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.

Efficient and reliable data collection is essential for agricultural Internet of Things (IoT) systems, where timely sensing supports precision farming. Unmanned aerial vehicle (UAV)-assisted collection provides flexible and low cost coverage. However, in wide and remote fields, UAV-assisted collection faces two coupled challenges. First, agricultural field operations and field-state changes, such as irrigation, rainfall, harvesting, canopy occlusion, machinery movement, and fluctuations in solar exposure, can jointly and unevenly perturb traffic, communication, and energy processes, leading to bursty arrivals, degraded air-to-ground (A2G) links, and variations in harvested energy. Second, under single-link communication, the UAV can directly access only one node in each slot, making frequent global state refresh difficult and causing stale beliefs to accumulate rapidly after abrupt changes. These effects lead to biased scheduling, unnecessary maneuvering, packet loss, and excess energy consumption. To address this problem, this paper proposes a three stage framework for joint node scheduling and UAV trajectory control. It first refreshes selected node states to correct stale beliefs and then encodes refreshed and inferred states through a reliability-aware Transformer. Finally, it performs conservative hybrid-action control to coordinate discrete scheduling and continuous motion while mitigating value overestimation under partial observability and non-stationarity. Simulations under point-shift, cluster-shift, and global-shift scenarios show that the proposed method consistently reduces packet loss, improves energy efficiency, and achieves faster recovery than representative reinforcement learning (RL) and heuristic baselines.

With the deepening of the Internet of Things (IOT) and industrial digital transformation, the core of positioning services is shifting from “serving people” to “connecting everything”, which poses a comprehensive challenge to indoor positioning technology in terms of high accuracy, low latency, low power consumption, and low cost. Traditional radio frequency positioning technology has shown many limitations in this context, while visible light positioning (VLP) technology has become a highly promising supplementary solution due to its unique advantages, such as the absence of authorized spectrum, no electromagnetic interference, high security, and the ability to balance lighting. However, traditional VLP methods heavily rely on accurate channel models and are difficult to cope with complex non-line-of-sight environments, resulting in increasingly prominent performance bottlenecks. In recent years, the rapid development of machine learning technology has provided a new paradigm for solving the above-mentioned problems. From the perspective of the IOT and edge computing, this paper systematically summarizes the latest progress of how machine learning can improve the performance of indoor VLP.We first elaborate on the architecture and basic principles of edge-oriented VLP systems. Then, a comprehensive review and comparison are performed on VLP methods based on traditional machine learning and deep learning. Moving on, we provide the analysis on how they improve system accuracy and robustness through data-driven approaches. Moreover, this article delves into the application and value of different learning paradigms, such as centralized learning, online learning, and federated learning in VLP systems. In addition, we have developed a multi-dimensional evaluation system that includes core positioning accuracy and edge performance indicators to comprehensively measure the feasibility of the system in practical deployment. Finally, we present the identified challenges and future research directions in this under-explored field from aspects of standardized scenario modeling, high generalization base models, dynamic environment robustness, heterogeneous terminal adaptation, and edge lightweight models.

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.

Vehicular twin networks (VTN) create virtual agents of vehicular entities through digital twin (DT) technology, replacing physical counterparts in connecting and exchanging traffic information in cyberspace, overcoming physical range constraints and extending information sources for enhanced vehicular decision support. However, the inherent openness of VTN renders communication between DTs, vulnerable to security threats, such as tampering and impersonation, especially in scenarios where DTs are distributed across multiple cloud domains. These issues result in erroneous decisions to threaten vehicular safety because DTs may receive compromised information. To address these challenges, this article proposes a secure and revocable access authentication scheme in the cross-domain VTN. In the scheme, DTs should be authorized first to obtain identity-bound symmetric functions before joining the VTN, and then perform secure access authentication and key agreement with others based on chameleon hash functions for both intradomain and cross-domain communication. Moreover, a dynamic revocation mechanism is introduced to remove malicious DTs from VTN. Formal verification using the Tamarin tool demonstrates that the proposed scheme achieves diverse security properties. Performance evaluation further shows that the proposed scheme outperforms most related schemes in terms of computational and communication overhead.

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.

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.

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.

With the rapid development of cloud computing, more and more users tend to store their data remotely to the cloud. Taking into account data security and resource utilization comprehensively, in addition to providing users with basic remote data integrity verification, cloud servers also need to conduct redundancy checks. However, current deduplication schemes primarily focus on static file-level data and auditing processes, rendering them inadequate for managing resources with dynamic attributes. In this paper, we propose a fine-grained deduplication and dynamic auditing model (FiDD) for cloud storage to address these challenges. FiDD utilizes homomorphic verifier-based data tags to seamlessly integrate deduplication and auditing processes, allowing both block-level and file-level deduplications. Additionally, FiDD employs doubly linked lists and multi-set hash functions to enhance the efficiency of data updates. The security of FiDD is validated through rigorous security proofs, while its efficiency is demonstrated through comprehensive experimental analysis. The experiments demonstrate an average improvement of at least 35% in audit efficiency and at least 50% in dynamics efficiency. Consequently, FiDD enhanes the security of data management in cloud computing while improving its overall efficiency.

This paper investigates the secrecy performance optimization of an uplink satellite communication system exposed to dual-domain eavesdropping threats. Specifically, a cooperative relaying architecture is considered, where a ground user (GU) transmits confidential information to a target satellite (TS), assisted by an amplify-and-forward (AF) relay satellite (RS). Simultaneously, a ground-based malicious node acts as an AF relay to enhance the interception capability of a satellite eavesdropper (SE). To improve secure transmission, a secrecy rate maximization problem is formulated by jointly optimizing the transmit powers of the GU and RS, subject to quality-of-service constraints at the TS. The resulting non-convex problem is solved efficiently using a successive convex approximation algorithm. Simulation results demonstrate that the proposed cooperative relaying scheme significantly enhances secrecy performance compared to traditional non-cooperative baselines. These findings highlight the potential of cooperative multi-satellite relaying as an effective approach to securing next-generation satellite communication networks against sophisticated eavesdropping threats.

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.

Knowledge graph completion (KGC) methods aim to predict missing links by learning from existing facts in a knowledge graph. Different from KGC, Temporal Knowledge Graph Completion (TKGC) further incorporates the time validity of facts (tagged timestamps) during the learning and inference to improve the completion accuracy. Many TKGC methods achieve this by projecting the static entity representations (time-invariant) of KGC embedding methods to time-dependent representations, which vary across timestamps. However, when measuring a fact, these TKGC methods only consider its subject/object entity representations corresponding to the tagged timestamp, but ignore their historical contexts that normally carry essential supportive information. With this observation, we propose a novel context aggregation (ContxE) method to include historical contexts of subject/object entities for TKGC. To achieve that, we propose a linear-rotary time embedding to obtain time-dependent entity representations that can preserve temporal relationships, and a relation-based attention to aggregate historical context for the score measurement. Comprehensive experiments on three temporal knowledge graph datasets show that the proposed ContxE achieves improved knowledge graph completion results compared to strong counterpart methods.

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.

Sparse vector coding (SVC) is a promising short-packet transmission method for ultra reliable low latency communication (URLLC) in next generation mobile communication systems. However, the storage burden of codebook and high decoding complexity limit its application in Internet of Things (loT) devices with constrained storage space and computational capabilities. To tackle this challenge, a semi-tensor SVC (ST-SVC)-based short-packet transmission scheme is proposed in this paper. The core idea behind ST-SVC is that it utilizes the semi-tensor product (STP) model in random spreading process, replacing the matrix multiplication model used in traditional SVC schemes. At the transmitter, a low-dimensional codebook is utilized to perform random spreading on a high-dimensional sparse vector carrying information bits. At the receiver, by exploiting the Kronecker structure induced by the STP model, a low-complexity parallel support identification algorithm is proposed for ST-SVC decoding. The proposed scheme breaks through the dimension matching condition required between the codebook matrix and high-dimensional sparse vector in traditional SVC schemes, allowing the loT devices to store an ultra-low-dimensional codebook, which significantly reduces storage overhead. Simulation results demonstrate that the proposed ST-SVC scheme can achieve a substantial reduction in both storage overhead and decoding latency compared to state-of-the-art SVC schemes, with only a slight performance loss in block error rate.

The widespread adoption of the Internet of Medical Things (IoMT) has spurred the development of blockchain-based models for medical data sharing, designed to ensure both data trustworthiness and privacy protection. In these models, data owners upload medical data to the blockchain via anonymized transactions, enabling researchers to access and analyze the on-chain data. Existing approaches often utilize coin-mixing techniques to obscure the relationships between transacting parties on the blockchain, thereby enhancing privacy. However, such solutions face challenges related to inefficiency and limited data usability, making them unsuitable for deployment in blockchain-based medical scenarios. To address the issue, we propose Medishuffle, an auditable coin-mixing protocol tailored for IoMT applications. Medishuffle introduces a polynomial-based group signature algorithm to facilitate the shuffling process, leveraging identity anonymity instead of traditional data encryption to reduce computational overhead. Additionally, the traceability feature of group signatures enables auditors to reconstruct the sequence of obfuscated transactions when necessary. Through theoretical analysis, we demonstrate that Medishuffle ensures both the unforgeability and traceability of signatures under the random oracle model in the query process. Using experiments, we show that Medishuffle outperforms existing solutions by offering enhanced functionality without a significant increase in computational overhead, making it a practical and efficient solution for IoMT-based medical data sharing.

