In 6G networks, Integrated Sensing and Communication (ISAC) is a key technology owing to its capability in enhancing spectral efficiency. Yet, in urban wireless environments, characterized by persistent random interference, maintaining stringent environmental prerequisites is infeasible. This bottleneck renders robust sensing under non-ideal conditions difficultly. In this paper, we propose a paradigm for multi-Base stations (BSs) cooperative sensing. In this paradigm, we simultaneously consider both reflected and scattered signals during the target UAV movement to improve sensing accuracy. As for the multipath signals arising from such paradigm, we have developed a mathematical model for the echo signals received at the ISAC BSs and illustrated how channel time-variability affects the receiving signals. Based on analysis for the time-varying nature of the real channel environment, we introduce a range power gain algorithm based on fuzzy positioning and name it Fuzzy Range Enhancement-Transmission Spatial Gain (FRE-TSG) inspired by Linearly Constrained Minimum Variance (LCMV). Furthermore, to robustly use multiple sensing results, we design a result-level fusion method Data-Driven-Maximum Consensus ISAC (2D-MaCeS) matching algorithm. This algorithm fully exploits the spatiotemporal diversity of multiple sensing results and reduces dependence on prior information. Finally, we validate the efficacy and robustness of the proposed algorithm through extensive simulation experiments.

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.

Anomaly detection in Industrial Control Systems (ICS) is crucial for ensuring operational safety, preventing equipment damage, and maintaining production continuity in critical infrastructure. As ICS become increasingly complex and interconnected, anomaly detection faces significant challenges in maintaining system security and operational integrity. Traditional statistics-based methods fail to capture complex, high-dimensional industrial data patterns. Existing deep learning approaches primarily focus on time-domain analysis, struggling with normal operational noise and system oscillation anomalies, causing detection errors. Additionally, these methods rely solely on data-driven correlation patterns, missing anomalies that violate physical coupling relationships between ICS components, leading to false negatives. In this paper, we propose TF-Detector, a dual-scale anomaly detection framework that decomposes ICS data into time-domain and frequency-domain representations. To capture the process interaction between sensors, TF-Detector employs process graphs enhanced with dynamic correlation patterns to model industrial relationships, and utilizes graph neural networks to extract complex features. Comprehensive evaluations conducted on two real-world ICS datasets demonstrate that our approach significantly outperforms state-of-the-art anomaly detection methods, improving average precision by 15.61% and 2.36%, respectively.

With the advancement of smart city development, integrating Unmanned Aerial Vehicles (UAVs) and the Internet of Vehicles (IoV) has emerged as a significant research focus. This paper investigates DDoS attack detection within the context of the vehicle-UAV integrated network. In response to the challenge of balancing accuracy and delay faced by traditional algorithms and the limitations of fixed network feature selection methods, this study proposes a novel algorithm incorporating the Pied Kingfisher Optimizer (PKO) feature selection approach. Dynamic feature selection is achieved by integrating PKO with the LSTM-CNN network, enabling efficient and high-precision DDoS detection. Additionally, relevant dynamic datasets tailored for the vehicle-UAV integrated network have been developed. Experimental results demonstrate that the proposed algorithm outperforms traditional methods on our custom-generated dynamic datasets.

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.

With the advancement of Internet of Things (IoT) technology, smart IoT systems have become integral to industrial production and daily life. However, they face significant security and privacy vulnerabilities from different aspects. To enhance the security mechanisms, “Meta Computing” techniques (also called “Network-as-a-Computer, NaaC”) integrate all available computing resources and support zero-trust environments. Traditional anomaly detection methods consider the correlation between two events, which can be bypassed by constructing fake events that indirectly influence target devices, leading to false negatives. To address this issue, behavior-context-based approaches struggle with the complexity and variability of behavior patterns, resulting in false positives due to their inability to tolerate slight differences in event sequences representing the same system behavior. In this paper, we propose an anomaly detection approach in smart IoT systems based on the contextual semantics of behavior graphs. Our method captures critical event semantics while tolerating variations in noncritical events to aggregate and summarize the behavior semantics. We cluster benign behaviors and use whether the testing behavior instance falls into the benign behavior clusters as the criterion for anomaly detection. Our experiment results show that our approach effectively differentiates between anomalous and benign behaviors, significantly reducing false positives and negatives compared to state-of-the-art methods.

