Existing differentially private graph neural network (GNN) methods predominantly rely on a uniform privacy budget and coarse-grained perturbation mechanisms, implicitly assuming homogeneous privacy sensitivity across graph nodes. However, real-world graphs exhibit pronounced community structures and topological heterogeneity, causing uniform privacy protection to simultaneously under-protect structurally critical regions and over-perturb peripheral ones, thereby degrading both privacy guarantees and model utility. To address this limitation, we propose a community-aware personalized differential privacy (DP) framework for GNNs, which enables fine-grained node-level privacy protection by explicitly leveraging community structure. Specifically, we apply the Louvain algorithm to detect community structures and introduce a community importance evaluation method that integrates structural connectivity with node attribute similarity. Based on these importance scores, we design a personalized privacy budget allocation scheme that adaptively assigns differentiated privacy budgets to nodes under a global (ϵ, δ)-DP constraint. We theoretically prove that the proposed mechanism satisfies node-level (ϵ, δ)-DP, and further demonstrate that it significantly enhances robustness against practical privacy threats such as attribute inference and link reconstruction attacks. Extensive experiments on five real-world graph datasets show that our approach consistently outperforms state-of-the-art DP-GNN baselines across multiple architectures, including GCN, GIN, and GraphSAGE, achieving a superior privacy–utility trade-off.

Black-box adversarial attacks require only querying model outputs rather than accessing internal gradients, making them more covert and posing a substantial threat to the security of deep neural networks. However, existing score-based black box methods typically rely on single-scale finite difference gradient estimation; the step size is constrained by a pronounced tradeoff between bias and variance, which in high-dimensional settings often inflates estimation variance and destabilizes the estimated direction. To address this, we propose Robust Multi-Scale Gradient Estimation (RMSGE) to improve query efficiency and stability for black box attacks. RMSGE uses a multi-scale finite difference strategy to mitigate the conflict between bias and variance and integrates two heterogeneous sampling schemes, Natural Evolution Strategy (NES) and Simultaneous Perturbation Stochastic Approximation (SPSA), to enhance both global exploration and local sensitivity in gradient estimation. In addition, RMSGE applies momentum smoothing across scales and sampling distributions to suppress noise accumulation in high-dimensional spaces and employs a voting mechanism to fuse gradient information from multiple sources, further improving the stability and robustness of the update direction. Theoretical analysis shows that RMSGE preserves the first-order unbiasedness of finite difference estimation while effectively alleviating the tradeoff between bias and variance. Extensive experiments demonstrate that RMSGE achieves substantially higher attack success rates and query efficiency than existing black box methods across multiple benchmark datasets, validating its effectiveness.

In the online social networks (OSNs) and social Internet of Things (SIOT), communities of interest (CoI) are often used to facilitate information sharing among user devices. However, the risk of information leaks across communities persists due to inadequate control over users’ sharing behavior. In this paper, we propose a novel trust-based community sharing mechanism to control users who are contributing to high privacy leakage across communities of an OSN. In detail, we firstly formulate privacy loss in community based on the sensitivity and willingness of users in sharing. Secondly, we use this loss as the key determinant when updating trust to dynamically hold users accountable for privacy leakage. Thirdly, we use an adjustable threshold to enable or disable sharing users and evaluate the amount of information shared before and after control, as well as the changes in community user trust. Finally, we propose an optimization method based on the upper confidence bound to make a trade-off between information sharing and leakage through a payoff function over discretized thresholds. Simulations on three real OSNs datasets — BlogCatalog, Flickr, and YouTube — demonstrated that our proposed mechanism can effectively reduce community privacy loss by achieving the best payoff score of 1076.53, while the state-of-the-art baselines PDC-InfoSharing and UTV scored 506.33 and 957.98, respectively.

