therefore, cooperative PLA (CoPLA), which introduces multiple supervised nodes as task-sharing entities, is emerged as a promising solution to address this concern. However, in existing CoPLA studies, some critical issues have been overlooked. For example, the consideration of which collaborative nodes are eligible or best suited for cooperation to maximize the authentication gains. Moreover, the security threats posed by untrusted collaborators are equally challenging. In this article, we propose a federated learning (FL)-based CoPLA scheme that utilizes a group of edge devices to jointly build an authenticator. This ensures privacy preservation and higher robustness. To figure out the optimal collaborator selection in CoPLA, an adaptive search procedure via reinforcement learning (RL) is customized. Furthermore, we introduce a lightweight reputation estimation method to evaluate each collaborator’s credibility, thereby uncovering underperforming devices or hidden internal attackers. Finally, simulations and real-world experiments are carried out. The results show that the authentication accuracy of our scheme is 9.52% higher than that of blind cooperation. And, it outperforms other existing CoPLA schemes in terms of time efficiency and robustness.

Physical layer security (PLS) has been widely employed in studies of computation offloading under traditional centralized networks. Distinguished from existing studies of only combating passive eavesdropper, we propose an efficient user-centric secure two-stage (UCSTS) access point (AP) selection method to combat simultaneously active and passive eaves-dropping, which is exploiting the distributed feature of cell-free massive multiple-input-multiple-output (MIMO) scenarios. Furthermore, we propose a novel secure computation task offloading (SCTO) model to guarantee the security of both uplink and downlink transmission. Aiming at reducing total energy consumption with high security, a minimum energy consumption optimization problem is solved by alternative optimization (AO) algorithm. Simulation results show that the proposed model can well combat Eve while reducing the total energy consumption, and the security of the proposed selection method is better than the AN-based scheme.

Physical layer authentication as a promising complement for upper layer authentication is the first line of defense against malicious attacks in wireless communication. However, the smart spoofer can learn the rules from receiver’s authentication process and dynamically choose the proper time sending spoofing signal which poses a severe threat to wireless communications. According to this, the Stackelberg game-based physical layer authentication strategy is proposed in this paper to model the interactions between the receiver and the smart spoofer. We first consider the static game-based authentication under the worse condition that the smart spoofer acts as the leader with privilege over the receiver. Moreover the Stackelberg equilibrium of static authentication strategy is derived. Then, we propose a dynamic game-based strategy according to reinforcement learning technique named Policy Hill Climbing, in which the spoofer always choose equilibrium solution and the receiver is unaware of the system parameters, such as the channel timevarying coefficient. Simulation results are presented to validate the effectiveness of the proposed authentication strategy, and the Policy Hill Climbing algorithm improves the utility compared with Q-learning-based algorithm.

The boom of the Internet of Things (IoT) has exposed many security issues in recent years. Cooperative jamming, including the continuous jamming strategy (CJS) and intermittent jamming strategy (IJS), is an effective approach toward secure wireless communication in the physical layer. CJS used to be a primary physical-layer security technology that sends cooperative jamming signals at the expense of energy consumption. Different from CJS, IJS is more energy efficient. The feasibility of IJS has been proved in a slotted scenario, which motivates us to design IJS in a nonslotted scenario. In this article, we discuss the feasibility of IJS for a nonslotted transmission IoT system and formulate an optimization problem based on a sense-harvest-jam policy. This problem is to find the optimal matching precision between durations of artificial noise and legitimate signals. To solve this problem, we exploit a backpropagation-neural-network model to analyze jamming duration proportion and derive the optimal proportion for the binary phase-shift keying modulation. Finally, we design a matching precision optimization algorithm to achieve the optimal nonslotted secure transmission. Simulation results on jamming efficiency demonstrate that the proposed IJS has preferable secure performance than the CJS under energy constraints.