Integrating Connected and Autonomous Vehicles (CAVs) with federated learning (FL) has garnered widespread attention in recent years, particularly in object detection. However, within the Internet of Vehicle (IoV) context, employing FL to handle complex visual tasks faces several challenges, such as difficulties in obtaining labeled data, heterogeneity in user data across vehicles, and limitations in timely assessing user contributions. To address these challenges, we propose a federated semi-supervised learning architecture for object detection, accompanied by a contribution evaluation and aggregation method based on heterogeneous data. Specifically, we designed a federated semi-supervised training process for the IoV, utilizing an object detection framework based on Faster R-CNN and a teacher–student architecture. To demonstrate its effectiveness, we conducted a communication feasibility analysis using real-world vehicular network data and an analysis of the algorithm’s convergence properties. Additionally, we developed a data-based user contribution assessment and aggregation framework to evaluate the distribution and quality of data from vehicle users to aid the FL center. Finally, simulation results show that the proposed federated semi-supervised algorithm can effectively train and converge to a model that outperforms traditional FL. Ablation experiments further validate the efficacy of the data-based assessment method.

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.

This paper investigates a centralized cooperative cognitive radio network (CCRN) where a primary base station (PBS) transmits a message to a primary user while a secondary user transmitter (SU-Tx) function as a friendly jammer. The jammer sends jamming signals to protect the PBS’s messages from a potential eavesdropper (Eve). However, the SU-Tx also attempts to covertly transmit its own messages to a secondary user receiver using the allocated spectrum resource, contravening the PBS regulations. To address this issue, the PBS requests its partner CBS to help detect jammer’s behavior. Specifically, we propose a generative adversarial network (GAN) optimization framework that models the strategic game between the CBS monitoring and the covert transmission of cooperative jammers. We introduce a novel GAN-based beamforming design algorithm, termed GAN-BD, to determine the power allocation at the jammer for covert communication. Additionally, we develop the detection error probability (DEP) at the CBS and derive its expression using a hypothesis testing problem. Through extensive simulation results, we demonstrate that the proposed GAN-BD algorithm can achieve near-optimal solutions for conducting covert communication, leveraging knowledge of the current network environment and exhibiting rapid convergence capabilities. The simulation results highlight the effectiveness of our GAN-BD algorithm.

In the Internet of Vehicles (IoV), task offloading is crucial for enhancing computational efficiency and managing resource constraints. However, the presence of information asymmetry and the inherent selfish behavior of vehicles pose significant challenges to task offloading in IoV. Thus, to improve the reliability and efficiency of offloading in scenarios with information asymmetry, an innovative two-stage offloading strategy that employs reinforcement learning to optimize task allocation and scheduling is introduced in this paper. To be specific, the first stage in the proposed offloading strategy involves an initial task allocation strategy based on task attributes, the state of the intermediate buffer queue, and the maximum task reception capacity of Roadside Units (RSUs). The second stage explores partial offloading strategies, investigating the impact of intermediate incentives on offloading decisions. By focusing on maximizing social utility, the destinations, offloading rates, and advantageous portions of intermediate incentives for partial offloading tasks are determined in this paper as well. Moreover, the Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC) algorithms are used to solve the optimization problem efficiently. Finally, simulation results demonstrate that the proposed innovative strategy significantly outperforms traditional approaches in terms of task completion reliability and efficiency, providing a robust solution for IoV scenarios with information asymmetry.

Physical layer authentication (PLA) as a promising solution has gained widespread attention due to its high security and lightweight deployment. A fixed authentication center is vulnerable to attacks, making both centralized PLA (CPLA) with a single authentication center and collaborative PLA with multiple collaboration nodes at risk of a single point of failure. In this paper, we propose a leader election algorithm for distributed physical layer authentication, where multiple receivers are col-laboratively trained under a leader and complete authentication independently. Specifically, the one with the strongest model generalization ability is elected as the leader by individual receivers voting based on data quality. The role of the leader is to filter out the underperforming receivers, assign reasonable weights to local models, and construct an authentication white list to achieve better authentication performance. Simulation results show that the proposed scheme outperforms the randomly selected leader and the traditional PLA schemes.

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.

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.

