Federated fine-tuning of Mixture-of-Experts (MoE)-based large language models (LLMs) is challenging due to their massive computational requirements and the resource constraints of participants. Existing works attempt to fill this gap through model quantization, computation offloading, or expert pruning. However, they cannot achieve desired performance due to impractical system assumptions and a lack of consideration for MoE-specific characteristics. In this paper, we propose Flux, a system designed to enable federated fine-tuning of MoE-based LLMs across participants with constrained computing resources (e.g., consumer-grade GPUs), aiming to minimize time-to-accuracy. Flux introduces three key innovations: (1) quantization-based local profiling to estimate expert activation with minimal overhead, (2) adaptive layer-aware expert merging to reduce resource consumption while preserving accuracy, and (3) dynamic expert role assignment using an exploration-exploitation strategy to balance tuning and non-tuning experts. Extensive experiments on LLaMA-MoE and DeepSeek-MoE with multiple benchmark datasets demonstrate that Flux significantly outperforms existing methods, achieving up to 4.75× speedup in time-to-accuracy.

The rapid adoption of electric vehicles (EVs) has created new opportunities for decentralized energy trading, where EVs can act as mobile energy providers in peer-to-peer markets. Blockchain provides a secure foundation for such systems, ensuring trust and accountability. However, its inherent transparency creates privacy risks, as it enables the tracking of trading activities. Existing privacy-preserving mechanisms typically focus on concealing payment transactions but often expose other critical interactions, such as matching coordination. To address these challenges, we proposePriVET, a privacy-preserving framework for vehicular energy trading. PriVET leverages smart contracts for trade matching and uses an enhanced Paillier encryption scheme to support encrypted comparisons, ensuring secure coordination without revealing sensitive data. Additionally, a Bloom-filter– based Geohash encoding is used to protect location privacy during spatial matching. We evaluate PriVET through both theoretical analysis and practical experiments. In a simulation environment, the transaction computation time for 100 vehicles is shown to be under 30ms, with communication overhead kept below 20KB. These results demonstrate that PriVET provides robust privacy protection while maintaining minimal overhead, making it a practical solution for real-world blockchain-based energy trading scenarios.

Assessing the trustworthiness of network elements, including devices, communication links, and end-to-end paths, is critical for building secure and reliable networks. However, the heterogeneity of these components and the dynamic nature of their security states make trust assessment in large-scale networks highly challenging. This paper presents a configurable end-to-end network trustworthiness assessment scheme featuring a hierarchical, life-cycle-based architecture for evaluating trust from initial network deployment through long-term operation to retirement. In the proposed scheme, trustworthiness is assessed at three levels: device, link, and path. At the device level, a Dempster-Shafer evidence theory-based model integrates multi-dimensional trust attributes to quantify each device’s trustworthiness. At the link level, a dynamic multi-attribute model combines static and dynamic trust attributes and employs a clustering algorithm to calculate link trust scores. At the path level, a distributed fusion-based model uses fuzzy logic to evaluate end-to-end path trust by aggregating the trust levels of constituent devices and links. Simulation results demonstrate that the proposed scheme significantly improves both the accuracy and efficiency of trustworthiness assessment in large-scale networks.

Driven by recent advances of large models and agents, embodied artificial intelligence (AI) emerges as a transformative paradigm for next-generation AI, endowing agents with physical forms and the ability to perceive, reason, and act within real-world environments. Unlike disembodied or virtual AI agent systems, embodied agents co-evolve cognition, control, and embodiment through continuous feedback loops, enabling applications ranging from humanoid robots to autonomous vehicles. In this survey, we first introduce a dual-brain architecture of embodied AI and examine its foundational technologies and key characteristics. We then analyze the security and privacy landscape, identifying critical vulnerabilities and evaluating existing/potential countermeasures. Finally, we outline emerging trends and open research directions in this emerging field, charting a roadmap toward efficient, secure, and ethically aligned embodied AI ecosystems.

The open and vulnerable nature of wireless channels exacerbates security risks in integrated sensing and communication (ISAC) systems, especially when the sensing targets act as potential eavesdroppers (Eves), and these risks intensify with collusion among Eves. To address this challenge, this paper investigates a novel strategy for a robust reconfigurable intelligent surfaces (RIS)-assisted secure ISAC system, where an ISAC base station facilitates simultaneous secure communication with legitimate users and sensing of multiple targets that may serve as Eves. We examine two different interaction mechanisms among Eves, namely, non-colluding Eves (NCE) and colluding Eves (CE), under both perfect and imperfect channel state information (CSI) assumptions. For both mechanisms, we formulate the optimization problem of maximizing users’ sum secrecy rate by jointly designing the transmit beamforming and RIS phase-shifts. This optimization is subject to constraints on transmit power, sensing requirements, and unit-modulus RIS phase shifts. The resulting non-convex problems are solved via alternating optimization (AO) algorithms. Specifically, in order to handle the severely non-convex and coupled objective function and multi-link accumulated channel error constraints caused by CE as well as imperfect CSI, we employ the majorizationminimization algorithm and the S-procedure to convert these problems into tractable forms. Simulation results validate the effectiveness of our proposed algorithms. We highlight that, at the expense of a 15% reduction in the users’ sum rate, our proposed algorithm achieves up to a 185% increase in the sum secrecy rate. Furthermore, we quantify the sensing-security trade-off by analyzing the reduction of the sum secrecy rate induced by sensing requirements, and we reveal the impacts of various factors on the sum secrecy rate, such as RIS element number, channel estimation errors, and sensing thresholds.

This paper investigates covert transmission in semantic communication systems, with a particular focus on protecting sensitive information embedded in images. While current research on semantic secure communication emphasizes semantic confidentiality, it often overlooks the covertness of semantic content. To bridge this gap, we propose a semantic covert communication scheme comprising a semantic covert artificial noise module and a deep learning module. The semantic covert artificial noise module ensures that the covertness-oriented artificial noise (CoAN) lies within the null space of the legitimate user’s channel, so that it perturbs only the eavesdropper while preserving the legitimate user’s ability to recover semantic information. The deep learning module is designed to distinguish sensitive content from the background in an image. Furthermore, the semantic covert communication neural network is trained using a customized information-bottleneck–inspired loss function that simultaneously enhances the semantic expressiveness of the representation observed by the legitimate user and improves the semantic representational capability of the CoAN at the eavesdropper, thereby inducing the eavesdropper to misinterpret the transmitted information as background. Simulation results demonstrate that the proposed scheme conceals sensitive information from the eavesdropper’s perspective while maintaining high-fidelity semantic recovery for the legitimate user.

The rise of AI-generated images has sparked serious concerns about their potential misuse across various domains, prompting the urgent need for robust detection methods. Despite advancements, many current approaches prioritize short-term gains at the expense of long-term effectiveness. This paper critiques the overly specialized approach of fine-tuning pre-trained models for short-term gains on a single AI image dataset, while disregarding the long-term imperative of achieving generalization and knowledge retention. To address this trade-off issue, we propose a novel learning framework (PoundNet) for the generalization of AI-generated image detection on a pre-trained vision-language model. PoundNet incorporates a learnable prompt design and a balanced objective to preserve broad knowledge from upstream tasks (object classification) while enhancing generalization for downstream tasks (AI-generated image detection). We train PoundNet on a single standard AI image dataset, following common practice in the literature. We then evaluate its performance across 10 large-scale public AI-generated image detection datasets with 5 main evaluation metrics, forming the largest benchmark test set for assessing the generalization ability of AI-generated image detection models, to our knowledge. The comprehensive benchmark evaluation demonstrates that PoundNet successfully balances generalization with knowledge retention, achieving a remarkable relative improvement of 19% in AI-generated image detection performance compared to state-of-the-art methods, while maintaining a strong performance of 63% on object classification tasks.

With the widespread use of social media platforms and people’s increasing dependence on them, social media has emerged as one of the most important channels for advertorials. However, there is currently a lack of research on detecting advertorials on social media platforms. This research focuses on detecting advertorials, a type of advertisement that frequently conceals itself within normal articles, blurring the nature of advertising and deceiving users. To effectively carry out research on advertorial detection, we have constructed a multi-topic advertorial dataset in Chinese with rich social information. This dataset is obtained from the Chinese question-answering platform ZHIHU, and it is publicly available to facilitate further research. 1 Furthermore, we propose AdDetector, a novel dual-tower model that detects advertorials by jointly leveraging the article’s textual and social information. In addition, we use fine-grained sentence-level classification labels to improve the model’s generalization capability on previously unseen topic articles. Experiment results show that our model significantly improves the \(F_1\) score by 1.29% in the intra-domain advertorial detection setting and 1.52% in the transfer setting in comparison with several strong baselines. The extensive ablation studies and thorough performance analyses also validate the complementary and beneficial values of the novel components of AdDetector. We also make our source code publicly available to facilitate future studies. 2 This research provides crucial support for user protection and advertising management.

The need for enhanced transaction privacy in decentralized finance (DeFi) is critical. However, existing coin mixing solutions often reveal telltale patterns on the blockchain, exposing users to heuristic analysis. This paper presents DeFiMix, an indistinguishable coin mixing scheme engineered to obscure transaction flows while guaranteeing fairness and security. DeFiMix achieves this through a dual-layer mechanism. First, an off-chain secret handshake protocol enables anonymous negotiation between senders and mixers, effectively breaking the link between transactions and participants. Second, on-chain transactions are structured using time-locks and concurrent signatures to resemble common DeFi activities such as staking and lending, rendering them indistinguishable from ordinary operations. Using security analysis and extensive simulations, we validate DeFiMix’s ability to prevent transaction linkage while remaining practically viable. The results underscore DeFiMix’s strong indistinguishability and fairness, alongside its minimal computational demands, establishing it as a compelling solution for privacy-focused transactions within the DeFi ecosystem.

With the rapidly growing demand for collaborative data analysis, Jaccard Coefficient (JC) computation over multisets has been widely adopted in data deduplication to enhance large-scale data processing efficiency, but meanwhile incurs some security issues such as the leakage of input data sets. Thus, Secure Jaccard Coefficient Computation over Multisets (SCJM) schemes have been proposed. However, existing solutions that indirectly compute JC fail to protect the privacy of intersection and union cardinalities, incur high computational overhead, and rely on approximation techniques that cannot support high-precision analysis or simulation-based security proofs. To address these problems, we propose a secure and efficient protocol to accurately compute JC over multisets. Specifically, the protocol computes JC for small-scale data domains using secure oblivious ratio computation, ensuring that under the semi-honest adversarial model, the intersection and union cardinalities remain concealed from all parties, including the decryption key holder, during and after the computation. And it achieves a linear computational cost ofO(em-1) without accuracy loss, whereem-1denotes the maximum number of repetitions of an element in the multiset. The protocol can also be extended to compute the cardinalities of intersection and union. To further enhance efficiency, we introduce a cloud-assisted encryption scheme, which improves computational efficiency by 25.5% to 30.4% compared with the non-cloud-assisted scheme. Additionally, we provide a secure proof of the proposed protocol in the ideal-real paradigm. Experimental results show efficiency advantages of our protocol over the state-of-the-art solution.

Collaborative perception (CP) is a critical enabler for enhancing situational awareness, traffic safety, and mobility in connected autonomous vehicles (CAVs). By integrating sensory data from multiple CAVs, CP effectively mitigates perceptual blind spots, reduces the likelihood of traffic accidents, and alleviates congestion within complex environments. To advance CP capabilities within dynamic and resource-constrained network conditions, this paper proposes a proactive collaborative perception strategy that enables CAVs to selectively share perceptual data with other CAVs based on real-time network status and anticipated perceptual demands. Specifically, a collaborative framework, integrating communication and perception, is designed to enhance CP performance with data processing and fusion techniques. Within this framework, an innovative adaptive data compression algorithm is introduced, which dynamically adjusts the compression ratio based on the monitored real-time signal-to-noise ratio, therefore optimizing the data transmission efficiency. Additionally, a mathematical model is formulated to jointly optimize the communication and perception resources, and a multi-agent reinforcement learning algorithm based on Global State Proximal Policy Optimization (GSPPO) is developed to enhance communication resources distribution and CAV selection in complex and dynamic network environments. Experimental results demonstrate that the proposed proactive CP strategy can effectively reducing communication latency without compromising perception accuracy.

To advance the development of maritime intelligent transportation systems (MITS), underwater wireless sensor networks (UWSNs), composed of numerous sensor nodes, have been widely deployed for underwater information perception. However, UWSNs face critical challenges in achieving cost-effective and timely data collection due to their large-scale deployment and stringent data timeliness requirements. To address this challenge, this paper proposes an efficient data collection scheme for UWSNs through the collaboration between uncrewed surface vehicles (USVs) and autonomous underwater vehicles (AUVs). Specifically, we first introduce a cooperative framework where AUVs select appropriate USVs to form USV-AUV clusters. Within each cluster, AUVs are responsible for sensing data collection, while USVs act as relay nodes, moving toward the destination (e.g., data center). We then devise an evolutionary game-theoretic cluster forming mechanism, deriving evolutionarily stable strategies (ESS) through replicator dynamics analysis, which guarantees a provable Nash equilibrium. Next, we present a hierarchical optimization method that models the interaction between UWSNs and the cluster as a two-agent Markov decision process, where a dual-agent Q-learning algorithm is designed to jointly optimize the decisions of both entities. Finally, extensive simulations demonstrate that the proposed scheme outperforms conventional methods in improving the efficiency of sensing data collection for UWSNs.

Distinguishing between benign and poisoned gradients hidden behind cryptographic masks is a critical challenge in privacy-preserving federated learning (FL). Existing robust aggregation defenses suffer from two primary limitations: (1) susceptibility to manipulation, where adversaries induce deviations from standard protocols to bypass statistics-based defenses (e.g., mean or median), and (2) limited detection granularity, where the reliance on coarse statistics under encryption fails to identify subtle or coordinated poisoning behaviors. To address these issues, we propose RankFL, a poison-robust and privacy-preserving FL scheme that leverages order sorting over ciphertext gradients. RankFL utilizes an efficient Paillier-based two-party comparison protocol to construct a joint order tree, facilitating quartile-driven filtering of malicious updates without compromising individual gradient privacy. Furthermore, we introduce RankFL-Extend, which incorporates zero-knowledge proof-of-knowledge and bidirectional verification to secure the ranking process against active adversaries. We provide a rigorous theoretical analysis to establish the scheme's privacy, indistinguishability, and convergence guarantees. Extensive experiments across diverse datasets and attack scenarios demonstrate that the proposed scheme achieves a$3\%$accuracy improvement over state-of-the-art defenses under poisoning attacks.

With the rapid growth of mobile and edge computing, federated learning (FL) has emerged as a key technology to enable collaborative model training on mobile devices while preserving user privacy. Secure aggregation is an essential component in FL to protect local gradients and compute the global model, but it is vulnerable to threats from high-latency. When some clients arrive late, the pairwise masks among clients cannot be canceled properly, forcing the server to learn the late clients' masks in order to complete the aggregation. As a result, network uncertainty puts the aggregation process at risk of either service interruption or privacy leakage. While double masking is treated as the most effective solution to achieve both robustness and privacy, its computational and communication costs are prohibitive, especially for resource-constrained mobile devices. To address these challenges, we propose an Efficient Mobile-Cloud Collaborative Aggregation for Federated Learning with Latency Resilience (EFL-LR). We leverage Shamir's secret sharing and a key-homomorphic pseudorandom function to ensure privacy for high-latency clients while reducing computation overheads to$\mathcal {O}(n\log ^{2} n + d)$for clients and$\mathcal {O}(n+d)$for the server. Formal security analysis confirms its latency resilience and privacy guarantees. Experimental results show that EFL-LR achieves 2–$3\times$lower client-side computation cost and accelerates server-side aggregation recovery by at least$10\times$.

The Internet of Vehicles (IoV) is undergoing a transformative evolution, enabled by advancements in future 6 G network technologies, to support intelligent, highly reliable, and low-latency vehicular services. However, the enhanced capabilities of loV have heightened the demands for efficient network resource allocation while simultaneously giving rise to diverse vehicular service requirements. For network service providers (NSPs), meeting the customized resource-slicing requirements of vehicle service providers (VSPs) while maximizing social welfare has become a significant challenge. This paper proposes an innovative solution by integrating a mean-field multi-agent reinforcement learning (MFMARL) framework with an enhanced Vickrey-Clarke-Groves (VCG) auction mechanism to address the problem of social welfare maximization under the condition of unknown VSP utility functions. The core of this solution is introducing the “value of information” as a novel monetary metric to estimate the expected benefits of VSPs, thereby ensuring the effective execution of the VCG auction mechanism. MFMARL is employed to optimize resource allocation for social welfare maximization while adapting to the intelligent and dynamic requirements of IoV. The proposed enhanced VCG auction mechanism not only protects the privacy of VSPs but also reduces the likelihood of collusion among VSPs, and it is theoretically proven to be dominant-strategy incentive compatible (DSIC). The simulation results demonstrate that, compared to the VCG mechanism implemented using quantization methods, the proposed mechanism exhibits significant advantages in convergence speed, social welfare maximization, and resistance to collusion, providing new insights into resource allocation in intelligent 6 G networks.

Current Text-to-Image generative models struggle to continuously learn multiple distinct entities or concepts, limiting their scalability and hindering practical deployment in dynamic environments. We formulate this task as Continual Conceptual Entity Learning (CEL) and propose a novel framework called Continual Entity Adapter Learning (CEAL). CEAL leverages a compact set of tunable parameters, termed SuperLoRA, to efficient and scalable learning of new entities. We propose a dynamic rank-increasing strategy to train the SuperLoRA, balancing computational efficiency with performance. To evaluate our method, we create three benchmarks encompassing generic objects, human faces, and artistic styles. Experimental results demonstrate that CEAL effectively learns new entities while preserving prior knowledge, outperforming existing methods in both entity fidelity and parameter efficiency.

Federated learning (FL) is a widely studied framework for privacy-preserving collaborative training among multiple clients. However, real-world deployments reveal a persistent challenge: free-riders, i.e., participants who benefit from the system without contributing meaningful updates. If not properly addressed, free-riders can discourage honest contributors and ultimately impair both the fairness and efficiency of FL ecosystems. Existing defenses mainly rely on post-hoc per-client, per-round evaluation, leading to limited deterrence and high resource overhead, particularly in large-scale deployments. To tackle these problems, we propose FLET, a novel federated learning framework with test tasks. FLET introduces dedicated test tasks into the training process, blending them with real FL tasks while concealing their types from participants. These test tasks, generated from the reference datasets, serve as decoys that enable accurate detection of free-riding behavior. To enforce accountability, an economic penalty mechanism is employed, achieving both proactive (ex-ante) deterrence and reactive (expost) detection.We analyze the interactions between the server and participants through a free-riding suppression game model with asymmetric information (i.e.,task type), and develop a strategic information disclosure scheme (i.e.,revealing task requirements) to mislead attackers and proactively shape participant behavior. We characterize both pure-strategy and mixed-strategy perfect Bayesian Nash equilibria, and propose a lightweight strateg-ymaking algorithm that guides players toward equilibrium strategies under different conditions with modest overhead. Extensive experiments validate that FLET effectively suppresses free-riding and enhances the utility of both the server and participants. Our findings provide insights for designing cost-effective free-riding defenses in practical FL.

Due to the immutable nature of smart contracts, online contract diagnosis is the only viable approach for revealing vulnerabilities in deployed contracts. Existing online approaches face significant challenges in terms of efficiency, adaptability, and reliance on vulnerability labels. This paper proposes ConWatcher+, a new adaptive and label-efficient online contract diagnosis framework from the diffusion perspective, which is capable to detect yet unknown attacks under evolving tactics without reliance on vulnerability labels. ConWatcher+ simulates the Advanced Persistent Threat (APT) tactics commonly used in yet unknown attacks by continuously applying minor perturbations to legitimate interaction behaviors. It then reversely learns the denoising process, guided by potential logic vulnerabilities (i.e., functionality dependencies), to adaptively identify stealthy anomalies and detect yet unknown attacks without needing vulnerability labels. ConWatcher+ proceeds in five steps. First,real-time data extraction. We design a cost-effective contract runtime information collector, incorporating on-demand data retrieval and event-driven data update mechanisms to reduce communication overhead in online contract diagnosis. Second,interaction behavior modeling. Via bytecode-level, account-level, revenue-level modeling, and side-channel level behavior modeling, we propose behavior-aware multivariate time series model to accurately represent long-term contract interactions with multi-faceted behaviors. Third,APT-like noise adding. We leverage the forward diffusion model to produce minor and stochastic APT-like noises with efficiency. Fourth,reverse denoising learning. To effectively guide reverse denoising using functionality dependencies, we devise an adaptive contract-level analysis engine equipped with heterogeneous control flow graph modeling and heterogeneous message passing mechanisms to extract function-level and bytecode-level functionality dependencies. Last,contract anomaly detection. We establish a label-efficient attack detector based on reconstruction error for contract anomaly detection. It combines complex dependency analysis and deterministic inference to ensure high-quality data reconstruction and low detection latency. Extensive empirical validations on a manually constructed dataset, covering both mainstream and novel vulnerabilities, demonstrate ConWatcher+’s effectiveness, adaptability, and label efficiency, with an average F1-score of 0.92 across all types of attacks without prior knowledge of corresponding vulnerabilities.

Fine-tuning large language models (LLMs) is critical for adapting pretrained models to specialized downstream tasks. Federated LLM fine-tuning enables privacy-aware model updates by allowing data owners (DOs) to contribute a global LLM without exposing local data. However, full-parameter fine-tuning in federated settings incurs significant computational and communication overhead, while frequent gradient exchanges increase the risk of privacy leakage, such as memorized data inference. Parameter-efficient fine-tuning (PEFT) with differential privacy (DP) offers a low-overhead alternative with formal privacy guarantees, but fails to strike privacy-utility tradeoff under heterogeneous privacy preferences: individual DOs may inject excessive DP noise to maximize privacy, whereas the curator aims to minimize noise to preserve model quality. In this paper, we present an innovative game-theoretical framework that enables dynamic privacy trading within differentially private federated LLM fine-tuning. In the game, DOs strategically adjust their local DP noise levels in exchange for customized incentives from the curator, thereby balancing privacy and utility. We begin by establishing a theoretical convergence bound that quantifies the influence of locally injected noise on the global model utility. Under this bound, we analytically characterize the pure-strategy Nash equilibrium of the game, accounting for DO heterogeneity, curator budget constraints, and noise estimation errors. For mixed-strategy settings with incomplete information, we design a hierarchical reinforcement learning algorithm that jointly learns DOs’ optimal noise-saving strategies and the curator’s optimal pricing policy without presupposing their private information. Experiments on real-world datasets demonstrate that the proposed scheme improves DO utility, reduces curator cost, mitigates free-riding, and accelerates convergence compared to existing methods.

In wireless networks with integrated sensing and communications (ISAC), edge intelligence (EI) is expected to be developed at edge devices (ED) for sensing user activities based on channel state information (CSI). However, due to the CSI being highly specific to users’ characteristics, the CSI-activity relationship is notoriously domain dependent, essentially demanding EI to learn sufficient datasets from various domains in order to gain cross-domain sensing capability. This poses a crucial challenge owing to the EDs’ limited resources, for which storing datasets across all domains will be a significant burden. In this paper, we propose theEdgeCLframework, enabling the EI to continually learn-then-discard each incoming dataset, while remaining resilient to catastrophic forgetting. We design a transformer-based discriminator for handling sequences of noisy and nonequispaced CSI samples. Besides, we propose a distilled core-set based knowledge retention method with robustness-enhanced optimization to train the discriminator, preserving its performance for previous domains while preventing future forgetting. Experimental evaluations show that EdgeCL achieves 89% of performance compared to cumulative training while consuming only 3% of its memory, mitigating forgetting by 79%.

Integrated sensing and communication (ISAC) has emerged as a promising paradigm for next-generation mobile wireless communication systems. In this paper, we propose a novel framework for full-duplex ISAC systems that jointly incorporates dynamic tracking and robust secure beamforming. Specifically, a dual-functional radar-communication base station employs an extended Kalman filter to dynamically estimate the trajectories of mobile downlink (DL) users. To mitigate the impact of imperfect channel state information from multiple eavesdroppers, a robust beamforming strategy is devised by quantifying angular uncertainty via the Cramér–Rao bound (CRB). A total transmit power minimization problem is formulated under secrecy rate constraints for both DL and uplink (UL) communications, while simultaneously ensuring sensing accuracy through CRB and beam tracking mean squared error metrics. The optimization jointly considers the beamforming matrices, artificial noise covariance matrix, and UL power allocation strategy. To address the formulated non-convex problem, the S-procedure is employed to transform semi-infinite constraints into linear matrix inequalities. Successive convex approximation technique is then iteratively applied to obtain high-quality solutions. Extensive simulations verify the effectiveness of the proposed framework, demonstrating superiority in secrecy rate and power efficiency compared to benchmark schemes. Moreover, the results highlight the inherent trade-offs between secure communications performance and sensing accuracy, thereby offering insights into the design of future full-duplex ISAC systems.

In this paper, we propose a full-duplex integrated sensing and communication (ISAC) system enabled by a movable antenna (MA). By leveraging the characteristic of MA that can increase the spatial diversity gain, the performance of the system can be enhanced. We formulate a problem of minimizing the total transmit power consumption via jointly optimizing the discrete position of MA elements, beamforming vectors, sensing signal covariance matrix and user transmit power. Given the significant coupling of optimization variables, the formulated problem presents a non-convex optimization challenge that poses difficulties for direct resolution. To address this challenging issue, the discrete binary particle swarm optimization (BPSO) algorithm framework is employed to solve the formulated problem. Specifically, the discrete positions of MA elements are first obtained by iteratively solving the fitness function. The difference-of-convex (DC) programming and successive convex approximation (SCA) are used to handle non-convex and rank-1 terms in the fitness function. Once the BPSO iteration is complete, the discrete positions of MA elements can be determined, and we can obtain the solutions for beamforming vectors, sensing signal covariance matrix and user transmit power. Numerical results demonstrate the superiority of the proposed system in reducing the total transmit power consumption compared with fixed antenna arrays.

Radio maps (RMs) are essential for environment-aware communication and sensing, providing location-specific wireless channel information. Existing RM construction methods often rely on precise environmental data and base station (BS) locations, which are not always available in dynamic or privacy-sensitive environments. While sparse measurement techniques reduce data collection, the impact of noise in sparse data on RM accuracy is not well understood. This paper addresses these challenges by formulating RM construction as a Bayesian inverse problem under coarse environmental knowledge and noisy sparse measurements. Although maximum a posteriori (MAP) filtering offers an optimal solution, it requires a precise prior distribution of the RM, which is typically unavailable. To solve this, we propose RadioDiff-Inverse, a diffusion-enhanced Bayesian inverse estimation framework that uses an unconditional generative diffusion model to learn the RM prior. This approach not only reconstructs the spatial distribution of wireless channel features but also enables environmental building outlines perception, just relying on pathloss, through integrated sensing and communication (ISAC). The proposed method operates on routine communication measurements, without new waveforms, specialized feedback, or protocol changes, thereby enabling a plug-and-play ISAC capability. Remarkably, RadioDiff-Inverse is training-free, leveraging a pre-trained model from Imagenet without task-specific fine-tuning, which significantly reduces the training cost of using a generative large model in wireless networks. Experimental results demonstrate that RadioDiff-Inverse achieves state-of-the-art performance in accuracy of RM construction and environmental reconstruction, and robustness against noisy sparse sampling.

In this paper, we investigate fairness-aware intelligent reflecting surface (IRS)-assisted multiple-user uplink communication systems. Current IRS-assisted communication methods do not consider the fairness issues of communication performance for multiple users. We formulate the IRS phase shift and receive beamforming design as an α-fair utility maximization problem by considering different fairness metrics, including zero fairness and proportional fairness, followed by an iterative algorithm to solve this problem. To overcome the high computational complexity of the iterative algorithm, we introduce a deep learning-based framework, which leverages convolutional neural networks with residual and attention mechanisms for real-time phase shift prediction. Extensive numerical simulations demonstrate that the proposed method achieves performance comparable to the traditional approach while significantly reducing computational overhead, making it a promising solution for future fairness-aware IRS-assisted communication systems.

Generative artificial intelligence (GAI), e.g., diffusion model is recognized as a promising paradigm for enhancing intelligent transportation systems in vehicular networks. However, the existing implementation of GAI in vehicular networks is limited due to the massive data requirements of GAI and the considerable resources for model training, particularly in distributed vehicular network environments. Federated learning (FL) offers a promising solution by enabling distributed collaborative training for GAI. Therefore, in this paper we present an FL-empowered diffusion model training scheme for vehicular networks. Specifically, first, a novel utility evaluation model based on local model training accuracy is designed to assess the contribution of each vehicle's local model. The interactions between the edge computing servers and vehicles are modeled using a Stackelberg game, while a non-cooperative game determines the optimal strategy among vehicles. To account for the heterogeneity of vehicles and the uncertainty of associated risks, we incorporate prospect theory (PT) to represent subjective utility. Afterward, a backward induction mechanism is devised to determine the Stackelberg equilibrium for deriving the optimal decisions of edge computing servers and vehicles. Finally, simulations are conducted to illustrate that the proposed scheme significantly improves the sum utility rate in comparison to other baseline schemes.

Vehicular digital twins (DTs) create virtual representations of physical vehicles, enabling real-time data exchange to enhance intelligence and ensure safe driving. Reducing DT content delivery latency in infrastructure-deficient, sparsely populated areas is crucial. This paper develops a novel Satellite-UAV multi-path content delivery framework for data synchronization in vehicular DT applications. Satellites offer wide coverage but suffer from high latency, while UAVs provide rapid deployment and low-latency communication. The framework leverages these unique characteristics to facilitate simultaneous content downloading through multiple paths, thereby reducing latency. A Stackelberg game model is used to motivate effective resource allocation by UAVs. Given the typically private utility model of DTs, a learning-based algorithm is developed to determine optimal pricing strategies for UAVs. Simulation results demonstrate significant enhancements in UAV utility and reduced DT costs, meeting diverse service requirements.

Telecommunication fraud refers to deceptive activities in the field of communication services. This research focuses on a category of fraud identified as ''mutation telecommunication fraud". There is currently a lack of research on mutation telecommunication fraud detection, allowing this type of fraud to persist uncaught. We identify that detecting mutation fraud requires capturing multi-source patterns, including user communication graphs and temporal-spatial Voice of Call (VOC) features. Specifically, we introduce MutationGuard, which leverages Graph Neural Networks (GNN) to capture changes in user communication graphs. For VOC records, we map call start times onto a 3D cylindrical surface, thereby representing each VOC record in spatial coordinates and utilizing proposed LFFE and TCFE modules to capture local fraud behaviors and temporal behavior changes. The proposed neural modeling approach that facilitates multi-source information fusion constitutes a significant advancement in detecting mutation fraud. Experiment results reveal a significant improvement in the AUC score by 1.52% and the F1 score by 1.36% on the proposed telecommunication fraud dataset. Particularly, our method shows a significant improvement of 13.93% in accuracy on mutation fraud data. We also validate the effectiveness of our method on the publicly available Sichuan Telecommunication Fraud dataset.

As the implementation of machine learning (ML) systems becomes more widespread, especially with the introduction of larger ML models, we perceive a spring demand for massive data. However, it inevitably causes infringement and misuse problems with the data, such as using unauthorized online artworks or face images to train ML models. To address this problem, many efforts have been made to audit the copyright of the model training dataset. However, existing solutions vary in auditing assumptions and capabilities, making it difficult to compare their strengths and weaknesses. In addition, robustness evaluations usually consider only part of the ML pipeline and hardly reflect the performance of algorithms in real-world ML applications. Thus, it is essential to take a practical deployment perspective on the current dataset copyright auditing tools, examining their effectiveness and limitations. Concretely, we categorize dataset copyright auditing research into two prominent strands: intrusive methods and non-intrusive methods, depending on whether they require modifications to the original dataset. Then, we break down the intrusive methods into different watermark injection options and examine the non-intrusive methods using various finger-prints. To summarize our results, we offer detailed reference tables, highlight key points, and pinpoint unresolved issues in the current literature. By combining the pipeline in ML systems and analyzing previous studies, we highlight several future directions to make auditing tools more suitable for real-world copyright protection requirements.

