With the rapid expansion of sensor networks across domains such as environmental monitoring, industrial automation, and smart healthcare, ensuring secure and reliable data storage in resource-constrained environments has become a critical challenge. Traditional centralized storage systems struggle with data tampering, privacy leakage, and vulnerability to collusion among nodes. Blockchain technology, characterized by decentralization, immutability, and traceability, provides a promising foundation for trustworthy sensor data management. Among various consensus mechanisms, Delegated Proof of Stake (DPoS) has been recognized for its efficiency and low energy consumption, yet it faces two critical issues: limited incentives for ordinary sensor nodes to participate in voting and the risk of collusion that undermines fairness and stability. To overcome these limitations, this study proposes a blockchain-enabled sensor data storage framework incorporating a four-party evolutionary game model. The model explicitly captures the strategic interactions among cluster head nodes, ordinary sensor nodes, competing gateway nodes, and supervisory nodes, while integrating reputation evaluation, penalty enforcement, and supervisory oversight. Through evolutionary game analysis, the proposed framework reveals the stability conditions of node behaviors and identifies strategies that promote fair and secure consensus. Simulation results verify that the mechanism enhances node participation, suppresses collusion, accelerates consensus convergence, and achieves superior throughput and fault tolerance compared with existing schemes. This research provides theoretical insights and practical guidance for designing secure, efficient, and scalable blockchain-enabled sensor network data storage systems.

With the large-scale popularity of wireless terminals, user equipment (UE) puts forward higher requirements for wireless network service quality. By using device-to-device (D2D) communication technology for cooperative relay transmission, any UE can be used as a potential relay UE (RUE). Making RUEs relay data for other UEs can improve the total throughput of the network. Considering the energy consumption of data relay transmission, this paper proposes a novel relay selection and resource allocation method for an energy harvesting (EH) cognitive D2D network based on a coalition game. This method first analyzes the resource allocation scheme for RUEs, either in underlay or interweave cognitive mode. Secondly, the energy harvesting incentive mechanism is used to encourage the RUE to obtain additional time to collect energy as a reward during the relay process, which can improve the willingness of the RUE to establish D2D relay links for other UEs. Finally, according to the EH incentive mechanism, the coalition comparison rules are determined. The cooperative behavior between UE and RUE is analyzed based on the coalition game, and then the relay selection problem of cognitive D2D network communication is solved. The result of the coalition game divides the user set into several subcoalition partitions, and the coalition is equivalent to the relay selection result. Simulation results show that compared with the traditional method, the proposed relay selection and resource allocation method based on a coalition game can improve the throughput of the whole network.

Covert communication ensures reliable communication with a legitimate user while preventing a warden from detecting the behavior of transmission. Most of the existing literature on covert communication focuses on far-field covert communication scenarios. However, the advancement of 6G technologies, particularly the integration of extremely large-scale multiple-input multiple-output (XL-MIMO) systems and Terahertz (THz), highlights the emergence of near-field communications. Moreover, wideband XL-MIMO exacerbates the near-field beam-squint effect. Therefore, how to covertly determine the location of the target user and establish precise transmission in the near field remains a major challenge. In this work, we investigate near-field covert communication. Specifically, leveraging the emerging extremely large-scale antenna array (ELAA) architecture with true-time-delay lines (TTDs), a transmitter Alice employs an iterative search strategy based on near-field beam squint to achieve precise localization of the legitimate receiver Bob. Subsequently, the TTDs are adjusted to eliminate the near-field beam-squint effect, thereby enabling effective data transmission (DT). Considering a finite blocklength, we propose a novel design framework that jointly optimizes the number of subcarriers and the transmit power. This framework aims to maximize the effective covert rate (ECR) of the Alice-Bob link while ensuring that the communication remains covert against the warden Willie's detection. Theoretical analysis and numerical results demonstrate the effectiveness of our proposed method and confirm the existence of optimal values among the parameters under consideration. Furthermore, compared with far-field, the proposed near-field approach achieves more accurate beam focusing and lower detection probability, making it particularly suitable for covert communications.