In this paper, we study a communication-efficient distributed learning scheme through a holistic integration of federated learning (FL) and particle swarm optimization, called DSL, which is suitable for the implementation of intelligent IoT applications. Since only one selected optimum from all local devices need to report its local model updates to the parameter server, the communication cost of DSL is much reduced compared to its counterpart of standard FL. However, the DSL is vulnerable to adversarial attackers. To achieve Byzantine-resilient DSL, we propose to introduce a shared dataset for scoring local updates to screen attackers. We further provide the convergence analysis to theoretically demonstrate that CB-DSL is superior than the standard FL. Experiment results show that the learning performance of our proposed CB-DSL outperforms the existing benchmarks with only a small amount of globally shared data. It enjoys higher robustness against Byzantine attacks than the vanilla DSL, and has better communication efficiency than the standard FL11Our code can be found at: https://github.com/fuanxiyin/CB-DSL.git..

Federated learning (FL) methods face major challenges including communication bottleneck, data heterogeneity and security concerns in edge IoT scenarios. In this paper, inspired by the success of biological intelligence (BI) of gregarious organisms, we propose a novel edge learning approach for swarm IoT, called communication-efficient and Byzantine-robust distributed swarm learning (CB-DSL), through a holistic integration of AI-enabled stochastic gradient descent and BI-enabled particle swarm optimization. To deal with non-independent and identically distributed (non-i.i.d.) data issues and Byzantine attacks, a very small amount of global data samples are introduced in CB-DSL and shared among IoT workers, which not only alleviates the local data heterogeneity effectively but also enables to fully utilize the exploration-exploitation mechanism of swarm intelligence. Further, we provide convergence analysis to theoretically demonstrate that the proposed CB-DSL is superior to the standard FL with better convergence behavior. In addition, to measure the effectiveness of the introduction of the globally shared dataset, we also evaluate the model divergence by deriving its upper bound. Numerical results verify that the proposed CB-DSL outperforms the existing benchmarks in terms of faster convergence speed, higher convergent accuracy, lower communication cost, and better robustness against non-i.i.d. data and Byzantine attacks11Our code can be found at:https://github.com/fuanxiyin/CB-DSL.git..

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.

With the growing computing power in the Internet of Vehicles (IoV), machine learning is increasingly utilized. Yet, IoV faces challenges like privacy, security, and trust issues between vehicles and infrastructure, hindering efficient information usage and machine learning. This paper introduces Semi-Supervised Federated Learning (SSFL) for object recognition in IoV. The proposed approach is designed to enhance the generalization capability and improve the performance of the algorithm by adapting the SSFL framework to the specific characteristics of IoV data deployment and algorithms. A teacher-student structured approach leverages labeled and unlabeled data, and a deployment scheme optimizes training. Results surpass traditional methods, promising improved IoV object recognition accuracy and efficiency.

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.

For distributed learning among collaborative users, this paper develops and analyzes a communication-efficient scheme for federated learning (FL) over the air, which incorporates 1-bit compressive sensing (CS) into analog-aggregation transmissions. To facilitate design parameter optimization, we analyze the efficacy of the proposed scheme by deriving a closed-form expression for the expected convergence rate. Our theoretical results unveil the tradeoff between convergence performance and communication efficiency as a result of the aggregation errors caused by sparsification, dimension reduction, quantization, signal reconstruction and noise. Then, we formulate a joint optimization problem to mitigate the impact of these aggregation errors through joint optimal design of worker scheduling and power scaling policy. An enumeration-based method is proposed to solve this non-convex problem, which is optimal but becomes computationally infeasible as the number of devices increases. For scalable computing, we resort to the alternating direction method of multipliers (ADMM) technique to develop an efficient implementation that is suitable for large-scale networks. Simulation results show that our proposed 1-bit CS based FL over the air achieves comparable performance to the ideal case where conventional FL without compression and quantification is applied over error-free aggregation, at much reduced communication overhead and transmission latency.

In this paper, we focus on federated learning (FL) over the air based on analog aggregation transmission in realistic wireless networks. We first derive a closed-form expression for the expected convergence rate of FL over the air, which theoretically quantifies the impact of analog aggregation on FL. Based on that, we further develop a joint optimization model for accurate FL implementation, which allows a parameter server to select a subset of edge devices and determine an appropriate power scaling factor. Such a joint optimization of device selection and power control for FL over the air is then formulated as an mixed integer programming problem. Finally, we efficiently solve this problem via a simple finite-set search method. Simulation results show that the proposed solutions developed for wireless channels outperform a benchmark method, and could achieve comparable performance of the ideal case where FL is implemented over reliable and error-free wireless channels.

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.