Retrieval-based augmentation enhances the capabilities of large language models (LLMs) by incorporating external knowledge into the response generation process. However, existing retrieval-augmented frameworks often lack fine-grained access control and risk exposing sensitive content, particularly in voice-based interactive systems where queries are open-ended and personalized. This risk becomes especially pronounced when the retrieved information includes proprietary or user-specific data. To mitigate these challenges, we propose PrivRAG, a privacy-preserving retrieval protocol that integrates access control and response-level privacy protection throughout the generation pipeline. Specifically, each document in the knowledge base is assigned an attribute-based access policy represented as a logical tree, ensuring that only authorized users can retrieve relevant content. The interactions between user and LLM-driven assistant is modeled as a multi-turn process, where user attributes are inferred through probabilistic reasoning over observed responses. Based on these inferred attributes, the system selectively accesses permitted knowledge segments and generates responses accordingly. To further protect sensitive content, the response is transformed using a formal privacy-preserving mechanism that combines calibrated noise injection for numerical fields with semantic generalization for textual entities. Empirical evaluations on synthetic interactions demonstrate that PrivRAG effectively enforces access control while preserving user privacy, with minimal degradation in response quality across voice-based use cases.

This article investigates the energy-efficient computation offloading problem in a meta computing environment, integrating multiple-input multiple-output (MIMO) technologies and statistical channel state information (CSI). Existing resource allocation approaches always depend on instantaneous CSI, which is impractical for highly dynamic meta computing scenarios due to rapid CSI fluctuations. To address this issue, we propose a novel computation offloading scheme that jointly optimizes transmit power and computation resource allocation for multi-antenna users by leveraging statistical CSI. Specifically, a closed-form expression for the ergodic capacity and a tight bound for the MIMO diversity transmission scheme are derived. These theoretical results decouple the complex joint optimization into two tractable subproblems, where the ergodic capacity expression is utilized for global computation resource assignment via a Kuhn–Munkres (KM) algorithm, while the bound significantly simplifies the transmit power optimization problem. Experimental results demonstrate that the proposed scheme achieves substantial energy consumption reductions compared to conventional methods, highlighting the effectiveness of utilizing statistical CSI in meta computing resource allocation.

Text-to-image models based on diffusion processes, such as DALL-E, Stable Diffusion, and Midjourney, are capable of transforming texts into detailed images and have widespread applications in art and design. As such, amateur users can easily imitate professional-level paintings by collecting an artist's work and fine-tuning the model, leading to concerns about artworks' copyright infringement. To tackle these issues, previous studies either add visually imperceptible perturbation to the artwork to change its underlying styles (perturbation-based methods) or embed post-training detectable watermarks in the artwork (watermark-based methods). However, when the artwork or the model has been published online, i.e., modification to the original artwork or model retraining is not feasible, these strategies might not be viable.

In mobile edge computing systems, base stations (BSs) equipped with edge servers can provide computing services to users to reduce their task execution time. However, there is always a conflict of interest between the BS and users. The BS prices the service programs on the basis of the user demand to maximize its own profit, whereas the users determine their offloading strategies based on the prices to minimize their costs. Moreover, service programs need to be precached to meet immediate computing needs. Due to the limited caching capacity and variations in service program popularity, the BS must dynamically select which service programs to cache. Since service caching and pricing have different needs for adjustment time granularities, we propose a two-time scale framework to jointly optimize service caching, pricing and task offloading. For the large time scale, we propose a game-nested deep reinforcement learning algorithm to dynamically adjust service caching according to the estimated popularity information. For the small time scale, by modeling the interaction between the BS and users as a two-stage game, we prove the existence of the equilibrium under incomplete information and then derive the optimal pricing and offloading strategies. Extensive simulations based on a real-world dataset demonstrate the efficiency of the proposed approach.

For future Internet of Vehicles (IoV), communications and computing will converge to provide services. Federated learning (FL), as one of the typical distributed computing technologies, needs to be integrated with IoV. For such integration, FL suffers from the straggler effect that the entire learning speed is lowered down, because of the existence of the devices, such as low-powered road side units and vehicles, taking more time to complete their tasks. Although the existing mechanisms reduce straggler effects by adopting asynchronous mechanisms and clustering mechanisms, they lack the detailed analysis of the reasons and the impacts of each cause, leading to inefficiencies in the design of algorithm. Additionally, most of the existing work only considered the impact of a single factor in computation, communication, or data distribution, which lacks comprehensive on research for causes of stragglers effects. The bottleneck is that it is laborious to observe the time delay precisely with the existing high-calculating evaluations. In this article, we elaborately explore the effects of computing power, communication capability, and data distributions on the straggler effects with carefully designing and conducting the extensive experiments. After investigations, we propose a novel learning completion time estimation formula for low computing capability devices with mini-batch stochastic gradient decent (SGD). We compare our proposed estimation formula with the one based on floating operation per second (FLOPs). Through the evaluations, our formula can demonstrate the improvement up to 72.4% at docker and 32.4% at Raspberry Pi device compared to the existing work.

While many existing movie recommendation systems have been integrated to personalize entertainment in FAVs, they face challenges in addressing the diverse and dynamic preferences of multiple passengers. To tackle these issues, this article introduces the multigate mixture of experts for multiinterest model (MEMI) specifically designed for FAVs. The proposed model employs a Transformer-based multi-interest extractor within a multigate mixture of experts (MMoE) structure to capture a range of person interests while managing network complexity. Additionally, a novel peak interest alignment (PIA) loss function is introduced to improve consistency between the training and inference phases, ensuring more accurate recommendations. Experimental evaluations using the Movielens dataset demonstrate that the proposed model significantly outperforms existing systems, providing more personalized and effective movie recommendations.

In this paper, we investigate the trade-off issue between energy efficiency and communication quality in the unmanned aerial vehicle (UAV) enabled cargo delivery system. For a cellular-connected cargo UAV delivering parcels from the warehouse to each user’s location, minimizing both the energy consumption and expected outage time is essential. However, a trade-off exists between these two factors, optimizing one aspect is bound to diminished performance in the other. To jointly reduce the UAV’s energy consumption and expected outage time, we formulate an optimization problem with the objective function to minimize the weighted sum of UAV’s energy consumption and expected outage time. With the aid of radio map, a hybrid deep reinforcement learning (HDRL) algorithm, consisting of an improved ant colony optimization algorithm and the dueling double deep Q network algorithm, is proposed to solve the formulated problem. The delivery sequence and the flight trajectory of the UAV are then jointly optimized by solving the problem with the HDRL algorithm. Numerical results demonstrate that the proposed algorithm effectively reduces both energy consumption and outage time, while achieving a performance improvement of approximately 6% to 50% compared to the comparisons. Moreover, the communication quality of the UAV improves with an increased weight factor, yet gives rise to a higher energy consumption.

As the Internet of Vehicles (IoV) advances, the security concerns surrounding vehicular networks have grown increasingly critical due to the openness of networking among vehicles, inadvertently creating more opportunities for adversaries to infiltrate and potentially disrupt vehicle operations. Intrusion detection systems (IDSs) stand as a promising solution, effectively mitigating the myriad of threats and security concerns that plague vehicles. In this article, we delve into the realm of IDSs within vehicular networks and propose an innovative collaborative intrusion detection framework based on blockchain technology and auction game. First, we integrate a vehicular blockchain into the IDS, offering a holistic approach to tackling both internal and external threats within vehicular networks. Second, we introduce a novel assistant-delegated Byzantine fault tolerance (A-DBFT) consensus algorithm, designed to bolster the efficiency of intrusion detection within the blockchain while maintaining the efficacy of the consensus mechanism. Third, we develop an auction game mechanism that incentivizes assistants and verifiers to actively initiate and participate in auctions, thereby enhancing the overall security of our intrusion detection scheme. Ultimately, we present simulation results that validate the superiority of our proposed scheme compared to conventional approaches.

With the advancement of communication technologies within the smart grid Internet of Things (SGIoTs), virtual power plants (VPPs), which aggregate distributed energy resources, are increasingly encouraged to participate in energy provisioning services. However, the inherent variability in energy supplies among different VPPs, combined with the dynamic and heterogeneous nature of energy demands, poses significant challenges for efficient energy provisioning in a competitive environment involving multiple VPPs and numerous energy users. To address these challenges, this paper proposes a cooperative energy provisioning scheme based on a coalition-Stackelberg game to enhance demand response management for VPPs in SGIoTs. Firstly, a coalition formation game model is employed to create VPP clusters, enhancing both the reliability of energy supply and the profitability of individual VPPs. Subsequently, a multi-leader multi-follower (MLMF) Stackelberg game is utilized to model the competitive interactions between VPP coalitions and energy users, aiming to maximize the respective utilities of both parties. The existence of the Stackelberg equilibrium is rigorously analyzed and derived through an alternating direction method of multipliers (ADMM)-based energy requirement decision algorithm and an asynchronous gradient descent iteration-based pricing algorithm. These methods enable the determination of optimal electricity prices for VPP coalitions and optimal energy requirement decisions for individual users. Finally, extensive simulations are conducted to demonstrate that the proposed scheme significantly enhances the utilities of both VPPs and energy users compared to conventional approaches.

Federated learning (FL) model marketplaces require qualified workers to collaboratively train customized models. However, recruiting optimal workers on a limited budget in non-independent and identically distributed (non-IID) data settings remains a fundamental issue. Moreover, inadequate quality verification exposes the marketplace to spoofing and poisoning attacks, while verifying data and model quality without accessing local storage remains a significant dilemma. To bridge the research gap, this paper proposes a knowledge-aware model customization scheme in FL model marketplaces, to facilitate zero-trust worker recruitment and verification while ensuring privacy preservation. Specifically, (i) we design a knowledge-aware quality evaluation mechanism by leveraging the knowledge of workers, i.e., soft-label predictions of their local models on a privacy-free reference dataset (provided by the customer), to assess their data quality in a privacy-preserving manner. (ii) We formulate the optimal worker recruitment problem under budget constraints as an NP-hard integer programming problem and design a dynamic programming-based optimal worker recruitment algorithm with budget feasibility and computational efficiency. (iii) We devise a two-stage zero-trust quality verification mechanism by utilizing zero-knowledge proof (ZKP) to exclude distrustful workers, thereby preventing spoofing and poisoning attacks. Extensive experimental results demonstrate that the proposed scheme enhances model customization performance by up to 34.3% on label-skewed non-IID data and 36.2% on feature-skewed non-IID data compared with existing representatives.

Recently, quantum federated learning (QFL) is advocated to leverage the robust computing power of quantum edge computing devices (QECDs) within unmanned aerial vehicle (UAV)-assisted wireless networks, to enhance the efficiency of distributed learning. However, the presence of malicious and selfish behaviors among some QECDs poses significant challenges for QFL model training to achieve high accuracy and rapid convergence. To tackle this issue, we introduce a trustenhanced incentive scheme for QFL in the UAV-assisted wireless networks. Specifically, a QECD-empowered QFL framework is first presented in the UAV-assisted wireless networks, where the QECDs independently train local models with their private data by using the quantum computing capabilities, while UAVs aggregate these trained local models to update the global model. Then, to ensure security and eliminate malicious participants, we devise a Bayesian inference-based trust assessment mechanism to select honest QECDs for local model training. Furthermore, we design a Stackelberg game-based incentive mechanism to incentivize QECDs to cooperatively provide high-quality training services. Afterwards, through game analysis using the backward induction method, we prove the existence of a Stackelberg equilibrium. The optimal payment strategies of the UAVs are obtained using the deep Q-learning network (DQN) algorithm in dynamic networks, and the optimal training contribution strategy of each QECD is derived using the convex optimization method. Finally, extensive simulations demonstrate that the proposed scheme can significantly enhance the accuracy and training speed of QFL in UAV-assisted wireless networks.

This paper focuses on semi-supervised crowd counting, where only a small portion of the training data are labeled. We formulate the pixel-wise density value to regress as a probability distribution, instead of a single deterministic value. On this basis, we propose a semi-supervised crowd counting model. First, we design a pixel-wise distribution matching loss to measure the differences in the pixel-wise density distributions between the prediction and the ground-truth; Second, we enhance the transformer decoder by using density tokens to specialize the forwards of decoders w.r.t. different density intervals; Third, we design the interleaving consistency self-supervised learning mechanism to learn from unlabeled data efficiently. Extensive experiments on four datasets are performed to show that our method clearly outperforms the competitors by a large margin under various labeled ratio settings.

In this paper, we investigate a secure communication architecture based on unmanned aerial vehicle (UAV), which enhances the security performance of the communication system through UAV trajectory optimization. We formulate a control problem of minimizing the UAV flight path and power consumption while maximizing secure communication rate over infinite horizon by jointly optimizing UAV trajectory, transmit beamforming vector, and artificial noise (AN) vector. Given the non-uniqueness of optimization objective and significant coupling of the optimization variables, the problem is a non-convex optimization problem which is difficult to solve directly. To address this complex issue, an alternating-iteration technique is employed to decouple the optimization variables. Specifically, the problem is divided into three subproblems, i.e., UAV trajectory, transmit beamforming vector, and AN vector, which are solved alternately. Additionally, considering the susceptibility of UAV trajectory to disturbances, the model predictive control (MPC) approach is applied to obtain UAV trajectory and enhance the system robustness. Numerical results demonstrate the superiority of the proposed optimization algorithm in maintaining accurate UAV trajectory and high secure communication rate compared with other benchmark schemes.

This paper explores a simultaneous tasks offloading and communications (STOC) scheme in mobile edge computing (MEC) networks, supported by the combination of simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) and the unmanned aerial vehicle (UAV). Different from the traditional MEC schemes, the proposed scheme concurrently considers the computation and communication capabilities of the MEC networks, which is actually more practical in reality. Specifically, an optimization problem is devised to maximize the weighted sum of the minimum computed task data and communication data, while ensuring the quality of service (QoS) constraints for STOC through joint design of time scheduling, resource allocation, active and passive beamforming, alongside with the UAV trajectory planning. This non-convex problem with strong couplings among variables is challenging to solve directly. Then, a novel alternating optimization method is proposed, leveraging the successive convex approximation (SCA) and semi-definite relaxation (SDR) techniques. We provide sufficient numerical results to validate the effectiveness of the proposed STOC scheme, which demonstrate that the proposed scheme supported by STAR-RIS and UAV outperforms five benchmark schemes in terms of performance gain. It is important to note that the proposed scheme offers a feasible and realistic way for the implementations of STOC in practical MEC networks.

Network path validation aims to give more control over the forwarding path of data packets in a path-aware network, which shields the network from security threats and allows end hosts to receive better services. Therefore, network path validation becomes a vital primitive for secure and reliable Internet services in the next generation networks. The path validation enables end hosts and intermediate router nodes to check whether a packet has followed the intended path. However, the existing solutions fail to protect path privacy and incur significant bandwidth and computation overhead on packet transferring, which degrades packet delivery performance. In this paper, we propose the StealthPath to protect path privacy and improve delivery efficiency. Firstly, StealthPath uses lightweight cryptographic primitives to generate nested proofs and ensures all nodes on the path to check the compliance of the forwarding path efficiently. Secondly, StealthPath hides the forwarding path in the proofs and reduces the proof size from linear to constant, which protects the path information and path length, and decreases the bandwidth consumption. Moreover, StealthPath allows on-path nodes to extract their proofs and the next hop address from proof without leaking on-path node index. Finally, StealthPath is proved to resist various attacks and preserves the path privacy. The experiments show that StealthPath saves nearly 60% header size and bandwidth, and is more efficient than state-of-the-art schemes.

The convergence of aUtonomous aerial vehicles (UAVs) and federated learning (FL) has emerged as a promising paradigm to facilitate artificial intelligence (AI) services with enhanced privacy preservation. However, notwithstanding the inherent advantages of FL in terms of privacy protection, attackers can still exploit inference attacks to deduce raw data of UAVs. The existing studies predominantly assume FL servers (hereafter servers)to be fully rational and have access to all privacy preference information of UAVs (i.e., information symmetry scenario), in the design of privacy-preserving incentive schemes. To tackle these challenges, we propose a privacy-preserving incentive scheme for UAV-aided FL in the presence of information asymmetry while considering the serverexhibits bounded rationality. Specifically, a practical UAV-aided FL framework is first introduced to enable AI model training between UAVs and the server with bounded rationality. In addition, based on differential privacy, we quantify the privacy level of UAVs and subsequently analyze its impact on the aggregation accuracy of the server. This scenario entails two conflicting objectives: the server aims for higher-quality local models to achieve better aggregation accuracy, while UAVs prioritize injecting more noise into their local models to enhance privacy protection. To reconcile the conflicting objectives, we develop an incentive mechanism based on contract theory to optimize the server’s aggregation accuracy in the presence of information asymmetry. Furthermore, we employ prospect theory (PT) to the above contract to capture biases in the server’s subjective decision-making process. Besides, we deduce closed-form solutions for optimal contracts under PT and expected utility theory (EUT), where participants are assumed to be fully rational. Finally, simulation results validate the superiority of our proposed scheme in motivating UAVs to share high-quality local models and improving the aggregation accuracy of the server.

Federated learning (FL) has become a promising technology to provide edge Artificial Intelligence (AI) due to its advantages in privacy protection and reduced communication costs. However, FL is still confronted with privacy leakage issues because the sharing local model may expose the training data information. Existing works typically utilize secure aggregation techniques to eliminate privacy leakage, where local model parameters in FL are obfuscated before they are sent to the aggregator. Nevertheless, secure aggregation makes poisoning attacks more convenient given that existing anomaly detection methods mostly require access to plaintext local models. A Poisoning-Defense Secure Aggregation in FL (PDSA-FL) is proposed to enhance the privacy protection of honest clients and defend against poisoning attacks from malicious clients. Specifically, a Secure Aggregation scheme based on Random Parameters Decomposition (SARPD) is designed to protect client privacy during the FL aggregation process and eliminates the impact of dropped clients on the aggregation results. Secondly, a Poisoning Detection method based on Similarity Grouping (PDSG) is proposed to mitigate the impact of poisoning attacks on the global model of FL without leaking client model parameters. The security analysis discusses the effectiveness of the proposed PDSA-FL in terms of privacy protection. Extensive simulation results show that PDSA-FL can effectively defend against poisoning attacks, significantly improve the convergence performance of global models, and reduce the computation time of clients.

Increasing attention is being paid to machine unlearning, which supports individuals’ “right to be forgotten.” While most studies focus on the efficiency and effectiveness of unlearning algorithms, the evaluation of machine unlearning effectiveness remains underexplored. Offering robust evaluation services for unlearning is critical, not only to uphold privacy legislation but also to assess and improve existing unlearning methods. Lots of existing methods employ backdoor methods to evaluate unlearning effectiveness, which can only verify the unlearning effect of backdoored samples and negatively impact the model utility as they need to embed backdoors into the model first. In this paper, we propose an evaluating machine unlearning (EMU) method, which aims to evaluate the effectiveness of unlearning and verify data removal without the aforementioned adverse effects. Machine unlearning inherently creates a difference on the model before and after unlearning. The model difference contains information about the unlearned samples, which can be extracted through reconstruction models for unlearning effectiveness evaluation. To efficiently generate the model differences as input for evaluation, we simulate the model changes based on the influence function theory. Additionally, we design a multi-task information bottleneck structure to enhance the scalability of EMU and simplify the analysis of different learning tasks. We provide a theoretical analysis of how the similarity between erased and remaining samples, as well as task types, affects the extent of unlearning—factors that have been largely overlooked. Extensive experiments on various model architectures and representative datasets confirm our analysis, demonstrating the effective evaluation for unlearning without any degradation in the service model utility.

The integration of unmanned aerial vehicles (UAVs) and artificial intelligence (AI) has garnered significant interest as a promising paradigm for facilitating intelligent and pervasive mobile crowdsensing (MCS) services. In traditional AI methodologies, the centralization of large volumes of privacy-sensitive sensory data shared by UAVs for model training entails substantial privacy risks. Federated learning (FL) emerges as an appealing privacy-preserving paradigm that enables participating UAVs to collaboratively train shared models while safeguarding the privacy of their data. However, given that the execution of FL tasks inherently requires the consumption of resources such as power and bandwidth, rational and self-interested UAVs may not actively engage in FL or launch free-riding attacks (i.e., sharing fake local models) to mitigate costs. To address the above challenges, we propose a truthful incentive scheme in FL-based UAV-assisted MCS. Specifically, we first present a learning framework tailored for realistic scenarios in UAV-assisted MCS that enhances privacy preservation and optimizes communication efficiency during AI model training for collaborative UAVs, where the sensing platform (i.e., the aggregation server) is the finite-rational decision maker. Then, based on prospect theory (PT), we design an incentive mechanism to motivate UAVs to participate in FL. In this mechanism, a PT-based game is exploited to model the interactions between the sensing platform and UAVs, where the equilibrium is derived. Moreover, we employ a zero-payment mechanism to curb the self-interested behavior of UAVs. Finally, simulation results show that the proposed scheme can facilitate high-quality model sharing while suppressing free-riding attacks.

Differential private learning is widely used in machine learning (ML) to protect continuous and scalar-valued data. The demand for discrete and matrix-valued computations is increasing, particularly in quantized neural networks and graph learning, which require discrete-valued parameters and large-scale matrix operations for efficient data processing. However, privacy protection for discrete and matrix-valued data is less explored. Traditional differential private mechanisms fail to maintain the discrete nature of data after perturbation and often overlook data correlations, struggling to balance privacy and utility. In this paper, we propose a Discrete Matrixing Differentially Private Learning (DM-DPL) framework, which protects the privacy of discrete and matrix-valued data during ML training by adding discrete matrix-variate Gaussian noise. First, we propose a novel Discrete Matrix-Variate Gaussian (DMVG) mechanism with rigorous conditions necessary to guarantee (ϵ, δ)-differential privacy. Additionally, we present an eigenvalue-weighted analysis-based precision budget allocation strategy, designed to maintain the utility of significant dimensions while providing consistent privacy guarantees. Finally, the results illustrate that our approach significantly surpasses existing state-of-the-art methods when applied to quantized federated learning. To the best of our knowledge, this is the first work to specifically protect discrete and matrix-valued data during ML training.

Federated graph attention networks (FGATs) are gaining prominence for enabling collaborative and privacy-preserving graph model training. The attention mechanisms in FGATs enhance the focus on crucial graph features for improved graph representation learning while maintaining data decentralization. However, these mechanisms inherently process sensitive information, which is vulnerable to privacy threats like graph reconstruction and attribute inference. Additionally, their role in assigning varying and changing importance to nodes challenges traditional privacy methods to balance privacy and utility across varied node sensitivities effectively. Our study fills this gap by proposing an efficient privacy-preserving FGAT (PFGAT). We present an attention-based dynamic differential privacy (DP) approach via an improved multiplication triplet (IMT). Specifically, we first propose an IMT mechanism that leverages a reusable triplet generation method to efficiently and securely compute the attention mechanism. Second, we employ an attention-based privacy budget that dynamically adjusts privacy levels according to node data significance, optimizing the privacy-utility trade-off. Third, the proposed hybrid neighbor aggregation algorithm tailors DP mechanisms according to the unique characteristics of neighbor nodes, thereby mitigating the adverse impact of DP on graph attention network (GAT) utility. Extensive experiments on benchmarking datasets confirm that PFGAT maintains high efficiency and ensures robust privacy protection against potential threats.

In digital-twin-enabled heterogeneous vehicular networks (DT-HetVNets), vehicles need to synchronize data to their DTs deployed in the cloud for decision-making. However, for a vehicle which is simultaneously covered by a group of heterogeneous network infrastructures, the DT of the vehicle (DT-V) can connect with the DTs of infrastructures (DT-Is) in different infrastructure groups across regions in the virtual networks so that each DT-V may deceive the DT-Is by interacting with multiple DT-I groups and selecting the optimal one to synchronize data. To this end, we propose an on-demand data synchronization scheme for DT-Vs and DT-Is. In the scheme, infrastructures and vehicles are grouped based on their geographical locations and the arrival time of each vehicle through which the DT-Vs and DT-Is can interact with each other to make decisions in groups. Then, the requirements of DT-Vs (i.e., minimize synchronization cost and maximize synchronization satisfaction) and DT-Is (i.e., maximize profits) are considered to design their utility functions and the decision-making process between the DT-Vs in each group and the DT-Is in each group is formulated as a Stackelberg game to obtain their optimal strategies. After that, considering the deceptive behavior of vehicles, a joint optimization algorithm that integrates the Stackelberg game and the selection of each DT-V is designed to obtain the real equilibrium solution for DT-Vs and DT-Is to maximize their utilities. Simulation results show that our scheme can obtain the highest utilities compared with the traditional schemes.

Low earth orbit (LEO) constellation integrated maritime networks have recently attracted much interest due to the rapid development of maritime applications and services. LEO satellites have the advantages of wide coverage to provide seamless connection for maritime wireless devices. However, due to the limited battery and computing capacity of unmanned aerial vehicles (UAVs) for ocean information perception and processing, the computing-intensive and delay-sensitive oceanic data suffer from long latency and high energy consumption, which degrades the efficiency of maritime services. In this paper, to enhance the perception and offloading endurance of UAVs in maritime networks, we propose an energy efficient multi-access edge computing scheme for heterogeneous satellite-maritime networks, with the objective of minimizing the cumulative transmitted energy for UAVs. Specifically, we first present a heterogeneous satellite-maritime network framework in which LEO satellites and unmanned surface vehicles (USVs) equipped with edge servers can process workloads simultaneously. Next, considering the limited battery supply of UAVs, we propose a hybrid harvesting-and-offloading scheme for resource allocation, where UAVs first harvest energy from solar power and radio frequency power from USV, and then UAVs determine the offloading strategy for task processing. Moreover, a joint optimization problem is formulated to optimize the offloading decision, the time scheduling, and the transmitting power. We also exploit a vertical architecture to solve the formulated problem. Regarding each decomposed sub-problem, we propose efficient algorithms to derive the corresponding solutions. Finally, we provide numerical results to validate the performance of our proposed algorithms in comparison with several benchmark algorithms.

With the development of new generation communication technologies, the future maritime information networks pave the way to promote the exploration of ocean resources. Moreover, the underwater data center (UDC) is considered to be a significant data storage and computing unit in future maritime networks for providing ocean services. However, the current deployment of UDC faces the critical issues, i.e., the long-distance underwater transmission is unreliable and the energy consumption and resources of underwater transmission are overloaded. To address the two critical issues of unreliable data transmission and high resource overheads, in this paper, we present a hybrid semantic data transmission architecture in air-ocean integrated networks, which can perceive the sea surface data accurately and transmit it to the UDC for processing. Specifically, in surface layer, uncrewed aerial vehicles (UAVs) perceive ocean environment and send data to the buoy via non-orthogonal multiple-access (NOMA) transmission to improve the channel utilization. In underwater layer, the buoy sends the collected data to UDC via semantic transmission, while the semantic fidelity metric is utilized to improve the transmission efficiency. A resource allocation problem for energy minimization is formulated to jointly optimize the semantic scaling factor, the NOMA decoding order, the communication and computing resource allocations. We exploit a decomposition approach to transform the problem into two sub-problems, where the optimal resource allocations are obtained by proposing efficient algorithms. Finally, we provide simulations to verify the effectiveness and efficiency of our proposed scheme. The results demonstrate that our proposal has the advantages of lower energy consumption compared to several baseline schemes.

Mobile crowdsensing (MCS) has emerged as a prominent trend across various domains. However, ensuring the quality of the sensing data submitted by mobile users (MUs) remains a complex and challenging problem. To address this challenge, an advanced method is needed to detect low-quality sensing data and identify malicious MUs that may disrupt the normal operations of an MCS system. Therefore, this article proposes a prediction- and reputation-based truth discovery (PRBTD) framework, which can separate low-quality data from high-quality data in sensing tasks. First, we apply a correlation-focused spatio-temporal Transformer network that learns from the historical sensing data and predicts the ground truth of the data submitted by MUs. However, due to the noise in historical data for training and the bursty values within sensing data, the prediction results can be inaccurate. To address this issue, we use the implications among the sensing data, which are learned from the prediction results but are stable and less affected by inaccurate predictions, to evaluate the quality of the data. Finally, we design a reputation-based truth discovery (TD) module for identifying low-quality data with their implications. Given the sensing data submitted by MUs, PRBTD can eliminate the data with heavy noise and identify malicious MUs with high accuracy. Extensive experimental results demonstrate that the PRBTD method outperforms existing methods in terms of identification accuracy and data quality enhancement.

The rise of the Metaverse has ushered in a new era of social networking, offering users deeply engaging spaces to connect and participate in social activities. However, rendering these virtual environments is resource-intensive. With many users accessing simultaneously and requiring diverse Metaverse services, optimizing Metaverse resources to deliver the best quality-of-experience (QoE) for users is a significant challenge. In this paper, we propose a cooperative virtual reality (VR) rendering and dynamic resource leasing mechanism to address this issue. Specifically, we first introduce a cooperative VR scene pre-rendering framework between users and Planets (i.e., edge servers hosting users), and establish a new user QoE metric named EdgeVRQoE which considers both rendering delay and visual quality. We formulate the multidimensional rendering resources (e.g., GPU, CPU, and outbound bandwidth) leasing problem between Planets and users as a double-layer decision problem, and devise a hybrid action multi-agent reinforcement learning-based dynamic resource auction mechanism to efficiently allocate limited resources of Planets in a distributed and adaptive manner. Extensive simulations demonstrate that our proposed scheme outperforms the representatives in user QoE and resource utilization efficiency. Particularly, the proposed scheme shows at least an 18-fold improvement in QoE over other schemes, demonstrating its capability in providing immersive Metaverse experiences.

Recently, the promising unmanned aerial vehicle (UAV)-assisted wireless networks (UAWNs) have emerged by advocating the UAVs to provide wireless transmission services. However, owing to the ever-growing volume of data traffic and the untrusted network operation environment, efficiently and securely assigning limited bandwidth for high-quality wireless communication between UAVs and mobile users poses a significant challenge. To address this challenge, we propose a novel secure UAV-bandwidth allocation scheme to provision reliable wireless transmission services for mobile users in UAWNs. Specifically, we first introduce a novel blockchain-empowered framework for secure bandwidth allocation, designed to automate payment processes and deter malicious activities through the immutable logging of transactional and behavioral data. Wherein, a smart contract is designed to regulate the honest behaviors of both mobile users and UAVs during bandwidth allocation with a distributed manner. Besides, a delegated proof-of-stake (DPoS) with reputation consensus protocol is presented to ensure the authenticity and efficiency of the decision-making process. Further, we apply the Stackelberg game theory to model the dynamic of the bandwidth allocation between mobile users and UAVs. In this game, the UAVs act as game leaders to determine the bandwidth price, while each mobile user acts as a game follower, making decision on the bandwidth request. We utilize the backward induction method to derive the optimal strategies of both parties, culminating in the identification of the Stackelberg equilibrium of the formulated game. Finally, extensive simulations are carried out to show the superiority of the proposed scheme over conventional schemes in terms of security, efficiency, and fairness in bandwidth allocation.

Mixture-of-Experts (MoE) is an emerging technique for scaling large models with sparse activation. MoE models are typically trained in a distributed manner with anexpert parallelismscheme, where experts in each MoE layer are distributed across multiple GPUs. However, the default expert parallelism suffers from the heavy network burden due to the all-to-all intermediate data exchange among GPUs before and after the expert run. Some existing works have proposed to reduce intermediate data exchanges by transferring experts to reduce the network loads, however, which would decrease parallelism level of expert execution and make computation inefficient. The weaknesses of existing works motivate us to explore whether it is possible to reduce inter-GPU traffic while maintaining a high degree of expert parallelism. This paper gives a positive response by presentingLuffy, a communication-efficient distributed MoE training system with two new techniques. First,Luffymigrates sequences among GPUs to hide heavy token pulling paths within GPUs and avoid copying experts over GPUs. Second, we propose token condensation that identifies similar tokens and then eliminates redundant transmissions. We implementLuffybased on PyTorch and evaluate its performance on a testbed of 16 V100 GPUs.Luffysystem can achieve a speedup of up to$2.73\times $compared to state-of-the-art MoE training systems.