With the widespread application of the Internet of Things (IoT), data processing has gradually migrated to edge devices that are closer to the data source. This shift has significantly improved the ability of real-time data analysis while effectively reducing bandwidth requirements and latency. Furthermore, Federated Learning (FL) has been introduced as a decentralized training method to achieve collaborative training of multiple devices while ensuring local data privacy. However, malicious clients in FL may theft trained models for unauthorized use, which causes model misuse or copyright challenges. To address these issues, this paper proposes FedMLC (Malicious client detection, Leakage tracing, and Copyright verification), a server-side white-box watermarking scheme. FedMLC utilizes the embedded watermark at different stages to achieve both traceability and copyright verification, simplifying the watermarking process. Additionally, the watermarking can also detect malicious clients in FL. Specifically, FedMLC uses the regularization term to guide the parameter signs of the normalization layer to be consistent with the watermark sign, thereby achieving watermark embedding. Experimental results show that our FL model watermarking scheme excels in malicious client detection, leakage tracing, and copyright verification, with minimal impact on model performance, able to resist various attacks such as fine-tuning, pruning, and quantization.

In this paper, we consider an energy sustainable wireless Internet of Things (IoTs) network with a reconfigurable intelligent surface (RIS). Specifically, the Hybrid Access Point (HAP) performs beamforming to transfer energy to a set of devices, and the devices then use the harvested energy for Non-orthogonal multiple access (NOMA)-based data transmissions, where a RIS is employed to enhance both the energy harvesting and data transmissions. An optimization problem is formulated to maximize the sum-rate of IoT devices by jointly optimizing the energy beamforming of the HAP, the phase shifts of RIS, the selection of devices in NOMA transmissions with power control, and the time allocation for energy harvesting. As the formulated optimization problem is a complex mixed-integer non-linear programming (MNLP) problem, we decompose it into four subproblems and apply Block Coordinate Descent (BCD) to iteratively optimize each subproblem until convergence is achieved. A novel joint optimization algorithm is proposed to select a subset of devices with transmission power control to attain the maximum sum-rate. A closed-form expression for the optimal time allocation is further derived to strike a balance between the energy harvesting and the data transmissions, considering the residual energy resulting from the previous power control. Simulations validate that the proposed solution outperforms state-of-the-art algorithms in the literature.

Despite the rapid growth of electric vehicles (EVs), charging remains a time-consuming issue that requires effective management. An important solution uses digital twin (DT) technology, which acts as a virtual agent for EVs in the digital space. DT can analyze real-time vehicle data to develop optimal charging schedules and reserve charging providers in advance through the vehicular network, leading to more efficient charging processes. However, the vehicular network exposes the automated reservation process of the DT to security attacks. Additionally, there is a risk that the actual charging process may deviate from the scheduled requirements set by the DT, resulting in wasted charging resources. To address these issues, this article proposes a secure and efficient charging reservation scheme based on DT technology. To prevent malicious attacks, we first design a secure and privacy-preserving reservation authentication protocol using the extended Chebyshev chaotic maps, taking into account the computational resources of the EV. Furthermore, we develop a reputation mechanism to evaluate and incentivize the charging behavior of EVs. Formal verification and further discussions are conducted to show diverse security functionalities of the proposed scheme can be achieved. We evaluate that the proposed scheme outperforms existing schemes in terms of computation and communication overheads, while also assessing the impact of EV charging behavior on reputation and charging level.

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.

Low Earth Orbit (LEO) satellite networks offer a promising approach for achieving global coverage and high-speed Internet access. However, the existing communication frameworks within these networks may result in low robustness and reliability. This paper introduces QTER, a novel and lightweight routing framework specifically designed for LEO networks, aimed at addressing these challenges. Our contributions are threefold. First, QTER establishes a cost-effective and reliable routing framework that accommodates a variety of applications with distinct Quality of Service (QoS) requirements. By enabling multi-path routing, the framework minimizes costs while ensuring end-to-end reliability, thus enhancing adaptability to diverse service demands. Second, we leverage the unique structural characteristics of LEO satellite networks by modeling the satellite constellation as a three-dimensional network, wherein higher-shell satellites serve as management satellite nodes (MSNs) to coordinate routing strategies among lower-shell satellites. This architecture significantly improves system efficiency and adaptability. Third, we propose a failure recovery mechanism that allows MSNs to relay packets when lower-orbit satellites are rendered unavailable due to environmental factors, thereby enhancing system robustness. Extensive simulations demonstrate that QTER exhibits resilience to node failures and dynamic network conditions, achieving reductions in average cost and delay by 59.4% and 38.9%, respectively, compared to baselines.

With the rapid development and extensive deployment of the fifth-generation wireless system (5G), it has achieved ubiquitous high-speed connectivity and improved overall communication performance. Additionally, as one of the promising technologies for integration beyond 5G, digital twin in cyberspace can interact with the core network, transmit essential information, and further enhance the wireless communication quality of the corresponding mobile device (MD). However, the utilization of millimeter-wave, terahertz band, and ultra-dense network technologies presents urgent challenges for MD in 5G and beyond, particularly in terms of frequent handover authentication with target base stations during faster mobility, which can cause connection interruption and incur malicious attacks. To address such challenges in 5G and beyond, in this paper, we propose a secure and efficient handover authentication scheme by utilizing digital twin. Acting as an intelligent intermediate, the authorized digital twin can handle computations and assist the corresponding MD in performing secure mutual authentication and key negotiation in advance before attaching the target base stations in both intra-domain and inter-domain scenarios. In addition, we provide the formal verification based on BAN logic, RoR model, and ProVerif, and informal analysis to demonstrate that the proposed scheme can offer diverse security functionality. Performance evaluation shows that the proposed scheme outperforms most related schemes in terms of signaling, computation, and communication overheads.

Edge collaboration is expected to effectively relieve the load of base stations and enhance the driving experience of autonomous vehicles (AVs). However, in existing edge collaboration schemes, the frequent information exchange between AVs will consume a significant amount of resources. In addition, the existing schemes ignore the types of services, where services with different types may be combined into a composite service which affects the utility of AVs. To this end, we consider various types of services in autonomous vehicular networks (AVNs) and propose a digital twin (DT)-enabled edge collaboration scheme for composite services. Specifically, we first divide the DTs of service requesters (DT-SRs) into service request groups (SRGs) based on the same basic service requests and propose an architecture to facilitate the edge collaboration between the DTs of the leaders of SRGs (DT-L-SRGs) and the DTs of the service providers (DT-SPs). In this architecture, different service composition forms will result in different resource purchase strategies for DT-L-SRGs and different resource pricing strategies for DT-SPs. Therefore, we model the process of service composition as a coalition game to determine the optimal service composition form for each basic service. In the process of the coalition game, in order to obtain the optimal resource purchase strategy for each DT-L-SRG and the optimal resource pricing strategy for each DT-SP under different coalition structures, the interaction between the DT-L-SRGs and the DT-SPs is formulated as a Stackelberg game. By obtaining the game equilibrium, the optimal strategies of each DT-L-SRG and each DT-SP can be determined to measure the performance of the given coalition structure until a stable and optimal composite service structure is finally formed through multiple rounds of iterations. Compared with traditional schemes, the simulation results demonstrate that our scheme can bring the highest utilities to both the SRs and the SPs.

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.

Federated Learning (FL) enables efficient and privacy-preserving Edge Intelligence (EI) in Mobile Edge Computing (MEC). However, implementing FL-enabled EI services faces critical challenges, including data and device heterogeneity, limited network resources, uneven distribution of network infrastructure, etc., which may intensify with increasing system scale. These challenges are particularly acute in multi-provider environments where edge servers are suboptimally allocated across federations, leading to degraded convergence and increased training costs. In this paper, we present a novel Multiple Hierarchical Federated Learning (MHFL) architecture for large-scale FL and design an Edge Server Reallocation scheme (ESR-MHFL) to enhance training efficiency by optimally redistributing edge servers among federations based on their contribution to model convergence. We first develop a closed-form analysis model for MHFL to quantify training time, computation, and communication costs. To improve training efficiency, we analyze the impacts of edge server allocation on convergence and formulate server reallocation as a multi-item auction problem with theoretical guarantees. We then propose ESR-MHFL, which leverages Coalition Structure Generation (CSG) and greedy matching methods to simplify the reallocation problem and enhance efficiency. Extensive numerical simulations demonstrate that ESR-MHFL not only improves model accuracy while reducing training cost but also exhibits strong compatibility with existing client selection methods, achieving improved training efficiency. The total economic expenditure combining all components

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.

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.

The rapid development of Low Earth Orbit (LEO) satellite networks has provided ubiquitous Internet access to users around the world, especially in areas where there are no terrestrial networks. However, a dish can only communicate with one of the available satellites when uploading data in the current framework, resulting in low communication efficiency. As the number of satellites continues to increase, the current framework cannot make full use of the user-satellite link resources. In this paper, we first conduct a measurement of Starlink’s network performance and report some unique features. Then, we propose an adaptive multi-link data allocation framework for LEO satellite networks where a dish can communicate with multiple satellites at the same time to improve data transmission efficiency. With this framework, data can be split into chunks and uploaded simultaneously over multiple links. Our goal is to determine the data allocation strategies to jointly optimize the transmission latency and data processing costs. To this end, we propose a deep reinforcement learning-based algorithm integrated with the traffic prediction module to determine the optimal data allocation strategies in a dynamic network environment. Through extensive simulations, we demonstrate the effectiveness of our approach compared with baselines.