Mobile edge computing, a promising paradigm, brings services closer to a user by leveraging the available resources in an edge network. The crux of MEC is to reasonably allocate resources to satisfy the computing requirements of each node in the network. In this paper, we investigate the service migration problem of the offloading scheme in a power‐constrained network consisting of multiple mobile users and fixed edge servers. We propose an affinity propagation‐based clustering‐assisted offloading scheme by taking into account the users’ mobility prediction and sociality association between mobile users and edge servers. The clustering results provide the candidate edge servers, which greatly reduces the complexity of observing all edge servers and decreases the rate of service migration. Besides, the available resource of candidate edge servers and the channel conditions are considered to optimize the offloading scheme to guarantee the quality of service. Numerical simulation results demonstrate that our offloading strategy can enhance the data processing capability of power‐constrained networks and reach computing load balance.

In this paper, we propose a trustworthy friendly jammer selection scheme with truth‐telling for wireless cooperative systems. We first utilize the reverse auction scheme to enforce truth‐telling as the dominant strategy for each candidate friendly jammer. Specifically, we consider two auction cases: (1) constant power (CP) case and (2) the utility of the BS maximization (UBM) case. In both cases, the reverse auction scheme enforces truth‐telling as the dominant strategy. Next, we introduce the trust category and trust degree to evaluate the trustworthiness of each Helper transmitter (Helper‐Tx). Specifically, an edge controller calculates the reputation value of each Helper‐Tx periodically using an additive‐increase multiplicative‐decrease algorithm by observing its jamming behavior. With the historical reputation values, the edge controller (EC) classifies a Helper‐Tx into one of four trust categories and calculates its trust degree. Then, the EC selects the best Helper‐Tx based on the trust category and trust degree. Lastly, we present numerical results to demonstrate the performance of our proposed jammer selection scheme.

In this paper, we investigate how to select a trust-worthy Helper as a friendly jammer in a wireless cooperative system (WCS). The selected Helper sends out artificial noise to interfere with an eavesdropper. To ensure that the selected Helper is trustworthy, we design a Dirichlet reputation-based framework and adopt the reputation score to evaluate the trustworthiness of a Helper. To calculate the reputation scores, we develop an artificial noise detection method based on the energy detection with multiple thresholds. According to the multiple-threshold energy detection method, we provide ratings with multiple graded levels (e.g., good-average-bad). In the Dirichlet reputation-based framework, the graded ratings are directly expressed and reflected in the derived reputation scores. Firstly, the a posteriori reputation score is computed by combining the a priori reputation score with the new ratings. Next, a point value is assigned to each rating and the normalized reputation score is computed. Finally, we adopt the normalized reputation score to select a trustworthy Helper as a friendly jammer. Numerical results are presented to demonstrate the performance of our proposed Dirichlet reputation-based framework.

In smart grid systems, vulnerable lines may lead to cascading failures which can cause large-scale blackouts. Successfully detecting vulnerable lines can increase the stability of the smart grid systems and reduce the risk of cascading failures. By modeling a smart grid system into a directed graph, we investigate the problem of vulnerable line identification from a clustering perspective. By jointly considering the topological parameters and the electrical properties, we propose an affinity propagation-based bus clustering algorithm to classify buses into clusters, where the center of each cluster represents the most influential bus in each partition. According to the clustering results, we design a vulnerable line identification scheme, which captures different types of potential critical lines in the smart grid system. Experiments over the IEEE-39 bus system demonstrate the effectiveness and correctness of our proposed algorithm.