Driven by the carbon neutrality agenda, the collaborative development of the photovoltaic (PV) industry chain faces dual challenges: behavioral heterogeneity among multiple stakeholders and the absence of a decentralized trust mechanism. Existing research exhibits notable limitations in the completeness of participant modeling, the quantification of blockchain effects, and the dynamic adaptability of incentive mechanisms. To address these gaps, this paper constructs a blockchain-enabled cooperative governance framework encompassing four core actors: PV power generation enterprises, power grids, users, and government entities. A four-party evolutionary game model is developed, integrating blockchain-based trust mechanisms and cost constraints. The model innovatively endogenizes default penalties, trust enhancement, and behavioral preferences into the payoff structure, and designs a multi-dimensional incentive system including subsidies, rewards, and penalties. Numerical simulations demonstrate that the blockchain platform significantly enhances the stability of cooperation and accelerates strategic convergence. Furthermore, appropriately calibrated subsidies and dynamic performance-based rewards effectively incentivize collaboration, while user power supply preferences and default penalties are identified as key variables influencing system evolution. Compared with a traditional three-party model, the proposed four-party model exhibits superior performance in terms of convergence speed and system stability. This study provides theoretical support for building a trustworthy, efficient, and sustainable governance system for the PV industry, and offers actionable policy recommendations in areas such as cost regulation, tiered incentives, differentiated tariffs, and smart contract implementation.

This paper proposes a novel Intelligent Reflecting Surface (IRS)-assisted interweave Cognitive Internet of Vehicles (CIoV) network under malicious jamming attacks, where the IRS enhances communication performance by establishing additional links. In order to maximize the sum transmission rate of Vehicle-to-Infrastructure (V2I) links, we propose an optimization problem that jointly optimizes wireless resource allocation, such as spectrum and transmit power for Vehicle Users (VUs) and IRS phase shift. Because this problem is non-convex and complicated, we further propose a Heterogeneous Multi-agent Transformer-enhanced Dueling Double Deep Q-Network (HMA-TD3QN) based resource allocation method, where VUs and Secondary Base Station (SBS) act as distinct heterogeneous agents can independently perform resource allocation and phase shift optimization. The Transformer neural network architecture can better adapt to long sequence input states and extract relevant features from complex input states through the attention mechanism. Simulation results indicate that the proposed HMA-TD3QN method achieves improvements of 24.42%, 20.79%, and 22.25% over the basic HMA-DQN under three different jamming strategies, highlighting the effectiveness of IRS technology in enhancing the Quality of Service (QoS) and jamming resilience of CIoV network.

In this paper, a two-layer game based joint time and power allocation method for an underlay Energy Harvesting Cognitive Radio (EH-CR) network is proposed. The method first models the interplay between the Primary User (PU) and the Secondary Users (SUs) as a Stackelberg game and then models the interplay among the SUs as a Supermodel game in the underlay EH-CR network. Later, a coefficient for evaluating fair ness is introduced in order to promote fairness among the SUs. Subsequently, the utility function of the primary network and the utility function of the secondary network are defined based on their individual profits. By maximizing the secondary network's utility function, the Supermodel game's Nash Equilibrium (NE) solution is achieved. Then, by substituting the NE solution of the Supermodel game into the utility function of the primary network and then maximizing the utility function of the primary network, the NE solution of the Stackelberg game is obtained. Finally, a deterministic strategy can be obtained, which is the time coefficient of equalized spectrum sensing and the equalized power allocation scheme instead of a probabilistic strategy. Simulation outcomes demonstrate that, under the condition of maintaining the communication quality of the PU, the PU's revenue when PH0 = 0.8 can be improved by 18.2% and when PH0 = 0.6 can be improved by 13.3% compared with the conventional method.