Blockchain plays a key role in establishing secure and decentralized Industrial Internet of Things (IIoT) systems. Currently, the dependent transactions generated by IIoT devices require a packing process to select a set of non-conflicted transactions, which results in significant delay and deviation of the transaction response time. In this paper, we propose a novel transaction packing algorithm named Priority-Pack to address the above issue. Firstly, we use directed acyclic graphs to model the dependent transactions in IIoT systems to establish the mathematical relationships between transaction priority and waiting time as well as dependencies. Secondly, we propose an algorithm to specify a higher priority to a transaction with longer waiting time without violating transaction dependencies. It eliminates the time required to traverse the subsets of transactions in other algorithms. Thirdly, to further reduce the response delay for transactions with the same priority level, we choose to first pack transactions with smaller sizes. We prove that this selection can achieve the lowest average response time. Finally, simulations are conducted to benchmark the Priority-Pack against the state-of-the-art algorithms including Fair-Pack and Random-Pack. The results demonstrate that Priority-Pack outperforms the others in terms of average response time and deviations.

This article proposes a novel machine-learning-based routing optimization for the multiple reconfigurable intelligent surfaces (M-RIS)-assisted multihop cooperative networks, in which a practical phase model for reconfigurable intelligent surface (RIS) with the amplitude variation based on the corresponding discrete phase shift is considered. We aim to maximize the end-to-end data rate in the proposed network by jointly optimizing the data transmission path, the passive beamforming design of RIS, and transmit power allocation. To tackle this complicated nonconvex problem, we divide it into two subtasks: 1) the passive beamforming design of the RIS and 2) joint routing and power allocation optimization. First, for the passive beamforming design of RIS, we develop a distributed learning algorithm that employs a cascade forward backpropagation network in each relay node to solve the RIS coefficients optimization problem by directly using the optimization target to train the cascade networks. This solution can avoid the curse of dimensionality of traditional reinforcement learning algorithms in the RIS optimization problem. Then, based on the result of RIS optimization, we introduce the proximal policy optimization (PPO) algorithm with the clipping method to find solutions for joint optimization of routing and power allocation via achieving the long-term benefit in the Markov decision process (MDP). Simulation results show that the proposed learning-based scheme can learn from the environment to improve its policy stability and efficiency in the iterative training process for optimizing routing and RIS and significantly outperform the benchmark schemes.

In this paper, a distributed cyclic delay diversity (dCDD) is proposed to the cooperative infrastructure-to-vehicle (I2V) system comprising one road side unit (RSU). At a particular time, to connect a RSU and a target vehicle outside each other's transmission range, a multiple number of vehicles located within the transmission ranges of both the RSU and the target vehicle are configured to operate as the dCDD based decode-and-forward (DF) cooperative relays. By using dCDD in the I2V system, the transmission range of the RSU is extended without the need of full channel state information of the vehicles at the RSU, and the transmit diversity gain can also be achieved. For this new preliminary setting of the I2V system, we conduct performance analysis. The simulations are conducted to verify the outage probability. The asymptotic outage diversity gain is also investigated and justified by the link level simulations.

Unmanned aerial vehicles (UAVs) have been undergoing fast development for providing broader signal coverage and more extensive surveillance capabilities in military and civilian applications. Due to the broadcast nature of the wireless signal and the openness of the space, UAV eavesdroppers (UEDs) pose a potential threat to ground communications. In this paper, we consider the communications of a legitimate ground link in the presence of friendly jamming and UEDs within a finite area of space. The spatial distribution of the UEDs obeying a uniform binomial point process (BPP) is used to characterize the randomness of the UEDs. The ground link is assumed to experience log-distance path loss and Rayleigh fading, while free space path loss with/without the averaged excess path loss due to the environment is used for the air-to-ground/air-to-air links. A piecewise function is proposed to approximate the line-of-sight (LoS) probability for the air-to-ground links, which provides a better approximation than using the existing sigmoid-based fitting. The analytical expression for the secure connection probability (SCP) of the legitimate ground link in the presence of non-colluding UEDs is derived. The analysis reveals some useful trends in the SCP as a function of the transmit signal to jamming power ratio, the locations of the UAV jammer, and the height of UAVs.