Friendly jamming is an unconventional approach to secure wireless communications. Specifically, a friendly jammer transmits jamming signals to an eavesdropper while a legitimate transmitter is sending data. The jamming signals only interfere with the eavesdropper, and thus, prevent data from being disclosed to unintended parties. Mainstream jamming schemes adopt a continuous jamming strategy (CJS), where the jammer is required to constantly transmit jamming signals in the entire duration of the legitimate transmission. In certain scenarios, however, the CJS may lead to excessive jamming, and cause a waste of energy and the degradation of jamming efficiency. To address the drawbacks of the CJS, we propose the concept of an intermittent jamming strategy (IJS), where a jammer alternates between jamming and non-jamming modes during the legitimate transmission. In this paper, we study the feasibility of the IJS for physical layer security. We first introduce a new metric to jointly measure security requirements and energy costs. Next, we formulate and solve an optimization problem with respect to the jamming duration proportion and the jamming power. Finally, we verify the feasibility of the IJS through extensive simulation experiments under different modulation methods.

In this study, the authors investigate the problem of jammer selection (JS) for enhancing the secrecy goodput in a cooperative cognitive radio network with the multiple‐input–multiple‐output capability. First, they propose an optimal stopping theory‐based JS scheme in the presence of a single eavesdropper. The proposed scheme can accommodate the cases of beamforming or non‐beamforming jamming signals. Furthermore, in the presence of multiple eavesdroppers, they develop a random JS scheme with the beamforming design. Their theoretical analysis and simulation results demonstrate that the proposed schemes can effectively improve the secrecy goodput.

This paper aims to improve security performance of data transmission with a mobile eavesdropper in a wireless network. The instantaneous channel state information (CSI) of the mobile eavesdropper is unknown to legitimate users during the communication process. Different from existing work, we intend to reduce power consumption of friendly jamming signals. Motivated by the goal, this work presents a location-based prediction scheme to predict where the eavesdropper will be later and to decide whether a friendly jamming measure should be selected against the eavesdropper. The legitimate users only take the measure when the prediction result shows that there will be a risk during data transmission. According to the proposed method, system power can be saved to a large degree. Particularly, we first derive the expression of the secrecy outage probability and set a secrecy performance target. After providing a Markov mobile model of an eavesdropper, we design a prediction scheme to predict its location, so as to decide whether to employ cooperative jamming or not, and then design a power allocation scheme and a fast suboptimal helper selection method to achieve targeted and efficient cooperative jamming. Finally, numerical simulation results demonstrate the effectiveness of the proposed schemes.

In this paper, we investigate the problem of jammer selection for securing Cooperative Cognitive Radio Networks (CCRNs) with the Multiple-Input Multiple- Output (MIMO) capability. In the CCRN under our consideration, there exist a pair of Primary Users (PUs), a relay node, a number of Secondary User (SU) pairs, and an eavesdropper. The PUs need to select a pair of SUs as jammers to interfere with the eavesdropper so as to preserve the secrecy of their wireless communications. To address this problem, we propose an Optimal Stopping based Jammer Selection (OSJS) scheme. Specifically, OSJS examines the primary secrecy capacity for each candidate SU pair in a sequential order. The first SU pair that makes the primary secrecy capacity higher than an optimal threshold is selected as the jammers. The optimal threshold is calculated based on the distribution function of the primary secrecy capacity. We derive the distribution function from the chi-square distribution function of the Signal-to-Noise Ratio (SNR) under the MIMO channel conditions. Since our OSJS scheme does not have to check all the candidate SU pairs, much time can be saved for data transmissions. Our rigorous analysis and simulation results demonstrate that our proposed scheme can achieve secure communications with improved network throughput.

This paper considers energy and information cooperation between a single-antenna primary user (PU) pair and a multiple-antennae secondary user (SU) pair in a cognitive radio system. The secondary transmitter (ST) harvests energy from the primary signal and gains opportunity to transmit its own signal in return for helping relay the primary transmitter's (PT) traffic. A time-divided power splitting scheme is proposed to enable the energy and information cooperation with the objective of maximizing the throughput of the SU pair under the energy constraint of the ST and the received signal-to-inference plus noise ratio (SINR) constraint of the primary receiver (PR). Simulation results demonstrate the influence of the time division proportion and the power splitting parameter on the throughput of the SU pair and the PU pair.