The rapid advancement of Industry 5.0 has accelerated the adoption of the Industrial Internet of Things (IIoT). However, challenges such as data privacy breaches, malicious attacks, and the absence of trustworthy mechanisms continue to hinder its secure and efficient operation. To overcome these issues, this paper proposes an enhanced blockchain-based data storage framework and systematically improves the Delegated Proof of Stake (DPoS) consensus mechanism. A four-party evolutionary game model is developed, involving agent nodes, voting nodes, malicious nodes, and supervisory nodes, to comprehensively analyze the dynamic effects of key factors—including bribery intensity, malicious costs, supervision, and reputation mechanisms—on system stability. Furthermore, novel incentive and punishment strategies are introduced to foster node collaboration and suppress malicious behaviors. The simulation results show that the improved DPoS mechanism achieves significant enhancements across multiple performance dimensions. Under high-load conditions, the system increases transaction throughput by approximately 5%, reduces consensus latency, and maintains stable operation even as the network scale expands. In adversarial scenarios, the double-spending attack success rate decreases to about 2.6%, indicating strengthened security resilience. In addition, the convergence of strategy evolution is notably accelerated, enabling the system to reach cooperative and stable states more efficiently. These results demonstrate that the proposed mechanism effectively improves the efficiency, security, and dynamic stability of IIoT data storage systems, providing strong support for reliable operation in complex industrial environments.

Amid the "dual carbon" strategy and energy digital transformation, the photovoltaic (PV) industry chain suf fers from low collaborative efficiency due to information asymmetry and trust deficits. This paper proposes a blockchain-enabled dual-principal-single-agent incentive model, incorporating fairness preferences to ana lyze optimal contract design under dual information asymmetry. Results show that disadvantageous fairness preferences enhance enterprise effort, while dominant fairness preferences weaken incentives, and blockchain mechanisms reduce information-screening errors, improving system returns and policy efficiency by approxi mately 6% and 5%, respectively. This study provides a unified theoretical framework and practical insights for designing differentiated incentive policies and improving collaborative governance in renewable energy systems.

Driven by carbon neutrality and energy transition goals, photovoltaic (PV) generation has become a key component of China's power system, yet incentive inefficiency caused by information asymmetry and behavioral imbalance remains a policy challenge. This paper develops a non-cooperative principalagent model between grid companies and PV enterprises that incorporates dual information asymmetry and fairness preferences. Under complete rationality, disadvantageous inequity, and advantageous inequity, the optimal contracts and effort levels are derived. Results show that fairness preferences significantly reshape the incentive structure: under disadvantageous inequity, the grid company should raise quantity-based and reduce fixed subsidies to strengthen motivation, while the opposite applies under advantageous inequity. Fairness preferences also affect enterprise effort and generation output, moderated by market composition. The findings provide theoretical and policy guidance for designing differentiated and dynamic PV subsidy mechanisms to enhance renewable energy efficiency.

With the increasing demand of wireless communication, the data rates supported by wireless communication technology are required to be higher, which also leads to the shortage of spectrum resources. Cognitive radio (CR) is considered a promising technology that can make full use of spectrum resources. Currently, Intelligent Reflecting Surfaces (IRSs) are gaining popularity. They autonomously regulate the wireless communication environment to achieve higher data rates and better coverage. In this paper, an IRS-based model is proposed to solve the energy efficiency (EE) optimization problem, which is in multiuser multiple input single output (MISO) energy harvesting CR (EH-CR) communication systems. The model enables the secondary transmitter (ST) to utilize EH technology to capture radio frequency (RF) energy from the primary user (PU), thereby powering its own data transmission. Due to the complexity and continuity of action and state space, a softmax deep double deterministic policy gradients method based on prioritized experience replay (PER-SD3) is designed to jointly optimize the transmission beamforming vector of the ST and the IRS reflection phase shift matrix. Simulation results show that the proposed method can improve the EE of underlay EH-CR communication systems by 35.3% at most compared with the benchmark cases.