We propose a digital twin-assisted adaptive preloading scheme to reduce bandwidth waste as well as enhance user quality of experience (QoE) for short video streaming. Though preloading video content can reduce rebuffering and improve user QoE, non-sequential playback of short videos induced by user swipe can result in substantial bandwidth wastage in mobile networks. To tackle this problem, we first model the short video streaming system and carry out preloading threshold analysis. We then construct a digital twin-assisted adaptive preloading framework for short video streaming. By collecting and analyzing the user's historical throughput and tracking swipe timing information, a throughput prediction model and a probabilistic model can be constructed to accurately predict future throughput and user swipe behavior, respectively. Utilizing the predicted information and real-time running status data from a short video application, we design a preloading strategy to enhance bandwidth efficiency while achieving high user QoE. Simulation results demonstrate the effectiveness of our proposed scheme compared with the state-of-the-art schemes.

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.

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.

As with anything connected to the internet, Internet of Things (IoT) devices are also subject to severe cybersecurity threats because an adversary could exploit vulnerabilities in their internal software to perform malicious attacks. Despite the promising results of Deep Learning (DL)-based approaches, the lack of well-labeled IoT vulnerability samples available for training and explainability pose a critical challenge to deploy them in practice. In this paper, we propose, a novel DL-based approach for Effective and eXplainable IoT VULnerability detection. Specifically, inspired by recent advances of self-supervised learning in label-expensive tasks, we propose a new combinatorial contrastive loss to combine the strengths of large-scale unlabeled code corpus and limited IoT vulnerability samples. Then, given a binary detection result, provides a set of faithful and stable code statements positively contributing to the model’s predictions as understandable explanations. Experimental results indicate that outperforms state-of-the-art baselines by 33.44%-72.91% and 19.52%-98.78% with respect to the accuracy and F1 score metrics, respectively. For vulnerability explanation, improves over the best-performing baseline explainer PGExplainer by 22.97% in MSP, 49.55% in MSR, and 48.40% in MIoU, demonstrating that the explanations provided by can correctly point out the vulnerable statements relevant to the detected vulnerabilities.

In this paper, we aim to minimize the age of information (AoI) for a random access internet of things (IoT) network, where AoI is a metric to measure the freshness of information delivery. Since non-orthogonal multiple access (NOMA) can improve network throughput and connectivity, we exploit an AoI-oriented NOMA-based random access scheme, wherein devices simultaneously access wireless channel over multiple power levels with different access probabilities when their AoIs is not smaller than a threshold. We firstly study the comprehensive steady-state analysis of an AoI-independent NOMA-based random access scheme, which is a special case when the threshold is one. The AoI evolution is formulated as a markov chain based on the analyzed transmission success probability, and the probabilities of AoI states and the achieved AoI under generate-at-will are derived. Then, an AoI minimization algorithm is proposed to optimize the power access probabilities. Concerning stochastic-arrival, the steady-state probabilities of devices’ active state, successful transmission, and number of active devices, are derived to analyze the expected AoI. Finally, the steady-state probabilities of AoI states and the achieved AoI of AoI-dependent NOMA-based scheme are obtained. Simulation results validate our analysis, and demonstrate the significant performance improvement in terms of AoI. In specific, the proposed scheme can achieve AoI reduction by 65%, compared with random access without NOMA.

The consortium blockchain is being utilized in supply chains on the Internet of Things (IoT) for tracking and protecting supply chain data, such as manufacture, storage, and shipment. However, the supply chain data in a consortium blockchain is publicly accessible for all parties, which attracts widespread concerns about supply chain data privacy. Several existing attribute-based encryption (ABE)-based blockchain systems targeting to address the supply chain data privacy problem either bring about additional security problems or lack the feasibility analysis on IoTs. To address the aforementioned issues, in this article, a novel multiauthority ABE (MA-ABE)-based blockchain system is proposed to protect the data privacy for the supply chain on IoTs. Specifically, a four-way tradeoff optimization framework is designed so that the system decentralization, scalability, and storage consumption are not significantly affected by the improved privacy. The optimal attribute setting policies for different scale blockchain networks are dynamically generated by the nondominated sorting genetic algorithm II (NSGA-II). Extensive experiment results show that the proposed scheme remarkably improves data privacy protection for the supply chain without downgrading the other three key factors.

The escalating growth of the Internet of Things (IoT) has intensified the demand for dependable and efficient communication networks to accommodate the massive data volumes produced by interconnected devices. Satellite networks have emerged as a promising alternative, particularly in remote and underserved regions where terrestrial communication infrastructures are inadequate. Nevertheless, guaranteeing secure uplink communications in satellite-based IoT networks is a daunting task due to similar satellite channels and limited resources at IoT nodes. In this article, we explore the potential of unmanned aerial vehicle (UAV) to improve the secrecy performance of uplink transmissions in satellite-supported IoT networks. Specifically, we first introduce a framework for UAV-aided secure uplink communications, presuming a secure UAV-to-satellite connection. To mitigate the risks of ground eavesdroppers intercepting uplink transmissions, we develop a max–min secrecy rate optimization problem with uplink power constraints. To address this nonconvex problem, a streamlined two-stage optimization approach is proposed. In the inner stage, we combine uplink power allocation and UAV beamforming and propose a successive convex approximation (SCA)-based joint optimization algorithm to address them. In the outer stage, we propose a synergized bisection and coordinate descent algorithm to optimize UAV positioning. Convergence is attained by alternating iterations between these two stages. Particularly, the secrecy fairness among IoT users is reached by solving the max–min problem. Additionally, we offer a complexity analysis of the proposed algorithm and validate the efficacy of the presented approach through comprehensive simulation results.

The rapid development of fog-assisted vehicular crowdsensing systems (FVCSs) enables real-time vehicular data sharing, but redundant and similar data in report results in unnecessary costs. However, previous studies only focus on duplicate reports and neglect deduplication of similar data. Besides, transmitting crowdsensing data in Internet of Vehicles (IoV) exposes vulnerabilities to offline brute-force and fake report attacks. In this article, we present P2SimiDedup, a scheme for secure deduplication of similar crowdsensing reports. Specifically, we develop cryptographic primitives and introduce an improved generalized deduplication technique (GreedyGD) to achieve secure deduplication over similar crowdsensing data. Then, we construct a two-level deduplication framework that can perform secure and efficient similar-based deduplication at fog nodes and cloud server. Besides, P2SimiDedup can ensure that only data requesters can decrypt and recover crowdsensing data. The security analysis and evaluation results demonstrate that P2SimiDedup can achieve privacy-preserving deduplication for similar crowdsensing reports with moderate computational, communication, and storage costs.

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 vehicular networks (VNets), vehicular federated learning (VFL) is a new learning paradigm that can protect data privacy of vehicle nodes (VNs) while training models. In VFL, the importance of data (IoD) is a key factor that affects model training accuracy. However, due to the heterogeneity of data in the VFL, it is a challenge to evaluate the quality of data owned by different VNs and design an efficient federated learning scheme to enable the VNs to complete learning tasks collaboratively. In this paper, we consider the IoD and propose a redundancy-aware collaborative federated learning (RCFL) scheme for the VFL. In the scheme, by jointly considering the data quality and the cooperation among VNs, we first design a redundancy-aware federated learning architecture to efficiently provide learning services in VNets. Then, we develop a data importance model that integrates the non-independent and identically distributed (non-IID) degree and the redundancy of data (RoD) to evaluate the data quality and formulate the cooperation of the VNs as a coalition game to improve their data importance, where the equilibrium of the coalition game is obtained by designing a coalition formation algorithm. After that, by considering the diversified characteristics of data and the available resources of different VNs in each coalition, a coalition-based federated learning algorithm is designed to enable the distributed coalitions to complete the learning task cooperatively with the target of improving the learning accuracy. The simulation results show that the proposed scheme outperforms the benchmark schemes in terms of the IoD obtained by the VNs and the training accuracy.

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.

Knowledge graph embedding (KGE) that maps entities and relations into vector representations is essential for downstream applications. Conventional KGE methods require high-dimensional representations to learn the complex structure of knowledge graph, but lead to oversized model parameters. Recent advances reduce parameters by low-dimensional entity representations, while developing techniques (e.g., knowledge distillation or reinvented representation forms) to compensate for reduced dimension. However, such operations introduce complicated computations and model designs that may not benefit large knowledge graphs. To seek a simple strategy to improve the parameter efficiency of conventional KGE models, we take inspiration from that deeper neural networks require exponentially fewer parameters to achieve expressiveness comparable to wider networks for compositional structures. We view all entity representations as a single-layer embedding network, and conventional KGE methods that adopt high-dimensional entity representations equal widening the embedding network to gain expressiveness. To achieve parameter efficiency, we instead propose a deeper embedding network for entity representations, i.e., a narrow entity embedding layer plus a multi-layer dimension lifting network (LiftNet). Experiments on three public datasets show that by integrating LiftNet, four conventional KGE methods with 16-dimensional representations achieve comparable link prediction accuracy as original models that adopt 512-dimensional representations, saving 68.4% to 96.9% parameters.

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.