In this paper, we employ an unmanned aerial vehicle (UAV) to ensurethe freshness of sensing data, as measured by the age of information (AoI), in Internet of Things (IoT) networks. Specifically, the UAV switches between flying and hovering modes to collect data from widely distributed IoT devices. UAV trajectory planning, which determines the times and moments of data collection, is vital for optimizing the system AoI. Considering the limited UAV onboard energy and mission duration, AoI-optimal trajectory planning is formulated as a mixed-integer nonlinear programming (MINLP) problem. We first decompose the MINLP problem into two subproblems: a UAV time scheduling subproblem and a UAV path planning subproblem. Then, we propose a learning-based iterative (LBI) algorithm that consists of two modules: a successive convex approximation (SCA)-based module for solving the time scheduling subproblem, and a hierarchical asynchronous advantage actor-critic (A3C) module for addressing the path planning subproblem. The numerical results verify that the proposed LBI algorithm outperforms typical baselines in terms of the AoI performance.

This paper explores the fairness issues on physical layer security (PLS) in intelligent reflecting surface (IRS) assisted multiple-user uplink systems. Due to unknown instantaneous eavesdropper channel state information (CSI), it is not possible to acquire exact secrecy rate of a PLS system. In this paper, we introduce secrecy outage probability (SOP) as a security metric, instead of secrecy rate as used in most existing works, and formulate a minimization problem of maximum (min-max) SOP among multiple users. To solve this problem, we propose two independent approaches: one is alternating optimization (AO) and the other is a deep learning based (DL) scheme. The AO scheme decouples the problem into two sub-problems to alternately optimize phase shift matrix and receiver beamforming vectors, which gives a near-optimal performance but with a high complexity. The DL scheme, on the other hand, works based on neural networks through offline training, which is used for online generation of phase shift matrix and receiver beamforming vectors with a lower complexity. As traditional self-supervised neural networks cannot achieve a good solution to the max-min problem, we design a multiple-stage booster (MSB) framework to solve this problem. Simulations demonstrated that SOP is improved significantly with the proposed schemes compared to benchmark schemes. In particular, the AO scheme outperforms the DL-based approach slightly at the cost of a relatively high computational complexity.

In the upcoming sixth generation (6G) era, dynamic routing relying on link state information update is crucial for global data service in large-scale low-earth orbit (LEO) satellite networks. However, the existing dynamic routing methods use a static link state update scheme where all satellites distribute their link state information with the same fixed period, while the link state of the satellites are different and vary dynamically. This makes it difficult to achieve the balance among various network performance metrics such as link state update accuracy, signaling overhead, network throughput, and energy efficiency. To solve this issue, we propose an adaptive link state update scheme for the LEO satellite network, where each satellite can dynamically adjust its own link state distribution interval according to the observation on the inter satellite links (ISLs). Based on the proposed scheme, we define the information deviation to characterize the accuracy of the link state update and derive the signaling overhead of link state distribution. To improve further the network performance, a multi-objective optimization problem (MOP) is formulated to minimize the information deviation and the signaling overhead simultaneously. By applying the weighted sum method, we convert the formulated MOP into a single-objective optimization problem (SOP). Then, we adopt the distributed reinforcement learning approach and develop the deep Q-network (DQN) algorithm for each satellite to learn its optimal link state distribution decision strategy based on local information. Simulation results demonstrate the superiority of the proposed scheme.

In mobile edge computing systems, base station (BS) equipped with edge servers can provide computing services to users to reduce their task durations. The BS prices the service programs based on user demand to maximize its own profits. Additionally, due to limited caching capacity and variations in service programs popularity, the BS has to dynamically select which service programs to cache. To address the conflict between high profits requirement and system instability, we propose a two time-scale framework to optimize service caching, pricing and task offloading. Under the small time scale, by modeling the interaction between the BS and users as a two-stage game, we derive the optimal offloading strategy and pricing algorithm. Then, we deduce the existence of equilibrium points. Under the large time scale, we propose a game-nested deep reinforcement learning algorithm to dynamically adjust the service caching according to estimated popularity information. Extensive data based simulations demonstrate the efficiency of the proposed approach.

The trustworthiness assessments of devices (e.g., routers, switches, and controllers, etc.) are essential to build a trusted network. However, due to the diversity of network devices and time-varying natures of security states, it is challenging to effectively assess the trustworthiness of devices in large-scale networks. To address this challenge, in this paper, we propose a device trustworthiness assessment scheme based on the Dempster-Shafer (D-S) evidence theory. Specifically, we first devise a whole life-cycle device assessment architecture to assess the trustworthiness of devices from startup, long-term operation to retirement. We then present a user-editable device assessment model to establish the trustworthiness attribute sets and trustworthiness level division strategies based on user requirements. Furthermore, we design a D-S evidence theory-based trustworthiness assessment algorithm to fuse the measurement values of multi-dimensional trustworthiness attributes for obtaining the trustworthiness level of devices. Finally, simulation results demonstrate that the proposed scheme can improve the accuracy and efficiency of device trustworthiness assessment.

The 6G empowered Internet of vehicles paves the way to autonomous driving era, where the ultra-low latency communication and ultra-reliable connections promote the quality of service (QoS) for vehicle users. However, the high data traffic load and communication resource constraint pose a heavy burden to autonomous driving. This paper proposes a secrecy oriented slicing resource allocation scheme in 6G green vehicular networks. We consider that cellular vehicular user (CUE) and vehicular user equipment (VUE) and eavesdropper coexist in the networks, where VUE can reuse the resource block non-orthogonally with CUE, and the eavesdropper may overhear the data transmission of CUE and VUE. To meet the QoS and green communication requirements, we formulate a joint optimization for energy-efficient resource allocation subject to the data rate and secrecy capacity. Despite the non-convex of the formulated problem, we propose corresponding algorithms to derive the optimal resource allocation strategies. Simulation performance validate the effectiveness of our proposal in comparison with benchmark algorithms.

Mobile edge computing (MEC) enhances data processing by enabling users to offload tasks to edge servers with enough computation resource. In multi-user and multi-server scenario, the offloading scheduling is overwhelming complex and significantly influences the processing delay, which makes deep learning (DL) become an appealing approach. Yet, prior DL-based methods often overlook dynamic topology challenges due to the inflexibility of fixed neural network structures, leading to constrained performance. To tackle this challenge, a novel reinforcement learning framework named dynamic topology adaptive reinforcement learning (DTA-RL) is proposed in this paper. The MEC network is modeled as a graph based on the communication relationships between users and servers, and the offloading process is formulated as a Markov decision process (MDP). Building on the graph model and MDP, DTA-RL leverages graph attention networks to handle dynamic observation spaces and incorporates an attention mechanism for decision-making in environments with evolving action spaces. Simulation results illustrate that DTA-RL effectively reduces task processing delays and offloading failure rates within the MEC system. Furthermore, the pre-trained model can be seamlessly implemented in networks with new topology without experiencing significant performance degradation. The code is available at https://github.com/UNIC-Lab/DTA-RL.

Aerial intelligent reflecting surface (AIRS), integrating unmanned aerial vehicle (UAV) with IRS, has emerged as a promising paradigm to improve the transmission quality and security in emergency communication, space-air-ground-integrated network and mobile edge computing, etc. However, the size of a single AIRS is constrained by the limited energy and payload capacity of the UAV, as well as the path loss of the air-to-ground reflective link, which makes the gain from a single AIRS finite. To address these problems, we propose a hybrid aerial IRS-assisted cooperative secure transmission system, where an aerial active IRS and an aerial simultaneously transmitting and reflecting IRS (STAR-IRS) are employed to achieve reflection amplification and 360-degree ubiquitous coverage, respectively. Additionally, the cooperative beamforming gain generated by the secondary reflection between the hybrid AIRSs can further improve communication quality. Specifically, an optimization problem is proposed with the objective of maximizing the sum secrecy rate by jointly optimizing the transmitting beamforming and the reflection coefficients of each AIRS. We first reformulated the original non-convex problem by fractional programming method, and a three-layer alternating optimization algorithm is introduced to address the proposed problem with the successive convex approximation (SCA) as well as penalty convex-concave procedure (PCCP) techniques. Finally, extensive simulations are conducted to demonstrate that the proposed scheme substantially improves the sum secrecy rate compared to other baseline schemes.

A Digital Twin (DT) is a software agent of a physical entity in virtual space, transcending the limitations imposed by physical constraints to enable intelligent services. The emergence of the Internet of Digital Twins (IoDT), by connecting DTs as a network, provides a reliable solution for streamlining the sharing of extensive real-time data among DTs. However, ensuring the security and efficiency of highly sensitive DT data remains a significant challenge in the distributed and dynamic IoDT storage environment. In this paper, we propose a comprehensive scheme that integrates blockchain, Distributed Hash Table (DHT), and Attribute-Based Encryption (ABE) to establish an IoDT data-sharing system. First, we introduce an ABE scheme to achieve data confidentiality and fine-grained access control. By implementing a key generation protocol, we address key escrow concerns in ABE. Second, to ensure bidirectional data confidentiality in data sharing without revealing the identities of DTs, the DHT, and blockchain are jointly applied to achieve resource registration and discovery. Through an XOR mapping operation mechanism, DT data are distributed across different DHT network nodes, reducing DT nodes’ storage consumption and effectively addressing the scalability issue of IoDT storage. Finally, we provide performance evaluations to demonstrate the reliability and efficiency of our proposed scheme. Our scheme strikes a balance between efficiency and security, exhibiting efficient performance in IoDT data sharing compared to other ABE and blockchain schemes.

In the evolving landscape of sequential recommendation systems, this paper propels the frontier forward with the introduction of the knowledge graph enhanced multi-task learning (KGML) model. At its core, KGML harnesses the capability of big data analytics, enabling a nuanced understanding of both the immediate and enduring interests of users. This is achieved through the integration of item knowledge graphs and multitask learning, which are meticulously enriched with big data insights, thereby ensuring a comprehensive representation of item attributes and interconnections. Such a method not only elevates the model’s precision in tailoring recommendations for less popular items with limited data but also effectively counters the "Matthew Effect", where visibility becomes disproportionately skewed towards already popular items. Through rigorous validation across three public datasets, the KGML model demonstrates that the proposed approach significantly enhances the accuracy of sequential recommendations.

This paper introduces defensive adversarial attacks aimed at enhancing the security of semantic communication systems by confusing potential eavesdroppers. Existing research predominantly focuses on enhancing the accuracy of semantic communications while neglecting the security vulnerabilities posed by eavesdroppers. In this study, from the standpoint of physical layer security, defensive adversarial attacks are employed to introduce artificial noise into semantic communications, effectively concealing real information. This artificial noise is generated by deep neural networks to mislead eavesdroppers into perceiving the content of images as unrelated information, with little probability of disrupting normal semantic communications. Experimental results demonstrate that the proposed model can selectively mislead the decoding efforts of eavesdroppers, while ensuring uninterrupted decoding by legitimate receivers.

In this paper, we study intelligent multi-domain collaborative computing offloading within the space-air-ground integrated Internet of Things (SAG-IoT). While non-terrestrial transmission alleviates the burden on scarce terrestrial resources, it introduces significant propagation delay, rendering it unsuitable for all tasks. To address this issue, we categorize tasks into priority and general groups and design a dynamic priority resource management (DPRM) framework. This framework strategically pre-allocates resources to priority tasks, ensuring their completion on edge nodes. Within this framework, we formulate an optimization problem focused on offloading path selection and multi-dimensional resource management, to maximize the completion rates of general tasks while meeting the quality of service requirements for priority tasks. We introduce a hierarchical hybrid policy optimization based on DPRM (HHPO-DPRM) algorithm to tackle the aforementioned problem in highly dynamic network environments. Comparative analysis with two traditional algorithms underscores the effectiveness of our approach.

Virtual Reality (VR) paves the way to link Meta-verse and the real world, allowing users to enjoy immersive experiences. However, delivering high-quality full spherical VR service within limited rendering energy is a challenge. Mobile edge computing (MEC) is a promising paradigm to provide rendering computation services to users. It is widely held that the rendering of panoramic video presents a significant impediment in the VR system, with disregard for the importance of the data correlation leading to excessive energy consumption caused by repeated rendering. In this paper, we propose a cooperative rendering scheme in mm Wave-enabled wireless networks with MEC via a coalition formation game, among which we focus on the data correlation of the background environment of VR streams. Specifically, we first devise a multiple MEC servers rendering framework, and we formulate an optimization problem to maximize the system utility, which contains energy savings for MEC servers and users' quality of experience (QoE). Then, considering the overlap of the VR streams requested by users in Metaverse, a coalition formation game is employed to model the cooperations among MEC servers, such that the user's QoE is significantly improved. The simulation experiments show that our proposed algorithm is superior to benchmark algorithms in improving the users' QoE and reducing the total energy consumption of MEC servers.

Provenance graph-based auditing offers a promising direction for APT (Advanced Persistent Threat) detection with traceability guarantees. However, most of the existing methods are based on host-level causality analysis, which is ineffective in practical APT scenarios when well-organized adversaries exploit lateral movement attacks (e.g., multi-level proxies) across multiple compromised hosts. To bridge the research gap, this paper proposes a collaborative APT detection and tracing frame-work (TRACEGADGET) based on federal provenance graphs. TRACEGADGET can efficiently reveal the whole trace of APT lateral movements through the interactions between hosts in Intranet. Specifically, the proposed framework 1) characterizes the relevance weights of all events in the given provenance graph in comparison to the POI (Point of Interest) events, 2) identifies the network entries rankings of the POI events through backward trace analysis, 3) reveals the evolution of the alarm events and confirms the network exit of penetration chain through forward propagation, and 4) aligns the network entries and network exits to derive the complete path of the lateral movement attack. Finally, we construct a dataset consisting of 280,000 edges and more than 90,000 entities through ten sets of real APT attacks. We demonstrate the feasibility and effectiveness of the proposed framework in recovering APT attack links at the network level. Particularly, TRACEGADGET achieves 100% APT path reconstruction with high robustness in all the experiments.

This paper delves into the realm of intelligent and cooperative computing offloading within satellite-assisted Inter-net of Vehicles (Sat-IoVs). More specifically, it focuses on enabling efficient computing offloading for highly mobile vehicle users by formulating and executing an offloading path selection and multidimensional resource management (OPS-MDRM) optimization problem at a central controller. Given the complex amalgamation of continuous and discrete action spaces, along with various timescales inherent to the OPS-MDRM problem, we introduce a two-timescale framework. In this framework, we present a hierarchical hybrid policy optimization (HHPO) based on-policy algorithm to effectively tackle the aforementioned problem. Our comparative analysis against three traditional resource allocation methods underscores the outstanding performance achieved by the HHPO-based approach in the Sat-IoV networks.

Unmanned Underwater Vehicles (UUVs) have been increasingly used in underwater scenes, such as ocean exploration, marine rescue, and underwater pipeline. Underwater wireless optical communication (UWOC) technology has higher speed, higher bandwidth, and lower latency compared to traditional underwater communication technologies, making it a promising paradigm for the communication between UUVs. However, UWOC-enabled UUV networks face challenges in terms of the number of affordable network interfaces and the stringent energy limit due to the expensive cost of network interface for UUVs and the serious underwater working environment. In this paper, we propose an exact algorithm and an approximate algorithm to optimize the network topology to lower energy consumption for UUVs. The exact algorithm can effectively enumerate all the network topology to find the optimal one, which works for small-scale UUV networks with no more than 10 UUVs. The approximate algorithm uses local search algorithm to approach the exact answer in the time complexity of$O(kn^{2}\log n)$, which targets at large-scale UUV networks with 10 ~ 100 UUVs.$k$is the number of iterations, and$n$is the number of UUVs. Simulation results verify the accuracy and efficiency of the proposed two algorithms, and demonstrate when$k=2n^{2}$or$k=3n^{2}$, the relative error of the approximate algorithm is less than 0.5% when$n<10$and converges fast when$n=100$.

In recent years, with the widespread of the Internet and further big data, E-Commerce (EC) has emerged as a popular medium for users to engage in online transactions of products and services. Generally, Service Providers (SPs) of EC collect users' personal information and utilize advanced big data technologies to enhance their services. However, the price discrimination problem may also arise based on personalized information, where malicious SPs analyze users' historical orders to provide the same products or services at varying prices depending on their characteristics. In this paper, we propose a price auditing system called E-Commerce Price Auditing System (ECPAS) to resolve this problem. ECPAS consists of four smart contracts: User Registration Contract, Product Registration Contract, Insurance Purchasing Contract, and Price Auditing Contract, which realize EC price auditing and financial compensation for price discrimination based on a private blockchain. Meanwhile, ECPAS utilizes InterPlanetary File System (IPFS) to efficiently store product data. Experimental results demonstrate that ECPAS achieves a higher processing speed of 5 million price auditing per day while maintaining low gas and on-chain storage costs based on the IPFS.

Due to the stringent latency requirements for computationally intensive rendering in virtual reality (VR) transmission and the limitations of computational resources on VR devices, extensive research has focused on task offloading with joint communication and computing resource scheduling to address these issues. Traditional model-based theoretical methods face challenges with long online processing times, while data-driven methods lack interpretability. This paper proposes a knowledge-driven rendering task offloading strategy for immersive wireless VR with mobile edge computing (MEC). The rendering approaches include local, MEC, and collaborative offloading between VR devices and MEC servers. First, we formulate an optimization problem to maximize user quality of experience (QoE), which is defined as the weighted sum of latency and video resolution. To solve the optimization problem, we propose a knowledge-driven belief propagation (KD-BP) algorithm where the structure of the BP algorithm is regarded as knowledge. Specifically, the operations with high computational complexity in the BP algorithm are replaced by a deep neural network, termed the knowledge-fused deep learning (DL) method. Finally, numerical results show that when the number of users reaches 10, the proposed KD-BP algorithm significantly reduces online processing latency and closely matches the convergence speed and performance compared to the BP algorithm.

Differential-privacy federated learning (DP-FL) has emerged as a promising approach to mitigate the inherent risks associated with traditional FL architectures, which are susceptible to inferential attacks due to the continuous sharing and updating of model parameters. However, existing DP-FL frameworks typically assume that the perturbations introduced by clients remain constant throughout the FL process, overlooking the dynamic influence of these perturbations on model performance across different communication rounds. In this paper, we present a long-term privacy-preserving FL framework designed to address issues of optimal incentive design, considering the dynamic influence of perturbations on model performance. Specifically, we first analyze the effect of local perturbations on the model’s convergence performance during various communication rounds, elucidating the balance between learning performance and privacy loss. Then, to harmonize learning performance with privacy loss, we develop a long-term privacy-preserving incentive scheme, where the interactions between clients and the FL server throughout the FL process are modeled as a multi-stage privacy-preserving game. Furthermore, we utilize contract theory to derive the equilibrium of this game. Finally, simulations show that our scheme can incentivize clients to contribute high-quality models, thereby enhancing the accuracy of the global model, as compared to benchmarks.

Crowd computing in large-scale cellular networks typically involves a significant number of participants, leading to high spectrum interference, reduced communication efficiency, and low user trustworthiness. To overcome these challenges, this paper proposes trustworthy and spectrum-efficient crowd computing scheme in massive multiple-input multiple-output (MIMO) networks based on deep neural networks (DNNs). Existing machine learning-aided multiple antenna technologies usually ignore the pilot contamination, which reduces spectrum efficiency. Here, we leverage the DNN to devise detection and precoding algorithms by inputting an imperfect channel state information (CSI) big data and provides detection and precoding matrices as outputs, where the online-to-offline learning framework offloads the training task to servers to reduce the overhead of base station (BS). With the well-trained DNNs, the BS can generate the detection and precoding matrices with low computation overheads. Especially, minimum-mean-square-error (MMSE) triggers between received signals and sources considering channel estimation error are seen as labels to improve spectrum efficiency. Besides, a multi-factor trust model is designed to enhance user authentication security. The simulations and numerical analysis show the proposed scheme can provide higher spectral efficiency, compared to conventional methods.

The evolving digital twin technology translates physical entities into the digital realm, allowing the exploration of abundant digital resources to optimize the task execution of these physical entities. Real-time data synchronization between physical entities and their digital twins is essential for the effective functioning of digital twin systems. In this paper, we investigate the challenge of data synchronization in vehicular digital twin networks operating in open street scenarios, where multiple vehicles rely on cellular networks for continuous data synchronization with their digital twins. Given the contention for cellular bandwidth among vehicles, a coordination scheme is required to manage resource allocation. As vehicles are fully distributed driven by self-interests only, a game-theoretic approach is proposed that leverages a cloud center controller to guide the sharing of cellular resources among digital twins. An optimal incentive mechanism is introduced to encourage digital twins to adhere to the center's guidance, promoting global social welfare. Through extensive simulations, we demonstrate that the proposed scheme successfully motivates vehicles to follow the center's guidance, leading to efficient data synchronization and mutual benefit maximization.

The fusion of federated learning (FL) and unmanned aerial vehicles (UAVs) garnered significant attention as a propitious paradigm, enabling the provision of ubiquitous Artificial Intelligence (AI) services in a privacy-preserving manner. However, despite the intrinsic superiority of FL in safeguarding privacy, an attacker could utilize differential attacks to infer the original data of UAVs. To address the aforementioned challenges, we design a privacy-preserving incentive scheme for UAV-aided FL. In particular, a UAV-aided FL framework is first proposed to facilitate AI model training between UAVs and the server. Then, we quantify the privacy level of UAVs based on differential privacy and analyze its influence on the aggregation accuracy of the server. This scenario involves a complex trade-off between two conflicting objectives. On the one hand, the server desires to obtain higher quality local models for superior aggregation accuracy. On the other hand, UAVs prefer to add more noise to their local models for better privacy protection. Besides, by employing contract theory, we propose an incentive mechanism to optimize the server's aggregation accuracy under information asymmetry. Finally, simulation results validate the superiority and feasibility of our proposed scheme.

In smart grid services, the leakage of crowdsourced consumption data on smart meters (SMs) poses potential risks of privacy disclosure and data misuse. Existing solutions, which rely on complex encrypted computations, are often impractical for resource-limited SMs due to their high computation and storage resource requirements. To address these challenges, this article proposes a practical privacy-preserving scheme with fault tolerance for smart grid services named 3PFT. In our scheme, we employ a masking approach that ensures user privacy preservation on SMs while consuming minimal resources. Unlike existing masking schemes, 3PFT provides fault tolerance, supports complex data analysis tasks, and mitigates vulnerabilities to key leakage attacks. To achieve these objectives, we incorporate a secret sharing technique into the masking approach, enabling the recovery of the master key using only a portion of the data. Additionally, we design a flexible data aggregation protocol for 3PFT, facilitating the execution of diverse data analysis missions, such as load forecasting, in smart grids. Furthermore, we introduce a negotiation-based key update method to enhance the protocol’s forward security and alleviate the additional overhead on SMs. Finally, we provide a rigorous proof of privacy preservation and fault tolerance for our scheme and validate its feasibility and effectiveness through extensive simulations.

Network Intrusion Detection Systems (NIDSs) have emerged as a frontline defense against potential attacks in wireless Internet of Things (IoT) networks. However, existing machine learning methods follow an unstructured data processing patterns and can barely incorporate all information due to the network dynamicity as well as data imbalance. In this study, we propose Graph Isomorphism Network model based on Edge (GINE), an innovative graph-based algorithm tailored to pinpoint malicious network traffic within wireless IoT networks. Specifically, we initiate by presenting the wireless IoT network graph, capturing the global topological interactions of its edges. Subsequently, we design an edge representation learning algorithm, capable of encoding network data frames in a discerning pattern-aware manner. Moreover, we integrate a data interpolation module into the edges of our structured graph data targeting at data imbalance, which fosters a more balanced distribution across the various classes of edges. Our empirical analysis on select wireless IoT intrusion datasets shows GINE’s superiority, consistently outperforming state-of-the-art methods in classification metrics, including accuracy, F1-Score, False Alarm Rate, etc. Through a simulated wireless environment, we demonstrate GINE’s robust scalability, even in unpredictable wireless networks.

The widespread application of ultra-dense and multivariate Internet of Things (IoT) benefits from Network Function Virtualization (NFV) that provides flexible frameworks and effective management. NFV leverages the virtualization technologies to integrate the existing network functions of devices into standard servers, storages, and switches. Then, the network functions are achieved in software form to displace the private, dedicated and closed network devices. However, NFV also brings instability and challenges to the network management where the network dynamics, lack of visibility, and high frequency and abundant types of faults will increase the difficulty. Therefore, diagnosing the faults embedded in the generic NFV framework is crucial for the effective adoption of NFV to the IoT environment and thus ensuring the user services. This paper summarizes the differences and connections of fault diagnosis between the NFV framework and traditional networks, and introduces the challenges faced by NFV. Moreover, we provide a comprehensive survey of the state-of-the-art fault detection methods for the NFV framework. After an in-depth discussion of the fault propagation characteristics, we further present a detailed taxonomy of the fault localization approaches. Finally, we highlight the future research directions to provide ample space for improvement in applying NFV to the IoT environment.

Federated learning (FL) has gained widespread adoption in Internet of Things (IoT) applications, promoting the evolution of IoT toward Artificial Intelligence of Things (AIoT). However, IoT devices are still vulnerable to various privacy inference attacks in FL. While current solutions aim to protect the privacy of devices during model training, the published model is still at risk from external privacy attacks during model deployment. To address the privacy concerns throughout the entire FL lifecycle, this article proposes a privacy-enhanced and efficient federated knowledge transfer framework for IoT, named PEFKT, which integrates the knowledge transfer method and local differential privacy (LDP) mechanism. In PEFKT, we devise a data diversity-driven grouping strategy to tackle the non-independent and identically distributed (non-IID) issue in IoT. Additionally, we design a quality-aware soft-label aggregation algorithm to facilitate effective knowledge transfer, thereby improving the performance of the student model. Finally, we provide rigorous privacy analysis and validate the feasibility and effectiveness of PEFKT through extensive experiments on real data sets.

Sharing digital resources is a common practice in both work and personal life. Yet, sharing identical credentials, such as passwords or physical cards, not only poses significant security risks but also falls short in addressing the specific requirements of small local groups, such as parental controls, tracking user modifications, and easily updating access. To address this, we suggest a touch behavior-based method tailored for sharing in small local groups, designed to balance between ensuring relaxed security and maintaining practical functionality. Our approach aims to concurrently identify in-group users and detect out-of-group imposters. Specifically, our approach extracts effective identity representations that are robust to in-group variability and out-of-group uncertainty by learning a pair of touch-behavioral and within-group similarity embeddings. While the former captures the unique features of user touch characteristics, the latter reflects the typical group-wide similarity structure that an in-group user is expected to possess from a holistic perspective. Experimental results showcase the effectiveness of our method even with few samples for training. It maintains accuracy despite the group growing larger and shows resilience against the advanced attacks. This offers a promising way to keep group access both user-friendly and relatively secure, striking a crucial balance for small groups’ needs.

With the rapid development of artificial intelligence technology, unmanned surface vehicles (USVs) in marine Internet of Things (MIoTs) have become an important paradigm for marine environment exploration. However, in MIoTs, when collecting environmental information, USVs face a series of threats such as engine failure, grounding and collision, etc., resulting in damage to shipboard memory, vessel breakage and sinking, which may cause loss or damage of stored data. The USV fleet consisting of multiple USVs is recently advocated to enable collaborative communication and storage resource sharing. As such, in this paper, the USV fleet-assisted data backup scheme for the damaged USVs is proposed to guarantee the availability of stored data. First, a data backup framework for USV fleets is designed, where the USVs are classified into high-risk USVs and low-risk USVs according to the damage risk probability of sailing. Within the USV fleet, high-risk USVs (i.e., requesters) back up data to low-risk USVs (i.e., assistants) under emergency time. Second, the coalition game based on cost sharing is utilized to incentivize individual USVs to form the optimal USV fleets by maximizing the expected revenues, where the cost sharing fashion effectively ensures the stability of the coalitions. Finally, the joint optimization problem of the requesters’ allocating data decisions and the assistants’ receiving data decisions is formulated to maximize the average amount of data backup. The predictor-corrector interior point method (PIPM) and Q-learning method are leveraged to derive the reasonable solution of the formulated problem, with achieving the optimal allocating data decision and receiving data decision. Extensive simulation results demonstrate that the proposed scheme outperforms the benchmark schemes in terms of individual expected revenue, participation degree and the average amount of data backup.

Precise positioning is a key factor and enabler technology for many use cases on intelligent transportation systems (ITS) and connected and automated vehicles (CAVs). Recently, the quantum positioning system (QPS) based on quantum ranging has emerged as a novel way to improve security and precision. As a key process of QPS, the entangled photons based ranging technology has picosecond-level clock synchronization, and the ranging accuracy can reach the Heisenberg limit. If promising QPS is deployed in the ITS and CAVs, it will cause a profound change. However, the existing QPS still lacks accuracy and robustness in different scenarios. To solve this problem, we proposed an efficient and robust fusion positioning system based on entangled photons. In this system, we derive the ranging accuracy limit with many factors and propose a fast data grouping and selection algorithm to improve real-time performance. Furthermore, we propose a fusion extended fingerprint localization method for robust positioning in the dynamic environment. The effectiveness and robustness of the system are verified by extensive experiments. When the range is 15m, the ranging accuracy can be limited to 0.0018m. The proposed system achieves the probability of positioning errors 90% within 0.13m with only two APs.

The proliferation of unmanned aerial vehicles (UAVs) opens up new opportunities for on-demand service provision anywhere and anytime, but also exposes UAVs to a variety of cyber threats. Low/medium interaction honeypots offer a promising lightweight defense for actively protecting mobile Internet of things, particularly UAV networks. While previous research has primarily focused on honeypot system design and attack pattern recognition, the incentive issue for motivating UAVs’ participation (e.g., sharing trapped attack data in honeypots) to collaboratively resist distributed and sophisticated attacks remains unexplored. This paper proposes a novel game-theoretical collaborative defense approach to address optimal, fair, and feasible incentive design, in the presence of network dynamics and UAVs’ multi-dimensional private information (e.g., valid defense data (VDD) volume, communication delay, and UAV cost). Specifically, we first develop a honeypot game between UAVs and the network operator under both partial and complete information asymmetry scenarios. The optimal VDD-reward contract design problem with partial information asymmetry is then solved using a contract-theoretic approach that ensures budget feasibility, truthfulness, fairness, and computational efficiency. In addition, under complete information asymmetry, we devise a distributed reinforcement learning algorithm to dynamically design optimal contracts for distinct types of UAVs in the time-varying UAV network. Extensive simulations demonstrate that the proposed scheme can motivate UAV’s cooperation in VDD sharing and improve defensive effectiveness, compared with conventional schemes.

The rapid development of the Internet of Vehicles (IoV) has spurred innovations in Intelligent Transportation Systems (ITS), but it also faces increasingly sophisticated cybersecurity threats. Traditional defense mechanisms often fall short in handling emerging and complex attacks due to the lack of flexibility to adapt to the rapidly evolving IoV environment. An emerging solution is to employ Large Language Models (LLMs), such as ChatGPT, to enhance IoV security, which depends on the quality, quantity, and freshness of the threat data used for fine-tuning. In this paper, we introduce a collaborative vehicular threat sharing framework that utilizes vehicular honeypots to gather threat data for fine-tuning LLMs, thereby bolstering IoV security. Local differential privacy is leveraged to safeguard the vehicles’ privacy. Given that vehicles have different privacy preferences that may change over time, it is critical to design an appropriate incentive mechanism to encourage sustainable participation in the dynamic IoV environment. Moreover, since privacy preferences are the private information of the vehicles, an information asymmetry exists between the vehicles and the IDS cloud server. To address this challenge, we propose a dynamic contract-based incentive mechanism that considers the dynamically changing privacy preference during long-term participation. The optimal contract is derived to maximize the expected utility of the IDS cloud server. Extensive simulation results demonstrate the feasibility of our proposed dynamic contract based incentive mechanism and validate the effectiveness of the LLM-based threat classification in handling complex threats.