With the development of wireless communications and the intellectualization of machines, the Internet of Things (IoT) has been of interest to both industry and academia. Multihop routing and relaying are key technologies that will underpin IoT mesh networks in the future. This paper investigates optimal routing based on the trusted connectivity probability (T-CP) for multihop, underlay, device-to-device (D2D) communications with decode-and-forward relaying. Both random and fixed locations for base stations (BSs) are considered, where the former case assumes that the locations of the BSs are modeled as a Poisson point process (PPP). First, we derive two expressions for the connectivity probability (CP): 1) a tight lower bound and 2) an exact closed-form. Analysis is carried out for the cases where the channel state information (CSI) between BSs and the D2D transmitter is known (CSI-aware) and unknown (noCSI). Interference from active cellular user equipments (CUEs) is characterized by modeling CUE locations as a PPP. Moreover, motivated by results that have shown that social behavior leads to D2D devices communicating with nearby neighbors, we derive the trust probability for D2D connections by using a rank-based model. Finally, we propose a novel routing algorithm that can achieve the highest T-CP for any pair of D2D devices in a distributed manner. The derived analytical results are verified by Monte Carlo simulations. We show that the proposed routing algorithm achieves almost the same performance as that attained through an exhaustive search. When BSs are located randomly, the optimal path based on the CP is the shortest path between the D2D transmitter and receiver. However, for fixed BSs, the optimal path selection depends on the locations of the BSs, which provides a very useful insight in designing the multihop D2D system for 5G IoT.

In this work, we analyze the distance statistics corresponding to points in a Matern cluster (offspring points) and points that do not belong to that cluster (non-offspring points). We first derive the probability density function (PDF) of the distance between an offspring point and a non-offspring point of a Matern cluster. We then formulate the probability generating functional based on this PDF. Since many wireless networks (e.g., device-to-device (D2D) networks and cognitive radio systems) exhibit device clustering, this formalism enables us to efficiently formulate and evaluate expressions that describe the interference statistics and connection probability in clustered networks. We validate our theoretical analysis with numerical simulations, and illustrate that traditional methods of evaluating similar performance metrics (based on point process statistics instead of distance statistics) are unsuitable for use in such complex scenarios.

Efficient transmission power control is indispensable for cellular networks. It not only provides a high energy efficiency, but also maintains reliable connections. With the emergence of 5G mobile technology, the presence of device-to-device (D2D) communications within the cellular network has stimulated research on radio resource sharing. In this paper, we consider an underlay D2D network operating in a Rayleigh fading channel and propose a power allocation method that assigns transmit power levels to D2D UEs (DUEs) and cellular UEs (CUEs) such that the joint connection probability of DUEs and CUEs is maximized. The approach is formulated as a optimization problem, and we prove that the problem is log concave. Hence, the optimum powers for active UEs can be found easily using modern computational methods. Both the theoretical and simulated results show that the joint connectivity probability is improved by one to two orders of magnitude by applying the optimization procedure compared to conventional LTE open loop power allocation. This dramatic improvement comes at the cost of an increase in UE average transmit power. Thus, the proposed technique is well suited to 5G public safety and disaster relief communication modes where enhanced connectivity is the top priority.

If an automobile can recognize whether a place is refuel station, parking lot or maintenance shop automatically, and it can tell other automobiles where the place is, the automobile will be able to fulfill its basic needs like refueling, parking and maintaining without any manual intervention. People will be freed from asking passengers or searching in the map of Geographic Information System (GIS) about whereto refuel, park or maintain. In this paper, we propose a novel vehicle self-gratification system using vehicular sensors and mobile phones to achieve the aforementioned assumption. Also, through statistics analysis, some new and useful information like popularity of a maintenance shop and probability of an available parking space at a certain time can be generated and guide people to make better choices. Experiments in real driving environment show that our system can accurately recognize the locations of fuel stations, parking lots and maintenance shops, and the information of popularity and probability make people's choices not random but good.