This paper considers optimal traffic signal control in smart cities, which has been taken as a complex networked system control problem. Given the interacting dynamics among traffic lights and road networks, attaining controller adaptivity and scalability stands out as a primary challenge. Capturing the spatial-temporal correlation among traffic lights under the framework of Multi-Agent Reinforcement Learning (MARL) is a promising solution. Nevertheless, existing MARL algorithms ignore effective information aggregation which is fundamental for improving the learning capacity of decentralized agents. In this paper, we design a new decentralized control architecture with improved environmental observability to capture the spatial-temporal correlation. Specifically, we first develop atopology-aware information aggregationstrategy to extract correlation-related information from unstructured data gathered in the road network. Particularly, we transfer the road network topology into a graph shift operator by forming a diffusion process on the topology, which subsequently facilitates the construction of graph signals. A diffusion convolution module is developed, forming a new MARL algorithm, which endows agents with the capabilities of graph learning. Extensive experiments based on both synthetic and real-world datasets verify that our proposal outperforms existing decentralized algorithms.

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.

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.

In Space-air-ground integrated networks (SAGIN), the inherent openness and extensive broadcast coverage expose these networks to significant eavesdropping threats. Considering the inherent co-channel interference due to spectrum sharing among multi-tier access networks in SAGIN, it can be leveraged to assist the physical layer security among heterogeneous transmissions. However, it is challenging to conduct a secrecy-oriented access strategy due to both heterogeneous resources and different eavesdropping models. In this paper, we explore secure access selection for a scenario involving multi-mode users capable of accessing satellites, unmanned aerial vehicles, or base stations in the presence of eavesdroppers. Particularly, we propose a Q-network approximation based deep learning approach for selecting the optimal access strategy for maximizing the sum secrecy rate. Meanwhile, the power optimization is also carried out by an unsupervised learning approach to improve the secrecy performance. Remarkably, two neural networks are trained by unsupervised learning and Q-network approximation which are both label-free methods without knowing the optimal solution as labels. Numerical results verify the efficiency of our proposed power optimization approach and access strategy, leading to enhanced secure transmission performance.

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.

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.

It is pivotal and challenging for crowdsensing systems to guarantee the reliable uploading of sensory data from source devices (workers) to a centralized platform, in order to process sensing tasks accurately and fast. On one hand, with limited communication resources, uploading a massive amount of sensory data is not cost-effective. On the other hand, the disruption of uploading is inevitable because of stochastic network environments and worker dropout, resulting in extra wasting of resources. To address that, we focus on a collaborative uploading scenario and propose to reduce the uploading latency of sensory data by adaptive data allocation while retaining data integrity at the destination. A key technical challenge is to identify proper collaborative paths such that corresponding data allocation and uploading are reliable enough. As such, we formulate a joint optimization problem with the minimization goal of uploading latency by considering both path selection and data allocation. To mine helpful information from unstructured topology-aware data, we propose a new diffusion graph convolution module by forming information aggregation based on the diffusion process that characterizes the stochastic correlation of devices. After transforming the original problem into a primal-dual problem, an algorithm is then developed by adapting Advantage Actor-Critic (A2C) framework embedded with the diffusion graph convolution module. With extensive experiments, it is validated that the newly developed algorithm improves collaborative uploading by reducing uploading latency and also stabilizing the queue state of intermediate devices, compared to existing heuristic and learning-based methods.

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.

The paper explores the issue of multiple-user fairness of intelligent reflecting surface (IRS)-assisted physical layer security (PLS) in the digital twin (DT)-enabler power grid. Previous research works have focused on achieving secrecy rate fairness through beamforming or phase shift optimization. However, in the DT-enabler power grid, the secrecy rate is not available as the instantaneous channel state information (CSI) of the passive eavesdropper is unknown. To address these challenges, we apply an expression for secrecy outage probability, measured based on the statistical CSI of the eavesdropper for the scenario where multiple DT users are present. Using zero-forcing (ZF) precoding at the transmitter, we formulate the problem of achieving fairness in secrecy outage probability, and then solve it by optimizing the phase shift matrices. Simulation results demonstrate that the proposed methods can achieve higher fairness among users in comparison to existing IRS-assisted PLS schemes.

Deep learning has been successfully adopted in mobile edge computing (MEC) to optimize task offloading and resource allocation. However, the dynamics of edge networks raise two challenges in neural network (NN)-based optimization methods: low scalability and high training costs. Although conventional node-output graph neural networks (GNN) can extract features of edge nodes when the network scales, they fail to handle a new scalability issue whereas the dimension of the decision space may change as the network scales. To address the issue, in this paper, a novel link-output GNN (LOGNN)-based resource management approach is proposed to flexibly optimize the resource allocation in MEC for an arbitrary number of edge nodes with extremely low algorithm inference delay. Moreover, a label-free unsupervised method is applied to train the LOGNN efficiently, where the gradient of edge tasks processing delay with respect to the LOGNN parameters is derived explicitly. In addition, a theoretical analysis of the scalability of the node-output GNN and link-output GNN is performed. Simulation results show that the proposed LOGNN can efficiently optimize the MEC resource allocation problem in a scalable way, with an arbitrary number of servers and users. In addition, the proposed unsupervised training method has better convergence performance and speed than supervised learning and reinforcement learning-based training methods. The code is available at https://github.com/UNIC-Lab/LOGNN.

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.

Digital Mobile Radio (DMR) is widely used in mission-critical communication due to its cost-effectiveness. However DMR only provide voice service for users in a small range. To address these limitations, a protocol conversion gateway named DoIP was designed and implemented to allow DMR devices to access a variety of communication services over long distances using the internet. In our proposed hierarchical model, DoIP works in an add-on mode. To meet the requirements of real-time, reliable, and multimedia applications, we designed the DMR frame structure, SPI packet structure, and Internet Protocol (IP) packet structure for inter-layer transmission. We also proposed the mapping rules between different protocols and achieved the conversion of DMR frames to IP packets. Finally, we implemented the DoIP on a commodity DMR repeater and evaluated its performance. The comprehensive evaluation revealed that DoIP successfully realized protocol conversion between DMR and TCP/IP, with a conversion delay of 9.10 ms, a packet loss rate of 0.3%, and an average jitter of 1.90 ms.

Mobile Edge Computing (MEC), as a promising key technology, provides tremendous support for latency-sensitive applications in Internet of Vehicles (IoV). In this paper, we focus on the MEC-assisted computation offloading problem for mixed traffic scenarios that autonomous and human-driven connected vehicles coexist. With the objective of minimizing system average latency, a priority-based serial and parallel joint offloading scheme is designed and formulate the optimization problem as a Markov decision process (MDP). Then, we propose an adaptive offloading strategy based on deep reinforcement learning. Simulation results compared to contrast algorithm and baseline schemes demonstrate the superiority of the proposed priority-based offloading scheme, effectively reducing the system average latency and ensuring the latency requirements of latency-sensitive tasks.

Passive localization is commonly achieved through the direction finding and positioning technique, which uses a airborne or ground multi-station angle measuring system to intersect pointing lines for fast and omnidirectional positioning. However, as the number of targets increases, so does the occurrence of false points. This poses a challenge to the positioning performance of system, requiring the prompt elimination of false points. To address the issue, we propose a high detection rate passive localization method based on density peak clustering (DPC). In this method, a suitable non-ideal location model is established, and improved density peak clustering is utilized to achieve data association and target localization. Simulation results confirm the proposed positioning method’s superior performance and adaptation to the non-ideal conditions of multi-target localization.

The combination of digital twins (DT) and heterogeneous vehicular networks (HetVNets) can significantly enhance the resource integration capability and performance of the network. In DT-HetVNets, vehicles need to selectively synchronize the data to be updated or cached to their DTs deployed in the cloud for data interaction and decision-making. However, considering that vehicles have diversified data synchronization requirements and network infrastructures have differentiated access capabilities, how to formulate optimal network access strategies and resource pricing strategies for vehicles and network infrastructures becomes a key challenge in the data synchronization process. To this end, we propose a utility-based on-demand data synchronization scheme in DT-HetVNets. In this scheme, we first establish the DT model and communication model in DT-HetVNets. Then, we design the utility functions of the DTs of vehicles and infrastructures by comprehensively considering their requirements. According to the utility functions, we model the decision-making process between the DTs of vehicles and the DTs of infrastructures as a Stackelberg game, where an iterative algorithm is proposed to obtain the Stackelberg equilibrium. The simulation results show that our scheme can bring them the highest utilities compared with the traditional schemes.

The virtual world created by the development of the Internet, computers, artificial intelligence (AI), and hardware technologies have brought various degrees of digital transformation to people’s lives. With multiple demands for virtual reality increasing, the metaverse, a new type of social ecology that can connect the physical and virtual worlds, is booming. However, with the rapid growth of data volume and value, the continuous evolution of the metaverse faces the demands and challenges of privacy, security, high synchronization, and low latency. Fortunately, the ever-evolving blockchain and intelligent networking technologies can be used to satisfy the trusted construction, continuous data interaction, and computing demands of the metaverse. Therefore, it is necessary to conduct an in-depth review of the role and gains of blockchain, intelligent networking, and the combination of both in providing the immersive experiences of the metaverse. In this survey, we first discuss the development trend, characteristics, and architecture of the metaverse. Then, the existing work on blockchain, networking, and the combination of the two technologies are reviewed, including overviews, applications, and challenges. Next, applications of the metaverse are summarized, emphasizing the importance of the metaverse and the fields of development. Finally, we discuss some open issues, challenges, and future research directions.