In order to save computing power yet enhance safety, there is a strong intention for autonomous vehicles (AVs) in future to drive collaboratively by sharing sensory data and computing results among neighbors. However, the intense collaborative computing and data transmissions among unknown others will inevitably introduce severe security concerns. Aiming at addressing security concerns in future AVs, in this paper, we develop SPAD, a secured framework to forbid free-riders and promote trustworthy data dissemination in collaborative autonomous driving. Specifically, we first introduce a publish/subscribe framework for inter-vehicle data transmissions. To defend against free-riding attacks, we formulate the interactions between publisher AVs and subscriber AVs as a vehicular publish/subscribe game, and incentivize AVs to deliver high-quality data by analyzing the Stackelberg equilibrium of the game. We also design a reputation evaluation mechanism in the game to identify malicious AVs in disseminating fake information. Furthermore, for lack of sufficient knowledge on parameters of the network model and the user cost model in dynamic game scenarios, a reinforcement learning based algorithm with hotbooting is developed to obtain the optimal strategies of subscriber AVs and publisher AVs with free-rider prevention. Extensive simulations are conducted, and the results validate that our SPAD can effectively prevent free-riders and enhance the dependability of disseminated contents, compared with conventional schemes.

The increasing demand for online high-quality video streaming has brought huge challenges to the traditional client-server video streaming systems due to the high feedback delay, rigorous bandwidth requirement, and the lack of a mechanism of centralized resource management between users. In this work, we propose AMIS-MU, an edge computing-based mobile video streaming system that optimizes the watching experience of users via playback adaptation and channel resource allocation. AMIS-MU fully explores the power of edge servers from three perspectives. First, by pre-caching videos from the cloud, AMIS-MU analyzes video contents at the edge, and achieves a nearly imperceptible content-based playback speed adaptation. Second, as the edge server controls the channel resources of users in a centralized fashion, AMIS-MU adaptively updates the channel configuration to optimize the overall watching experience. Last, the plenty of computational power available at the edge enables a more intelligent playback control by using deep reinforcement learning (DRL). We propose a novel usage of DRL which significantly reduces the complexity of the cross-layer joint optimization problem and solve the non-convex channel resource allocation problem by Lyapunov optimization. Experiments show that AMIS-MU outperforms other existing algorithms in terms of average QoE and fairness.

Unmanned Aerial Vehicles (UAVs) have attracted extensive attention from both industry and academia owing to their high mobility, line-of-sight (LoS) characteristics of air-toground (A2G) channels, and low cost. However, the broadcast nature of wireless transmission and the LoS characteristics of A2G channels are vulnerable to eavesdropping attack, which leads to severe security issues. To enhance the security of UAV communication, we propose a framework that multiple UAVs cooperate to resist attacks (MURA). Specifically, we first propose an efficient incentive scheme based on the coalitional game to encourage UAVs to join the coalition. We prove that each UAV can maximize its utility by joining the coalition to form a grand coalition. Then, a secure UAV communication scheme is proposed to resist eavesdropping attack. Two types of scenarios are considered for UAV communication. In a completely rational scenario, in which participants make decisions aiming to maximize their utility, we utilize the Stackelberg game to model the interactions between UAVs and attacker. The existence and uniqueness of the equilibrium solution are proved, and the equilibrium solution is obtained. In an imperfectly rational scenario, the prospect theory (PT) is applied to capture the underlying rationality of the players. The PT valuations of the players, i.e., UAV and attacker, are deduced in detail. Meanwhile, the convergence of the PT valuations of UAV and attacker is proved. Finally, extensive simulation results show that the proposed scheme can effectively improve the utility of legal UAVs and ensure the security of the UAV networks compared with benchmarks.

Unmanned aerial vehicles (UAVs) combined with tagging applications (Apps) have recently attracted considerable attention to enable efficient mobile crowdsensing (MCS) applications in scenarios where an insufficient number of UAVs may be available to perform the sensing tasks. However, there remain potential security and incentive threats for App- and UAV-assisted crowdsensing owing to the presence of malicious UAVs and the selfishness of UAVs. To address these issues, we propose a two-stage secure incentive mechanism in the App- and UAV-assisted MCS. Specifically, we first develop an App- and UAV-assisted MCS framework, where the App tags the location of the sensing task as a point-of-interest (PoI) to attract registered UAVs, thus assisting the platform to complete the sensing task efficiently. To motivate the App to cooperate with the sensing platform, we design a double auction-based incentive mechanism for PoI-tagging tasks in the first stage, where the optimal price for PoI-tagging services is obtained by applying a double auction game. Furthermore, we evaluate each UAV through comprehensive consideration of the performance and security of UAVs for most task-suitable UAV recruitment and malicious UAVs prevention. Additionally, in the second stage, based on the Stackelberg game theory, an incentive mechanism for sensing tasks is proposed to encourage UAV participation. Finally, simulation results and security analysis validate that the proposed mechanism can greatly increase the utility of UAVs and the App while ensuring the security of the sensing process.

A key feature of federated learning (FL) is to preserve the data privacy of end users. However, there still exist potential privacy leakage in exchanging gradients under FL. As a result, recent research often explores the differential privacy (DP) approaches to add noises to the computing results to address privacy concerns with low overheads, which however degrade the model performance. In this paper, we strike the balance of data privacy and efficiency by utilizing the pervasive social connections between users. Specifically, we propose SCFL, a novel Social-aware Clustered Federated Learning scheme, where mutually trusted individuals can freely form a social cluster and aggregate their raw model updates (e.g., gradients) inside each cluster before uploading to the cloud for global aggregation. By mixing model updates in a social group, adversaries can only eavesdrop the social-layer combined results, but not the privacy of individuals. As such, SCFL considerably enhances model utility without sacrificing privacy in a low-cost and highly feasible manner. We unfold the design of SCFL in three steps. i) Stable social cluster formation. Considering users’ heterogeneous training samples and data distributions, we formulate the optimal social cluster formation problem as a federation game and devise a fair revenue allocation mechanism to resist free-riders. ii) Differentiated trust-privacy mapping. For the clusters with low mutual trust, we design a customizable privacy preservation mechanism to adaptively sanitize participants’ model updates depending on social trust degrees. iii) Distributed convergence. A distributed two-sided matching algorithm is devised to attain an optimized disjoint partition with Nash-stable convergence. Experiments on Facebook network and MNIST/CIFAR-10 datasets validate that our SCFL can effectively enhance learning utility, improve user payoff, and enforce customizable privacy protection.

Application (App)-assisted mobile edge crowd- sensing is a promising paradigm, in which Apps are in charge of tagging the location of the sensing tasks as point-of-interest (PoI) to assist the platform in recruiting users to participate in the sensing tasks. However, there exist potential security, incentive, and privacy threats for App-assisted mobile edge crowdsensing (AMECS) due to the presence of malicious Apps, the low-quality shared sensing data, and the vulnerability of wireless communication. Therefore, we propose a differential privacy-based incentive (DPI) scheme for AMECS to provide secure and efficient crowdsensing services while protecting users’ privacy. Specifically, we first propose an App quality management mechanism to correlate the behavior of each App with its quality and then select reliable Apps based on quality thresholds to assist the platform in recruiting users. With the designed mechanism, we further present an auction game-based incentive mechanism to encourage Apps to mark the location of the sensing tasks as PoI. To protect the privacy of users, a privacy-preserving sensing data sharing algorithm is devised based on differential privacy. Further, given the difficulty of obtaining accurate network parameters in practice, a reinforcement learning-based incentive mechanism is designed to encourage users to participate in sensing tasks. Finally, simulation results and security analysis demonstrate that the proposed scheme can effectively improve the utilities of users, ensure the security of the crowdsensing process, and protect the privacy of users.

Graph neural networks (GNNs) have attracted significant research attention because of their impressive capability in dealing with graph-structure data, such as energy networks, that are crucial for sustainable computing. We find that the communication of data loading from main memory to GPUs is the main bottleneck of GNN inference because of redundant data loading. In this paper, we propose RAIN, an efficient GNN inference system for graph learning. There are two key designs. First, we explore the opportunity of conducting similar inference batches sequentially and reusing repeated nodes among adjacent batches to reduce redundant data loading. This method requires reordering the batches based on their similarity. However, comparing the similarity across a large number of inference batches is a difficult task with a high computational cost. Thus, we propose a local sensitive hash (LSH)-based clustering scheme to group similar batches together quickly without pair-wise comparison. Second, RAIN contains an efficient adaptive sampling strategy, allowing users to sample target nodes’ neighbors according to their degree. The number of sampled neighbors is proportional to the size of the node's degree. We conduct extensive experiments with various baselines. RAIN can achieve up to 6.8X acceleration, and the accuracy decrease is smaller than 0.1%.

The Public Key Infrastructure (PKI) system is the cornerstone of today's security communications. All users in the service domain covered by the same PKI system are able to authenticate each other before exchanging messages. However, there is identity isolation in different domains, making the identity of users in different domains cannot be recognized by PKI systems in other domains. To achieve cross-domain authentication, the consortium blockchain system is leveraged in the existing schemes. Unfortunately, the consortium blockchain-based authentication schemes have the following challenges: high cost, privacy concerns, scalability and economic unsustainability. To solve these challenges, we propose a scalable and privacy-preserving cross-domain authentication scheme called Bifrost-Auth. Firstly, Bifrost-Auth is designed to use a decentralized oracle to directly interact with blockchains in different domains instead of maintaining a consortium blockchain and enables mutual authentication for users lying in different domains. Secondly, users can succinctly authenticate their membership of the domain by the accumulator technique, where the membership proof is turned into zero knowledge to protect users' privacy. Finally, Bifrost-Auth is proven to be secure against various attacks, and thorough experiments are carried out and demonstrate the security and efficiency of Bifrost-Auth.

This paper investigates the utilization of simultaneously transmitting and reflecting RIS (STAR-RIS) in supporting joint physical layer security (PLS) and covert communications (CCs) in a multi-antenna millimeter wave (mmWave) system, where the base station (BS) communicates with both covert and security users while defeating eavesdropping by wardens with the help of a STAR-RIS. Specifically, analytical derivations are performed to obtain the closed-form expression of warden’s minimum detection error probability (DEP). Furthermore, the asymptotic result of the minimum DEP and the lower bound of the secure rates are derived, considering the practical assumption that BS only knows the statistical channel state information (CSI) between STAR-RIS and the wardens. Subsequently, an optimization problem is formulated with the aim of maximizing the average sum of the covert rate and the minimum secure rate while ensuring the covert requirement and quality of service (QoS) for legal users by jointly optimizing the active and passive beamformers. Due to the strong coupling among variables, an iterative algorithm based on the alternating strategy and the semi-definite relaxation (SDR) method is proposed to solve the non-convex optimization problem. Simulation results indicate that the performance of the proposed STAR-RIS-assisted scheme greatly surpasses that of the conventional RIS scheme, which validates the superiority of STAR-RIS in simultaneously implementing PLS and CCs.

Topic segmentation aims to reveal the latent structure of a document and divide it into multiple parts. However, current neural solutions are limited in the context modeling of sentences and feature representation of candidate boundaries. This causes the model to suffer from inefficient sentence context encoding and noise information interference. In this paper, we design a new text segmentation model SegFormer with unidirectional attention blocks to better model sentence representations. To alleviate the problem of noise information interference, SegFormer uses a novel additional context aggregator and a topic classification loss to guide the model to aggregate the information within the appropriate range. In addition, SegFormer applies an iterative prediction algorithm to search for optimal boundaries progressively. We evaluate SegFormer's generalization ability, multilingual ability, and application ability on multiple challenging real-world datasets. Experiments show that our model significantly improves the performance by 7.5% on the benchmark WIKI-SECTION compared to several strong baselines. The application of SegFormer to a real-world dataset to separate normal and advertisement segments in product marketing essays also achieves superior performance in the evaluation with other cutting-edge models.

This paper focuses on the prevalent stage interference and stage performance imbalance of incremental learning. To avoid obvious stage learning bottlenecks, we propose a new incremental learning framework, which leverages a series of stage-isolated classifiers to perform the learning task at each stage, without interference from others. To be concrete, to aggregate multiple stage classifiers as a uniform one impartially, we first introduce a temperature-controlled energy metric for indicating the confidence score levels of the stage classifiers. We then propose an anchor-based energy self-normalization strategy to ensure the stage classifiers work at the same energy level. Finally, we design a voting-based inference augmentation strategy for robust inference. The proposed method is rehearsal-free and can work for almost all incremental learning scenarios. We evaluate the proposed method on four large datasets. Extensive results demonstrate the superiority of the proposed method in setting up new state-of-the-art overall performance. Code is available at https://github.com/iamwangyabin/ESN.

This paper investigates the power efficiency of physical layer security (PLS) in intelligent reflecting surface (IRS)-assisted multi-user uplink channels. Existing research works usually focus on enhancing secrecy performance, and neglect measures to improve power efficiency. In this paper, the optimization problem is formulated to minimize the sum radio frequency (RF) power of multiple users in the uplink channel subject to secrecy outage probability constraint. This problem is solved by an alternating optimization (AO) algorithm that includes three optimization sub-problems, i.e., phase shift matrix, receiving matrix, and RF power optimization. Furthermore, to reduce the complexity of the proposed AO algorithm, a deep learning (DL)-based approach is proposed to optimize the sophisticated phase shift matrix optimization process. Simulation results demonstrate that the proposed scheme can significantly reduce the average RF power, and the DL-based scheme achieves similar performance as AO algorithm while reducing the time complexity significantly.

In recent years, 5G-V2X has promoted the advancement of autonomous vehicles, enabling the latter to obtain more information via 5G networks. However, fast-moving vehicles have to perform frequent handover authentication with base stations in vulnerable wireless channels, which can cause access failures and affect smooth driving. The digital twin is the virtual agent in cyberspace to reliably provide real-time decisions and added-value services to improve the quality of communication for vehicles by analyzing raw data and interacting with the 5G core network. Based on the capabilities of digital twin, in this paper, we propose digital twin-assisted handover authentication scheme that uses the digital twin as the bridge to exchange necessary parameters in 5G-V2X, thereby intelligently assisting in completing mutual authentication and key negotiation between the vehicle and the target base station in advance and reducing the complexity of the handover process. Furthermore, the security and performance analysis demonstrates that our proposed scheme is secure and efficient.

The combination of applications (Apps) and mobile edge crowdsensing technology has been viewed as a promising paradigm, where Apps are responsible for marking the location of the sensing task as point-of-interest (PoI) to assist the platform in recruiting users. However, there still exist potential incentive and privacy threats associated with App-assisted mobile edge crowdsensing (AMECS) due to the selfish nature of Apps and the vulnerability of wireless communication. To this end, we propose a differential privacy-based incentive (DPI) scheme for AMECS to support secure and efficient crowdsensing while protecting the privacy of users. Specifically, we first propose an App quality management mechanism to correlate the behavior of the App with its quality and then choose reliable Apps based on quality thresholds. Afterwards, a privacy-preserving sensing data sharing algorithm is designed to protect the privacy of users. Furthermore, given the difficulty of obtaining accurate network parameters in real life, a reinforcement learning-based incentive mechanism is devised to motivate users to actively engage in sensing tasks. Finally, simulation results and security analysis demonstrate that the proposed scheme is effective in improving the utility of participants and protecting the privacy of users.

Cross-silo federated learning, as a distributed learning paradigm, allows clients to collaboratively train an artificial intelligence (AI) model and jointly share the model ownership without local data transfer or exposure. However, the valuable AI models are facing fatal intellectual property (IP) infringement threats when offering AI services. Existing researches on IP protection mainly focus on the centralized models (i.e., single ownership), but leave federated models (i.e., shared ownership) unexplored. In this paper, we propose IPSF, a novel shared IP protection framework with all-round verification for multiple owners under cross-silo federated learning. Specifically, instead of embedding private watermarks individually, we adopt joint watermarks and soft labels as a conjoint fingerprint, and present a watermark generative adversarial network (WM-GAN) mechanism to fuse private watermarks and facilitate the integrated verification. We also design a diversity-and similarity-oriented assessment mechanism to support mutual evaluation between private and joint watermarks. Through the designed assessment mechanism, the correlation and variability between private and joint watermarks are dynamically maintained to ensure the stability of WM-GAN and the fairness among users in verification. Extensive experiments validates that our IPSF achieves desirable fidelity and high robustness under attacks.

Underwater wireless acoustic networks (UWANs) integrating sensing and underwater acoustic communication technology have been widely used for the perception and collection of underwater information. However, due to the openness of UWAN's deployment environment and the broadcast nature of wireless communication, the data collection process of UWAN by using an autonomous underwater vehicle (AUV) may suffer from potential eavesdropping attacks, resulting in information leakage. In order to implement data secure collection, we propose a data secure collection scheme for UWAN assisted by unmanned surface vehicle (USV) to protect the security of data collection in the case of a malicious proactive eavesdropping AUV. Firstly, a friendly jamming strategy by USV for coping with the eavesdropping AUV is proposed. Secondly, the interaction between the data collection link and the eavesdropping link is modeled as a linear constrained bimatrix game problem, and the utility functions of the two links are established. After that, the equilibrium solution of the linear constrained bimatrix game is obtained by transforming the game into an optimal solution of a corresponding quadratic programming. The numerical results demonstrate that the proposed scheme can achieve higher utility of legitimate link compared with other schemes.

For future networks, communications and computing will converge to provide services; Federated Learning (FL), as one of the typical distributed computing technologies, needs to be integrated with networking. For such integration, FL suffers from the straggler effect that the entire learning speed can be lowered down, because of the existence of the devices taking more time to complete their tasks. There are many existing works targeting at reducing straggler effects; However, they lacks the detailed investigations on the reasons and the impact of each cause when integrating FL with networking. To carefully investigate those aspects, we classify the reasons of such effects into 3 categories, computing power, communication capability and data distributions, and conduct the extensive experiments with carefully designs. After investigations, it is observed that learning completion time cannot be estimated by formulation with FLoating-point Operations Per second (FLOPs) if the device's computing capability is low. Also the communication time can be reduced by intentionally selecting appropriate devices when the computing powers of devices are heterogeneous, and the model parameters can be discarded if the device holds independent and identically distributed (i.i.d.) dataset.

To prevent implicit privacy disclosure in sharing gradients among data owners (DOs) under federated learning (FL), differential privacy (DP) and its variants have become a common practice to offer formal privacy guarantees with low overheads. However, individual DOs generally tend to inject larger DP noises for stronger privacy provisions (which entails severe degradation of model utility), while the curator (i.e., aggregation server) aims to minimize the overall effect of added random noises for satisfactory model performance. To address this conflicting goal, we propose a novel dynamic privacy pricing (DyPP) game which allows DOs to sell individual privacy (by lowering the scale of locally added DP noise) for differentiated economic compensations (offered by the curator), thereby enhancing FL model utility. Considering multi-dimensional information asymmetry among players (e.g., DO's data distribution and privacy preference, and curator's maximum affordable payment) as well as their varying private information in distinct FL tasks, it is hard to directly attain the Nash equilibrium of the mixed-strategy DyPP game. Alternatively, we devise a fast reinforcement learning algorithm with two layers to quickly learn the optimal mixed noise-saving strategy of DOs and the optimal mixed pricing strategy of the curator without prior knowledge of players' private information. Experiments on real datasets validate the feasibility and effectiveness of the proposed scheme in terms of faster convergence speed and enhanced FL model utility with lower payment costs.

Federated learning (FL) is a promising distributed learning paradigm, which enables devices to collaboratively train an AI model without exposing participants' private data. However, FL is vulnerable to various attacks and thus remains exposed to privacy issues. For example, malicious parties can launch attacks to recover sensitive and private training data from the shared parameters. Leakage of privacy data can cause serious damage to data providers. Furthermore, user anonymity and data verification in FL also need to be considered. To tackle these problems, in this paper, a verifiable and privacy-preserving cooperative FL (VPPFL) scheme is proposed in UAV-assisted vehicular networks (UVNs). Specifically, to preserve the identity privacy of vehicles, elliptic curve cryptosystem (ECC) is used to generate pseudonyms for vehicles. To preserve the data privacy, Paillier homomorphic encryption algorithm is utilized to encrypt the updates of vehicles, whereby UAVs directly perform global aggregations on encrypted updates instead of raw ones. Additionally, pseudonym-based signature mechanism is presented for vehicles to generate verifiable signatures, so as to ensure the authenticity and validity of uploaded local model updates. Besides, to sufficiently use the multi-source data, multiple UAVs share the local updates packets with each other to execute global aggregation. Finally, simulations are carried out to demonstrate that the proposed scheme can achieve high accuracy and verification with providing strict privacy protection.

The emergence of the Metaverse has brought forth a new era of social networks, offering immersive virtual spaces for users to engage in social activities. However, the resource-intensive nature of rendering avatars and virtual scenes places considerable strain on end devices. To improve the Quality of Experience (QoE) for users, the utilization of edge servers’ resources becomes crucial. Moreover, accommodating the diverse QoE requirements and time dynamics of users (e.g., user join/departure, and social activities) escalates the complexity of resource allocation. In this paper, we propose an auction-based dynamic resource allocation algorithm to efficiently and economically allocate various limited resources (e.g., CPU, GPU, RAM, and VRAM) of edge servers to social user groups in a rapid and decentralized manner. First, with heterogeneous and dynamic varying resources at each Planet (i.e., edge server to host Metaverse users), we design an optimal Planet access scheme to help social user groups to determine which Planet to connect. Second, considering the dynamic nature of social applications, e.g., users dynamically join and depart the network with dynamic requirements on resources, we present a multi-round auction game between social user groups and edge servers to compete for the dynamic multi-dimensional resources before each scheduled time period. By using the above mechanisms, our scheme optimizes the dynamic resource utilization by considering the social feature of Metaverse. Using extensive simulations, we demonstrate that the proposed algorithm dynamically and effectively allocates resources for social Metaverse activities, outperforming conventional allocation approaches.

Human pose estimation is a crucial problem in computer vision, and it has numerous applications in diverse fields such as virtual reality, surveillance, human-computer interaction, and action assistance. With the advent of edge computing, it is a promising paradigm to perform real-time artificial intelligence (AI)-assisted action based on pose estimation at the edge. However, task scheduling optimization for human pose estimation in edge computing is a challenging problem, due to the limited computing resources. In this paper, we propose a novel framework for task scheduling optimization in human pose estimation at the edge. Our framework takes computing resources scheduling and task scheduling decision into account, with the objective of maximizing the quality of service (QoS) of the system. We use multiple depth cameras at different locations to build three-dimensional (3D) poses to maintain the accuracy of estimation and to assist in guiding action. We evaluate our proposed framework on a real-world dataset. The results demonstrate its effectiveness in improving system delay and estimation accuracy in comparison with benchmark methods. We also verify the sensitivity of our proposed framework, which can provide insights into optimal parameter settings for different scenarios.

Federated learning (FL) is capable of using the local data sets from large-scale nodes for distributed model training. In FL tasks, training and updating are usually repeated ping-pong processes, in that the model training process between devices (workers) and task publisher (TP) needs to be repeated for multiple rounds towards the global model convergence. However, a worker is typically selfish to save its local resource, and even with an incentive mechanism in place at the beginning of model training, a worker may not be honest to participate in all training rounds, leading to poor performance in global model convergence. To enable long-term cooperation in FL, however, has rarely been considered in the existing literature which motivates our work. In this paper, the multi-round FL is modeled as a dynamic repeated game. To exploit the long-term cooperation gain, a general trigger strategy is deployed as the punishment for free-riding and the Nash equilibrium (NE) of the repeated game is derived. Based on the game theoretic analysis, we develop a NE-driven incentive mechanism to guide the TP selects the most effective wages to motivate workers towards long-term cooperation and avoid midway free-riding. Simulation results show the effectiveness of our proposal.

Connected and Autonomous Vehicles (CAVs) provide significant societal benefits but pose serious security risks due to their high connectivity and openness. Traditional security measures like cryptography and intrusion detection systems (IDSs) are reactive and passive, posing significant challenges to securing CAVs. We propose a proactive and collaborative threat-sharing framework to tackle the above challenges and enhance CAV security through vehicular honeypots. The proposed framework leverages federated learning, which allows CAVs to share threat information decentralized while preserving their privacy. Additionally, we design an optimal incentive mechanism that considers three private information of CAVs, including deployment, training, and communication costs. Specifically, we leverage the self-disclosure property of the contract theory, which can effectively address information asymmetry and incentive mismatches between CAVs and the IDS server, motivating CAVs to participate in threat sharing. Finally, through a series of simu- lation experiments, we validate the feasibility of the contract and evaluate the effectiveness of our proposed incentive mechanism.

With the development of communication technology and virtual reality (VR) technology, virtual Metaverse services are gradually entering people’s lives to provide immersive experience. As one of the important travel tools for people, vehicles have the opportunity to become the carrier of Metaverse, thereby enhancing the driving experience and entertainment experience of vehicle users (VUs). However, due to the high-speed movement of vehicles, how to dynamically adapt to environmental changes to allocate transmission and computing resources so that VUs can better experience VR services in the Metaverse has become a challenge. To this end, in this paper, we propose an environment-aware dynamic resource allocation scheme for VR video services in vehicle Metaverse, aiming to efficiently allocate computing and communication resources to maximize the quality of experience (QoE) of VUs when requesting VR video services. Specifically, we first establish the system model which includes network model, communication model, and VR video model. Then, considering the dynamic changes in the driving environment, we design a QoE model for each VU based on its VR video buffer. After that, we design a deep deterministic policy gradient (DDPG) algorithm to optimally allocate communication and computing resources to maximize the QoE of each VU. The simulation results show that our scheme can bring the highest reward to the VUs compared with the benchmark schemes.

This paper investigates the utilization of simultaneously transmitting and reflecting RIS (STAR-RIS) in supporting joint physical layer security (PLS) and covert communications (CCs) in a multi-antenna millimeter-wave (mmWave) system. Specifically, analytical derivations are performed to obtain the closed-form expression of the warden’s minimum detection error probability (DEP) considering the practical assumption. Subsequently, an optimization problem is formulated with the aim of maximizing the average sum of the covert rate and the secure rate while ensuring the covert requirement and quality of service (QoS) for legal users by jointly optimizing the active and passive beamformers. Due to the strong coupling among variables, an iterative algorithm based on the alternating strategy and the semi-definite relaxation (SDR) method is proposed to solve the non-convex optimization problem. Simulation results indicate the superiority of STAR-RIS in simultaneously implementing PLS and CCs.

With the rapid growth of marine services and applications for achieving smart oceans, advanced marine communication networks have attracted increasing interests. However, the limited resources constrain the applications in marine communication networks. In this paper, we investigate a two-tier computation offloading scheme for unmanned aerial vehicle (UAV) aided marine communication networks via game theory to improve offloading efficiency. Specifically, these underwater wireless sensors (UWSs) are deployed at the seafloor, which partially offloads their sensed information to unmanned surface vessels (USVs) for assist computing. USV acts as a relay to offload part of its data to UAVs. We formulate three optimization problems to optimize the utility of UWSs, USVs, and UAVs, respectively. To address the formulated problems, we propose efficient algorithms to derive the solutions, which can maximize the utility of each participant. Finally, simulations are conducted to validate the performance of the proposed algorithms, and the results show the efficiency and effectiveness of the proposed algorithms in comparison with the benchmark schemes.

This article investigates physical-layer security (PLS) of a typical wireless-powered communication (WPC) system with the aid of intelligent reflecting surface (IRS) in the presence of a passive eavesdropper for Internet of Things (IoT), and proposes three IRS-aided secure WPC modes. Specifically, in mode-I, the IRS is deployed between hybrid access point (HAP) and wireless user (U) for co-located power station (PS) and access point (AP). In mode-II, the IRS is deployed between AP and U for separate PS and AP, while in mode-III, the IRS is deployed between PS and U for separate PS and AP. For each mode, the optimal phase shift is designed to maximize the reception of energy and information at the legitimate receiver. We comprehensively analyze the performance of each mode, and derive the closed-form expressions of connection outage probability (COP), secrecy outage probability (SOP), and effective secrecy throughput (EST) for each mode, respectively. The theoretical analysis and simulation results reveal that from the perspective of reliability, mode-I can achieve the best COP with the increased number of IRS elements, while mode-II and mode-III have a similar COP. From the perspective of security, mode-II can achieve the best SOP with the increased number of IRS elements, while mode-I and mode-III have a similar SOP. Moreover, under the condition of small transmission power at HAP/PS or small number of IRS elements, mode-I has the best EST, while as the power or the number increases, the EST of mode-II becomes the best one.

The rapid development of communication and computing techniques enables unmanned aerial vehicles (UAVs) to provide reliable and cost-effective wireless communication and computing services from the air. Compared to the conventional fixed infrastructure, UAVs have attractive attributes, such as high flexibility and operability, and, as a result, on-demand line-of-sight connection links. Therefore, UAV-assisted wireless networks have been envisioned as a promising paradigm to achieve enhanced coverage and connectivity for future wireless communications. Meanwhile, achieving high levels of energy efficiency, sensing, communication, and computing capacities, and security and privacy are critical to the success of UAV-assisted wireless networks. In order to improve the performance of UAV-assisted wireless networks, some frameworks and mechanisms have been developed in the past few years. In this article, we provide a comprehensive survey of these developments. Specifically, we conduct a brief overview for the architecture of UAV-assisted wireless networks from four domains (i.e., framework-related, technology-related, challenge-related, and solution-related) and four aspects (i.e., sensing-related, communication-related, computing-related, and application-related). Then, the integrated sensing, communication, and computing for UAV-assisted wireless networks is introduced, followed by the characteristics and requirements. We also provide the implementation and applications of UAV-assisted wireless networks. Next, we discuss the challenges and the state-of-the-art solutions for UAV-assisted wireless networks. Finally, the advanced technologies for UAV-assisted communication and computing networks are exploited, followed by the potential research directions.

The Internet of Drones (IoD) manages and coordinates communications between drones in Internet of Things (IoT) applications. Ensuring security and privacy in unmanned aerial vehicles (UAVs) networks, i.e., drones, is essential to protect data from cyber attacks. In this context, providing authentication is a major challenge due to the fact that drones are devices limited in power capabilities. The problem is aggravated by the dynamism of IoD networks due to the high mobility of drones, being sensitive to packet loss and handovers. Blockchain technology is attractive to address the problem of centralization of existing authentication protocols. In this article, we provide a decentralized, secure, and efficient authentication protocol, based on$\mu $Tesla, that relies on Blockchain to manage drone authentication. We analyze the security and performance of the proposed solution. Simulation results show that the proposed solution outperforms several approaches in the literature, achieving an authentication delay of less than 250 ms with a low information exchange of 1024 bits for 128-bit security level while maintaining low computational requirements.

This article investigates computation offloading in blockchain-empowered Internet of Things (IoT), where the task data uploading link from sensors to a base station (BS) is protected by intelligent reflecting surface (IRS)-assisted physical-layer security (PLS). After receiving task data, the BS allocates computational resources provided by mobile-edge computing (MEC) servers to help sensors perform tasks. Existing blockchain-based computation offloading schemes usually focus on network performance improvements, such as energy consumption minimization (ECM) or latency minimization, and neglect the Gas fee for computation offloading, resulting in the dissatisfaction of high Gas providers. Also, the secrecy rate during the data uploading process cannot be measured by a steady value because of the time-varying characteristics of IRS-based wireless channels, thereby computational resources allocation with a secrecy rate measured before data uploading is inappropriate. In this article, we design a Gas-oriented computation offloading scheme that guarantees a low degree of dissatisfaction of sensors, while reducing energy consumption. Also, we deduce the ergodic secrecy rate of IRS-assisted PLS transmission that can represent the global secrecy performance to allocate computational resources. The simulations show that the proposed scheme has lower energy consumption compared to existing schemes and ensures that the node paying higher Gas gets stronger computational resources.

By interacting, synchronizing, and cooperating with its physical counterpart in real time, digital twin (DT) is promised to promote an intelligent, predictive, and optimized modern city. Via interconnecting massive physical entities and their virtual twins with inter-twin and intra-twin communications, the Internet of DTs (IoDT) enables free data exchange, dynamic mission cooperation, and efficient information aggregation for composite insights across vast physical/virtual entities. However, as IoDT incorporates various cutting-edge technologies to spawn the new ecology, severe known/unknown security flaws, and privacy invasions of IoDT hinder its wide deployment. Besides, the intrinsic characteristics of IoDT, such as decentralized structure, information-centric routing, and semantic communications, entail critical challenges for security service provisioning in IoDT. To this end, this article presents an in-depth review of the IoDT with respect to system architecture, enabling technologies, and security/privacy issues. Specifically, we first explore a novel distributed IoDT architecture with cyber–physical interactions and discuss its key characteristics and communication modes. Afterward, we investigate the taxonomy of security and privacy threats in IoDT, discuss the key research challenges, and review the state-of-the-art defense approaches. Finally, we point out the new trends and open research directions related to IoDT.