As a promising distributed learning technology, analog aggregation-based federated learning over the air (FLOA) provides high communication efficiency and privacy provisioning under the edge computing paradigm. When all edge devices (workers) simultaneously upload their local updates to the parameter server (PS) through commonly shared time-frequency resources, the PS obtains the averaged update only rather than the individual local ones. While such a concurrent transmission and aggregation scheme reduces the latency and communication costs, it unfortunately renders FLOA vulnerable to Byzantine attacks. Aiming at Byzantine-resilient FLOA, this article starts from analyzing the channel inversion (CI) mechanism that is widely used for power control in FLOA. Our theoretical analysis indicates that although CI can achieve good learning performance in the benign scenarios, it fails to work well with limited defensive capability against Byzantine attacks. Then, we propose a novel scheme called the best effort voting (BEV) power control policy that is integrated with stochastic gradient descent (SGD). Our BEV-SGD enhances the robustness of FLOA to Byzantine attacks, by allowing all the workers to send their local updates at their maximum transmit power. Under worst-case attacks, we derive the expected convergence rates of FLOA with CI and BEV power control policies, respectively. The rate comparison reveals that our BEV-SGD outperforms its counterpart with CI in terms of better convergence behavior, which is verified by experimental simulations.

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.

Federated learning (FL) is an attractive paradigm for making use of rich distributed data while protecting data privacy. Nonetheless, non-ideal communication links and limited transmission resources may hinder the implementation of fast and accurate FL. In this paper, we study joint optimization of communications and FL based on analog aggregation transmission in realistic wireless networks. We first derive closed-form expressions for the expected convergence rate of FL over the air, which theoretically quantify the impact of analog aggregation on FL. Based on the analytical results, we develop a joint optimization model for accurate FL implementation, which allows a parameter server to select a subset of workers and determine an appropriate power scaling factor. Since the practical setting of FL over the air encounters unobservable parameters, we reformulate the joint optimization of worker selection and power allocation using controlled approximation. Finally, we efficiently solve the resulting mixed-integer programming problem via a simple yet optimal finite-set search method by reducing the search space. Simulation results show that the proposed solutions developed for realistic wireless analog channels outperform a benchmark method, and achieve comparable performance of the ideal case where FL is implemented over error-free wireless channels.

Considering the ongoing development of various devices and rich applications in intelligent Internet of Things (IoT) systems, it is a crucial issue to solve secure transmission of legitimate signals for massive data sharing in the systems. Cooperative jamming‐based physical layer security is explored to be a complement of conventional cryptographic schemes to protect private information. Yet, this method needs to solve a game between energy consumption and signal secure transmission. In this paper, we summarize the basics of cooperative jamming and universal security metrics. Using the metrics, we study a series of typical cooperative jamming strategies from two aspects, including power allocation and energy harvesting. Finally, we propose open issues and challenges of further works on cooperative jamming in an IoT system with energy constraints.

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.

With the great-leap-forward development of Internet of Things (IoT), it is extremely important to study secure wireless transmission for IoT systems. A cooperative jamming (CJ) strategy has been extensively studied to enhance physical layer security in IoT systems. However, CJ's secrecy performance has not been well studied in the scenario of collusive eavesdroppers. In this paper, we propose a CJ scheme for IoT systems to fight against multiple passive and collusive eavesdroppers of unknown channel state information. Considering nodes with multi-antenna in an IoT system, we design beamforming vectors to maximize signal-to- interference-plus-noise ratio (SINR) of the wireless link from a controller (transmitter) to an actuator (receiver). Under the worst-case assumption that eavesdroppers can aggregate all signals to enhance their eavesdropping abilities, we derive the closed- form expressions of the secrecy outage probability (SOP) with and without CJ. In addition, we employ a strict mathematical asymptotic analysis to provide insights into the effects of various system parameters on the SOP. Numerical results verify that the CJ scheme can effectively prevent eavesdropping and the effects of system parameters on SOP is consistent with theoretical analytical results.

In this research, we consider the resource allocation of spectrum sensing and transmit power for a cognitive sensor node with energy harvesting capability, operating in time-slotted fashion with causal knowledge of the channel state and the energy harvesting state. Taking into account the status of primary channel occupation and the sensing imperfection, we formulate this resource allocation problem as an infinite-horizon discrete-time Markov decision process (MDP) in which the cognitive sensor node aims at maximizing the long-term expected throughput. An optimal sensing-transmission (OST) policy which specifies the time duration allocated for spectrum sensing as well as the power level to be used upon the transmission is proposed. A structural property pertaining to the OST policy is revealed, that is the optimal long-term expected throughput is non-decreasing with the level of the battery available energy. Moreover, we study a special case with sufficiently high signal-to-noise (SNR) power ratio of the primary signal. We demonstrate that the optimal transmit power has a monotonic structure with respect to the battery energy states. Based on this monotonic structure, an efficient sensing-transmission algorithm with low-complexity is developed. The simulation results are presented to confirm the theoretical analysis and the predominance of our proposed policies.