The heterogeneous vehicular networks (HetVNets) can accelerate the deployment of Internet of Vehicles (IoV) and enrich the content distribution methods. However, the diverse requirements of vehicular users (VUs), the limited cache resources of roadside units (RUs), and the frequent interactions between VUs and RUs pose great challenges to efficiently distribute contents. To address these challenges, we propose an on-demand content delivery scheme in digital twin-enabled HetVNets (DT-HetVNets). Specifically, we first design an on-demand content delivery architecture in DT-HetVNets which uses DT communication mode to simplify the frequent interactions between VUs and RUs. With this architecture, by jointly considering the popularity of each content and the relevance between different contents, the personal content requirement of each DT of VU (DT-VU) can be perceived and the VUs within the coverage of the same RU can collaboratively request contents in groups. Then, we formulate the interaction between each group and the DT of the RU (DT-RU) as a double auction game to determine the transaction price of the perceived content, where the request information of the contents which are accepted by the groups can be shared between different DT-RUs based on the path of each group, enabling collaborative content recommendation between the RUs. After that, by jointly considering the contents recommended by different DT-RUs and the content popularity, the content caching model of each DT-RU is formulated as a knapsack problem, where a collaborative content caching algorithm is designed to obtain the optimal caching strategy with the target of making full use of the limited cache resources. Compared with the conventional schemes, the simulation results show that our scheme can not only bring the highest utility to the RUs but also lead to the highest hit ratio and the lowest delay.

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.

The autonomous vehicles (AVs), as intelligent mobile robots, can undertake tasks to facilitate various computation-intensive services in intelligent transportation system (ITS). Due to hardware device failures or environmental identification errors, the AVs controlled by intelligent algorithms may cause accidents during driving. However, the existing studies in the post-accident stage lack the analysis of the impact degree of the accidents and the computing tasks undertaken by the AVs to determine the optimal maintenance strategy. In this article, we consider the accidents in a continuous period of time and design a digital twin (DT)-enabled post-accident maintenance scheme. Specifically, by considering the computing tasks undertaken by the AVs and the impact degree of the accidents, we first design a DT-enabled post-accident maintenance architecture. With the designed architecture, an optimal maintenance method under an incomplete information scenario is then proposed to help each accident AV decide its optimal maintenance strategy. Besides, based on the maintenance strategies of the AVs and the capacities of the maintenance service providers (MSPs), the two-way selection problem between the AVs and the MSPs in the continuous period of time is modeled as a dynamic matching game to obtain the optimal AV-MSP pairs. Simulation results demonstrate that the proposed scheme outperforms the benchmark schemes in terms of the maintenance rate of the accident AVs, the average utility of the MSPs, and the average social welfare.

Unmanned aerial vehicles (UAVs) are widely used for surveillance and monitoring to complete target search tasks. However, the short battery life and moderate computational capability hinder UAVs to process computation-intensive tasks. The emerging edge computing technologies can alleviate this problem by offloading tasks to the ground edge servers. How to evaluate the search process so as to make optimal offloading decisions and make optimal flying trajectories represent fundamental research challenges. In this paper, we propose to utilize the concept of uncertainty to evaluate the search process, which reflects the reliability of the target search results. Thereafter, we propose a deep reinforcement learning (DRL) technique to jointly make optimal computation offloading decisions and flying orientation choices for multi-UAV cooperative target search. Specifically, we first formulate an uncertainty minimization problem based on the established system model. By introducing a reward function, we prove that the uncertainty minimization problem is equivalent to a reward maximization problem, which is further analyzed by a Markov decision process (MDP). To obtain the optimal task offloading decisions and flying orientation choices, a deep Q-network (DQN) based DRL architecture with a separated Q-network is then proposed. Finally, extensive simulations validate the effectiveness of the proposed techniques, and comprehensive discussions on how different parameters affect the search performance are given.

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.

In this paper, we investigate the secure transmission of multi-resource heterogeneous radio access networks (RANs) in space-air-ground integrated network (SAGIN) from the perspective of physical layer security. Considering the network heterogeneity, resource constrain, and channel similarity, it is challenging to implement the physical layer security in SAGIN. Particularly, digital twin (DT) is considered in the cyberspace of SAGIN to reflect the physical network entities (i.e., satellite, unmanned aerial vehicle (UAV), and terrestrial base station), which is assumed to comprehensively control and manage the heterogeneous RANs’ resources. To ensure secure transmissions of multi-tier heterogeneous downlink communications in SAGIN, a multi-point symbiotic security scheme is proposed through DT-assisted multi-dimensional domain synergy precoding, where the co-channel interference due to spectrum sharing among these heterogeneous RANs is recast to unevenly corrupt the main and wiretap channels of each legitimate user. Specifically, to realize the multi-point symbiotic security, a max-min problem is formulated to maximize the minimum secrecy rate of three heterogeneous downlinks. Since this problem is non-convex and challenging, a list of mathematical reformulations is derived and the successive convex approximation (SCA) based multi-dimensional domain synergy precoding algorithm is proposed to solve it. Moreover, the computational complexity of our proposed approach is analyzed and meaningful discussions are made. In addition, extensive simulations are carried out to evaluate the secrecy rate performance and verify the efficiency of our proposed approach.

Currently, the complex traffic environment challenges the fast and accurate response of a connected autonomous vehicle (CAV). More importantly, it is difficult for different CAVs to collaborate and share knowledge. To remedy that, this paper proposes a selective federated reinforcement learning (SFRL) strategy to achieve online knowledge aggregation strategy to improve the accuracy and environmental adaptability of the autonomous driving model. First, we propose a federated reinforcement learning framework that allows participants to use the knowledge of other CAVs to make corresponding actions, thereby realizing online knowledge transfer and aggregation. Second, we use reinforcement learning to train local driving models of CAVs to cope with collision avoidance tasks. Third, considering the efficiency of federated learning (FL) and the additional communication overhead it brings, we propose a CAVs selection strategy before uploading local models. When selecting CAVs, we consider the reputation of CAVs, the quality of local models, and time overhead, so as to select as many high-quality users as possible while considering resources and time constraints. With above strategic processes, our framework can aggregate and reuse the knowledge learned by CAVs traveling in different environments to assist in driving decisions. Extensive simulation results validate that our proposal can improve model accuracy and learning efficiency while reducing communication overhead.

Federated learning (FL), as a distributed machine learning technology, allows large-scale nodes to utilize local datasets for model training and sharing without revealing privacy, which has significant efficiency and advantages in artificial intelligence (AI)-based knowledge sharing of connected and autonomous vehicles (CAVs). However, for FL, there are challenges to ensure the security of knowledge, deal with the lazy behavior of participants, and enforce effective incentives. To bridge the gaps, in this paper, we first propose a hierarchical blockchain-supported FL architecture that utilizes the immutable and transparent properties of blockchain to enable secure storage and sharing of knowledge and transaction information with scalability. Then, considering the cost and laziness of the participants in the FL process, we propose an incentive mechanism combined with the supervision game to attract high-quality participants based on a comprehensive evaluation of model quality and participants’ reputation. Extensive simulation results validate that our proposal can improve learning accuracy and efficiency while ensuring security.

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.

A fundamental issue of the vehicular digital twin (DT) is efficiently synchronizing the data between the DT and the vehicular user (VUE). In this paper, we consider the heterogeneous vehicular networks (HetVNets) in which a VUE can connect to the network through different networks. The HetVNets can improve the efficiency of communication by providing seamless connections. However, the uneven distribution of VUEs and the dynamics of HetVNets make the environment more complex. Therefore, we propose the network selection algorithm for data synchronization between VUEs and DTs in the HetVNets, where the behaviour between the VUEs is considered as a competition for wireless resources. A learning-based prediction model residing in the DT is developed where the DT can predict the waiting time of each relay and transmit the predicted results to the VUE for decision-making. We model the network selection problem as a potential game considering both the transmission time and the waiting time obtained from the prediction model and prove the existence of Nash equilibrium (NE). We analyze the performance of the proposed algorithm, and simulation results show that our approach can effectively find the optimal strategy while achieving a fast convergence speed and high-level performance compared to the baselines.

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.

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.

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.

Recent efforts in reducing user involvement during device pairing have successfully introduced touch-to-access. To detect whether two devices are being held by the same person, existing touch-to-access solutions extract features from a shared information source to generate pairing keys. They focus on validating the device's authenticity by only requiring the user's simple touching of the device, however, ignore the device holder's legitimacy and pairing intent. Moreover, the pairing keys may be vulnerable to eavesdropping attacks since they are exchanged over an open wireless link (e.g., WiFi or Bluetooth). In this paper, we develop a secure device pairing mechanism that essentially uses the human body to generate and transmit user-specific pairing keys, ensuring the user's legitimacy and pairing intent, as well as improving key transmission reliability. Our work is based on the observation that the human body produces a unique response to the electrical signal flowing through it, and different bodies induce distinct responses to the signal. The built-in microphone on devices captures ambient sound as an entropy source and converts it into an electrical signal, which is subsequently processed and transmitted by the human body for device pairing. We build a prototype using off-the-shelf microphones and conduct extensive experiments with 31 participants to evaluate its security performance and usability. The results show that our system achieves a pairing success rate of 97.74% and an equal error rate of 2.28%.