With the continuous expansion of the power Internet of Things (IoT) and the rapid increase in the number of Smart Devices (SDs), the data generated by SDs has exponentially increased. The traditional cloud-based smart grid cannot meet the low latency and high reliability requirements of emerging applications. By moving computing, data, and services from the centralized cloud to Edge Servers (ESs), edge computing exhibits excellent performance in communication delay and traffic reduction. Simultaneously, service caching also shows attractive advantages in handling the surge in data traffic. In this paper, we consider the joint optimization of computing offloading and service caching in edge computing-based smart grid, and formulate the problem as a Mixed-Integer Non-Linear Program (MINLP), aiming to minimize the task cost of the system. The original problem is decomposed into an equivalent master problem and sub-problem, and a Collaborative Computing Offloading and Resource Allocation Method (CCORAM) is proposed to solve the optimization problem, which includes two low-complexity algorithms. Specifically, a gradient descent allocation algorithm is first proposed to determine the computing resource allocation strategy, and then a game theory-based algorithm is proposed to determine the computing strategy. Simulation results show that CCORAM with low time complexity is very close to the optimal method, and performs much better than other benchmark methods.

The explosively increasing development of marine communication networks will improve the quality of service (QoS) of marine applications (e.g., ocean farm and marine tourism), which has attracted much attention from both academia and industrial in recent years. However, real-time data processing for diverse marine tasks (especially those computing-intensive and latency-sensitive tasks) is still challenging due to the limited marine communication and computing resources. Mobile edge computing (MEC) driven by powerful computing capability is envisioned as a promising solution to address the issue for resource-constrained marine services. In this paper, we propose a hybrid offshore and aerial-based multi-access edge computing scheme in marine communication networks to improve the QoS of marine applications. Specifically, we consider a scenario that both offshore base-station and unmanned aerial vehicles (UAVs) are equipped with edge-servers, and the computation workloads of unmanned surface vehicle (USV) can be simultaneously offloaded to offshore base-station and UAVs via multi-access manner. To minimize the latency of completing USV’s workloads and reduce USV’s energy consumption, we formulate a joint optimization problem to optimize the offloading decision, transmission time, and computing-rate allocation, with the objective ofMinimizing theMaximumWorkloadsLatency (MMWL). Exploiting the features of the formulated problem, we present a layered structure approach and decompose it into three subproblems. We propose efficient algorithms to obtain the optimal solutions and validate the optimality of the proposed algorithms. Finally, we provide simulation results and analysis to demonstrate the effectiveness and efficiency of the proposed scheme and algorithms in comparison with benchmark algorithms.

The indoor navigation system supported by spatial crowdsourcing emerges as a promising application to provide customized location service for requesters. An important stage of crowdsourcing is to select trustworthy workers. Workers’ reputation, as an essential criterion of this selection, is usually evaluated by feedback ratings from requesters. However, the reputation in the crowdsourcing-based indoor navigation system is vulnerable to the collusion attack, that is malicious workers (i.e., attackers) collude with requesters to illegally increase reputation. In this paper, we propose a collusion detection scheme to distinguish attackers and provide a secure reputation mechanism. Specifically, we first identify collusive requesters categorized into three different levels according to their feedback rating behaviors. Then, the weighted logistic regression (WLR) is developed to distinguish the collusive requesters who provide exorbitant feedback ratings. Furthermore, we employ an outlying sequence detection based on the maximum mean discrepancy (MMD), to resist the multiple location queries initiated by the same collusive requester through analyzing the distribution distance. In addition, we propose a community detection algorithm, named Fastgreedy, to identify the collusion from many requesters. Finally, the extensive simulation results demonstrate that the proposed scheme can effectively detect collusive requesters and significantly outperform other methods.

Advanced persistent threat (APT) is a kind of stealthy, sophisticated, and long-term cyberattack that has brought severe financial losses and critical infrastructure damages. Existing works mainly focus on APT defense under stable network topologies, while the problem under time-varying dynamic networks (e.g., vehicular networks) remains unexplored, which motivates our work. Besides, the spatiotemporal dynamics in defense resources, complex attackers’ lateral movement behaviors, and lack of timely defense make APT defense a challenging issue under time-varying networks. In this paper, we propose a novel game-theoretical APT defense approach to promote real-time and optimal defense strategy-making under both periodic time-varying and general time-varying environments. Specifically, we first model the interactions between attackers and defenders in an APT process as a dynamic APT repair game, and then formulate the APT damage minimization problem as the precise prevention and control (PPAC) problem. To derive the optimal defense strategy under both latency and defense resource constraints, we further devise an online optimal control-based mechanism integrated with two backtracking-forward algorithms to fastly derive the near-optimal solution of the PPAC problem in real time. Extensive experiments are carried out, and the results demonstrate that our proposed scheme can efficiently obtain optimal defense strategy in 54481 ms under seven attack-defense interactions with 9.64% resource occupancy in stimulated periodic time-varying and general time-varying networks. Besides, even under static networks, our proposed scheme still outperforms existing representative APT defense approaches in terms of service stability and defense resource utilization.

Due to the limited battery and computing resource, offloading unmanned aerial vehicles (UAVs)’ computation tasks to ground infrastructure, e.g., vehicles, is a fundamental framework. Under such an open and untrusted environment, vehicles are reluctant to share their computing resource unless provisioning strong incentives, privacy protection, and fairness guarantee. Precisely, without strategy-proofness guarantee, the strategic vehicles can overclaim participation costs so as to conduct market manipulation. Without the fairness provision, vehicles can deliberately abort the assigned tasks without any punishments, and UAVs can refuse to pay by the end, causing an exchange dilemma. Lastly, the strategy-proofness and fairness provision typically require transparent payment/task results exchange under public audit, which may disclose sensitive information of vehicles and make the privacy preservation a foremost issue. To achieve the three design goals, we propose SEAL, an integrated framework to address Strategy-proof, fair, and privacy-prEserving UAV computation offLoading. SEAL deploys a strategy-proof reverse combinatorial auction mechanism to optimize UAVs’ task offloading under practical constraints while ensuring economic-robustness and polynomial-time efficiency. Based on smart contracts and hashchain micropayment, SEAL implements a fair on-chain exchange protocol to realize the atomic completion of batch payments and computing results in multi-round auctions. In addition, a privacy-preserving off-chain auction protocol is devised with the assistance of the trusted processor to efficiently protect vehicles’ bid privacy. Using rigorous theoretical analysis and extensive simulations, we validate that SEAL can effectively prevent vehicles from manipulating, ensure privacy protection and fairness, improve the offloading efficiency, and reduce UAV’s energy costs and expenses with low overheads.

Edge caching has been envisioned as a promising technology in heterogeneous networks (HetNets) to proximally cache (video) contents. Nevertheless, as massive resources (e.g., energy, storage, computing, and bandwidth) are consumed to cache contents, edge caching devices (ECDs) are unwilling to provide caching services. In addition, as the ECDs are usually deployed by untrusted third parties, the cached contents may be illegally accessed, which results in the mobile users’ privacy leakage. To efficiently address these problems, in this paper, we propose a novel secure edge caching scheme for video contents in HetNets. Specifically, to motivate the participation of ECDs, the Nash bargaining game is exploited to model the negotiations between the content provider and ECDs, where the optimal requested caching space of the content provider and the optimal caching price of each ECD are jointly analyzed. Apart from this, to protect the content secrecy, scalable video coding is employed to facilitate secure edge caching, where the ECDs are only utilized to cache the enhancement layers that cannot be independently decoded to reconstruct the original contents. Then, we formulate a non-convex 0–1 integer programming problem to optimize the enhancement layer caching on ECDs, and the modified alternating direction method of multipliers (ADMM) is used to solve the problem optimally. Finally, simulation results show that the proposed scheme provides secure and efficient content caching for mobile users.

The data aggregation technique has been widely adopted in the Internet of Things (IoT) to protect data privacy while ensuring data availability. Homomorphic encryption is a typical technique that guarantees accurate computing results. However, it brings heavy computation overhead for edge nodes and exposes the aggregated results to the central server, which significantly threatens the confidentiality of results. This article gets rid of the server-centric style existing in most data aggregation schemes and proposes a scalable and decentralized data aggregation scheme for edge-enabled IoT. In the proposed scheme, edge nodes can freely form, join, and exit from the data aggregation group to aggregate data correctly, securely, and efficiently. Besides, two structure-based data aggregation methods are proposed to reduce the aggregation overhead to$O(n\sqrt{n})$with constant rounds, as opposed to$O(n\log n)$with$O(n)$round. Symmetric encryption and online/offline signature computation are adopted to mitigate the online computation burden. Moreover, the proposed scheme can rigorously defend against forgery attack, eavesdropping attack, and collusion attack. The performance evaluation and experiment results show that the proposed scheme improves the efficiency of communication with affordable computation costs for edge nodes.

In this paper, we consider task offloading in edge computing systems, where tasks are offloaded by the base station to resourceful mobile users. With the consideration of unique characteristics in practical edge computing systems, such as dynamic arrival of computation tasks, and energy constraints at battery-powered mobile users, we formulate an incentive mechanism design problem by jointly optimizing task offloading decisions, and allocation of both communications (i.e., power and bandwidth), and computation resources. In order to tackle the nonlinear issue in the designed mechanism, a novel online incentive mechanism is proposed. We first convert the original mechanism design problem into several one-shot design problems by temporally removing the energy constraint. Then, we propose a new mechanism design framework, called the Integrate Rounding Scheme based Maxima-in-distributional Range (IRSM), and based on that, design a new incentive mechanism for each one-shot problem. Finally, we reconsider energy constraints to design a new nonlinear online incentive mechanism by rationally combining the previously derived one-shot ones. Theoretical analyses show that our proposed nonlinear online incentive mechanism can guarantee individual rationality, truthfulness, a sound competitive ratio, and computational efficiency. We further conduct comprehensive simulations to validate the effectiveness and superiority of our proposed mechanism.

Timely and accurate cellular traffic prediction is difficult to achieve due to the complex spatial-temporal characteristics of cellular traffic. The latest approaches mainly aim to model local spatial-temporal dependencies of cellular traffic based on deep learning techniques but lack the consideration of diverse global spatial-temporal correlations hidden in cellular traffic. To tackle this issue, we propose a novel multi-view spatial-temporal graph network (MVSTGN), which combines attention and convolution mechanisms into traffic pattern analysis, enabling the comprehensive excavation of spatial-temporal characteristics. Specifically, the MVSTGN realizes the above statement from three spatial-temporal views: 1) From a global spatial view, two spatial attention modules are proposed to capture the global spatial correlations between different regions at node and trend levels; 2) From a global temporal view, a temporal attention module is employed to capture and encode global temporal correlations between traffic at different times; 3) From a local spatial-temporal view, a dense convolution module is developed to further excavate the local spatial-temporal dependencies in cellular traffic. Consequently, a successful cellular traffic prediction strategy is constructed to fully explore the spatial-temporal characteristics from multiple views. The experimental results on a popular real-world cellular traffic dataset demonstrate that the MVSTGN achieves obvious improvements over baselines.

Unmanned aerial vehicles (UAVs) have the potential to establish flexible and reliable emergency networks in disaster sites when terrestrial communication infrastructures go down. Nevertheless, potential security threats may occur on UAVs during data transmissions due to the untrusted environment and open-access UAV networks. Moreover, UAVs typically have limited battery and computation capacity, making them unaffordable for heavy security provisioning operations when performing complicated rescue tasks. In this paper, we develop RescueChain, a secure and efficient information sharing scheme for UAV-assisted disaster rescue. Specifically, we first implement a lightweight blockchain-based framework to safeguard data sharing under disasters and immutably trace misbehaving entities. A reputation-based consensus protocol is devised to adapt the weakly connected environment with improved consensus efficiency and promoted UAVs’ honest behaviors. Furthermore, we introduce a novel vehicular fog computing (VFC)-based off-chain mechanism by leveraging ground vehicles as moving fog nodes to offload UAVs’ heavy data processing and storage tasks. To offload computational tasks from the UAVs to ground vehicles with idle computing resources, an optimal allocation strategy is developed by choosing payoffs that achieve equilibrium in a Stackelberg game formulation of the allocation problem. For lack of sufficient knowledge on network model parameters and users’ private cost parameters in practical environment, we also design a two-tier deep reinforcement learning-based algorithm to seek the optimal payment and resource strategies of UAVs and vehicles with improved learning efficiency. Simulation results show that RescueChain can effectively accelerate consensus process, improve offloading efficiency, reduce energy consumption, and enhance user payoffs.

The metaverse, as an instance of cyber–physical–social systems (CPSS) that originates in cyber–physical systems (CPS), features growing complexity, and diversity in terms of functionalities, as well as the exponentially increasing demand in network bandwidth and computational resources, thereby leading to exaggerated security threats. However, compared with the extensive attention received by the metaverse, solutions defending against the threats have not kept pace. A major obstacle to such solutions is virtuality–reality-synthesized threats. Therefore, it is imperative to design new paradigms to defend the metaverse effectively. In this article, we advance a parallel system, dubbed ParaDefender, to defend the metaverse against emerging new threats effectively. Inspired by parallel intelligence, ParaDefender comprises artificial cyberspace, computational experiments, and parallel execution. The basic idea is to make artificial and real cyberspaces executed in parallel to mutually guide each other for enhanced security, wherein the parallel execution is scenario driven in the sense that the scenarios originate from all possible spatial–temporal combinations of security threats in the metaverse. We also demonstrate how to land ParaDefender onto real-world applications, including the Industrial Internet of Things (IIoT) security operation application in the industrial metaverse, and the social governance application.

Smart contracts are programs running on Ethereum, whose deployment and use require gas. Gas measures the cost of performing specific operations as an index designed to quantify the computing power consumption. Existing unoptimized smart contracts make contract developers and users spend extra gas. To save gas and optimize smart contracts, this paper proposes a new tool named GaSaver for automatically detecting gas-expensive patterns based on Solidity source code. Specifically, we first identify 12 gas-expensive patterns in smart contracts and classify them into three categories: storage-related, judgment-related, and loop-related. Then, we deploy gas-expensive patterns and group them into three levels according to gas waste degree. By conducting extensive experiments on real data sets, we find that 89.68$\%$of the 1172 smart contracts suffer from gas-expensive patterns, 94.27$\%$of 1100 new smart contracts are gas-expensive, and 80.56$\%$of 72 widely used smart contracts are affected. Finally, the experiment results show that the proposed GaSaver can effectively optimize smart contracts. Besides, the proportion of gas-expensive cases in widely used smart contracts is lower than that in the newly released smart contracts.

With the increasing exploration of marine resources, various marine wireless devices have been rapidly deployed for different marine applications such as marine navigation, ocean environment monitoring, and seabed resource exploitation. However, due to long transmission delay and low data rate between marine wireless devices and the cloud, it is challenging to satisfy the service requirements of computing-intensive and delay-sensitive tasks. By migrating computing resources from cloud to the near side of ocean, the paradigm of marine edge computing networks, which integrates communication and computation capacities in marine wireless devices, is expected to support a variety of marine tasks (e.g., data collection, monitoring and processing) with low delay and high data rate. However, considering the rationality and selfishness of marine wireless devices and their limited computing-capacity, how to motivate marine wireless devices to conduct task processing becomes an important problem for improving computing efficiency. To address this issue, in this paper, we propose an incentive oriented two-tier task offloading scheme for marine edge computing networks via hybrid Stackelberg-auction game approach, with the objective of improving the offloading efficiency and maximizing marine wireless devices’ utilities. Specifically, for underwater acoustic transmission tier, we exploit multi-access task offloading scheme, in which underwater wireless sensor (UWS) uploads its workloads to an unmanned underwater vehicle (UUV) and a sea surface sink node (SN) via non-orthogonal multiple access (NOMA) transmission. We formulate the utility of each party and model the task offloading process among UWS, UUV and SN as a Stackelberg game to optimize the UWS’s offloading strategy, UUV’s and SN’s price strategies. For radio frequency transmission tier, SN can offload its partial workloads to an unmanned aerial vehicle (UAV) via frequency division multiple access (FDMA) transmission. We provide their utilities and model the offloading process between a SN and a UAV as a double auction game to optimize their bidding strategies. Extensive simulation results are provided to validate the performance of the proposed scheme. Numerical results demonstrate that the proposed algorithms can obtain the optimal solutions and increase the utilities for marine wireless devices.

With the accelerated popularization of 5G applications, accurate cellular traffic prediction is becoming increasingly important for efficient network management. Currently, the latest algorithms for cellular traffic prediction generally neglect extraction of the shallow features of cellular traffic and the prediction accuracy is hence limited. Therefore, we propose a global-local spatial-temporal transformer network (GLSTTN) that can fully excavate diverse spatial-temporal characteristics of cellular traffic for accurate cellular traffic prediction. Specifically, GLSTTN achieves this goal by constructing two modules: the global spatial-temporal module and the local spatial-temporal module. In the global spatial-temporal module, GLSTTN captures global correlations using stacked spatial-temporal blocks, where each block is composed of one spatial transformer and one temporal transformer. A skip connection is then used in each block to strengthen feature propagation. In the local spatial-temporal module, GLSTTN fully extracts the local spatial-temporal dependencies hidden in globally encoded features using densely connected convolutional neural networks. Extensive experiments demonstrate that GLSTTN achieves more accurate cellular traffic prediction than existing approaches on a real-world cellular traffic dataset.

This article investigates physical layer security (PLS) in reconfigurable intelligent surface (RIS)-assisted multiple-input multiple-output multiple-antenna-eavesdropper (MIMOME) channels. Existing researches ignore the problem that secrecy rate can not be calculated if the eavesdropper’s instantaneous channel state information (CSI) is unknown. Furthermore, without the secrecy rate expression, beamforming and phase shifter optimization with the purpose of PLS enhancement is not available. To address these problems, we first give the expression of secrecy outage probability for any beamforming vector and phase shifter matrix as the RIS-assisted PLS metric, which is measured based on the eavesdropper’s statistical CSI. Then, with the aid of the expression, we formulate the minimization problem of secrecy outage probability that is solved via alternately optimizing beamforming vectors and phase shift matrices. In the case of single-antenna transmitter or single-antenna legitimate receiver, the proposed alternating optimization (AO) scheme can be simplified to reduce computational complexity. Finally, it is demonstrated that the secrecy outage probability is significantly reduced with the proposed methods compared to current RIS-assisted PLS systems.

With the rapid advance of wireless communication technologies, the promising space-air-ground integrated networks (SAGINs) are advocated to provide ubiquitous multicast transmission services for the social community constituted by a group of mobile users that have strong social ties and similar content interests. However, due to the limited yet valuable spectrum resources, the network heterogeneity, and diverse service demands of mobile users, it is challenging to efficiently allocate bandwidth for social communities with the objective of achieving satisfactory quality of experience (QoE) in SAGINs. To address this problem, in this paper, we propose a hierarchical bandwidth allocation scheme to enable high-quality multicast services for social communities in SAGINs. Specifically, we first develop a hierarchical bandwidth allocation framework. Wherein, the low earth orbit (LEO) satellite is utilized to provide space-to-air (S2A) unicast bandwidth for unmanned aerial vehicles (UAVs) at a certain price. Each UAV is employed to provide air-to-ground (A2G) multicast bandwidth for ground social communities with a certain A2G multicast bandwidth charge. We then formulate the hierarchical bandwidth allocation problem as a four-stage Stackelberg game, where the target of each participant is to maximize its own utility. Afterward, through the game analysis by the backward induction method, the existence of the Stackelberg equilibrium is proved, where the closed-form solutions on the optimal policies of both the social communities and UAVs are derived by the convex optimization method, and the optimal pricing policies of the LEO satellite is achieved by a proposed gradient descent iteration algorithm. Finally, extensive experiments are conducted to demonstrate that the proposed scheme can greatly increase the utilities of social communities while consuming a less bandwidth compared to conventional schemes.

The proliferation of unmanned aerial vehicles (UAVs) opens up new opportunities for on-demand service provisioning anywhere and anytime, but it also exposes UAVs to various cyber threats. Low/medium-interaction honeypot is regarded as a promising lightweight defense to actively protect mobile Internet of things, especially UAV networks. Existing works primarily focused on honeypot design and attack pattern recognition, the incentive issue for motivating UAVs' participation (e.g., sharing trapped attack data in honeypots) to collaboratively resist distributed and sophisticated attacks is still under-explored. This paper proposes a novel game-based collaborative defense approach to address optimal, fair, and feasible incentive mechanism design, in the pres-ence of network dynamics and UAVs' multi-dimensional private information (e.g., valid defense data (VDD) volume, communication delay, and UAV cost). Specifically, we first develop a honeypot game between UAVs under both partial and complete information asymmetry scenarios. We then devise a contract-theoretic method to solve the optimal VDD-reward contract design problem with partial information asymmetry, while ensuring truthfulness, fair-ness, and computational efficiency. Furthermore, under complete information asymmetry, we devise a reinforcement learning based distributed method to dynamically design optimal contracts for distinct types of UAVs in the fast-changing network. Experimental simulations show that the proposed scheme can motivate UAV's collaboration in VDD sharing and enhance defensive effectiveness, compared with existing solutions.

This paper considers the downlink data synchronization from the digital twin (DT) to the vehicle, in which a vehicle drives through consecutive roadside units (RSUs) along its trip, and the DT on the cloud transmits the data to the vehicle through the relay of RSUs. To this goal, the DT first chops the data into blocks and cache them in the RSUs along the driving path of the vehicle. The vehicle can then retrieve the blocks when driving into the RSU's coverage to recover the data. Since RSUs have different cache capacities and communication costs, the DT needs to determine how to optimally distribute the data blocks at RSUs so that vehicles can finish downloading all the data before the deadline yet with the minimal cost. To determine the optimal delivery strategy of DT, we model the problem as an optimization framework subject to the time-varying wireless channel of RSUs, their service load and the communication cost. We then resort to the Lyapunov optimization to derive a distributed solution. Using extensive simulation results, we demonstrate that our scheme can effectively reduce the cost of data synchronization and improve the network load performance.

The emerging federated learning (FL) offers a feasible solution for the privacy preservation of users' sensitive data in training artificial intelligence (AI) models. Meanwhile, differential privacy (DP) is widely used in FL to ensure that data privacy is not disclosed during model training. However, in the practical deployment of DP in FL, a prominent challenge is that most existing FL solutions set the same privacy level for different users, resulting in over-protection for some users while insufficient protection for others. In this paper, we propose a novel federated learning framework with user-level personalized privacy protection (named FLUP) to meet the personalized privacy requirements of different users while maintaining high data utility. In this framework, we propose a user-level personalized DP mechanism that combines a personalized sampling algorithm and Gaussian perturbation to meet each user's personalized differential privacy corresponding to their privacy parameters. Then, we qualitatively analyze the impact of the sampling threshold on model performance. Furthermore, to balance user privacy requirements and AI model performance, we design a utility-aware game model to distributively determine the optimized sampling threshold and the users' differential privacy parameters. Finally, by conducting validation experiments, we demonstrate the feasibility and effectiveness of our proposed framework in terms of model performance as well as user privacy preservation.

Internet of Things (IoT) solutions have applications in various spheres of human life. There is a large number of IoT devices connected to the Internet and with a tendency to increase. Blockchain technology allows solving multiple security and centralization problems in these overcrowded networks. Fork occurrence affects the performance of many consensus mechanisms used in Blockchain, so it is important to analyze their performance considering different metrics and traffic patterns. This paper evaluates the performance of a blockchain-based IoT network in terms of the Age of Information (AoI) considering different traffic patterns and forking phenomena. Simulation results show that the AoI remains constant until the average time a batch stays in the queue is less than the average mining time and the decrease stops when the miners reach their maximum mining capacity. The simulation results allow to verify the agreement with the analytical model and to study the influence of the blockchain configuration parameters in the AoI, for different sensing mechanisms.

With the advancement of smart cities and the development of heterogeneous vehicular networks (HetVNets), vehicles can collect data and generate valuable information to obtain profits, thus forming a new vehicular self-media paradigm in HetVNets. However, in the HetVNets with potential security risks, the vehicular self-media market lacks the consideration of the values of the data owned by the media data producers (MDPs) and the capabilities of the media data sellers (MDSs) to improve their utilities. To this end, we propose a value-based secure self-media data trading scheme in the HetVNets. Specifically, we first design a vehicular self-media trading mechanism based on smart contracts to provide participants with a safe and reliable transaction environment. Then, we model the interactions between the MDPs and the MDSs as a Stackelberg game by considering the values of various media data and the sales capabilities of different MDPs. After that, we design an iterative method to obtain the optimal game strategies for the MDPs and the MDSs to maximize their utilities. Compared with the traditional schemes, the simulation results show that our scheme can obtain the optimal strategies for the MDPs and the MDSs and bring them the highest utilities.

The security of distributed communications in UAV rescue networks is promising to be provisioned by blockchain technology. However, due to high mobility, distributed UAVs cannot timely connect to the backbone to synchronize blocks, which can result in severe security issues (such as Forged deposit address and Double spend attack). These issues has been neglected in literature. This paper proposes a UAVs assisted and incentive based blockchain offline transaction scheme to address the above issues when UAVs and ground users are offline. Particularly, we consider vehicle-to-vehicle (V2V) charging transactions in disaster areas. First, we built an offline channel between charging and discharging electric vehicles (EVs), and then, we design an accountable assertions based UAVs aided penalty algorithm to prevent various attacks. Then, considering selfishness of users, we formulate an incentive model based on Stackelberg game to encourage EVs to participate to the offline V2V charging transactions. Our simulation results demonstrate that our proposed scheme obtain the optimal utilities for EVs, which outperforms the conventional schemes.

Recently, unmanned aerial vehicle (UAV) swarm has been advocated to provide diverse data-centric services including data relay, content caching and computing task offloading in vehicular networks due to their flexibility and conveniences. Since only offloading computing tasks to edge computing devices (ECDs) can not meet the real-time demand of vehicles in peak traffic flow, this paper proposes to combine edge computing and UAV swarm for cooperative task offloading in vehicular networks. Specifically, we first design a cooperative task offloading framework that vehicles' computing tasks can be executed locally, offloaded to UAV swarm, or offloaded to ECDs. Then, the selection of offloading strategy is formulated as a mixed integer nonlinear programming problem, the object of which is to maximize the utility of the vehicle. To solve the problem, we further decompose the original problem into two subproblems: minimizing the completion time when offloading to UAV swarm and optimizing the computing resources when offloading to ECD. For offloading to UAV swarm, the computing task will be split into multiple subtasks that are offloaded to different UAVs simultaneously for parallel computing. A Q-learning based iterative algorithm is proposed to minimize the computing task's completion time by equalizing the completion time of its subtasks assigned to each UAV. For offloading to ECDs, a gradient descent algorithm is used to optimally allocate computing resources for offloaded tasks. Extensive simulations are lastly conducted to demonstrate that the proposed scheme can significantly improve the utility of vehicles compared with conventional schemes.

Unmanned aerial vehicles (UAVs) empowered with artificial intelligence (AI) have become a new paradigm for marine monitoring and disaster rescue. In AI-enabled UAV applications, UAVs generate amounts of computation-intensive tasks (e.g., image recognition, video processing, and path planning, etc.) that cannot be locally executed by UAVs in time. How to offload the computation-intensive tasks of UAVs timely and effectively has become an urgent challenge. Multiple unmanned surface vehicles (USVs) integrated into a USV fleet is appealingly advocated to provide abundant computation resources for computation tasks. In this paper, we propose a collaborative computation offloading scheme with UAVs and USV fleets in maritime communication networks. Specifically, we first propose a collaborative computation offloading framework, where UAVs act as the requesters of computation offloading, and USV fleets are the assistants. Then, to minimize the overall execution time of computation tasks, UAVs determine the optimal ratio of compu-tation tasks offloaded to USV fleets in the worst case. Afterwards, the first sealed reverse auction with reserve price is utilized to incentivize USV fleets to assist in executing computation tasks of UAVs, where the reserve price guarantees the satisfied benefits of UAVs. Simulation results demonstrate that the proposed scheme reduces the overall execution time and improves the expected revenue of the USV fleet as compared to conventional schemes.

In this paper, we propose a new agency-guided semi-supervised counting approach. First, we build a learnable auxiliary structure, namely the density agency to bring the recognized foreground regional features close to corresponding density sub-classes (agents) and push away background ones. Second, we propose a density-guided contrastive learning loss to consolidate the backbone feature extractor. Third, we build a regression head by using a transformer structure to refine the foreground features further. Finally, an efficient noise depression loss is provided to minimize the negative influence of annotation noises. Extensive experiments on four challenging crowd counting datasets demonstrate that our method achieves superior performance to the state-of-the-art semi-supervised counting methods by a large margin. The code is available at https://github.com/LoraLinH/Semi-supervised-Crowd-Counting-via-Density-Agency.

Federated learning (FL) as a privacy-preserving machine learning (ML) algorithm provides an efficient distributed training paradigm. Existing FL frameworks still suffer from privacy leakage hazards such as membership inference attacks. The current popular defense approaches are mainly based on differential privacy (DP) strategy. However, privacy preservation is undertaken with an inevitable loss of model utility in DP. As a result, it performs miserably in practical deployments. To solve this problem, we modify the FL framework through the information bottleneck (IB) method to attain a trade-off between privacy protection and model utility. Firstly, we adapt the training process on client side by applying IB in the local training. It is intended to squeeze out privacy through the bottleneck. Secondly, we further modify the training process on server side. A validation process is used to evaluate whether the IB-based local training is squeezing out privacy. Clients that extrude the right information will occupy an important place in aggregation phase. Extensive experiments on classic datasets demonstrate the superiority of the proposed scheme in terms of privacy preservation and model utility.

The customization of edge computing services is one of the key research fields in sixth-generation (6G) heterogeneous vehicular networks (HetVNETs). With various personalized requirements of vehicles on computation-intensive applications, how to explore the heterogeneous computing resources in the 6G HetVNETs to guarantee vehicles with the customized Quality of Experience (QoE), therefore, becomes a challenge. In this article, we develop a novel secure scheme to provide personalized edge computing services for moving vehicles (MVs) in 6G HetVNETs. In the scheme, a smart-contract-based secure edge computing architecture is designed by jointly considering the attack models and the characteristics of the 6G network infrastructures (e.g., satellites, drones, base stations, and roadside units), where each network infrastructure manages a number of parking vehicles to complete computing services collaboratively. With this architecture, based on the available computing resources owned by different network infrastructures, the collaborative computing resource allocation algorithm is designed to help each network infrastructure decide a customized service strategy (CSS) to satisfy the QoE of MVs. After deciding the CSSs, a model based on the second price-sealed auction is formulated to describe the competition among the network infrastructures, where the Nash equilibrium of the game is obtained to guide their optimal bidding strategies to obtain the chance for completing the services. The security analysis and the simulation results show that the proposed scheme can defend against the attacks and lead to a lower cost for completing the services than the conventional schemes.

The vehicular crowdsensing, which benefits from edge computing devices (ECDs) distributedly selecting autonomous vehicles (AVs) to complete the sensing tasks and collecting the sensing results, represents a practical and promising solution to facilitate the autonomous vehicular networks (AVNs). With frequent data transaction and rewards distribution in the crowdsensing process, how to design an integrated scheme which guarantees the privacy of AVs and enables the ECDs to earn rewards securely while minimizing the task execution cost (TEC) therefore becomes a challenge. To this end, in this article, we develop a blockchain-based collaborative crowdsensing (BCC) scheme to support secure and efficient vehicular crowdsensing in AVNs. In the BCC, by considering the potential attacks in the crowdsensing process, we first develop a secure crowdsensing environment by designing a blockchain-based transaction architecture to deal with privacy and security issues. With the designed architecture, we then propose a coalition game with a transferable reward to motivate AVs to cooperatively execute the crowdsensing tasks by jointly considering the requirements of the tasks and the available sensing resources of AVs. After that, based on the merge and split rules, a coalition formation algorithm is designed to help each ECD select a group of AVs to form the optimal crowdsensing coalition (OCC) with the target of minimizing the TEC. Finally, we evaluate the TEC of the task and the rewards of the ECDs by comparing the proposed scheme with other schemes. The results show that our scheme can lead to a lower TEC for completing crowdsensing tasks and bring higher rewards to ECDs than the conventional schemes.

Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges between AVs for determining the members in each collaborative group will consume a lot of time and resources. In addition, since AVs have different computing capabilities and costs, the collaboration types of the AVs in each group and the distribution of the AVs in different collaborative groups directly affect the performance of the cooperative driving. Therefore, how to develop an efficient collaborative autonomous driving scheme to minimize the cost for completing the driving process becomes a new challenge. To this end, we regard collaboration as a service and propose a digital twins (DT)-based scheme to facilitate the collaborative and distributed autonomous driving. Specifically, we first design the DT for each AV and develop a DT-enabled architecture to help AVs make the collaborative driving decisions in the virtual networks. With this architecture, an auction game-based collaborative driving mechanism (AG-CDM) is then designed to decide the head DT and the tail DT of each group. After that, by considering the computation cost and the transmission cost of each group, a coalition game-based distributed driving mechanism (CG-DDM) is developed to decide the optimal group distribution for minimizing the driving cost of each DT. Simulation results show that the proposed scheme can converge to a Nash stable collaborative and distributed structure and can minimize the autonomous driving cost of each AV.

At present, the crowdsourcing-based indoor navigation system (CINS) has attracted extensive attention from both industry and academia owing to its low-cost and high-accuracy performance. Unfortunately, the system that relies on crowdsourced data is vulnerable to the collusion attack, which leads to severe security issues. To address the security issues in the CINS, we propose to utilize a fully trusted fog server platform to advocate secure transactions between service requesters and responders. First, we propose a novel reputation incentive mechanism based on the behaviors of responders. Then, we employ the offensive and defensive game to model the interactions between the fog server platform and the responders, whereby a social welfare optimization problem is formulated to maximize the social welfare of the system. Next, the game equilibriums are found by using the replicator dynamic equation while the game stability is discussed. Finally, the simulation results show that the proposed mechanism can effectively encourage responders to provide positive navigation services and obtain more social welfare of the system compared with the conventional mechanisms.

Proof of work (PoW), as the representative consensus protocol for blockchain, consumes enormous amounts of computation and energy to determine bookkeeping rights among miners but does not achieve any practical purposes. To address the drawback of PoW, we propose a novel energy-recycling consensus mechanism named platform-free proof of federated learning (PF-PoFL), which leverages the computing power originally wasted in solving hard but meaningless PoW puzzles to conduct practical federated learning (FL) tasks. Nevertheless, potential security threats and efficiency concerns may occur due to the untrusted environment and miners’ self-interested features. In this paper, by devising a novel block structure, new transaction types, and credit-based incentives, PF-PoFL allows efficient artificial intelligence (AI) task outsourcing, federated mining, model evaluation, and reward distribution in a fully decentralized manner, while resisting spoofing and Sybil attacks. Besides, PF-PoFL equips with a user-level differential privacy mechanism for miners to prevent implicit privacy leakage in training FL models. Furthermore, by considering dynamic miner characteristics (e.g., training samples, non-IID degree, and network delay) under diverse FL tasks, a federation formation game-based mechanism is presented to distributively form the optimized disjoint miner partition structure with Nash-stable convergence. Extensive simulations validate the efficiency and effectiveness of PF-PoFL.

Recently, mobile social cloud (MSC), formed by mobile users with social ties, has been advocated to allocate tasks of big data applications instead of relying on the conventional cloud systems. However, due to the dynamic topology of networks and social features of users, how to optimally allocate tasks to mobile users based on the trust becomes a new challenge. Therefore, this paper proposes a novel incentive scheme based on the trust of mobile users in the MSC to allocate the tasks of big data. First, a social trust degree is defined according to the social tie among users, the importance of task, and the available resources of networks. With the social trust degree, the task owner can select a group of mobile users as the candidates for task allocation. Second, a reverse auction game model is developed to study the interactions among the task owner and the candidates. With the reverse auction game model, the optimal strategy of task allocation can be obtained with a low cost for the task owner where the selected candidate of mobile users can also obtain the high profit. Finally, simulation experiments are carried out to prove that the proposal can outperform other existing methods with a low delay and a high efficiency to allocate tasks in the MSC.

In disaster areas, a large amount of data (e.g., rescue commands, road damage, and rescue experience) should be delivered among ground rescuing vehicles for safe driving and efficient rescue. When communication infrastructures are destroyed by disasters, unmanned aerial vehicles (UAVs) can be employed to perform immediate rescue missions in destroyed areas and assist data sharing for ground Internet of vehicles (IoV). However, in such UAV-assisted IoV under disaster situation, there exist potential security threats on data sharing among vehicles and UAVs because of the untrusted network environment, unreliable misbehavior tracing, and low-quality shared data. To address these issues, in this article, we develop alightweightvehicularblockchain-enabledsecure (LVBS) data sharing framework in UAV-aided IoV for disaster rescue. First, we propose a novel UAV and blockchain-assisted collaborative aerial-ground network architecture in disaster areas. Second, we develop a credit-based consensus algorithm in the lightweight vehicular blockchain to securely and immutably trace misbehaviors and record data transactions for UAVs and vehicles with improved efficiency and security in reaching consensus. Third, since UAVs and vehicles have little explicit knowledge of the whole network, we develop reinforcement learning-based algorithms to optimally schedule the pricing and quality of data sharing strategies for both data contributor and data consumer via trial and error. Finally, extensive simulations are conducted, which demonstrate that LVBS can effectively improve the security of consensus phase and promote high-quality data sharing.

With the prevalence of smart appliances, smart meters, and Internet of Things (IoT) devices in smart grids, artificial intelligence (AI) built on the rich IoT big data enables various energy data analysis applications and brings intelligent and personalized energy services for users. In conventional AI of Things (AIoT) paradigms, a wealth of individual energy data distributed across users’ IoT devices needs to be migrated to a central storage (e.g., cloud or edge device) for knowledge extraction, which may impose severe privacy violation and data misuse risks. Federated learning, as an appealing privacy-preserving AI paradigm, enables energy data owners (EDOs) to cooperatively train a shared AI model without revealing the local energy data. Nevertheless, potential security and efficiency concerns still impede the deployment of federated-learning-based AIoT services in smart grids due to the low-quality shared local models, non-independently and identically distributed (non-IID) data distributions, and unpredictable communication delays. In this article, we propose a secure and efficient federated-learning-enabled AIoT scheme for private energy data sharing in smart grids with edge-cloud collaboration. Specifically, we first introduce an edge-cloud-assisted federated learning framework for communication-efficient and privacy-preserving energy data sharing of users in smart grids. Then, by considering non-IID effects, we design a local data evaluation mechanism in federated learning and formulate two optimization problems for EDOs and energy service providers. Furthermore, due to the lack of knowledge of multidimensional user private information in practical scenarios, a two-layer deep reinforcement-learning-based incentive algorithm is developed to promote EDOs’ participation and high-quality model contribution. Extensive simulation results show that the proposed scheme can effectively stimulate EDOs to share high-quality local model updates and improve the communication efficiency.

As the natural disasters may destroy the ground communication infrastructures for the transportation systems, the communication relief in post-disaster networks is more crucial to reduce risk loss. The growing application of unmanned aerial vehicles (UAVs) holds great potential for disaster communication relief due to its flexibility and functionalities. In this paper, we investigate the channel allocation and data delivery problems for UAV-assisted cooperative transportation communications in post-disaster networks to provide communication and data delivery services for affected users. Specifically, we first introduce the UAV-assisted communication relief system, in which UAVs equipped with the communication and caching functionalities are deployed as the aerial base stations in post-disaster regions. Then, we propose the channel allocation scheme between UAVs and users by taking the interferences into consideration, and obtain the channel allocation strategy to improve the network throughput. Based on the optimal channel allocation strategy, users can deliver their data to UAVs for backup. Next, we propose the data delivery scheme to cope with the pricing problem for UAVs and the data delivery strategy for users to improve the efficiency of data delivery, with the objective of maximizing the utilities of both UAVs and users. The optimal strategy for both UAVs and users are derived according to the analysis of Stackelberg game. Finally, we conduct simulations to evaluate the performance of the proposed channel allocation and data delivery scheme, and the numerical results demonstrate that the proposed scheme can significantly improve the efficiency and effectiveness of channel allocation and data delivery in post-disaster networks, compared with benchmark schemes.

The autonomous vehicles (AVs) in smart city, as intelligent mobile robots, are expected to provide diversified services to facilitate the life of citizens. However, the attributes of the services requested by users are different and the statuses of the AVs managed by different central servers are dynamically changed. To execute the services with the minimum cost based on the requirements of users and the statuses of AVs therefore becomes a challenge. In this article, we establish an intelligent multi-attribute service response framework in smart city based on the request of users and the response of AVs. In the first phase of the framework, each central server decides the minimum service execution cost (SEC) to respond to the user’s service by considering the available resources of its AVs, where the minimization problems are formulated for the services with one attribute and the services with multiple attributes, respectively. To address the problems, the optimal AV selection (OAVS) algorithm for the services with one attribute and the OAVS-M algorithm for the services with multiple attributes are designed. In the second phase, based on the SEC of each central server, an auction game is developed to model the competition among the central servers to help the user select the optimal one to execute the service with the lowest service transaction price (STP). By achieving the Nash equilibrium of the game, the optimal strategy of each central server to win the chance for executing the service is obtained. The simulation results show that the designed framework can reduce the STP compared with the conventional schemes.

With the ever-increasing demand for the content delivery services in autonomous vehicular networks (AVNs), caching popular contents in the edge nodes in advance is expected to reduce the transmission delay. Current works on the contents cached in connected and autonomous vehicles (CAVs) or roadside units (RSUs) are facing the problems of limited caching size and high deployment cost. In this paper, by exploiting the advantages of high caching space and flexibility of truck platoons composed of connected and autonomous trucks (CATs), we propose a secure content delivery service for CATs based on coalition formation game. Firstly, in order to protect the security and privacy of content delivery services, a differential privacy model is proposed to protect the sensitive information of CATs. Meanwhile, the differential privacy model is combined with the incentive based trust evaluation model to monitor the behaviors of CATs. In the incentive based models, CATs are encouraged to improve their trust values to obtain higher utilities and find a balance between confidence levels and utilities. Moreover, a coalition formation game is established among CATs with the same driving route, in which all CATs can maximize their utilities with the formation of several minor coalitions. Finally, we conduct extensive simulations to demonstrate the effectiveness and superiority of the proposed scheme.

Natural disasters often cause huge and unpredictable losses to human lives and properties. In such an emergency post-disaster rescue situation, unmanned aerial vehicles (UAVs) are effective tools to enter the damaged areas to perform immediate disaster recovery missions, owing to their flexible mobilities and fast deployment. However, UAVs typically have very limited battery and computational capacities, which makes them harder to perform heavy computation tasks during the complicated disaster recovery process. This paper addresses the issue of the battery and computation resource limitation with a fog computing based UAV system. Specifically, we first introduce the vehicular fog computing (VFC) system in which the unmanned ground vehicles (UGVs) perform the computation tasks offloaded from UAVs. To avoid the transmission competitions yet enable cooperations among UAVs and UGVs, a stable matching algorithm is developed to transform the computation task offloading problem into a two-sided matching problem. An iterative algorithm is then developed which matches each UAV with the most suitable UGV for offloading. Finally, extensive simulations are carried out to demonstrate that the proposed scheme can effectively improve utilities of UAVs and reduce average delay through comparison with conventional schemes.

Cloud computing is an essential technique to provide item traceability for industrial internet of things (IIoT) systems by providing item data sharing services. However, a malicious cloud server may prevent industrial participants from acquiring accurate traceability of items by providing inconsistent item data. To fix this issue, we propose Acics, an item data consistency auditing scheme in untrusted cloud services for cloud-aided IIoT systems. Acics presents two variants named S-Acics and L-Acics. S-Acics enables industrial participants to audit item data consistency for each item and circularly play the auditing role. L-Acics further enables industrial participants to audit item data consistency for a sampled subset of items while resisting data selection attack via a new separated storage mechanism. Finally, Acics integrates a fair payment mechanism built on smart contract to incentivize the cloud server to provide consistent item data access service for industrial participants. The experiment results show that our solution can audit item data consistency with reasonable cost.

Space-air-ocean integrated networks (SAOINs), composed of low earth orbit (LEO) satellites, unmanned aerial vehicles (UAVs), and unmanned surface vehicles (USVs), have been advocated to provide seamless, high-rate, and reliable wireless transmission services for USVs. However, due to the restrictions of limited resources (e.g., spectrum bandwidth, transmission power, etc.), diverse demands of USVs, and selfishness of both UAVs and LEOs, there comes a significant challenge to provision high-quality wireless data rate for USVs to achieve their satisfied quality of experience (QoE). To this end, in this paper, we propose a hierarchical on-demand wireless data rate provisioning scheme to provide ubiquitous transmission services for USVs. Specifically, we first devise a hierarchical wireless data rate provisioning framework. The LEO satellite with an extensive wireless coverage is utilized to provide LEO satellite-to-UAV (L2U) data rate for UAVs with a certain L2U data rate price. Each UAV is employed to provide UAV-to-USV (U2U) data rate for covered multiple USVs with a certain U2U data rate price. We then propose a modified three-stage Stackelberg game to model the wireless data rate assignments among LEO satellites, UAVs, and USVs, where the time-varying data rate demands of USVs are considered to formulate the utility maximization problem. Afterwards, the backward induction approach is leveraged to attain the Stackelberg equilibrium as the solution of the formulated problem, where the closed-form expressions on the optimal strategies of both USVs and UAVs under different data rate budgets are obtained by the nonlinear programming method. Besides, an accelerated conjugate gradient descent (ACGD) based iteration algorithm is also designed to obtain the optimal strategies of the LEO satellites on the L2U data rate prices. At last, extensive simulations are carried out to demonstrate that the proposed scheme can significantly increase the utilities of USVs, as compared to other benchmark schemes.

Federated learning provides a solution for data privacy protection, while enabling training over the local data samples, without exchanging them. However, it is far from practical and secure because data privacy is still vulnerable due to the well-studied attacks, e.g., membership inference attacks and model inversion attacks. In this paper, to further prevent data leakage against these attacks, we propose FL-PATE, a differentially private federated learning framework with knowledge transfer. Specifically, participants with sensitive data are grouped to train teacher models under federated learning settings, and the knowledge of teacher models is transferred to a publicly accessible student model for prediction via aggregating teacher models' outputs of public datasets. A modified client-level differential privacy mechanism is used to guarantee each participant's data privacy during the corresponding teacher model's training process. The proposed framework preserves participant's privacy against membership inference attacks and the differential privacy cost is fixed. The privacy analysis and experiments demonstrate that trained teacher and student models have an excellent performance in accuracy and robustness theoretically and empirically.

Unmanned aerial vehicles (UAVs) combining with crowdsensing technology has been viewed as a promising paradigm for performing sensing tasks in extreme scenarios such as earthquakes, etc. However, potential security issues could incur on data sharing between UAVs and task publishers owing to the vulnerability of central nodes and selfishness of distrusted UAVs. To cope with these problems, we propose a novel blockchain-based crowdsensing framework with reputation incentive (BCFR) in UAV-assisted mobile crowdsensing. Specifically, we first propose a novel reputation incentive scheme to choose UAVs with a high reputation to perform sensing tasks, thereby protecting data sharing between UAVs and task publishers from internal attack (i.e., some UAVs with insufficient resources may turn into malicious UAVs to provide wrong sensory data to the task publishers). Then, we design a blockchain-based secure data transmission scheme to securely record data transactions of UAVs. Furthermore, since UAVs with limited resources are difficult to perform compute-intensive mining tasks, edge computing is incorporated to increase the success probability of block creation. The interactions between UAVs and edge computing provider (ECP) are modeled as a two-stage Stackelberg game to motivate UAVs participating in the block creation process while providing high-quality services. Finally, we conduct extensive simulations to demonstrate that the proposed BCFR scheme can effectively improve successful mining probabilities and utilities of UAVs, and ensure the security of data sharing among UAVs and task publishers.

Autonomous vehicles (AVs) are coming with great potentials to bring safer, greener, and more convenient transportation systems. As AVs rely on radar, camera, and other advanced sensors to sense its surroundings, a salient challenge of AVs is the intrinsic limitations of onboard sensors (e.g., limited awareness range, blind spots, and failure in foggy days). To tackle this problem, we propose a collaborative data sharing scheme for AVs to make up for sensor deficiencies by promoting sensory information sharing in autonomous driving. However, this brings another fundamental issue on how to ensure trust in shared sensory data from distrustful collaborators and how to motivate AVs to participate in data sharing. This work studies this issue by modeling it as a quality-aware optimal sensing task scheduling problem. Specifically, we design an edge computing-enabled architecture where AVs can form collaborative sensing groups in executing sensing tasks. After that, a quality-aware auction-based incentive mechanism is developed to promote AVs’ participation and high-quality data sharing. We also design a reputation model to recruit trustworthy AVs to perform sensing tasks based on their behaviors and social identities. Due to the NP-hardness of problem, we devise a heuristic algorithm to determine the optimal winners and payments in auction with truthfulness and individual rationality guarantees. Lastly, extensive simulations validate that our approach can effectively improve sensing data quality and user utility, compared with conventional schemes.

The rechargeable battery of a plug-in electric vehicle (PEV) endows the PEV with dual roles in the power grid as power load and mobile energy storage (MES). Owing to the technical advancement of autonomous driving, private PEVs that are parked most of the day can be used as private MESs (PMESs) to autonomously deliver energy for overloaded charging stations (CSs). In this paper, we investigate an energy compensation problem where PMESs are scheduled to deliver energy to overloaded CSs so that the energy balance can be achieved while the energy delivery time can be minimized. Based on the time-variant CS operation status and traffic conditions, we propose a pricing-based scheduling scheme that considers both PMES navigation and incentive price design. First, to navigate PMESs in the energy-capacitated transportation system, a minimum-cost flow problem is formulated to minimize the energy delivery time. Then, the incentive price is determined to encourage PMESs to follow the optimal navigation results for energy delivery. Simulations are conducted based on the traffic data of California highway to validate the effectiveness of the proposed scheduling scheme.

Unmanned aerial vehicles (UAVs) have attracted extensive attention from both industry and academia owing to their high mobility, and characteristics of line of sight (LoS) propagation. However, wireless communication is vulnerable to eavesdropping attacks because of the broadcast characteristics. To enhance secure UAV communications with the ground nodes, we propose a novel framework that multiple UAVs cooperate to resist attack (MURA). First, we propose an incentive mechanism based on coalitional game to encourage legal UAVs to join the coalition. We prove that each legal UAV can only maximize its profits by joining the coalition to form a major coalition. Then, a secure UAV communication scheme is proposed to resist the eavesdropping attacks. Two types of scenarios are considered for the UAV communication: in a completely rational scenario, we utilize the Stackelberg game to model the interactions between the legal UAVs and attacker. In an imperfectly rational scenario, the cumulative prospect theory (PT) is applied to the game to capture the underlying rationality of the players. Finally, simulation results show that the proposed scheme can significantly improve the security of the UAV network compared with traditional schemes.

Recently, unmanned aerial vehicles (UAVs) have been employed to provide wireless communication services, which promotes the emergence of promising UAV-assisted heteroge-neous networks (UHetNets). However, due to the ever-increasing amount of data traffic and diverse wireless service demands of mobile users, it is challenging to efficiently allocate limited secure bandwidth for safe communication. To tackle this problem, in this paper, we propose a game theoretical secure bandwidth allocation scheme in UHetNets. Specifically, we first design a UAV-assisted bandwidth allocation framework, where each UAV as a flying base station reuses the secure spectrum to enhance the utilization rate of wireless resource. To allocate the restricted secure band-width, we further introduce the utility functions of both UAVs and mobile users, based on the real-time bandwidth capacity of each UAV and the demand of each mobile user. Stackelberg game is then utilized to model the dynamic interactions between UAVs and mobile users. Afterwards, we devise a gradient descent based optimal decision searching algorithm to achieve the Stackelberg equilibrium. The simulation results, at last, show the effectiveness of the proposed scheme to improve the utilities of both mobile users and UAVs.

Unmanned aerial vehicles (UAVs) have brought numerous potentials to establish flexible and reliable emergency networks in disaster areas when terrestrial communication infrastructures go down. Nevertheless, potential security threats may occur on UAVs during data transmissions due to the untrustful environment and open-access UAV networking. Moreover, UAVs typically have limited battery and computation capacity, making them unaffordable to execute heavy security provisioning operations when carrying out complicated rescue tasks. In this paper, we develop RescueChain, a secure and efficient information sharing scheme for UAV-aided disaster rescue. Specifically, we first implement a lightweight blockchain-based framework to safeguard data sharing under disasters and immutably trace misbehaving entities. A reputation-based consensus protocol is devised to adapt the weakly connected environment with improved consensus efficiency and promoted UAVs' honest behaviors. Furthermore, we introduce a novel vehicular fog computing based off-chain mechanism by leveraging ground vehicles as moving fog nodes to offload UAVs' heavy data processing and storage tasks. To optimally stimulate vehicles to share their idle computing resources, we also design a two-layer reinforcement learning based incentive algorithm for UAVs and ground vehicles in the highly dynamic networks. Simulation results show that RescueChain can effectively accelerate consensus process, enhance user payoffs, and reduce delivery latency, compared with representative existing approaches.

This work proposes AMIS, an edge computing-based adaptive video streaming system. AMIS explores the power of edge computing in three aspects. First, with video contents pre-cached in the local buffer, AMIS is content-aware which adapts the video playout strategy based on the scene features of video contents and quality of experience (QoE) of users. Second, AMIS is channel-aware which measures the channel conditions in real-time and estimates the wireless bandwidth. Third, by integrating the content features and channel estimation, AMIS applies the deep reinforcement learning model to optimize the playout strategy towards the best QoE. Therefore, AMIS is an intelligent content- and channel-aware scheme which fully explores the intelligence of edge computing and adapts to general environments and QoE requirements. Using trace-driven simulations, we show that AMIS can succeed in improving the average QoE by 14%-46% as compared to the state-of-the-art adaptive bitrate algorithms.

In this article, we propose a physical layer security (PLS) scheme to secure vehicle-to-vehicle (V2V) communications, where vehicles share spectrum resources with cellular users via underlaying device-to-device (D2D) technologies. Existing PLS-assisted V2V communication schemes neglect the issue of the unknown eavesdropper's instantaneous channel state information (CSI), and the management of spectrum resource reuse-incurred interference should be enhanced. Here, we design an artificial noise (AN)-assisted beamforming scheme to protect the V2V communications that does not require the eavesdropper's instantaneous CSI. Especially, the spectrum resource reuse-incurred interference can be eliminated by detection methods. Also, we deduce the expression of secrecy outage probability as the security metric, and minimize the secrecy outage probability via power allocation. The simulations demonstrate that the proposed scheme can reduce the secrecy outage probability compared to conventional methods.

Designing efficient and secure mutual authentication and key agreement (MAAKA) protocols for Internet of Medical Things (IoMT) has been shown to be challenging, mainly due to the different security and privacy requirements in complex settings. Existing schemes generally are subject to a number of limitations, ranging from performance to security issues. In this article, we introduce a provably secure and lightweight MAAKA (PSL-MAAKA) protocol for fully public channels in IoMT. First, the proposed scheme is lightweight since the major operations in the stage of authentication and key agreement are hash operation and XOR operation, respectively. Second, this article proves the security of the presented protocol taking the advantage of the random oracle model. Next, this article gives that security requirements in IoMT could be satisfied through our presented MAAKA protocol. Finally, we demonstrate that it enjoys optimal performance than other competing schemes, in terms of communication overhead, computation overhead, and storage overhead.

The infectious disease presents great hazards to public health, due to their high infectivities and potential lethalities. One of the effective methods to hinder the spread of infectious disease is vaccination. However, due to the limitation of resource and the medical budget, vaccinating all people is not feasible in practice. Besides, the vaccinating effects are difficult to be timely observed through traditional ways, such as outpatient services. To tackle the above problems, we propose an e-healthcare mobile social Internet of Things (MSIoTs)-based targeted vaccination scheme to fast contain the spread of the infectious disease. Specifically, we first develop an e-healthcare MSIoT architecture by integrating the e-healthcare system and MSIoTs, whereby the spread status of the infectious disease is timely collected. Furthermore, a graph coloring and spreading centrality-based optional candidate searching algorithm is devised to hunt for the candidates that are powerfully capable of preventing infectious disease. Especially, in order to reduce the vaccination cost, we design an optimal vaccinated target selection algorithm to choose a minimum number of targets whose locations are differentially distributed. Extensive simulations demonstrate that the proposed scheme can effectively prevent infectious disease as compared to conventional schemes.

As both the scale of mobile networks and the population of mobile users keep increasing, the applications of mobile social big data have emerged where mobile social users can use their mobile devices to exchange and share contents with each other. The security resource is needed to protect mobile social big data during the delivery. However, due to the limited security resource, how to allocate the security resource becomes a new challenge. Therefore, in this paper we propose a joint match-coalitional game based security-aware resource allocation scheme to deliver mobile social big data. In the proposed scheme, first a coalition game model is introduced for base stations (BSs) to form groups to provide both wireless and security resource, where the resource efficiency and profits can be improved. Second, a matching theory based model is employed to determine the selecting process between communities and the coalitions of BSs so that mobile social users can form communities to select the optimal coalition to obtain security resource. Third, a joint matching-coalition algorithm is presented to obtain the stable security-aware resource allocation. At last, the simulation experiments prove that the proposal scheme outperforms other existing schemes.

The exponential growth of data generated from increasing smart meters and smart appliances brings about huge potentials for more efficient energy production, pricing, and personalized energy services in smart grids. However, it also causes severe concerns due to improper use of individuals' private data, as well as the lack of transparency and auditability for data usage. To bridge this gap, in this article, we propose a secure and auditable private data sharing (SPDS) scheme under data processing-as-a-service mode in smart grid. Specifically, we first present a novel blockchain-based framework for trust-free private data computation and data usage tracking, where smart contracts are employed to specify fine-grained data usage policies (i.e., who can access what kinds of data, for what purposes, at what price) while the distributed ledgers keep an immutable and transparent record of data usage. A trusted execution environment based off-chain smart contract execution mechanism is exploited as well to process confidential user datasets and relieve the computation overhead in blockchain systems. A two-phase atomic delivery protocol is designed to ensure the atomicity of data transactions in computing result release and payment. Furthermore, based on contract theory, the optimal contracts are designed under information asymmetry to stimulate user's participation and high-quality data sharing while optimizing the payoff of the energy service provider. Extensive simulation results demonstrate that the proposed SPDS can effectively improve the payoffs of participants, compared with conventional schemes.

Smart city emerges a promising paradigm for improving operational efficiency of city and comfort of people. With embedded multi-sensors, Unmanned Aerial Vehicles (UAVs) hold great potential for collecting sensing data and providing social services in smart city. However, due to the limited battery lifetime and processing capacities of UAVs, the efficient offloading scheme of UAVs is urgently needed in smart city. Therefore, in this article, a vehicle-assisted computing offloading architecture for UAVs is proposed to improve offloading efficiency by harnessing the moving vehicles in smart city. We first develop an offloading model for UAVs to determine the offloading strategy. Next, to select the optimal vehicles for offloading, we formulate a matching scheme based on the preference lists of UAVs and vehicles to derive the optimal matching between UAVs and vehicles. After that, to improve the offloading efficiency and maximize the utilities of UAVs and vehicles, the transaction process of computing data between UAVs and vehicles is modeled as a bargaining game. Moreover, an offloading algorithm for UAVs and vehicles is proposed to obtain the optimal strategy. Finally, simulations are performed to validate the efficiency of the proposed offloading scheme. The results demonstrate that the proposed offloading scheme can significantly save resource and improve the utilities of UAVs and vehicles.

The increasing development of Internet of Things (IoT) has led to the emergence of Internet of connected vehicles (IoCVs). These vehicles with various functionalities have the potential prospects for improving the quality of experience (QoE) of vehicle users. Moreover, the use of unmanned aerial vehicles (UAVs) in flying networks extends the connectivity and universality of IoT, and these UAVs with caching and communication capacities can support various services. However, due to the heterogeneity of vehicular networks and flying networks, the communication performance and content distribution between UAVs and IoCVs expose new challenges in heterogeneous networks (HetNets). Therefore, in this paper, a novel content distribution mechanism between UAVs and IoCVs is proposed to improve the QoE of vehicle users. Specifically, we first develop a novel content distribution architecture for UAVs and IoCVs in HetNets, where the content is distributed by UAV content providers to IoCVs. Next, we establish an optimization problem of content distribution between UAVs and IoCVs to minimize the transmission delay. In order to stimulate UAVs and IoCVs to join content distribution, the utilities of UAVs and IoCVs are formulated, respectively. Moreover, we design a coalition game between UAVs and IoCVs to determine the optimal strategy of content distribution. Finally, simulation results demonstrate that the proposed mechanism can significantly improve the performance of content distribution compared with the conventional mechanisms.

To accommodate the surge of data traffic in unmanned aerial vehicle (UAV) applications, software defined air-ground integrated networks (SD-AGNs) hold great potentials for efficient resource allocation and intelligent security countermeasures for UAVs. In SD-AGNs, virtualized bandwidth, computing and security resources owned by terrestrial mobile edge computing (MEC) nodes can be dynamically allocated to satisfy UAVs' diverse demands in data transmission and security protection. However, with complicated cooperative interactions among MEC nodes and competition among UAVs, it is of great challenge to allocate both the security and wireless resource in SD-AGNs. In this paper, we propose a security-aware resource sharing scheme for UAVs to jointly allocate bandwidth and security resource in SD-AGNs, using a game-theoretic approach. Specifically, we first investigate a software-defined collaborative mechanism to promote resource utilization for MEC nodes through coalition formation and resource sharing within each coalition. Then, a coalitional game model is presented to construct the Nash-stable coalition structure for MEC nodes. Furthermore, by modeling the interactions among UAVs as a non-cooperative game, their optimal demands of wireless and security resource, as well as the Nash equilibrium, are analyzed in the competitive environment. Simulation results show that the proposed scheme can effectively improve resource efficiency and reduce average delay.

WiFi fingerprint-based positioning system emerges to offer fundamental location information for indoor mobile users. It facilitates the check-in to point of interest (POI) through submitting received signal strength (RSS) fingerprints in order to evaluate the crowd traffic. However, the crowd traffic evaluation with RSS fingerprints is vulnerable to the malicious check-in attacks. Attackers who are not at the target POI may still submit the self-modified RSS fingerprints located at the target POI in order to illegally increase its crowd traffic and eventually profit from this fake information. In this paper, we propose a defense scheme against malicious check-in based on access point (AP) selection to significantly reduce the success rate of fingerprint modification from attackers. Specifically, we first exploit fingerprint distance between POIs for AP selection. Then, we explore the mutual information between different POI classes to select APs with high robustness. In addition, the level set method (LSM) is developed to search the optimal modified fingerprint to assess attacker's costs. The extensive simulation results show that the proposed scheme can effectively resist attackers with high accuracy and facilitate crowd traffic evaluation of target POI according to the submitted RSS fingerprints.

As the ever-increasing capacities of internet of things (IoT), unmanned aerial vehicle (UAV)-assisted IoT becomes a promising paradigm for improving network connectivity, extending the coverage of network and computing offloading. However, due to the limitation of battery lifetime and computing capacities of UAVs, the offloading scheme for UAVs presents a new challenge in IoT. Therefore, in this paper, an energy-efficient edge offloading scheme is proposed to improve the offloading efficiency of UAVs. Firstly, based on the data transmission delay of UAVs and computing delay of edge nodes, the matching scheme is designed to obtain the optimal matching between UAVs and edge nodes. Secondly, the energy-efficient offloading scheme for UAVs and edge nodes is modeled as a bargaining game. Then, the offloading strategy based on incentive algorithm is developed to improve the offloading efficiency. Finally, the simulation results demonstrate that the proposed offloading scheme can significantly promote the effectiveness of offloading compared with the conventional schemes.