Cognitive Internet of Vehicles (CIoV) adds the cognitive engine based on traditional Internet of Vehicles (IoV), which can improve spectrum utilization. However, spectrum sensing data falsification (SSDF) attacks pose a threat to CIoV network security. To ensure the full utilization of spectrum resources and protect primary users transmission, this article combines blockchain with CIoV to defend against SSDF attacks in the presence of vehicle users (VUs) entering and leaving the network. Specifically, this article introduces a virtual currency called Sencoins serve as credential for VUs to purchase transmission shares. And this article proposes a reward and punishment mechanism and a hybrid Proof-of-Stake (PoS) and Proof-of-Work (PoW) mining model to thwart the motivation of the VUs to launch SSDF attacks. On this basis, this article investigates the dynamics of SSDF attack strategy choice of VUs, and uses the largest Lyapunov exponent (LLE) to determine the critical value of Sencoins that avoids the system to exhibit chaotic behavior. To describe the uncertainty of the population proportion of VUs that choose different attack strategies due to high-speed movement and the VUs entering and leaving the CIoV network, this article introduces Gaussian white noise into the replication dynamics equation and builds the It & ocirc; stochastic evolutionary game model, and solves it according to the stability judgment theorem of stochastic differential equations and stochastic Taylor expansion. Finally, simulation results verify that the proposed method can quickly and effectively thwart SSDF attacks in the CIoV network. And compared with traditional methods, the proposed method can improve the efficiency of defending against SSDF attacks by 567% and the average throughput by 25%.

The explosive growth of Internet of Things (IoT) data demands secure and reliable storage, where traditional centralized solutions often fall short. Blockchain offers decentralization and tamper resistance, making it a promising foundation for IoT. However, IoT blockchain systems based on delegated proof-of-stake (DPoS) face challenges such as weak node incentives, unfair reward distribution, and low consensus efficiency. This article proposes a fairness-aware incentive mechanism that accounts for both node capability and effort under information asymmetry. By incorporating fairness pReferences into the contract design, the mechanism improves participation and motivates sustained effort. Theoretical analysis and simulation results show that our approach enhances throughput by about 15% while achieving revenue fairness, incentive compatibility (IC), and stronger consensus performance. The mechanism's adaptability makes it suitable for diverse IoT application scenarios.

In the near-field extremely large-scale multiple-input multiple-output (XL-MIMO) systems, the large bandwidth and massive antenna arrays lead to a significant beam-squint effect, causing subcarriers at different frequencies to focus on distinct locations and resulting in severe array gain loss. However, this effect can be leveraged to enable rapid near-field beam training. Based on this, we propose a joint optimization solution for location estimation (LE) and data transmission (DT) based on extremely large antenna array (ELAA) and true-time-delay lines (TTDs) to address the challenges of near-field covert communication. Specifically, with the help of TTDs, the transmitter Alice leverages the beam-squint effect for rapid localization of the legitimate receiver Bob in the LE phase, then suppresses the effect in the DT phase to concentrate energy, thereby minimizing the detection probability at the warden Willie. Theoretical analysis and simulation show that the proposed scheme maximizes the effective covert rate (ECR) by balancing the number of subcarriers and the transmit power while meeting covertness constraints.

This paper investigates a covert communication with symbiotic backscatter, based on a segmented simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS), ensuring reliable communication between transmitter Alice and warden user Willie, while simultaneously enabling covert communication with user Bob. The structure of the STAR-RIS is segmented into a primary zone (P zone) and a covert zone (C zone), adopting the energy splitting (ES) and time switching (TS) protocols, respectively. Reliable transmission between Alice and Willie is maintained via the P zone. In contrast, the C zone conveys covert information to Bob by embedding the backscatter signal into the primary system signal during the transmission phase while further maintaining reliable communication with Willie during the reflection phase. By analyzing the connection outage probability (COP), the allocation of time resources between the two phases of the C zone is studied. Furthermore, under the constraint of covertness, both the allocation of elements and transmission/reflection coefficients are adjusted to optimize and achieve the highest effective covert rate. The results of the simulation reveal a notable improvement in covert communication performance due to the proposed system.