We present a secure device pairing approach, called BiTouch, using the human body as a conductor to generate and transmit user-specific pairing keys for advancing touch-to-access policy. BiTouch is designed based on the observation that the human body responds uniquely to electrical signals flowing through it. Built-in microphones on devices are essentially used to capture ambient sound as entropy and convert it into an electrical signal, which is subsequently transmitted by the body for device pairing. We implement BiTouch using off-the-shelf microphones and evaluate it with 31 participants. The results demonstrate that BiTouch ensures the user's legitimacy and key transmission reliability, and achieves a pairing success rate of 97.74% and an equal error rate of 2.28%.

User authentication is critical to privacy preservation. Most of the existing works focus on single-user authentication, which may not work efficiently and practically in multi-user scenarios. To this end, we present a Multi-user Authentication system (M-Auth) that employs a single COTS mmWave radar to capture the user's unique breathing pattern. It exploits the phenomenon that radio frequency (RF) signals are affected by chest displacements due to breathing. We specifically design an auxiliary rotating gadget to dynamically adjust radar orientation, making it more effective in capturing respiration signals from multiple users. To profile individual components from the entangled RF signals, we leverage mmWave's high directivity to locate each user and separately focus on reflections from different positions. We propose a signal energy comparison method to eliminate the irrelevant body movements for preserving fine-grained respiration traits. Afterward, we develop a feature selection pipeline to elicit the most informative features and train a machine learning-based classifier to identify each user. M-Auth is practical due to its non-contact and passive nature, and it is secure as respiration is unique and difficult-to-forge. Extensive experiments involving 37 participants demonstrate that M-Auth is effective in verifying legitimate users and thwarting spoofing attacks, with an authentication accuracy of over 96 % and an attack detection rate of over 95%.

In the heterogeneous vehicular networks (HetVNets), the base stations (BSs) can exploit the massive amounts of valuable data collected by vehicles to complete federated learning tasks. However, most of the existing studies consider the scenario of one task requester (TR) and ignore the fact that multiple TRs may concurrently generate their model training requests in the HetVNets. In this paper, we consider the scenario of multi-TR and multi-BS and propose a digital twin enabled scheme for multitask federated learning to address the two-way selection problem between the TRs and the BSs. We first analyze the diversified requirements of the TRs in the HetVNets. Then, we develop a novel model that jointly considers the available training data, the declared price, and the training experience to evaluate the differentiated training capabilities of the BSs. After that, based on the requirements of the TRs and the training capabilities of the BSs, the two-way selection problem between the TRs and the BSs is formulated as a matching game in the digital twin networks, where a matching algorithm is designed to obtain their optimal strategies. The simulation results demonstrate that the proposed scheme can obtain the highest model accuracy and bring the highest utility to the TRs compared with the conventional schemes.

Task scheduling is a critical problem when one user offloads multiple different tasks to the edge server. When a user has multiple tasks to offload and only one task can be transmitted to server at a time, while server processes tasks according to the transmission order, the problem is NP-hard. However, it is difficult for traditional optimization methods to quickly obtain the optimal solution, while approaches based on reinforcement learning face with the challenge of excessively large action space and slow convergence. In this paper, we propose a Digital Twin (DT)-assisted RL-based task scheduling method in order to improve the performance and convergence of the RL. We use DT to simulate the results of different decisions made by the agent, so that one agent can try multiple actions at a time, or, similarly, multiple agents can interact with environment in parallel in DT. In this way, the exploration efficiency of RL can be significantly improved via DT, and thus RL can converges faster and local optimality is less likely to happen. Particularly, two algorithms are designed to made task scheduling decisions, i.e., DT-assisted asynchronous Q-learning (DTAQL) and DT-assisted exploring Q-learning (DTEQL). Simulation results show that both algorithms significantly improve the convergence speed of Q-learning by increasing the exploration efficiency.

Continuous authentication is crucial for protecting user’s privacy throughout their login session. Existing studies employ wireless sensing technologies to provide device-free and unobtrusive authentication; the user’s behavior is continually assessed without their direct involvement until it deviates from their normal pattern. However, these works primarily concentrate on single-user authentication, which poses challenges in multiuser scenarios, such as smart homes and offices, where more than one user usually exists. In this article, we propose HeartPrint, a continuous multiuser authentication system, that employs a single commodity mmWave radar to capture the unique self-driving heartbeat motions from multiple users. Specifically, HeartPrint leverages the effect of skin surface vibrations caused by heartbeat on radio frequency (RF) transmissions. To profile individual heartbeat signals from the entangled components that are induced by multiple users, we first use a clustering method to position each user in the environment, then focus on the signal reflected from each position separately. The irrelevant body movements are eliminated from the RF signal by using a proposed signal energy comparison method for preserving fine-grained heartbeat traits. We then develop a pipeline to extract the most informative features for characterizing each user and feed them to an elaborated classifier for user authentication. We evaluate HeartPrint with 54 participants and demonstrate that it achieves an average authentication accuracy of over 95%. Additionally, we show that it is resilient against spoofing attacks, with an average attack success rate of less than 3%.

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.

The research on Wi-Fi sensing has been thriving over the past decade but the process has not been smooth. Three barriers always hamper the research: 1) unknown baseband design and its influence; 2) inadequate hardware; and 3) the lack of versatile and flexible measurement software. This article tries to eliminate these barriers through the following work.First, we present an in-depth study of the baseband design of the Qualcomm Atheros AR9300 (QCA9300) NIC. We identify a missing item of the existing channel state information (CSI) model, namely, the CSI distortion, and identify the baseband filter as its origin. We also propose a distortion removal method.Second, we reintroduce both the QCA9300 and software-defined radio (SDR) as powerful hardware for research. For the QCA9300, we unlock the arbitrary tuning of both the carrier frequency and bandwidth. For SDR, we develop a high-performance software implementation of the 802.11a/g/n/ac/ax baseband, allowing users to fully control the baseband and access the complete physical-layer information.Third, we release the PicoScenes software, which supports concurrent CSI measurement from multiple QCA9300, Intel Wireless Link (IWL5300), and SDR hardware. PicoScenes features rich low-level controls, packet injection, and software baseband implementation. It also allows users to develop their own measurement plugins.Finally, we report state-of-the-art results in the extensive evaluations of the PicoScenes system, such as the >2-GHz available spectrum on the QCA9300, concurrent CSI measurement, and up to 40 and 1 kHz CSI measurement rates achieved by the QCA9300 and SDR. PicoScenes is available athttps://ps.zpj.io.

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.

Diverse technologies, such as machine learning and big data, have been driving the prosperity of the Internet of Things (IoT) and the ubiquitous proliferation of IoT devices. Consequently, it is natural that IoT becomes the driving force to meet the increasing demand for frictionless transactions. To secure transactions in IoT, blockchain is widely deployed since it can remove the necessity of a trusted central authority. However, the mainstream blockchain-based IoT payment platforms, dominated by Proof-of-Work (PoW) and Proof-of-Stake (PoS) consensus algorithms, face several major security and scalability challenges that result in system failures and financial loss. Among the three leading attacks in this scenario, double-spend attacks and long-range attacks threaten the tokens of blockchain users, while eclipse attacks target Denial of Service. To defeat these attacks, a novel bidirectional-linked blockchain (BLB) using chameleon hash functions is proposed, where bidirectional pointers are constructed between blocks. Furthermore, a new committee members auction (CMA) consensus algorithm is designed to improve the security and attack resistance of BLB while guaranteeing high scalability. In CMA, distributed blockchain nodes elect committee members through a verifiable random function. The smart contract uses Shamir’s secret-sharing scheme to distribute the trapdoor keys to committee members. To better investigate BLB’s resistance against double-spend attacks, an improved Nakamoto’s attack analysis is presented. In addition, a modified entropy metric is devised to measure eclipse attack resistance across different consensus algorithms. Extensive evaluation results show the superior resistance against attacks and demonstrate high scalability of BLB compared with current leading paradigms based on PoS and PoW.

Real-time fault diagnosis on vehicles can effectively avoid potential accidents, which, however, is difficult and challenging to be widely deployed due to the low computational capability and limited data storage of electric vehicles (EVs). To address this issue, we propose a vehicle-mounted fault diagnosis system with low computational complexity and small data storage, for achieving real-time monitoring of vehicle status. To facilitate the accurate and optimized feature selection, we had been collecting 6.52-GB real data from three EVs in 12 months. Motivated by those data, we first propose a multilabel feature selection algorithm to obtain the feature weights, based on which the optimal number of features is then calculated through the backpropagation neural network (BPNN), thus minimizing the computational cost of real-time fault diagnosis regarding sample dimensions. To further simplify the fault diagnosis system, i.e., reducing the minimum required capacity of data storage, we design a real-time diagnosis sliding window (RDSW) where the window moves forward as new samples arrive and the stale data outside the window are discarded. In particular, we calculate the optimal size of RDSW, which controls the minimum required number of samples to guarantee the accuracy of real-time fault diagnosis. Owing to the mechanism of RDSW, vehicles no longer need to store massive data to guarantee the accuracy of real-time fault diagnosis. In addition, the results of real-time fault diagnosis at each vehicle can be shared with other vehicles in cooperative intelligent transportation systems (C-ITS). Finally, comprehensive simulation is conducted to validate the effectiveness of the proposed diagnosis system in terms of accuracy, complexity and storage capacity.

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.

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.