Natural disasters often cause huge and unpredictable losses to human lives and properties. In such an emergency post-disaster rescue situation, unmanned aerial vehicles (UAVs) are effective tools to enter the damaged areas to perform immediate disaster recovery missions, due to their flexible mobilities and fast deployment. However, the UAVs typically have very limited batteries and computational capacities, which make them unable to perform heavy computation tasks during the complicated disaster recovery process. This paper addresses the issue with a fog computing based UAV system. In specific, we first introduce the vehicular fog computing (VFC) system in which the unmanned ground vehicles (UGVs) perform the computation tasks offloaded from UAVs. To resolve the transmission competitions yet enable cooperations among UAVs and UGVs, a stable matching algorithm is developed to transform the computation task offloading problem into a two-sided matching problem. An iterative algorithm is then developed which matches each UAV with the most suitable UGV for offloading. Finally, extensive simulations are carried out to demonstrate that the proposed scheme can effectively improve utilities of UAVs and reduce average delay through comparison with conventional schemes.

With the technical advancement of transportation electrification and Internet of vehicle, an increasing number of electric vehicles (EVs) and related infrastructures (e.g., service stations with both charging and communication services) are deployed in the intelligent highway systems. Not only can EVs enter the service station areas for charging, but they can also upload/download cached data at service stations to access multiple networking services. However, as EVs are operated individually with their unique travelling patterns, questions arise as how to incent EVs so that both energy and communication resources are optimally allocated. In this paper, we propose an online pricing mechanism of EV charging and data caching for service stations along the highway. First, we design an online reservation system at each EV to decide the best service station to park when the EV enters the highway. Furthermore, based on the variant power system status, an online pricing mechanism is devised to update the charging and caching price based on Q-learning, by which EVs can be motivated to arrive at the designated station for services. Finally, simulation results validate the effectiveness of the proposed scheme in improving the station's utility.

This paper investigates the reliable computation offloading in vehicular edge computing (VEC) systems. Compared with the traditional task replication method in which task replicas are typically assigned to multiple service vehicles at the same time, in our work, a task vehicle allocates the computation tasks and communication resources to its neighboring service vehicles through the vehicle-to-vehicle (V2V) links, and avoids the degradation of delay and computation efficiency. Specifically, an optimization problem is formulated to minimize the task completion delay and ensure offloading reliability. Then, an algorithm based on the penalty and the concave-convex procedure (CCCP) method is proposed to effectively solve the formulated optimization problem. The simulation results show that the task completion delay of the proposed algorithm is only 30% of that in the traditional task replication method.

The popularization of mobile devices connected to the network promotes the rise and development of the emerging mobile Internet of Things (MIoT). Crowdsensing is a promising mode to perceive data in MIoT, where the collection of sensing data is outsourced to the public crowd carrying mobile devices. However, this crowdsensing mode inevitably makes privacy compromised, due to the workers' sensitive information in the sensing data. As such, how to incentivize workers' participation with privacy preservation becomes a challenge. To tackle this problem, in this article, we propose an auction-based privacy-preserving incentive scheme (APIS) for sensing task allocation in MIoT. Specifically, integrating the idea of software-defined network (SDN), we first present a cloud and edge cooperation-based crowdsensing framework, where the cloud is designed as the controller to collect sensing results from the distributed edge nodes and each edge node outsources sensing tasks to participating workers. To motivate workers' participation, we devise a differential privacy-based auction mechanism, whereby each worker can utilize her privacy budget to control how much privacy can be leaked and decide the sensing precision by the sensing time. Moreover, to maximize the utility of the sensing platform, we design a greed-based algorithm to select the winning workers and determine payments to winners. Finally, we conduct extensive simulations to verify the effectiveness of APIS and demonstrate its superiority.

To improve mobile users' quality-of-experience (QoE) in the mobile cyber-physical system (MCPS), caching layered-coding contents on edge nodes that are close to mobile users has been advocated as a promising solution, which can efficiently lower the content delivery delay and mitigate the overhead of backhaul network. However, due to the complexity of trust management and the limited caching capacities of edge nodes, designing an efficient edge caching scheme for mobile users becomes a challenge. Meanwhile, the content caching in MCPS also faces some security problems, where edge nodes may return incorrect results or viruses to mobile users, and mobile users would deliberately refuse to pay for caching services. To tackle these problems, we propose a novel blockchain-based trustworthy edge caching scheme for mobile users in MCPS. Specifically, we first exploit blockchain to supervise the caching transactions between the edge nodes and mobile users in a distributed manner, whereby the caching service information cannot be modified and denied by any entities. Furthermore, we devise a trust management mechanism for mobile users to search the trustworthy caching services from diversified edge nodes, where the trust degree of the edge node is real-time evaluated and updated by mobile users based on the quality of caching service. To take full advantage of caching resources, we design a max-min-based resource allocation algorithm, with which the trustworthy edge node could fairly allocate its caching resource based on mobile users' optimal demands. The simulation results show that the presented scheme not only improves the utilities of edge nodes but also increases the QoE of mobile users.

The unmanned surface vehicles (USVs) have been regarded as a promising paradigm to automatically perform emergency tasks in a dynamic maritime traffic environment. However, the performance of maritime communication between USVs and offshore platforms becomes a critical challenge, and the efficiency of task allocation for USVs in the smart ocean is low. In this article, a novel task allocation scheme for USVs in the smart ocean Internet of Things (IoT) is proposed to improve the efficiency of task allocation. First, the offshore platform is developed to provide maritime communication for USVs in the smart ocean IoT. Second, the network resource allocation process between USVs and offshore platforms is modeled as the second price sealed auction game, where the optimal bidding strategy of USV is derived by the Q-learning to maximize the utilities of USVs and offshore platforms. Third, the task allocation scheme is proposed to improve the number of allocated tasks. Finally, the performance of the proposed scheme is conducted based on extensive simulations. The simulation results show that the proposed scheme can significantly improve the number of allocated tasks compared with the conventional schemes.

Driven by the ever-increasing demands of vehicular services, edge computing has become a promising paradigm to facilitate edge services in vehicular networks by using edge computing devices (ECDs). To enhance the service experience, we develop a reservation service framework, where the reservation service request of a vehicle needs to be relayed to one of the ECDs which is ahead of its driving direction. However, due to the various behaviors of vehicles, not all the vehicles are trustworthy and willing to join in the service request relay process. Therefore, how to exploit the cooperation between ECDs and vehicles to relay the service request by considering the dynamic traffic status and the behaviors of vehicles becomes a challenge. As an effort to address this problem, we propose a trusted relay selection scheme for edge services to facilitate the proposed reservation service framework. Specifically, we first design the request relay mechanism based on the dynamic traffic status to guarantee the efficiency of the relay process. Then, the reputation management mechanism is presented to constrain the behaviors of vehicles, where a vehicle with high reputation value can enjoy the price discount for computing service. Based on the designed request relay and reputation management mechanisms, a reputation-based auction approach is then proposed to select relay vehicles (RVs) to reduce the cost of the relay service. Simulation results show that the proposed reservation service framework can manage vehicles efficiently and lead to the lowest cost for the relay services compared with the conventional schemes.

Edge caching has become one of promising technologies in mobile social networks (MSNs) to proximally provide popular contents for mobile users. However, since caching contents inevitably consume resources (e.g., power, bandwidth, storage, etc.), edge caching devices maybe selfish to cheat the content provider for earning service fees. In addition, due to the open access of edge caching devices, the edge caching service is vulnerable to various attacks, such as man-in-the-middle attack and content tamper attack, etc., resulting in the degradation of content delivery performance. To efficiently tackle the above problems, in this paper, we propose a secure edge caching scheme for the content provider and mobile users in MSNs. Specifically, we first develop a secure edge caching framework consisting of the content provider, multiple edge caching devices, and some mobile users. To motivate the participation of edge caching devices, Stackelberg game is exploited to model the interactions between the content provider and edge caching devices. The content provider serves as the game-leader to determine the payment strategy of secure caching service and each edge caching device is the game-follower to make the strategy on the quality of secure caching service. Especially, the zero payment mechanism is adopted to suppress the selfish behaviors of edge caching devices. Apart from this, for lack of the knowledge on interactions between the content provider and edge caching devices in dynamic network scenarios, we also employ the Q-leaning to derive the optimal payment strategy of the content provider and the security strategy of edge caching device. Extensive simulations are conducted, and results demonstrate that the proposed scheme can efficiently motivate edge caching devices to provide the content provider and mobile users with high-quality secure caching services.

In this paper, a mobile edge computing framework with multi-user computation offloading and transmission scheduling for delay-sensitive applications is studied. In the considered model, computation tasks are generated randomly at mobile users along the time. For each task, the mobile user can choose to either process it locally or offload it via the uplink transmission to the edge for cloud computing. To efficiently manage the system, the network regulator is required to employ a network-wide optimal scheme for computation offloading and transmission scheduling while guaranteeing that all mobile users would like to follow (as they may naturally behave strategically for benefiting themselves). By considering tradeoffs between local and edge computing, wireless features and noncooperative game interactions among mobile users, we formulate a mechanism design problem to jointly determine a computation offloading scheme, a transmission scheduling discipline, and a pricing rule. A queueing model is built to analytically describe the packet-level network dynamics. Based on this, we propose a novel mechanism, which can maximize the network social welfare (i.e., the network-wide performance), while achieving a game equilibrium among strategic mobile users. Theoretical and simulation results examine the performance of our proposed mechanism, and demonstrate its superiority over the counterparts.

The software defined heterogeneous vehicular networks (SD-HetVNETs), which consist of cellular base stations (CBSs) and roadside units (RSUs), have emerged as a promising solution to address the fundamental problems imposed by the surge increase of vehicular content demand. However, due to the ever increasing requirement of the vehicles' quality of experience (QoE) and the network vendors' utilities, there come new challenges to motivate CBS to cooperate with RSU for content delivery in order to maximize their utilities and improve the efficiency of the networks. Therefore, in this paper, we propose a collaborative content delivery scheme to improve the utilities of the participants (i.e., CBS, RSU and vehicles) in the SD-HeVNETs, where the CBS can cooperate with RSUs by serving a group of vehicles with multicast technology. We first define the utility models to map the profits of the participants in the networks and formulate the utilities of CBS and RSU as two optimization problems. Then, we exploit the double auction game to motivate CBS to cooperate with RSU for the multicast assisted content delivery to address the two maximization problems. Next, the optimal bidding strategies of CBS and RSU in the game are analyzed when the Bayesian Nash equilibrium is achieved. With the optimal bidding strategies, both CBS and RSU can bid for the multicast assisted content delivery services to maximize their utilities based on the network status. Finally, the performance of the proposed cooperative scheme is evaluated by using simulations. The simulation results demonstrate that the utilities of all the participants in the networks can be enhanced and the efficiency of the networks can be improved.

To meet the exponentially increasing mobile services and applications, heterogenous networks (HetNets) have been envisioned as a promising technology. In HetNets, multiple caching-enabled small-cell based stations (SBSs) are deployed within the coverage of a macro-cell base station (MBS) to cache multimedia contents for mobile users. However, due to security threats of untrusted SBSs, the cached contents may be illegally accessed by owners of these untrusted SBSs, resulting in the content privacy leakage. To tackle this problem, we propose a secure edge caching scheme for layered multimedia contents in HetNets. Specifically, considering the layered features of contents, we first develop a secure edge caching framework based on the cooperations of SBSs and MBS. In this framework, the critical base layer subfile of the content are directly delivered by the trusted MBS, whereas the enhancement layer subfiles are cached on untrusted SBSs. Furthermore, according to the limited caching capacities of SBSs and dynamic content demands of mobile users, we formulate the enhancement layer subfile caching problem as a non-convex 0-1 integer programming problem. To solve this problem, we devise a distributed alternating direction method of multipliers (ADMM) and secure the edge caching for each SBS to iteratively search the optimal caching strategy. Simulation results show that the proposed scheme provides secure and efficient multimedia content caching for mobile users.

With the rapid advances of wireless technologies and popularization of mobile devices, edge computing boosts mobile social networks (MSNs) to allow mobile users to deliver, share, and exchange contents with each other. In particular, with edge caching, various content services can be provided to mobile users with improved Quality-of-Experience (QoE). However, edge caching is vulnerable to cache pollution attack (CPAttack), degrading content delivery. To tackle these problems, in this paper, we propose a hidden Markov model (HMM) based detection scheme against CPAttack in edge computing enabled MSNs. Specifically, we first present the CPAttack model with the observations of malicious behaviors. According to the CPAttack model, the caching state of each edge device is characterized in terms of request rate and cache hit rate. The HMM is exploited to detect the CPAttack with observation sequence of caching states. The simulation results demonstrate that the proposed scheme can efficiently improve edge devices' capability to detect CPAttack.

Nash noncooperative power game is an effective method to implement interference cooperation in downlink multiuser multiple-input multiple-output (MU-MIMO). Power equilibrium point of Nash noncooperative power game can achieve a satisfactory tradeoff between self-benefits of Internet of Things (IoT) users and interference between IoT users which largely enhance the edge IoT user throughput. However, either power strategy space, i.e., the enabled range of power allocation for IoT users, or overall BS transmit power in the existing Nash noncooperative power games is generally static. This limits the performance of systems, especially in IoT systems, etc., in 5G. As an effort to address these problems, we design a novel framework of Nash noncooperative game with iterative convergence for downlink MU-MIMO. We first decompose the MU-MIMO into multiple virtual single-antenna transmit-receive pairs with a stream analytical model. Afterwards, based on streams, we propose a noncooperative water-filling power game with pricing (WFPGP) where the power strategy space of each stream can be dynamically determined byiterative water-filling. We derive the sufficient condition for the existence and uniqueness of WFPGP game, in which the verification of the sufficient condition can be executed in a distributed manner. By simulations, we verify the performance of WFPGP compared to other Nash noncooperative games.

The smart community (SC), as an important part of the Internet of Energy (IoE), can facilitate integration of distributed renewable energy sources and electric vehicles (EVs) in the smart grid. However, due to the potential security and privacy issues caused by untrusted and opaque energy markets, it becomes a great challenge to optimally schedule the charging behaviors of EVs with distinct energy consumption preferences in SC. In this paper, we propose a contract-based energy blockchain for secure EV charging in SC. First, a permissioned energy blockchain system is introduced to implement secure charging services for EVs with the execution of smart contracts. Second, a reputation-based delegated Byzantine fault tolerance consensus algorithm is proposed to efficiently achieve the consensus in the permissioned blockchain. Third, based on the contract theory, the optimal contracts are analyzed and designed to satisfy EVs' individual needs for energy sources while maximizing the operator's utility. Furthermore, a novel energy allocation mechanism is proposed to allocate the limited renewable energy for EVs. Finally, extensive numerical results are carried out to evaluate and demonstrate the effectiveness and efficiency of the proposed scheme through comparison with other conventional schemes.

Caching contents on edge computing-enabled small cell base stations (ECSBSs) has become a promising technology for mitigating burdens of macro cell base stations and offloading data from mobile users. However, as ECSBSs may be malicious, providing a secure caching scheme becomes a challenge. In this paper, we propose a novel secure caching scheme in heterogeneous networks for multihoming users. First, to provide the cached contents, a trust mechanism is designed to verify the reliability of each ECSBS. Then, in order to guarantee the integrity of cached contents and preserve the privacy of mobile users, a Chinese remainder theorem-based privacy preservation protocol is proposed. Next, we investigate the interactions among mobile users and ECSBSs by Stackelberg game, where the trusted ECSBSs are selected to provide caching resources for mobile users with multihoming access. In addition, we analyze the Stackelberg equilibrium to jointly maximize the utilities of ECSBSs and mobile users. Extensive simulations validate the efficiency of the proposed scheme with the reliability and effectiveness to cache contents.

Vehicular energy network (VEN), as an important part of the Internet of Things for the smart city, can facilitate the renewable energy (RE) transportation over a large geographical area by means of electric vehicles (EVs) through wireless power transfer technology. However, due to the potential security vulnerability in VEN, EV users can be attacked by external or internal adversaries. In addition, owing to the selfishness of EVs, it is a great challenge to optimally schedule the charging/discharging behaviors of EVs to realize regional energy balance in VEN. To tackle the above issues, this paper proposes a blockchain-based secure incentive scheme for energy delivery in VEN. First, a novel permissioned energy blockchain system is introduced in VEN to implement secure energy delivery services for EVs and energy nodes through the use of distributed ledgers and cryptocurrency. Second, a proof of reputation consensus protocol is proposed to efficiently reach consensus in energy blockchain, where the reputation derivation is constructed based on the local trust computing and credibility computing. Third, motivated by the pricing mechanism, an incentive model is developed to stimulate EVs to cooperatively deliver RE to various areas with different electricity loads while maximizing EVs' utilities. Finally, extensive numerical results are provided, which demonstrate the efficiency of the proposed scheme through the comparison with conventional schemes.

Content dissemination, in particular, small-volume localized content dissemination, represents a killer application in vehicular networks, such as advertising distribution and road traffic alerts. The dissemination of contents in vehicular networks typically relies on the roadside infrastructure and moving vehicles to relay and propagate contents. Due to instinct challenges posed by the features of vehicles (mobility, selfishness, and routes) and limited communication ability of infrastructures, to efficiently motivate vehicles to join in the content dissemination process and appropriately select the relay vehicles to satisfy different transmission requirements is a challenging task. This paper develops a novel edge-computing-based content dissemination framework to address the issue, composed of two phases. In the first phase, the contents are uploaded to an edge computing device (ECD), which is an edge caching and communication infrastructure deployed by the content provider. By jointly considering the selfishness and the transmission capability of vehicles, a two-stage relay selection algorithm is designed to help the ECD selectively deliver the content through vehicle-to-infrastructure (V2I) communications to satisfy its requirements. In the second phase, the vehicles selected by the ECD relay the content to the vehicles that are interested in the content during the trip to destinations via vehicle-to-vehicle (V2V) communications, where the efficiency of content delivery is analyzed according to the probability that vehicles encounter on the path. Using extensive simulations, we show that our framework disseminates contents to vehicles more efficiently and brings more payoffs to the content provider than the conventional methods.

The heterogeneous vehicular networks (HetVNETs), which apply the heterogeneous access technologies (e.g., cellular networks and WiFi) complementarily to provide seamless and ubiquitous connections to vehicles, have emerged as a promising and practical paradigm to enable vehicular service applications on the road. However, with different costs in terms of latency time and price, how to optimize the connection along the vehicle's trip toward the lowest cost represents fundamental challenges. This paper investigates the issue by proposing an optimal access control scheme for vehicles in HetVNETs. In specific, with different access networks, we first model the cost of each vehicle to download the requested content by jointly considering the vehicle's requirements of the requested content and the features of the available access networks, including conventional vehicle to vehicle communication and the heterogeneous access technologies. A coalition formation game is then introduced to formulate the cooperation among vehicles based on their different interests (contents cached in vehicles) and requests (contents to be downloaded). After forming the coalitions, vehicles in the same coalition can download their requested contents cooperatively by selecting the optimal access network to achieve the minimum costs. The simulation results demonstrate that the proposed game approach can lead to the optimal strategy for the vehicle.

The papers in this special issue focus on trustworthiness in multimedia communications. Recently, social multimedia content is being delivered to users with a high quality of experience (QoE) with the advance of multimedia technologies and social networks. However, as a huge amount of social users have various demands to exchange and share multimedia content with each other, it becomes a new challenge for the current social multimedia analytics and delivery to deal with the various attacks perpetrated by malicious users or through spam contents. Therefore, the trust and risk management for social multimedia content based on the social tie of users become of prime importance to face the unpredicted threats and subsequent damage. This Special Section aims to provide a premier forum for researchers working on the trust-based social multimedia analytics and delivery. It also provides the opportunity for both academic and industrial researchers to discuss recent results and provide solutions to the above-mentioned challenges.

The pervasiveness of information and communications technologies as well as intelligent electronic devices leads to an expanded attack surface in smart grids, making it increasingly challenging to withstand the high-profile false data injection (FDI) attacks. In this paper, we propose a Proactive Approach to Mitigate FDI Attacks (PAMA) in smart grids. With PAMA scheme, the critical information - power grid connections and configurations as well as the original measurement data - used for constructing FDI attacks is well protected from leakage or theft, so that FDI attacks are effectively mitigated. Specifically, we transform the state estimation and FDI detection application into a distributed one equipped with converted information from the critical information provided by the control center. In addition, the original measurement data is also protected by using a secure hybrid Paillier cryptosystem. Our PAMA scheme is proved to be secure and effective in mitigating FDI attacks on smart grids. The computational complexity and the communication overhead are evaluated on the standard IEEE 14-bus test system. Keywords__Smart grids, false data injection (FDI) attack, Paillier cryptosystem, state estimation.

Indoor navigation system provides customized path planning for requesters who are unfamiliar with the indoor environment, such as shopping mall and airport. Spatial crowd-sourcing technology can be applied to indoor navigation to offer fundamental services related to location. However, spatial crowdsourcing-based indoor navigation is vulnerable to the intrusion of injected anomalous paths from attackers. In this paper, we propose an anomalous path detection (APD) scheme to classify attackers according to their reputation management and abnormal trajectory sequence. Specifically, we first develop a crowdsourcing system to support the indoor location service using the fog as the spatial crowdsourcing server. Then, we identify two levels of attackers, i.e., the malicious responders and the semi-honest responders in the indoor environment according to their attacking purposes. Through the responders' historical records from the fog server, we analyze a series of trajectory sequences consisting of the distance between the current position and the destination to distinguish the semi-honest responders from the normal. In addition, we propose a semi-supervised learning with hidden Markov model (HMM) to detect the semi-honest responders. Finally, the extensive simulations show that the APD scheme can achieve higher accuracy with the acceptable false rate.

In a smart community (SC) with renewable energy sources (RES), flexible charging service can be provisioned to electric vehicles (EVs), where EVs can choose clean energy, traditional energy, or the mixture of them on demand. Considering the existence of various entities in the SC and the limited generation capacity of RES, it becomes of significance yet very challenging to optimally schedule the charging service for EVs with different consumption preferences. In this paper, we propose a charging scheme for EVs in a SC integrated with RES by using a game theoretical approach. Firstly, a three-party energy network is proposed to model the interactions among the power grid, EVs, and aggregators in the smart grid. Secondly, the trust model is presented to improve safety of power trading by evaluating the reliability of aggregators. Thirdly, based on the four-stage stackelberg game, the optimal strategies of three energy entities are analyzed by solving the stackelberg equilibrium (SE). Furthermore, a weighted max-min fairness (WMMF) based algorithm is proposed to fairly allocate the limited renewable power for EVs. Finally, extensive simulations are carried out to evaluate and demonstrate the effectiveness of the proposed scheme through comparison with conventional schemes.

Recently, social network based e-healthcare service has emerged as a promising way to control the spread of infectious diseases. However, the large-scale deployment in reality faces a fundamental challenge to reduce the cost where social features of mobile users and the properties of networks should be considered. To tackle the above problem, this paper presents a green social cyber physical system (CPS) based e-Healthcare scheme to control infectious diseases. Firstly, based on the analysis of social features, the high influential users are selected to inoculate immune drugs when an infectious disease is identified. Secondly, we develop an epidemic spreading model with the dynamic equations to analyze the efficiency of immune strategy. With the proposed model, the spread of infectious diseases can be effectively monitored and the spreading range of the infectious can be predicted. In addition, simulation experiments prove that the proposal can be more efficient to prevent infectious diseases from being spread than conventional methods.

Caching contents in small cell base stations (SBSs), namely, caching-enabled SBSs, has become a promising technology for mitigating burdens of macro cell base stations and backbone links. However, as some SBSs may be malicious, how to provide a secure caching scheme becomes challenging. In this paper, we propose a novel secure caching scheme in heterogeneous networks for multi-homing users. Firstly, to achieve availability of cached contents, a trust mechanism is designed to verify the reliability of each SBS. Then, we investigate the interactions among mobile users and SBSs according to Stackelberg game, where the trusted SBSs are selected to provide caching space for mobile users with multi-homing access. In addition, we investigate the Stackelberg equilibrium (SE) to jointly maximize the utilities of SBSs and mobile users. Extensive simulations validates the efficiency of the proposed scheme by evaluating the reliability and effectiveness to cache contents.

The autonomous vehicles (AVs), like that in knight rider, were completely a scientific fiction just a few years ago, but are now already practical with real-world commercial deployments. A salient challenge of AVs, however, is the intensive computing tasks to carry out on board for the real-time traffic detection and driving decision making; this imposes heavy load to AVs due to the limited computing power. To explore more computing power and enable scalable autonomous driving, in this paper, we propose a collaborative task computing scheme for AVs, in which the AVs in proximity dynamically share idle computing power among each other. This, however, raises another fundamental problem on how to incentivize AVs to contribute their computing power and how to fully utilize the pool of group computing power in an optimal way. This paper studies the problem by modeling the issue as a market-based optimal computing resource allocation problem. In specific, we develop a software-defined network (SDN) architecture and consider a star topology where a centered AV outsources its computing tasks to the surrounding AVs for its autonomous driving. A market mechanism is developed in which the surrounding AVs sell their computing power at a cost based on their local idle computing resources. Then, we classify the tasks requested by the centered AV into two types which are task with time to live (TTL) and task without TTL, respectively. With different task types, we define corresponding cost models of the centered AV and formulate them as two minimization problems. The optimal solutions of the problems are achieved to guide the centered AV to wisely allocate computing tasks to surrounding AVs towards minimal cost. Finally, the performance of the proposed scheme is evaluated using simulations, which show that the proposed scheme can result in the guaranteed computing performance yet the lowest costs compared with other conventional schemes.

Caching content with fog computing at the edge nodes has been a promising alternative to mitigate burdens of backbone networks and improve mobile users' quality of experience in mobile social networks (MSNs). However, as edge node may be vulnerable due to the attacks from malicious users, the design of secure caching schemes for the fog/edge enabled MSNs becomes a new challenge. In this paper, to tackle the above problem, we propose a secure caching scheme for disaster backup in MSNs with fog computing. Specifically, to protect the privacy, a partitioning and scrambling method is first designed to encrypt the contents. Then, the encrypted contents are replicated to multiple replicates, where these replicates are delivered and stored in different servers. Based on the recovery time objective and content delivery latency, an auction game model is developed to determine the optimal servers, where both edge nodes and cloud servers can obtain the maximum utilities. Extensive simulations are conducted to show the effectiveness and reliability of the proposed scheme.

To save energy during content delivery in green cities, caching contents on edge nodes that are placed near mobile social users has been advocated recently. However, how to allocate the limited caching resources with secure content delivery becomes a new challenge. Therefore, in this paper, we present a novel theoretical model to deliver secure content with edge nodes in order to save energy for green cities. First, we present a reverse auction game to encourage edge nodes to cooperatively provide caching services with incentives. With the model, mobile users can determine the candidate of edge node to cache content based on the interaction between mobile users and edge nodes. Second, a trust management method is designed to evaluate the reliability of the selected candidate of edge node by considering the direct trust evaluation. Finally, extensive simulations show that the proposal can save energy with a secure content delivery where both the delay to obtain the content and the caching ratio can be improved compared with the conventional methods.

Heterogeneous vehicular networks (HetVNETs), which applies the heterogenous access technologies (e.g., cellular and WiFi) complementarily to provide seamless and ubiquitous connections to vehicles, have emerged as a promising and more practical paradigm to enable vehicular service applications on the road. However, with different access technologies presenting different costs in terms of download latency and bandwidth cost, how to optimize the connection along the vehicle's trip towards the lowest cost represents fundamental challenges. This paper investigates the issue by proposing an optimal access control scheme for vehicles in HetVNETs. In specific, with different access links, we first model the cost of each vehicle to download its content by jointly considering the conventional vehicle to vehicle (V2V) communication and the available access links. A coalition formation game is then introduced to formulate the cooperation among vehicles based on different interests (content cached in vehicles) and requests (content needs to download). After forming the coalition, vehicles in the same coalition can download their requested content by selecting the optimal access link to achieve the minimum cost. Simulation results demonstrate that the proposed game approach can lead to the optimal strategy for vehicles and reduce the cost.

In this paper, we studied the transmission performance of underwater wireless networks, where underwater network users (UNUs) can transmit their data through wireless optical and acoustic communication in a certain range to improve the overall underwater networks. By jointly considering UNUs' volume of data transferred and overall network transmission performance, we introduced an auction game based optical and acoustic communication mechanism (AGOC). With AGOC mechanism, the base transceiver station (BTS) sells wireless optical communication chances through auctions. The users will decide whether to bid according to their own situation, and then the winner could use wireless optical to transmit finally. The simulation results verified the effectiveness of our proposed algorithm. It also be concluded that AGOC mechanism could improve the overall underwater wireless network performance through reducing the number of UNUs contending for the wireless optical channel.

With the rapid development of wireless communication and mobile devices, heterogeneous networks have emerged as a promising paradigm to enable users' data services. However, it lacks an experience blocking theory to optimize data services. Furthermore, due to the limited resources of spectrum, the spectrum sharing based on the quality of experience (QoE) in heterogeneous networks becomes a new challenge. Therefore, to tackle the above challenge, we present an experience blocking (EB) ratio based game-theoretic approach for spectrum sharing in heterogeneous networks where the small cell can lease the spare spectrum from macro cell. Specifically, firstly, a novel EB ratio based model is proposed to evaluate the efficiency of spectrum usage in a cell. Then a Stackelberg game is employed to formulate the interaction between macro cell and small cell according to the EB ratio. Finally, an EB table is given to evaluate the blocking status of a cell and simulation results show that the proposed scheme can improve the efficiency of spectrum sharing better than other schemes.

Nash non-cooperative power game can be effectively used to find the power equilibrium point to enhance edge user throughput in downlink multi-user multiple-input multipleoutput (MU-MIMO) wireless networks. However, the power strategy space of the existing Nash non-cooperative power games are generally statically pre-determined without adapting to the changing wireless environment. To address this problem, we explore a novel framework of Nash non-cooperative power game with dynamic and environment-adaptive power strategy space. Then, we propose a non-cooperative water-filling power game with pricing (WFPGP), wherein the power strategy space is dynamically determined by iterative water- filling algorithm. We derive the sufficient condition for the existence and uniqueness of the WFPGP game which can be implemented in a distributed manner. Simulation results are used to compare the performance of WFPGP with the other Nash non- cooperative power games and confirm the stable and superior performance advantages of WFPGP.

As an emerging solution to mitigate the problems of the shortage of power resources, electric vehicles (EVs) have advocated to provide safety and convenient driving recently. However, with the ever increasing number of EVs and the new demand of services, how to optimally schedule the charging services becomes a challenge. Therefore, in this paper we present a novel pricing mechanism to optimally schedule the charging demands with user utilities. Firstly, a framework with a nonpreemptive priority charging service is shown for users to queue up. Secondly, based on queuing theory, a novel pricing mechanism is designed to balance the load of charging station by considering the characteristics of different regions and the status of queue. Thirdly, the user utility is studied according to the distance, waiting time as well as the expense, in order to improve the user utility. Finally, simulation results show that the proposed scheme can optimally distribute the charging demand and improve the user utility more efficiently than other conventional methods.

How to control the access of the huge amount of big data becomes a very challenging issue, especially when big data are stored in the cloud. Ciphertext-policy attribute-based encryption (CP-ABE) is a promising encryption technique that enables end-users to encrypt their data under the access policies defined over some attributes of data consumers and only allows data consumers whose attributes satisfy the access policies to decrypt the data. In CP-ABE, the access policy is attached to the ciphertext in plaintext form, which may also leak some private information about end-users. Existing methods only partially hide the attribute values in the access policies, while the attribute names are still unprotected. In this paper, we propose an efficient and fine-grained big data access control scheme with privacy-preserving policy. Specifically, we hide the whole attribute (rather than only its values) in the access policies. To assist data decryption, we also design a novel attribute bloom filter to evaluate whether an attribute is in the access policy and locate the exact position in the access policy if it is in the access policy. Security analysis and performance evaluation show that our scheme can preserve the privacy from any linear secret-sharing schemes access policy without employing much overhead.