Smart cyber-physical systems (CPSs) include Internet of things (IoT), smart grids, smart cities, smart transportation, and smart "Anything" (e.g., homes and hospitals).ese systems require different levels of security and protection based the sensitivity of their data.Nonetheless, we are living in a world where cyber attacks, privacy violations, phishing scams, and data breaches have become commonplace.Smart CPSs are also subject to security violations and privacy breaches, which stem from the vulnerabilities of existing computers and communications technologies.In addition, as smart CPSs get more complex, more vulnerabilities will emerge.Hackers will be able to launch increasingly sophisticated attacks in the future due to the ever-shi ing cyber physical landscape.Hence, innovative research is needed for security assurance and privacy preservation in smart CPSs for new architectural models, system designs, and cryptographical protocols.In this special issue, we received submissions from both academia and industry in the relevant fields.Following a strict review process, we accepted papers for this special issue.Each of the papers was peer-reviewed by at least three experts in the field.In the following, we provide a brief introduction to each paper.ere are four papers aiming to design and analyze security schemes and privacy preserving strategies for IoT applications.e paper titled "Function-Aware Anomaly Detection Based on Wavelet Neural Network for Industrial Control Communication" proposed a function-aware anomaly detection approach to detect these cyber intrusions and anomalies.Next, the authors of the paper titled "A Compatible OpenFlow Platform for Enabling Security Enhancement in

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.

Cooperative jamming is deemed as a promising physical layer-based approach to secure wireless transmissions in the presence of eavesdroppers. In this paper, we investigate cooperative jamming in a two-tier 5G heterogeneous network (HetNet), where the macrobase stations (MBSs) at the macrocell tier are equipped with large-scale antenna arrays to provide space diversity and the local base stations (LBSs) at the local cell tier adopt non-orthogonal multiple access (NOMA) to accommodate dense local users (LUs). In the presence of imperfect channel state information, we propose three robust secrecy transmission algorithms that can be applied to various scenarios with different security requirements. The first algorithm employs robust beamforming (RBA) that aims to optimize the secrecy rate of a marcouser (MU) in a macrocell. The second algorithm provides robust power allocation (RPA) that can optimize the secrecy rate of an LU in a local cell. The third algorithm tackles a robust joint optimization (RJO) problem across tiers that seek the maximum secrecy sum rate of a target MU and a target LU robustly. We employ convex optimization techniques to find feasible solutions to these highly non-convex problems. The numerical results demonstrate that the proposed algorithms are highly effective in improving the secrecy performance of a two-tier HetNet.

An underlay cognitive radio network (CRN) with non- orthogonal multiple access (NOMA) is a promising multiple access scheme to solve the problem of scarce spectrum. This novel NOMA-enabled underlay CRN can also enhance the transmission secrecy via employing the deliberately introduced interference. In this paper, we intend to investigate the secrecy capacity of a pair of primary users (PUs) and randomly deployed secondary users (SUs) in the NOMA- enabled underlay CRN. Considering an existing eavesdropper in the network, we derive a closed-form expression of the secrecy sum rate (SSR) of all SUs. Then, we further formulate an SSR optimization problem for both PUs and SUs and design a simulated annealing algorithm to find the optimal power allocation. Simulation results demonstrate that the secrecy performance of all NOMA-enabled SUs is higher than that of SUs with frequency division multiple access (FDMA). In other words, the use of the NOMA technology can improve the secrecy performance of the cognitive radio system.

In this paper, we investigate secure communications in energy harvesting enabled cooperative cognitive radio networks (CCRNs). In such CCRNs, a pair of primary users (PUs) can only communicate with each other through a relay. To protect data transmission between PUs, we propose a cooperative jamming strategy with energy harvesting technology. In the first phase, a PU as the source (PU-S) broadcasts signals, while another PU as the destination (PU-D) sends artificial noise (AN) and all secondary uses (SUs) harvest energy from the received mixed signals. In the second phase, one SU selected as a relay (SU-R) uses its harvested energy to forward PU's signals, while another SU selected as a jammer (SU-J) employs its harvested energy to send AN. According to this, we formulate a non-convex problem aiming to improve the secrecy rate of PUs and divide this problem into three optimization subproblems. Finally we provide a feasible joint solution by a two-tiered iterative algorithm. Numerical results demonstrate the secrecy performance of our proposed jamming strategy.