With the widespread deployment of Power Internet of Things (PIoT), wireless communication security faces increasingly critical challenges from eavesdropping attacks. Traditional methods for localizing eavesdropping nodes struggles to fully leverage network structural information, often result in high model complexity and limited practicality. To address these issues, this article proposes an innovative graph convolutional attention (GCAT) localization algorithm. This method utilizes graph convolution to capture the global topological features of the network while introducing an attention mechanism to adaptively aggregate key regions, thus enabling a fine-grained spatiotemporal representation of eavesdropping behavior. Extensive simulation experiments show that GCAT significantly outperforms traditional methods in terms of accuracy, recall, and other metrics, providing new insights for securing PIoT communication. Notably, GCAT is able to maintain stable performance at extremely low labeling rates, which are less than 5%, demonstrating excellent few-shot learning ability. Simultaneously, its inference latency can be controlled within 30 ms, and the number of parameters is reduced by more than 30% compared to mainstream graph neural network models, making it easy to deploy in resource-constrained environments. The research results of this article can provide crucial technical support for the construction of an active defense system for PIoT, and have significant implications for ensuring the cybersecurity of energy systems, and promoting the safe and controllable development of ubiquitous PIoT applications and smart grids.

This work proposes a six-dimensional movable antenna (6DMA) enhanced covert communication scheme, enabling flexible adjustments of both positions and rotations of 6DMA surfaces to improve covert performance. We first formulate an optimization problem to maximize effective throughput by jointly designing the positions, rotations, and transmit beamforming of the 6DMAs, subject to a covert constraint, a total power constraint, and the antenna spatial feasibility constraints. To address the non-convex optimization challenge, we employ an alternating optimization framework combined with semidefinite relaxation (SDR) method. Results show that the proposed scheme achieves higher effective throughput than fixed or partially adjustable antennas, demonstrating its effectiveness in enhancing covert communication through flexible adjustments.

In this paper, we construct a framework for covert communication assisted by a full-duplex (FD) receiver with movable antennas (MAs). Specifically, we consider uniformly distributed artificial noise (AN) to confuse the warden and give the closed-form expressions for the communication covertness and the transmission outage probability. In the considered system, we formulate a problem to maximize the covert throughput by optimizing the antenna positions and the transmit power of AN. To cope with the high coupling of the proposed problem, we derive the minimal area region based on the characteristics of the movable antennas’ channel gain, thereby reducing the range of exhaustive search. Numerical results show that MAs can significantly improve covert throughput in comparison with the fixed-position antennas (FPAs). In addition, there exists an optimal distance for the transmit and receive antennas of the FD receiver, which can maximize the covert transmission throughput.

The 6G mobile communication system leverages wideband extra-large multiple-input multiple-output (XL-MIMO) to achieve ultra-high data rates, which also leads to new security vulnerabilities due to the near-field spherical wavefront characteristics. This work investigates the problem of near-field multi-user covert communication and proposes a joint location estimation (LE) and data transmission (DT) design based on the beam split effect. The proposed method employs a time delay phase shifters (TD-PS) precoding architecture to generate focusable beams at controllable locations across different subcarriers, enabling simultaneous localization of multiple users. Subsequently, beam split is suppressed to support covert communication involving a legitimate user, Bob, and multiple wardens, Willies. In addition, an optimization problem is formulated to maximize the effective covert rate (ECR) under both non-colluding and colluding detection strategies, and a two-stage optimization approach is used to solve the non-convex problem. Numerical results show that the non-colluding detection strategy achieves a higher ECR compared to the colluding one, and demonstrate the effectiveness of the proposed design in balancing covertness and transmission efficiency.

To break through the topological restriction imposed by conventional reflecting/transmitting-only reconfigurable intelligent surface (RIS) in covert communication systems, a simultaneously transmitting and reflecting RIS (STAR-RIS) is adopted in this paper. A transmitter Alice communicates with both users Willie and Bob, where Bob is the covert receiver. Moreover, Willie also plays a warden seeking to detect the covert transmission since it forbids Alice from illegally using the communication resources like energy and bandwidth allocated for them. To obtain the maximum covert rate, we first design the transmission schemes for Alice in the case of sending and not sending covert information and further derive the necessary conditions for Alice to perform covert communication. We also deduce Willie's detection error probability, the minimum value of which obtained as well in terms of an optimal detection threshold. Furthermore, through the design of Alice's transmit power for covert transmission together with transmission and reflection beamforming at STAR-RIS, we achieve the maximum effective covert rate. Our numerical results show the correctness of the proposed theorems and indicate that utilizing STAR-RIS to enhance covert communication is feasible and effective.