To improve the performance of mobile video delivery, caching layered videos at a site near to mobile end users (e.g., at the edge of mobile service provider's backbone) was advocated because cached videos can be delivered to mobile users with a high quality of experience, e.g., a short latency. How to optimally cache layered videos based on caching price, the available capacity of cache nodes, and the social features of mobile users, however, is still a challenging issue. In this paper, we propose a novel edge caching scheme to cache layered videos. First, a framework to cache layered videos is presented in which a cache node stores layered videos for multiple social groups, formed by mobile users based on their requests. Due to the limited capacity of the cache node, these social groups compete with each other for the number of layers they request to cache, aiming at maximizing their utilities while all mobile users in each group share the cost involved in the cache of video contents. Second, a Stackelberg game model is developed to study the interaction among multiple social groups and the cache node, and a noncooperative game model is introduced to analyze the competition among mobile users in different social groups. Third, leveraging the backward induction method, the optimal strategy of each player in the game model is proposed. Finally, simulation results show that the proposed method outperforms the exiting counterparts with a higher hit ratio and lower delay of delivering video contents.

Content dissemination, in particular small-volume popular content dissemination, represents a killer application of vehicular networks, which is also fundamental to the delivery of advanced infotainment applications, such as vehicular social networks, road traffic alerts, etc. The content dissemination in vehicular networks relies on moving vehicles to relay and propagate contents. Due to challenges including diverse mobilities of vehicles, strict timeliness and limited vehicular communication bandwidth, to appropriately select the relay vehicles towards the optimal system performance is a challenging task. This paper investigates the issue by devising a novel content dissemination scheme composed of two phases. In the first phase, contents are uploaded to a road-side cache infrastructure called roadside buffer (RSB). By examing the transmission capability of vehicles, the RSB then selectively disseminates content files to drive-thru vehicles with an optimal relay selection scheme. In the second phase, the vehicles selected by the RSB relay the content to other vehicles which have interest in the content during the trip to destinations. Using extensive simulations, we show that our scheme disseminates content to vehicles more efficiently than the conventional method.

To cope with the rapidly expanding network scale and population, the heterogeneous content centric networks (HCCNs) have emerged, where the naming content can be shared among different sub-networks by using interest packets. However, In the HCCNs, as these sub-networks are managed by different operators and some sub-network may exhibit selfish behaviors due to the limited resource, how to design a cooperative scheme for relay service to deliver naming content becomes a new challenge. Therefore, this paper proposes a bargaining game based cooperative scheme for relay service in HCCNs. Specifically, at first we present an incentive framework that each sub-network can obtain currency by providing other subnetworks with relay service. Then, by using a cooperative node as an agent, a sub-network can select an optimal adjacent subnetwork to obtain content. Next, a bargaining game is introduced to model the interaction between two sub-networks, which leads to a Subgame perfect Nash equilibrium as the agreement of two players to maximize their benefits. Finally, simulation experiments prove that the proposed scheme can outperform other conventional methods to reduce the delay and overhead.

Due to the rapid increase of mobile user population and the dynamical change of network topology in mobile social networks (MSNs), how to efficiently deliver content among mobile social users becomes a new challenge. In this paper, an incentive content delivery mechanism based on the weighted directed graph is proposed to encourage users to obtain and provide content in the MSNs. Specifically, firstly we introduce a weighted directed graph to study the features of obtaining and providing content among mobile social users. Secondly, based on the social features including the average closeness and vertex betweenness, we present the sealed-bid auction based incentive mechanism to overcome selfish behavior and efficiently deliver content in the MSNs. Finally, with a real dataset numerical experiments are carried out to prove that the proposal can accurately show the properties of the MSNs and can be efficient for content delivery.

Mobile charging stations (MCSs) can provide electric vehicles (EVs) with better charging services than the fixed charging stations, as the flexible and efficient charging sites can be available. However, how to schedule the tasks from the EVs and optimally place the MCSs becomes a new challenge. Therefore, in this paper we present a novel approach to help EVs' charging with MCSs through heterogeneous networks. Firstly, a novel heterogeneous network model is presented to improve the communication between EVs and MCSs by using macro cells and small cells. Next, a novel model is developed to make optimal decisions for MCSs to schedule the tasks from EVs. Then, a chaotic evolution particle swarm optimization (CEPSO) algorithm is presented to determine the optimal placement of MCSs based on the charging demand and the maintenance cost. Finally, the simulation experiments prove that the proposed approach can outperform the conventional methods.

Due to the rapid increases in both the population of mobile social users and the demand for quality of experience (QoE), providing mobile social users with satisfied multimedia services has become an important issue. Media cloud has been shown to be an efficient solution to resolve the above issue, by allowing mobile social users to connect to it through a group of distributed brokers. However, as the resource in media cloud is limited, how to allocate resource among media cloud, brokers, and mobile social users becomes a new challenge. Therefore, in this paper, we propose a game theoretic resource allocation scheme for media cloud to allocate resource to mobile social users though brokers. First, a framework of resource allocation among media cloud, brokers, and mobile social users is presented. Media cloud can dynamically determine the price of the resource and allocate its resource to brokers. A mobile social user can select his broker to connect to the media cloud by adjusting the strategy to achieve the maximum revenue, based on the social features in the community. Next, we formulate the interactions among media cloud, brokers, and mobile social users by a four-stage Stackelberg game. In addition, through the backward induction method, we propose an iterative algorithm to implement the proposed scheme and obtain the Stackelberg equilibrium. Finally, simulation results show that each player in the game can obtain the optimal strategy where the Stackelberg equilibrium exists stably.

Nowadays, due to the increasing population of mobile users and popularity of social applications, the social relationships among mobile users become more various and affect the information spreading in mobile social networks (MSNs) more deeply than before. For example, there are multiple social relationships between mobile users, such as friends, relatives, classmates and so on. However, as most of existing models mainly consider the relationship among mobile users to be the same during the information spreading, these models can not mimic the information spreading in the MSNs well, where multiple relationships among mobile users needs to be studied thoroughly. Therefore, in this paper, to resolve the above issue, we firstly divide social relationships into four types: blood relationship, geographical relationship, work relationship, and interest relationship. Then we develop an analytical model to evaluate the influences of multiple relationships on the information spreading process in MSNs. With real traces, simulation results show the accuracy of the presented model. Numerical results demonstrate that different social relationships have different effects on the information spreading.

With the increasing sensing and communication in cyber physical social system (CPSS), the data volume is growing much rapidly in recent years. Secure deduplication has attracted considerable interests of storage provider for data management efficiency and data privacy preserving. One of the most challenging issues in secure deduplication is how to manage data and the convergent key when users frequently update it. To solve this problem, D. Koo et al. use bilinear paring as the key method. However, bilinear paring requires high computation cost for implementations. In this paper, we propose a session-key-based convergent key management scheme, named SKC, to secure the dynamic update in the data deduplication. Specifically, each data owner in SKC can verify the correctness of the session key and dynamically change it with the data update. Furthermore, to enable group combination and remove the aid of gateway (GW), a convergent key sharing scheme, named CKS, is presented. Security analysis demonstrates that both SKC and CKS can protect the confidentiality of the data and the convergent key in the case of dynamic updates. The simulation results show that our SKC and CKS can significantly reduce computation complexity and communication during the data uploading phase.

With the rapid development of vehicular and ICT technologies, Vehicular Content Networks (VCNs) play an important role in facilitating better road safety and comfortable driving. Because the content, such as traffic reports and disaster warning messages, must be delivered efficiently and timely, how to guarantee and improve the performance of VCNs becomes a hot issue. Recently, caching with the parked vehicles shows its efficiency to distribute the large-sized vehicular content. However, the related caching algorithm has not been studied thoroughly. In this paper, based on the analyses of the arrival of vehicles, mobility in parking area and access pattern of vehicular content, we propose an algorithm to decide how to use the cached replicas in the parking area to provide users with the requested content. Simulation results prove that our proposal can outperform the conventional methods.

With the development of mobile devices, especially the emergence of smart phones, the mobile social networks (MSNs) have emerged to provide a variety of mechanisms for users to share their content. However, because the number of the mobile users still keeps growing rapidly, the MSNs become more complex than before and the features including evolution and topology need to be studied for communication system optimization. Therefore, in this paper, a great deal of data on social interactions among mobile users are collected to reveal the evolution and topological features of the MSNs. Firstly, the evolution feature of the MSN with the time is detailedly studied. Then, the statistical features of MSN including degree distribution, node distance, node closeness, and betweenness are analyzed. From the results of the analysis on the evolution features, we find that the MSN will become complex over time. In addition, the analysis of the topological properties shows that the MSN is a typical scale-free network and has strong small-world features.

In order to achieve better statistical Quality-of-Service (QoS) provisioning for cognitive radio networks (CRN), in this paper, we develop a hybrid underlay/overlay transmission mode for CRNs. Specifically, by applying the theory of effective capacity and taking PN's activity statistics into consideration, we first analyze the maximum achievable throughput of the CRN under two dominant transmission modes, namely underlay and overlay, respectively, and provide efficient algorithms to derive optimal transmission strategies for the two modes. Following the analyses, we then propose a hybrid underlay/overlay transmission mode, through which the cognitive users' QoS requirements can be better guaranteed and network throughput can be further improved. Moreover, we analyze the optimal transmission strategies for both underlay and overlay modes under two limiting cases. Analyses indicate that 1) for the loose QoS requirement, optimal transmission strategies for both underlay and overlay modes become the water-filling algorithm; and 2) for the stringent QoS requirement, the cognitive user will transmit with constant rate. Furthermore, the impact of imperfect channel estimations on our proposed transmission mode is discussed. Simulation results are provided to demonstrate the impacts of delay QoS requirements and PN's activity statistics on maximizing the delay-constrained throughput for both underlay and overlay modes and verify the effectiveness of our proposed transmission mode. Moreover, for the overlay mode, we observe that 1) a unique optimal sensing time exists under the given QoS constraint; and 2) the optimal sensing time surprisingly increases as the QoS constraint gets more stringent.

Shrew Distributed Denial-of-Service (DDoS) attacks are stealthy, concealing their malicious activities in normal traffic. Although it is difficult to detect shrew DDoS attacks in the time domain, the existent energy exposes them in frequency domain. For this purpose, online Power Spectral Density (PSD) analysis necessitates real-time PSD data conversion. In this paper, an optimized FPGA based accelerator for real-time PSD conversion is proposed, which is based on our innovative component-reusable Auto-Correlation (AC) algorithm and the adapted 2N-point real-valued Discrete Fourier Transform (DFT) algorithm. Further optimization is achieved through the exploration of algorithm characteristics and hardware parallelism for this case. Evaluation results from both simulation and synthesis are provided. The overall design can be easily placed in a Xilinx Virtex2 Pro FGPA.

With the rapid revolution of wireless transmissions technologies, Internet access through wireless mobile terminals has attracted numerous attentions from not only the research community but also industry and end users. In the past 20 years, efforts contributed by fundamental research, industry standardization, and construction of infrastructure have turned the theoretic wireless internet into reality. The major approach of current wireless-internet service provisioning can be usually decomposed by two parts. Specifically, the mobile users often first connect to a telecommunication networks, typically the cellular networks or the Wi-Fi hotspots. Then, the telecommunication networks are responsible for connecting to the internet and fulfilling the data request from mobile users. This approach maximally makes use of the existing infrastructures of both wireless and wired communications networks and has efficiently accommodated current service load of wireless internet connections. However, as the demands on internet connection over wireless networks increase explosively, we face many new challenges in wireless internet service provisioning, which are introduced by the limitation on bandwidth available to internet services, the difficulty in QoS guarantees, the shortage of radio spectrum resources, the heterogeneous networking structures, and so forth. First, telecommunications networks need to accommodate the conventional text, voice call, and video call services between mobile users as well as internet services. The proportion of resources allocated for wireless internet services cannot be arbitrarily high. Second, as aforementioned, the wireless internet services need to handle hybrid wireless and wired connections with different channel features, which make QoS guarantees an unsolved problem. Moreover, the Wi-Fi connections use the industrial, scientific, and medical radio bands, which are shared by many other wireless networks. Thus, the provisioning of wireless internet services over Wi-Fi hotspots suffers from the severe spectrum shortage problem. Last but not the least, although multiple co-existing telecommunications networks bring a better chance for robust internet connection, how to take advantage of the heterogeneous connection resources with well-balanced benefit and costs has not been well addressed nor thoroughly studied. Despite the extensive research work proposed in recent years, the aforementioned issues for wireless internet still remain open problems. This special issue is targeted at addressing the urgent need in the research community as well as the industry to discuss the recent progress and future research directions on wireless Internet. The response to our Call for Papers on this special issue was overwhelming, with over 200 articles submitted from diverse places all over the world. We held a rigorous peer-review process with each paper reviewed by at least experts in corresponding research area, and finally selected nine excellent papers published in this special issue. These nine papers cover a variety of hot topics and cutting-edge technologies in wireless Internet, involving cross-layer design, QoS-provisioning, game-theoretic approach for resource allocation, cognitive femtocell, video quality enhancement over wireless channels, fair scheduling, and so forth. The first paper, Bargaining-based Spectrum Sharing in Cognitive Radio Network 1, contributed by Y. Yan et al. proposes a bilateral bargaining mechanism between two secondary users (SUs) to avoid interference to primary users. The general network scenario with multiple SUs can be decomposed into multiple pairs of bilateral bargaining accordingly. The authors model the bargaining process through dynamic finite/infinite horizon multi-stage games and identify the corresponding equilibria. As a result, spectrum sharing across SUs can be effectively achieved. In the second paper, Queuing Analyses and Statistically-bounded Delay Control for Two-hop Green Wireless Relay Transmissions 2, Q. Du et al. studies the statistically-bounded delay control and green communications over two-hop wireless relay links. The authors showed that subject to the delay-QoS constraint, asymmetric resource allocation over the two-hop transmission is efficient in terms of reducing resource consumption. The corresponding results also give valuable guidance for position selection for relay nodes towards delay-QoS constrained relay transmissions. In the third paper, Face Detection Algorithm Based on Hybrid Monte Carlo Method and Bayesian Support Vector Machine 3, contributed by L. Wang et al., a hybrid Monte Carlo method of the Bayesian support vector machine is proposed to solve the high-dimension and long-training-time problems. Under the proposed scheme, the training time can be effectively reduced as well as the face detection result is more accurate. In the fourth paper, Energy-efficient Radio Remote Units Placement for Single-user Uplink in C-RAN 4, authored by S. Shao et al., the authors derive the energy efficiency of the newly emerged radio access network (RAN), called C-RAN, in order to achieve QoS requirements in terms of circuitry energy consumption. Optimal placement of remote radio unit, targeting at minimization of energy consumed per bit, is obtained and is validated via numerical simulations. In the fifth paper, Multi-user Multimedia Communication Over Orthogonal Frequency-division Multiple Access Downlink Systems 5, the author, L. Fan et al., investigates the problem of the multi-user multimedia communication over orthogonal frequency-division multiple-access (OFDMA) downlink systems. The authors proposed a cross-layer design towards maximizing the video quality over all users with limited network resource. Global optimal solution is derived via Lagrange dual decomposition method, and the effectiveness of the proposed scheme is verified via abundant simulations. In the sixth paper, A Fair Scheduling Scheme Based on Collision Statistics for Cognitive Radio Networks 6, contributed by W. Tang et al., develops a fair scheduling scheme by using collision statistics for cognitive radio networks, which aims at not only improving the fairness across all SUs but also control the collisions among SUs. This goal is achieved by predicting idle probabilities of available channels. Simulations demonstrate that the proposed scheme significantly improve the fairness with little impact on spectrum utilization. In the seventh paper, Adaptive Spectrum Access Strategies in the Context of Spectrum Fragmentation in Cognitive Radio Networks 7, Y. Lu et al. present two adaptive spectrum access strategies, both of which not only select the best transmission channel but also efficiently solve the spectrum fragmentation problem. The first strategy partially remedies the fragmentation problem using higher-layer solutions. The second strategy suppresses the impact of spectrum fragmentation at the physical layer by combining k spectrum fragments to form a single transmission. The superior of the authors’ proposed schemes over existing solutions are validated via simulations. In the eighth paper, Energy-efficient Resource Allocation in Multi-user Relay-based OFDMA Networks 8, contributed by J. Zhang et al., the energy-efficient resource allocation for two-hop uplink multi-user relay-based system is studied. A near-optimal resource allocation scheme to maximize the overall energy efficiency is first proposed, accompanied by a low-complexity resource allocation algorithm. Fairness constraint among users is also taken into consideration. Simulation experiments show the improvement in terms of energy-saving as well as outage probability reduction. The ninth paper, A DOF-based Dynamic Spectrum Auction Algorithm in Cognitive Femtocell 9, authored by G. Wu et al., develops a double optimization framework (DOF) based dynamic spectrum auction algorithm in cognitive femtocell. A dynamic spectrum auction algorithm under this framework employs two-stage optimization processing techniques to maximize the auction revenue. The authors proposed scheme is shown to outperform the greedy algorithm and Vickrey–Clarke–Groves auction, while keeping the high spectrum utilization efficiency. We would like to express our appreciation of all authors' excellent contributions. We also thank all reviewers for their dedication in reviewing the papers. Their valuable suggestions and comments play an important role in further improving the quality of the papers. Moreover, we appreciate the strong support and precious advice from Professor Geoffrey C. Fox, Editor-in-Chief of Concurrency and Computation: Practice and Experience, and thanks to Paterno c. Mojados JR. for his help during the publication process. Finally, we hope that readers in both academia and industry find this special issue interesting and contribute new results and developments in related research areas.

ABSTRACT In this paper, the main properties of digital television broadcasting signals based on the Digital Video Broadcasting—Terrestrial (DVB‐T) standard are analyzed, and these properties are utilized to design a new pilot‐aided detector for spectrum sensing in cognitive radio networks. The proposed detector consists of a processing unit and a combination and decision unit. In the processing unit, multiple statistics that correspond to different enhanced pilot components are computed. In the combination and decision unit, three newly proposed combination schemes are adopted to combine these statistics, and then, a final decision on the presence or absence of the DVB‐T signals is made on the basis of the Neyman–Pearson criterion. The proposed pilot‐aided detector exploits both the periodic continual and scattered pilots that are intrinsic in the DVB‐T signals, processes the observed data timely, experiences short sensing duration, and requires no time synchronization information. Furthermore, the proposed pilot‐aided detector is able to distinguish DVB‐T signals from interference. Theoretical analysis and simulation results show that spectrum bands that are not currently occupied by the DVB‐T systems can be detected accurately by using the proposed pilot‐aided detector. Simulation results also demonstrate the significant performance gain of the proposed detector compared with the counterparts.Copyright © 2011 John Wiley & Sons, Ltd.

XOR based RAID 6 systems outperform other RAID systems. Among the XOR (exclusive OR) based RAID-6 schemes, RDP has better performance than others by a narrow margin. However, the RDP code scheme cannot take full advantage of parallel hardware implementation of XOR codes. In this paper, we propose an extension of the double-erasure-correcting RDP code called EDP, which consists of a parallel decoding scheme. Thus, EDP can improve the decoding velocity of RDP by about 40% without any change to the current RDP configuration for storage.

Dynamic spectrum access (DSA) strategy selection and the associated resource allocation are critically important issues for cognitive networks, because they need to not only satisfy the interference constraint caused to the primary users (PU), but also meet the delay quality-of-service (QoS) requirements for the secondary users (SU). In this paper, we develop the optimal resource allocation schemes for the underlay and overlay DSA strategies, respectively, in delay-QoS constrained cognitive networks. Specifically, for the underlay strategy, we find that 1) when the maximum average interference power is less than the maximum average transmit power, the cognitive network will gradually converge to an interference-power constrained system as the QoS constraint becomes more stringent; 2) when the maximum average interference power is larger than the maximum average transmit power, the cognitive network reduces to a transmit-power constrained system not varying with the QoS requirement. For the overlay strategy, we observe that 1) a unique optimal sensing time exists under the given QoS constraint; 2) the optimal sensing time increases as the QoS constraint gets more stringent. Following these results, we further propose a selection criterion across underlay and overlay DSA strategies. By applying this criterion, the SU can determine whether to use underlay or overlay for DSA under the given QoS constraint and the PUs' spectrum-occupancy probability.

Cognitive radio has been proposed to improve the spectrum utilization by allowing the unlicensed secondary users (SUs) to access the spectrum resources licensed to the primary users (PUs) opportunistically. However, the cognitive radio has rarely been employed to enhance the system performance of the licensed PUs that endure spectrum scarcity. To improve the outage-probability quality-of-service (QoS) of the mobile station (MS) in macrocellular networks, we propose two Cognitive Amplify-and-forward Relay (CAR) schemes in this paper. In our proposed CAR schemes, the MS and relay utilize both the licensed spectrum band (LSB) provided to the macrocellular network and the opportunistic spectrum band (OSB) discovered by the base stations. Simulation results show that compared with the conventional transmission schemes without cognitive relay, our proposals can effectively improve the outage performance of MS in macrocellular networks by exploiting both the space diversity and spectrum diversity.

SUMMARY In this paper, we propose a cross‐layer based power allocation scheme with statistical delay QoS guarantees for the cognitive (secondary) amplify‐and‐forward relay link, which coexists with one primary link by sharing particular portion of the spectrum. Specifically, our derived power allocation scheme aims at maximizing the effective capacity of the cognitive relay link, which can be seen as the maximum arrival rate supported by the system under given QoS constraints. In our work, not only the average total transmit power and average interference power constraints are considered, but also the impact of the interference from the primary link to the cognitive relay link is taken into consideration. Simulation results show that the effective capacity of the cognitive relay link varies with the statistical QoS constraints. In particular, the stringent QoS constraint will cause low effective capacity. Moreover, we observe that the average total transmit power and average interference power are two important parameters, which will obviously impact the performance of the cognitive relay link. In addition, we find that the transmission of the primary link will significantly affect the performance of the cognitive relay link, such that a larger transmit power of the primary link will cause the performance degradation of the cognitive relay link. Copyright © 2011 John Wiley & Sons, Ltd.

The dynamical network topology is the source of most challenges in VANETs (Vehicle Ad hoc Networks). In urban area, however, it is feasible to meet the challenge by taking advantage of the heavy traffic. This paper proposes a location aware virtual infrastructure (LAVI) based on recognition memory. Combining the memory of past cooperation with the location information, the mobile nodes can construct cooperative groups with recognized peers and in turn to provide a virtual infrastructure.

Along with variant advantages, cloud storage also poses new security challenges. Potential users are reluctant to move important and sensitive data to cloud unless security challenges have been well addressed. This paper reports our on-going efforts to address three data security issues in cloud storage: repudiation, fairness, and roll-back attacks. We proposed a novel fair multi-party non-repudiation (MPNR) protocol, which provide a fair non-repudiation storage cloud and is capable of preventing roll-back attacks.

To improve driving comfort and provide entertainment services, vehicular communication networks (VCNs) have appeared as an emerging solution, which consists of road-side units (RSUs) and on-board units (OBUs) to distribute multimedia contents. However, as most of OBUs always request the stored contents in the RSUs, how to update the contents in these RSUs when the original changes at its original servers has become an important issue to be dealt with. This paper proposes a novel method to resolve the above problem. Firstly, based on the characteristics of peers and geographical information, we decide which replica of which content in RSUs should be updated when its original changes. Secondly, by comparing the delivery cost of wired and wireless transmission, we decide whether the updated content should be delivered from a fixed peer or other mobile peers. Lastly, the detailed algorithm is presented and summarized.

Mobile Ad Hoc Networks (MANETs) allow mobile hosts to form a communication network without a prefixed infrastructure. Although it provides high flexibility, it also brings more challenges for MANETs to fight against malicious attacks. However, the property of mobility and redundancy also inspires new ideas to design defence strategy. In this paper, we propose a strategy to mitigate DDoS attacks in MANETs. Assume that a malicious attacker normally targets specific victims. The attacker will give up if the attack failed to achieve the desired goals after a certain length of attacking time. In our protection strategy, we take advantage of high redundancy and select a protection node. Once a DDoS attack has been detected, the suspicious traffic will be redirected to the protection node. The victim will function normally, and it is reasonable to expect that the attacker will stop the meaningless efforts. Through intensive simulation experiment using NS-2, we have verified the effectiveness of our approach and evaluated the cost and overhead of the system.

In this paper, we introduce a hybrid approach for overlay construction and data delivery in an application-layer multicast. We combine the strong points of a tree-based structure and a mesh-based data delivery to form ToMo, a two-layer hybrid overlay. We try to reduce the number of replicated packets at a source, and reduce an effect when slow connection peers are located near the source. The overlay is constructed in the fashion of a mesh layer over a tree layer. This structure allocates the source to multicast each piece of the packet to a specific group of child peers only. Different from other approaches, we employ only push-based data delivery in order to minimize the latency. The redundancy is avoided by defining a set of well-organized mesh connections. Furthermore, in our approach, the isolated peers affected by parent departure are not facing data loss during the rejoin process since they still receive data from their neighbors via mesh connections. Simulations through ns2 demonstrate the efficiency of this solution.

The hybrid application layer multicast (ALM) has been shown its efficiency by leveraging the conventionally main structures of application layer multicast, tree-based and mesh-based. However, how to select the proper node to construct the overlay and how to establish the connection between any two nodes are still unsolved. Therefore, this paper is to design a novel construction algorithm for the hybrid ALM to resolve the above two issues. Firstly, by carrying out the analysis of nodes' characteristics, all nodes are divided into groups and a node priority is proposed to select the super node within each group. Secondly, by using the selected super node, all nodes are hierarchically controlled and different kinds of connections are carried out in the ALM, where the connection between super node and other nodes is set to be a tree to enhance the efficient utilization of network resource while the connection between other normal nodes is decided to be a mesh to reduce the overhead. Simulation results show that the proposal outperforms other conventional methods.

Migrating from server-attached storage to distributed storage brings new vulnerabilities in creating a secure data storage and access facility. Particularly it is a challenge on top of insecure networks or unreliable storage service providers. For example, in applications such as cloud computing where data storage is transparent to the owner. It is even harder to protect the data stored in unreliable hosts. More robust security scheme is desired to prevent adversaries from obtaining sensitive information when the data is in their hands. Meanwhile, the performance gap between the execution speed of security software and the amount of data to be processed is ever widening. A common solution to close the performance gap is through hardware implementation. This paper proposes D-DOG (Data Division and Out-of-order keystream Generation), a novel encryption method to protect data in the distributed storage environments. Aside from verifying the correctness and effectiveness of the D-DOG scheme through theoretical analysis and experimental study, we also preliminarily evaluated its hardware implementation.

With the development of recent vehicular communication technologies, distributing multimedia contents in the vehicular communication networks (VCNs) has become more and more popular, to provide conveniences and entertainment services during the time of driving. However, as multimedia contents are changed and updated dynamically, how to keep the consistency between the original and these replicas in VCNs is very important. Therefore, this paper designs a novel algorithm to control the consistency for the VCNs. In our proposal, after the analyses of the status of road-side units, on-board units and local geographical information, we divide all replicas into two groups, where one is necessary for update and the other are not. Then, we compare the cost to update replicas by using wireless and wired connection, and propose a method to make selection between them. The performance of our proposal is tested by simulation experiments. And the results show that our method can reduce the delay successfully.

The performance of spatial multiplexing-based multiple-input multiple output (MIMO) systems using zero forcing (ZF) detector in frequency selective fading channels is analyzed in this paper. The approximation of a linear combination of Wishart matrices is used to derive the probability density function (p.d.f.) of output signal-to-noise ratio (SNR) expression. Analytical error rate expressions for the system are obtained with the assumption that inter-path interference is omitted. Simulations are carried out to evaluate the analytical results. We relate the diversity order of frequency selective fading channels with the multipath power delay profile.

This paper designs a tree construction algorithm by distributing the layered steaming over the ALM in order to improve both the throughput and user delay. Firstly, by carrying out theory analysis, we define an out/in degree and the corresponding constraints to manage the layered streaming and nodes to improve the throughput. Besides, a novel method, called ALM-Bi-cast, is also proposed and analyzed to reduce user delay during the data-transmission. Secondly, by using the defined degrees and the ALM-Bi-cast, we present a tree construction algorithm and test it by simulations with NS-2. Simulations show that the proposal can get better results than other conventional methods. Finally, we carry out an implementation of our proposal, by distributing the video encoded by H.263+ over the ALM nodes placed in Tokyo City and Kyusyu Prefecture. Implementation further improves the out-performance of our proposal.

With the growing use of dynamic content by mobile content distribution systems, how to manage dynamically changing files has become an important issue, since the cached replicas on different mobile sites must be updated if the originals have been changed. Therefore, this paper proposes a priority based selection method to enhance the efficient utilization of network resource and support the client mobility for mobile contents delivery network (M-CDN). On one hand, a consistency priority is calculated by analyzing the characteristics of mobile surrogates. If a given content which has been changed on its original node, only the replicas with the high consistency priority instead of all replicas are updated. On the other hand, an update priority is also proposed. If a replica has been selected for update, the latest version will be sent from the site decided by the update consistency. Simulation results show that the proposed new approach outperforms other conventional methods.

Abstract: Deployed in a hostile environment, the individual Sensor Node (SN) of a Wireless Sensor Network (WSN) could be easily compromised by an adversary due to constraints such as limited memory space and computing capability. Therefore, it is critical to detect and isolate compromised nodes in order to avoid being misled by the falsified information injected by adversaries through compromised nodes. However, it is challenging to secure the flat topology networks effectively because of the poor scalability and high communication overhead. On top of a hierarchical WSN architecture, a novel algorithm based on Weighted Trust Evaluation (WTE) to detect malicious nodes for hierarchical sensor networks is proposed in this paper. The hierarchical network can reduce the communication overhead among SNs by utilising clustered topology. The proposed algorithm models a cluster of SNs and detects malicious nodes by examining their weights that represent the reliability of SNs. Through intensive simulations, the accuracy and effectiveness of the proposed detection algorithm are verified.

Content Delivery Overlays improves end-user performance by replicating Web contents on a group of geographically distributed sites interconnected over the Internet. However, with the development whereby overlay systems can manage dynamically changing files, an important issue to be resolved is consistency management, which means the cached replicas on different sites must be updated if the originals change. In this paper, based on the analytical formulation of object freshness, Web access distribution and network topology, we derive a novel algorithm as follows: (1) For a given content which has been changed on its original server, only a limited number of its replicas instead of all replicas are updated. (2) After a replica has been selected for update, the latest version will be sent from an algorithm-decided site instead of from its original server. Simulation results verify that the proposed algorithm provides much better consistency management than conventional methods with the reduced the old hit ratio and network traffic.

With the development whereby mobile content distribution systems can manage dynamically changing files, an important issue to be resolved is consistency management, which means the cached replicas on different mobile sites must be updated if the originals change. This paper is to design an integrated consistency-control algorithm for mobile contents delivery network (M-CDN) to enhance the efficient utilization of network resource and support the client mobility. Firstly, by carrying out an analysis of mobile surrogates' characteristics, for a given content which has been changed on its original node, only a limited number of its replicas instead of all replicas are updated. Secondly, if a replica has been selected for update, the latest version will be sent from an algorithm-decided site instead of from its original server. Simulation results show that the proposal outperforms other conventional methods.

Content distribution overlays improves end-user performance by replicating Web contents on a group of geographically distributed sites interconnected over the Internet. However, with the development whereby overlay systems can manage dynamically (Ganguly et al., 2005) changing files, an important issue to be resolved is consistency management, which means the cached replicas on different sites must be updated if the originals change. In this paper, based on the analytical formulation of object freshness time, Web access distribution and network topology, we derive a novel algorithm as follows: (1) for a given content which has been changed at its original server, only a limited number of its replicas instead of all replicas are updated. (2) After a replica has been selected for update, the latest version will be sent from an algorithm-decided site instead of from its original server. Simulation results verify that the proposed algorithm provides much better consistency management than conventional methods with the reduced update overhead and network traffic

In contrast to conventional P2P systems in wired networks that consist of static peers, mobile P2P are subjected to the limitations of battery power, wireless bandwidth, and the dynamically changed network topology. Challenges arise in how to improve the source discovery and data replication. In this paper, we talk about an integrated searching and prefetching algorithm for the segmented streaming in mobile peer-to-peer (P2P) Networks. Firstly, each stream is divided into several segments and each segment is assigned a priority based on theory analyses. Then, for a given segment, the different number of queries is sent to search it and the length of the query for this segment is also dynamically decided by the segment-priority to avoid the unnecessary overhead. Next, along the path where a stream is sent from the requester node, parts of the nodes on this path are selected to pre-fetch the requested segment to reduce the user delay for the next possible request. Finally, Simulation results show that better performance than the conventional methods can be achieved