The concept of smart grid gained tremendous attention among researchers and utility providers in recent years. How to establish a secure communication among smart meters, utility companies, and the service providers is a challenging issue. In this paper, we present a communication architecture for smart grids and propose a scheme to guarantee the security and privacy of data communications among smart meters, utility companies, and data repositories by employing decentralized attribute based encryption. The architecture is highly scalable, which employs an access control Linear Secret Sharing Scheme (LSSS) matrix to achieve a role‐based access control. The security analysis demonstrated that the scheme ensures security and privacy. The performance analysis shows that the scheme is efficient in terms of computational cost.

In the Internet of Things (IoT), aggregation and release of real‐time data can often be used for mining more useful information so as to make humans lives more convenient and efficient. However, privacy disclosure is one of the most concerning issues because sensitive information usually comes with users in aggregated data. Thus, various data encryption technologies have emerged to achieve privacy preserving. These technologies may not only introduce complicated computing and high communication overhead but also do not work on the protection of endless data streams. Considering these challenges, we propose a real‐time stream data aggregation framework with adaptive ω‐event differential privacy (Re‐ADP). Based on adaptive ω‐event differential privacy, the framework can protect any data collected by sensors over any dynamic ω time stamp successively over infinite stream. It is designed for the fog computing architecture that dramatically extends the cloud computing to the edge of networks. In our proposed framework, fog servers will only send aggregated secure data to cloud servers, which can relieve the computing overhead of cloud servers, improve communication efficiency, and protect data privacy. Finally, experimental results demonstrate that our framework outperforms the existing methods and improves data availability with stronger privacy preserving.

Recently, a new paradigm of multiple access (MAC) along with one eavesdropper to achieve secrecy transmissions has been getting in focus. However, all existing work on such multiple access wiretap channel (MAC-WT) mainly concentrates on the secrecy performance of the system as a whole from an information theoretic perspective. In this work, we investigate the secrecy performance of a single transmitter in the quasi-static Rayleigh fading MAC-WT on basis of two decoding methods, zero-forcing (ZF) and minimum mean-square error (MMSE), jointly with successive interference cancellation (SIC). We evaluate the secrecy performance in three metrics: positive secrecy capacity probability, secrecy outage probability and effective secrecy throughput. The analytical and simulation results show that, 1) the SIC order has great impacts on the secrecy performance for both methods; 2) MMSE-SIC outperforms ZF-SIC, while the performance gap can be overcome via adjusting SIC order, or increasing SNR, or enhancing the spatial diversity gain; 3) in high SNR regime, the secrecy performance is only determined by the relative distance to eavesdropper over legitimate receiver rather than the SNR.

In this paper, we consider a time-slotted energy harvesting cognitive radio network with the save-then-transmit protocol, in which the secondary user (SU) is powered by energy harvested from the ambient environment. The primary concern of this study is to design an optimal harvesting-access policy to maximize the throughput of the SU, where the harvesting part of the policy specify the time duration allocated for harvesting energy, and the access part of the policy specify the power level to be used upon transmission. Jointly considering the presence of primary users, diversity of channel quality, time and energy consumption for the sensing process as well as sensing errors, we formulate the above design problem as an infinite-horizon Markov decision process, and propose an algorithm to find the optimal harvesting-access policy using the value iteration. We then investigate the relationship between the throughput, the available energy in the battery and the battery capacity. It is indicated that the achievable throughput is monotonously increases with the available energy in the battery if the available energy is under a threshold, which is determined by the battery capacity. Next, in order to reduce the computational complexity, we propose an optimal myopic policy with a closed-form expression. Finally, the performance of the proposed policies and the impacts of system parameters are evaluated through numerical results.

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.

Many mobile big data applications require the computation of dot‐product of two vectors. For examples, the dot‐product of an individual's genome data collected by a body area network and the gene biomarkers of a health centre can help detect diseases in m‐Health, and that of the interests of two persons can facilitate profile matching in mobile social networks. Nevertheless, mobile big data typically contain sensitive personal information and are more accessible to the general public as they are collected by mobile devices carried by human beings. Therefore exposing the inputs of dot‐product computation discloses sensitive information about the two participants, leading to severe privacy violations. The authors tackle the problem of private dot‐product computation targeting mobile big data applications in which secure channels are hardly established, and the computational efficiency is highly desirable. We first propose two basic schemes and then present the corresponding advanced versions to improve computational efficiency and enhance the privacy‐protection strength. Furthermore, we theoretically prove that our proposed schemes can simultaneously achieve privacy‐preservation, non‐repudiation, and accountability. Our numerical results verify the performance of the proposed schemes in terms of communication and computational overheads.