Accurately discovering hazards and issuing appropriate warnings to drivers in advance or performing autonomous control is the core of the Collision Avoidance (CA) system used to solve traffic safety problems. More comprehensive environmental awareness, diversified communication technologies, and autonomous control can make the CA system more accurate and effective, thereby improving driving safety. In addition, the assistance of Artificial Intelligence (AI) technology can make the CA system adapt to the environment and facilitate fast and accurate decisions. Considering the current lack of a thorough survey of driving safety with sensing, vehicular communications, and AI-based collision avoidance, in this paper, we survey existing researches for state-of-the-art data-driven CA techniques. Firstly, we discuss the major steps of CA and key research issues. For each step, we review the existing enabling techniques and research methods for CA in detail, including sensing and vehicular communication for safe driving, as well as CA algorithm design. Particularly, we present a comparison between the most common AI algorithms for different functions in the CA system. Testbeds and projects for CA are summarized next. Finally, several open challenges and future research directions are also outlined.

The complex traffic and road environment pose considerable challenges to the accuracy, timeliness, and adaptive ability of connected and autonomous vehicles (CAVs) in making driving decisions. This paper uses vehicle collaboration and integrates the adaptive learning capabilities of machine learning and the interpretation capabilities of expert systems (ESs) in a unified architecture to form a hybrid autonomous driving guidance system, which not only solves the “bottleneck” of knowledge acquisition during the construction of expert systems but also solves the “black box” phenomenon of machine learning in the decision-making process. First, an autonomous driving strategy based on deep reinforcement learning (DRL) is proposed for CAVs to make decisions and extract corresponding rules. Next, we design an ES knowledge base expansion method including rule extraction, rule sharing, and rule test. Particularly, vehicular blockchain is adopted to ensure user privacy and data security during the rule-sharing process. Third, hybrid autonomous driving guidance combining ES and machine learning is proposed for CAVs to make accurate and efficient decisions in different driving environments. Once the strategy is well trained, it can effectively guide CAVs to cope with the complex traffic environment. Extensive simulations validate the performance of our proposal in terms of decision-making accuracy, effectiveness, and safety.

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.

Recent advancements in edge computing and edge caching provide a feasible solution to support a plethora of new applications such as on-demand videos, AR/VR, road surveillance. However, to apply edge caching in vehicular scenarios is still difficult due to the unkonwn request pattern of vehicular users and intermittent service links between vehicles and edge servers (e.g., Road Side Units, RSUs). In this paper, we aim to investigate the vehicular edge caching problem in practical vehicular scenarios by considering higher hit ratio, while avoiding interruption of caching services. Specifically, to obtain a higher hit ratio, we firstly propose an on-demand adaptive cache algorithm. The algorithm can adjust the eviction time of cached contents by tracking the dynamics of requests and content popularity. We then develop an analysis framework to model the interruption performance of caching services from RSUs. Through diffraction approximation theory, the service process can be modeled as a joint process of the movement and stopping of vehicles to deduce the interruption ratio. To apply the on-demand adaptive cache algorithm in practical scenarios, the final caching decisions should be corrected by incorporating the interruption performance. Therefore, a$\alpha $-fair utility-oriented vehicular edge caching scheme is developed, which can achieve the tradeoff of hit ratio and interruption ratio. Performance evaluation shows the advantages of our proposed vehicular caching scheme in hit ratio, accuracy of analysis model, utility, respectively.

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.

The development of autonomous driving poses significant demands on computing resource, which is challenging to resource-constrained vehicles. To alleviate the issue, Vehicular edge computing (VEC) has been developed to offload real-time computation tasks from vehicles. However, with multiple vehicles contending for the communication and computation resources at the same time for different applications, how to efficiently schedule the edge resources toward maximal system welfare represents a fundamental issue in VEC. This article aims to provide a detailed analysis on the delay and cost of computation offloading for VEC and minimize the delay and cost from the perspective of multi-objective optimization. Specifically, we first establish an offloading framework with communication and computation for VEC, where computation tasks with different requirements for computation capability are considered. To pursue a comprehensive performance improvement during computation offloading, we then formulate a multi-objective optimization problem to minimize both the delay and cost by jointly considering the offloading decision, allocation of communication and computation resources. By applying the game theoretic analysis, we propose a particle swarm optimization based computation offloading (PSOCO) algorithm to obtain the Pareto-optimal solutions to the multi-objective optimization problem. Extensive simulation results verify that our proposed PSOCO outperforms counterparts. Based on the results, we also present a comprehensive analysis and discussion on the relationship between delay and cost among the Pareto-optimal solutions.

In this paper, we investigate secure transmissions in integrated satellite-terrestrial communications and the green interference based symbiotic security scheme is proposed. Particularly, the co-channel interference induced by the spectrum sharing between satellite and terrestrial networks and the inter-beam interference due to frequency reuse among satellite multi-beam serve as the green interference to assist the symbiotic secure transmission, where the secure transmissions of both satellite and terrestrial links are guaranteed simultaneously. Specifically, to realize the symbiotic security, we formulate a problem to maximize the sum secrecy rate of satellite users by cooperatively beamforming optimizing and a constraint of secrecy rate of each terrestrial user is guaranteed. Since the formulated problem is non-convex and intractable, the Taylor expansion and semi-definite relaxation (SDR) are adopted to further reformulate this problem, and the successive convex approximation (SCA) algorithm is designed to solve it. Finally, the tightness of the relaxation is proved. In addition, numerical results verify the efficiency of our proposed approach.

Efficient path planning is the key enabling technology for the realization of intelligent transportation systems (ITS). However, due to poor real-time performance and lack of effective incentive methods, it is difficult for traditional path planning schemes to significantly improve the efficiency of traffic management. In addition, existing solutions that use driving distance and driving time as indicators cannot meet the personalized requirements of vehicle users. To this end, by considering the personalized requirements of vehicle users, we propose a digital-twin (DT) enabled path planning scheme to facilitate traffic management. To be specific, based on the collection of traffic data, we first establish a DT architecture for traffic scheduling to reduce the delay of path planning. Then, according to the traffic density of different road sections, we regard road sections as resources and set different rewards for different road sections to encourage vehicles to obey the scheduling instructions. In addition, by jointly considering the driving time and rewards, we further design personalized utility models to map the requirements of different vehicle users. After that, based on the personalized requirement of the vehicle user, we use a$Q$-learning algorithm to obtain the optimal path with the target of maximizing the user's utility. The simulation results show that the proposed scheme can bring higher utility to the vehicle users than the conventional schemes.

Most of existing works about data offloading do not consider the participation of mobile subscribers (MSs) when designing the budget allocation, such that the fairness performance is challenging. For a mobile data offloading system consisting of a base station run by a service provider (SP), multiple MSs, and several third-party WiFi access points (APs), in this paper we study how the SP allocates the budget among APs and arranges the offloading data rate for MSs such that the fairness of each MS’s profit is guaranteed. By jointly considering the preferences of MSs for different APs and budget limit, we propose a two-phase participatory budget and rate allocation (PBRA) scheme where a core solution is designed for the budget allocation in the first phase to guarantee the fairness of all MSs, and an optimal rate allocation based on the core solution is designed to maximize the expected amount of data offloading in the second phase. Simulation results demonstrate the efficacy of our proposed PBRA scheme. Specifically, our proposed scheme can achieve the fairness among all MSs with a less performance loss in terms of the expected amount of data offloading by comparing with three benchmark schemes.

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 the Internet of Vehicles (IoVs), cooperative perception allows vehicles to share the sensor data with each other, so as to increase the perception range of vehicles beyond their field of view. This enables vehicles to obtain more accurate sensing information while driving and improves the safety of vehicles on the road. However, malicious nodes can send false information and poison the cooperative perception process. Therefore, how to guarantee the security of cooperative perception is crucial. In this paper, we propose a blockchain-based scheme for the post hoc electronic forensics of cooperative perception. Applying blockchain in cooperative perception is however challenging. Due to the high mobility of vehicles, the connection of vehicles to the Internet is intermittent and unpredictable. In this scenario, the security and effectiveness of blockchain can not be guaranteed as vehicles are offline.11Offline refers to that the vehicles can not connect to the Internet, but they can use vehicle-to-vehicle communication to share data. and can not update the blockchain on time. On addressing the issue, We develop an offline blockchain scheme that is composed of offline and online phases. In the offline phase, vehicles transact by sending a commitment. In the online phases before and after the offline phase, the deposit and arbitration mechanisms are proposed to defeat potential attacks in the offline phase. Lastly, the consortium blockchain is deployed to store the records of the cooperative perception process so that the records are tamper-proof and can be traced. Using extensive evaluations, we show the effectiveness of the proposed scheme.

With the evolving Internet of Vehicles (IoVs), the onboard resources of vehicles in computing and communication are experiencing fast growth. The sharing of road information and computing results among vehicles in proximity can effectively improve the utility of IoVs. However, remote inter-vehicular resource sharing, e.g., information and computing resource sharing, remains an under-explored issue. Motivated by this, we propose a novel digital twin based fair trading platform built upon consortium blockchain to enable city-wide vehicular resource sharing. Specifically, we first develop a digital twin based vehicular platform to enable vehicular resource sharing in the cloud. To track and secure the resource sharing among digital twins, the consortium blockchain is deployed, which is enforced by the designed smart contracts with an efficient Proof-of-Stake (PoS) consensus algorithm. In addition, an innovative incentive mechanism is devised to motivate the city-wide resource sharing for vehicles, which can maximize the profits of task publishers. Using extensive evaluations, we show the effectiveness of the proposed system.