Proximity detection is one of the most common location-based applications in daily life when users intent to find their friends who get into their proximity. Studies on protecting user privacy information during the detection process have been widely concerned. In this paper, we first analyze a theoretical and experimental analysis of existing solutions for proximity detection, and then demonstrate that these solutions either provide a weak privacy preserving or result in a high communication and computational complexity. Accordingly, a location difference-based proximity detection protocol is proposed based on the Paillier cryptosystem for the purpose of dealing with the above shortcomings. The analysis results through an extensive simulation illustrate that our protocol outperforms traditional protocols in terms of communication and computation cost.

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.

A Mobile Social Network (MSN) is a typical Delay Tolerant Network (DTN) composed of mobile nodes with social characteristics. Routing design and optimization are difficult because of the fast topology changes in such network. However, realistic mobility trace shows that mobile nodes in MSNs generally visit a few locations called homes, frequently, while other locations are visited less frequently. In order to evaluate the influence of the home on the routing design and improve the delivery success ratio, we propose two novel home-based multi-copy routing protocols, HBMRZ and HBMR. In HBMRZ, we assume that the message holder has zero-knowledge about the destination when it delivers the message, while in HBMR, the message holder not only knows its own homes but also the destination. Nevertheless, both of the protocols can broadcast message on the basis of the known home information. In addition, extensive simulations are conducted. Numerical simulation demonstrates that it is important to exploit home factor in routing protocols for delivering the message accurately. By using home characteristic, HBMRZ and HBMR achieve a better performance on the delivery success ratio than existing MSN routing algorithms, including Epidemic and Spray&Wait.

Multiple-Input Multiple-Output Cooperative Cognitive Radio Networks (MIMO-CCRNs) have been proposed recently to further improve the spectrum efficiency. In MIMO-CCRNs, secondary users (SUs) equipped with multiple antennas can cooperatively relay the primary signals for the primary users (PUs) while concurrently accessing the same spectrum to send the secondary traffic for themselves. This communication model is intrinsically vulnerable to eavesdropping as more wiretapping opportunities can be exploited by attackers. In this paper, we propose to employ cooperative jamming at physical (PHY) layer to achieve secure transmissions. In cooperative jamming, certain SUs are employed as helpers to send jamming signals to jam the eavesdropper without interfering the legitimate receivers. We optimize the design of the beamformers (including the information beamformer and the jamming beamformer) as well as the power allocation vector to maximize the secrecy capacity in two scenarios. Simulation results demonstrate that the secrecy capacity is remarkably enhanced in our cooperative jamming scheme. To the best of our knowledge, this work is the first one to investigate the PHY layer security issue for MIMO-CCRNs.

Vehicular ad hoc networks (VANETs) are regarded as essential ways for vehicles to share information with each other. As communication range is limited, multiple relay nodes are often required for establishing the multi-hop routing path between the source node and the destination node. Dependability of such routing path may be compromised due to different motion states of vehicles. In this paper, we address this challenge by designing a new next-hop selection scheme named LPLS (Long Path Lifetime Scheme), in which each relay node uses the optimal stopping theory to choose a suitable next-hop node. Especially, this selection scheme can balance the tradeoff between routing path lifetime and selection efficiency. The analysis and simulation results show that our next-hop selection scheme presents better performance with comparison to other reference schemes applied in the routing algorithms AODV and MOPR.

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.

Auction mechanism has been widely applied to cognitive radio networks to motivate spectrum redistribution among unlicensed users and spectrum holders, in which unlicensed users with cognitive radio capability can access the licensed spectrum by compensating spectrum holders with monetary payment. However, most of prior studies on auction are mainly restricted to channel allocation with the assumption of fixed transmitting power and/or neglecting spectrum holders’ interference restrictions. Few of them jointly considers the channel and power allocation. Additionally, power allocation may cause non-identical interference relationships among unlicensed users due to the variable interference ranges. In this paper, we propose two truthful double auction schemes under single-channel demand and multi-channel demand, respectively. We theoretically prove that the two auction schemes both achieve the desired economic properties. Results from numerical evaluation demonstrate our analysis.

Traditional spectrum sensing methods require that a secondary user (SU) senses the spectrum at the beginning of each time slot. A closer look at the network activities of a cognitive radio network reveals that the access pattern of a primary user (PU) typically consists of a succession of transmission periods, alternating with idle periods, each of which lasts a number of time slots. Based on this observation, it becomes clear that forcing the SU to sense the channel at the beginning of each time slot is unnecessary and may lead to considerable waste of energy. The main objective of this paper is to investigate new approaches for spectrum sensing by exploring the tradeoffs between energy consumption and secondary network throughput. To this end, we propose a stochastic, energy-aware model to derive the optimal spectrum sensing interval an SU can use to dynamically determine when the next spectrum sensing should be performed. The proposed model allows an SU to adaptively derive the sensing interval based on its required quality of service and current network state, including the PU's network activities and traffic load. Extensive simulation study is performed to assess the effectiveness of our proposed approach in achieving high accuracy with reduced energy consumption. The analysis of the results show that careful tuning of key parameters leads to improved energy efficiency and increased secondary network throughput.

As one of the most powerful tools in game theory, double auction is widely utilized to tackle the spectrum allocation problem in a secondary market. In this paper, we propose a multi-unit double auction framework in which the conflict graph-based bidder group formation, the winner determination strategy, and the spectrum pricing are elaborately designed. Through an in-depth theoretical analysis, we prove that our auction scheme can achieve three critical properties including the individual rationality, the ex-post budget balance, and the truthfulness. Extensive simulation results validate that the proposed auction framework can significantly improve the user satisfaction degree.

The benefits of cognitive radio networks have been well recognized with the dramatic development of the wireless applications in recent years. While many existing works assume that the secondary transmissions are negative interference to the primary users (PUs), in this paper, we take secondary users (SUs) as positive potential cooperators for the primary users. In particular, we consider the problem of cooperative relay selection, in which the PUs actively select appropriate SUs as relay nodes to enhance their transmission performance. The most critical challenge for such a problem of cooperative relay selection is how to select a relay efficiently. But due to the potentially large number of secondary users, it is infeasible for a PU transmitter to first scan all the SUs and then pick the best one. Basically, the PU transmitter intends to observe the SUs sequentially. After observing a SU, the PU needs to make a decision on whether to terminate its observation and use the current SU as its relay or to skip it and observe the next SU. We address this problem by using the optimal stopping theory, and derive the optimal stopping rule. To evaluate the performance of our proposed scheme, we conduct an extensive simulation study. The results reveal the impact of different parameters on the system performance, which can be adjusted to satisfy specific system requirements.

The problem of channel quality prediction in cognitive radio networks is investigated in this paper. First, the spectrum sensing process is modeled as a Non-Stationary Hidden Markov Model (NSHMM), which captures the fact that the channel state transition probability is a function of the time interval the primary user has stayed in the current state. Then the model parameters, which carry the information about the expected duration of the channel states and the spectrum sensing accuracy (detection accuracy and false alarm probability) of the SU, are estimated via Bayesian inference with Gibbs sampling. Finally, the estimated NSHMM parameters are employed to design a channel quality metric according to the predicted channel idle duration and spectrum sensing accuracy. Extensive simulation study has been performed to investigate the effectiveness of our design. The results indicate that channel ranking based on the proposed channel quality prediction mechanism captures the idle state duration of the channel and the spectrum sensing accuracy of the SUs, and provides more high quality transmission opportunities and higher successful transmission rates at shorter spectrum waiting times for dynamic spectrum access.

Spectrum sharing is an emerging mechanism to resolve the conflict between the spectrum scarcity and the growing demands for the wireless broadband access. In this paper we investigate the achievable transmission capacity of a wireless backhaul mesh network that shares the spectrums of the underutilized cellular uplink over the underlay spectrum sharing model with several commonly adopted medium access control protocols: slotted-ALOHA, CSMA/CA, and TDMA. By employing stochastic geometry, we derive the probabilities for a packet to be successfully transmitted in the primary cellular uplink and the secondary mesh networks. The achievable transmission capacity of the secondary network with outage probability constraints from both the primary and the secondary systems is obtained according to Shannon's Theory. The capacity region and the achievable capacity when the outage probabilities equal their corresponding threshold values are analyzed numerically and the results illustrate the effect of adjusting the mesh network parameters on the achievable transmission capacity under different MAC protocols.