In smart cities, on-street parking space prediction is the key yet difficult point in smart parking system. However, conventional prediction methods generally neglect spatial and temporal dependencies and cannot predict long-term parking events accurately. To this end, we propose a parking space prediction scheme based on the spatial-temporal graph convolution networks (STGCN). We first consider the instantaneous status of the parking to calculate the on-street parking occupancy rate (POR). Then, based on the POR, we exploit a time convolution module and a graph convolution module to extract spatial and temporal dependencies of the parking spaces, respectively. Next, we design the parameters of the STGCN to predict the POR of all the parking spaces based on the spatial and temporal dependencies. Finally, based on the real-world data sets, we compare the proposed scheme with the benchmark models. The experimental results show that the proposed scheme has the best performance in predicting the POR.

Recently, the abnormal intrusion detection has become an urgent problem in high-speed railway system. One way to solve this problem is the optical fiber distributed acoustic sensing (DAS) system that can monitor the intrusion events and provide early warning. However, most long-distance DAS systems are unable to distinguish signal types to improve the detection performance. Moreover, the traditional fiber optic sensing system is susceptible to interference from environmental factors, resulting in false detections and alarms. To this end, with the adoption of DAS system, we propose a railway intrusion detection system based on Faster R-CNN. In our system, we first design the DAS system to collect the optical fiber acoustic signals. Then, the collected signals are normalized in temporal and spatial dimensions and converted into Spatio-temporal images. After that, we design the Faster R-CNN algorithm to extract the Spatio-temporal features to detect and classify five types of abnormal intrusion events. The experimental results demonstrate that the average detection precision of our system for all abnormal intrusion events is above 89%. In addition, compared with the conventional methods, our system achieves the highest detection precision. Meanwhile, the system can distinguish the non-threatening background noise, which is of great help to reduce the system false positive rate.

Autonomous vehicles(AVs) have developed rapidly in recent years and derived many AV-related services. However, the stringent requirement of real-time sensing, computing, and communication may severely overload the AV, resulting in a poor passenger experience. In this work, we leverage the digital twin(DT) concept to help the information fusion and computing for AV, propose a vehicular digital twin(VDT) framework. Specifically, DT is maintained in the cloud and synchronized with its corresponding AV in real-time. AV uploads necessary information to the DT, and DT makes feedback through processing and analysis. The DT can also communicate with other DTs to obtain information. Due to the real-time information synchronization of AV and DT in an open communication environment, ensuring communication security and protect privacy is needed. To tackle those issues, we give a concrete authentication protocol for secure communication between AV and DT. The security analysis and performance evaluation show that our proposed protocol not only has less computation cost but also can satisfy the necessary security requirements.

Due to the pervasive adoption of sensor-embedded mobile devices yet increasing demand on data and computing resources, mobile crowdsensing is a promising paradigm with rapid growth. One of the most challenging issues is how to maximize the utilities of crowdsensing platforms under inaccurate distributed sensing. The nature of such inaccuracy is due to the fact that energy-based sensing can be greatly impacted by thermal and environmental noise, which significantly affects task allocation strategies of crowdsensing platforms. Because of the allocation efficiency and fairness concerns, auction-based mechanisms have been extensively used in crowdsensing systems. However, the existing auction-based mechanisms for crowdsensing do not take sensing inaccuracy into consideration, while guaranteeing that each participator obtains her maximal utility by bidding with her true cost for tasks. To tackle this issue, in this article, we propose OSIER, an optimal mobile crowdsensing incentive under sensing inaccuracy. Specifically, a quantitative analytical framework on characterizing the impact of sensing inaccuracy on a crowdsensing platform is presented, and an optimization problem involving sensing inaccuracy is solved to achieve a maximum utility of the platform. Furthermore, depending on whether a user needs to perform all tasks simultaneously or not, indivisible tasks and divisible tasks are discussed, and OSIER-I and OSIER-D are presented for these two kinds of tasks. Simulation results verify the truthfulness of OSIER, and given a sample set with 5%-20% noise in spectrum sensing, OSIER can achieve 10% higher utilities than the existing crowdsensing mechanisms on average.

Neighbor information perception with high accuracy and low overhead is quite essential for vehicular networks, which is accomplished by the neighbor discovery scheme. Following the scheme, nodes exchange short discovery messages for advertising their existence and sensing neighboring vehicles. To combat high vehicle mobility and severe channel fading, the discovery message is always exchanged frequently in vehicular networks. This, however, introduces superabundant communication overhead. In this article, a novel neighbor discovery method with mobility awareness and channel randomness adaptability is proposed for investigating the aforementioned issue. First, a closed-form expression is derived, which captures the quantitive relation of the neighbor discovery performance to the vehicle mobility and channel randomness. Guided by our theoretical analysis, the optimal neighbor discovery scheme is developed to adjust the discovery frequency adaptively based on mobility and channel. Thus, an optimal tradeoff between the discovery accuracy and overhead is achieved in vehicular networks. Simulation results coincide with our analysis results, which further demonstrates that the proposed discovery scheme outperforms the periodic and existing adaptive methods in terms of discovery accuracy and overhead.

The positioning accuracy of the existing vehicular Global Positioning System (GPS) is far from sufficient to support autonomous driving and ITS applications. To remedy that, leading methods such as ranging and cooperation have improved the positioning accuracy to varying degrees, but they are still full of challenges in practical applications. Especially for cooperative positioning, in addition to the performance of methods, cooperators may provide false data due to attacks or selfishness, which can seriously affect the positioning accuracy. By fully exploiting the characteristics of blockchain and edge computing, this paper proposes a vehicular blockchain-based secure and efficient GPS positioning error evolution sharing framework, which improves vehicle positioning accuracy from ensuring security and credibility of cooperators and data. First, by analyzing the GPS error, a bridge can be established between the sensor-rich vehicles and the common vehicles to achieve cooperation by sharing the positioning error evolution at a specific time and location. Particularly, the positioning error evolution is obtained by a deep neural network (DNN)-based prediction algorithm running on the edge server. We further propose to use blockchain technology for storage and sharing the evolution of positioning errors, mainly to guarantee the security of cooperative vehicles and mobile edge computing nodes (MECNs). In addition, the corresponding smart contracts are designed to automate and efficiently perform storage and sharing tasks as well as solve inconsistencies in time scales. Extensive simulations based on actual data indicate the accuracy and security of our proposal in terms of positioning error correction and data sharing.

With the fast development of Internet of Vehicles (IoV), various types of computation-intensive vehicular applications pose significant challenges to resource-constrained vehicles. The emerging Vehicular Edge Computing (VEC) and Edge Intelligence (EI) can alleviate this situation by offloading the computation tasks of vehicles to the roadside edge servers. However, with many vehicles contending for the communication and computation resources at the same time, how to quickly and efficiently make an optimal computation offloading decision for individual vehicles represents a fundamental research issue. In this paper, we propose a self-learning based distributed computation offloading scheme for IoV. Note that without any centralized controller, a fully distributed algorithm is necessary. The proposed scheme is devised based on a game-theoretic model. Specifically, through establishing an offloading framework with communication and computation for IoV, the computation offloading problem is first formulated as a distributed offloading decision-making game, in which each vehicle as a player makes its best response decision to minimize its joint cost (including latency and offloading cost). The existence of Nash Equilibrium can be proved. We then propose a self-learning based distributed computation offloading (DISCO) algorithm to reach the Nash Equilibrium, where a mutually satisfactory solution among vehicles is obtained and no vehicle is willing to change its decision. Using extensive simulations, we verify that DISCO can outperform the counterparts and achieve at least an order-of-magnitude improvement on time overhead and 88% performance gain on message overhead, only at up to 12% performance loss on joint cost over the centralized scheme.

Channel aggregation (CA) is proposed in IEEE 802.11ax to allow wireless users to aggregate multiple available channels, either contiguous or non-contiguous, to improve the network throughput. In this paper, the performance of CA is extensively investigated. It is shown that a simple CA that aggregates all available channels does not always promote but may degrade the network performance due to the increased inter-channel contentions in a random access wireless local area network (WLAN). Thus, it is of critical importance to select an appropriate set of channels for CA. To this end, we propose an efficient probabilistic channel aggregation scheme to maximize the network throughput under the quality of service constraints. That is, an ax user aggregates each secondary channel with a certain probability based on the traffic load of the secondary channel. A Proximal Policy Optimization (PPO) based approach is further applied to intelligently tune the aggregating probabilities of secondary channels to maximize the network throughput. Numerical results show that the proposed algorithm can greatly improve the network throughput compared with existing CA algorithms in the literature.

Implementing real-time and onboard fault diagnosis on electric vehicles can effectively avoid potential dangers. However, the low calculating ability and limited storage capacity of electric vehicles hamper the development of real-time and onboard fault diagnosis. To address the issue, combining neural network and fuzzy logic, we propose a low complexity onboard vehicle fault diagnosis method to monitor the vehicle status and give early warning of accidents. In twelve months, we first utilize three electric vehicles and collect 6. 52GB real data related to vehicle components. Motivated by those data, we conducted an in-depth research on the major vehicle faults, and divided them into four types which are no fault, battery fault, sensor fault, and module fault. Furthermore, we propose a BP neural network based multiple training method to define the correlation between data types and fault types. Then, applying the correlation and data, a fuzzy logic based classification method is proposed to evaluate the vehicle status and give early warning. Finally, a comprehensive simulation is conducted, which indicates that the accuracy is 88%.

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.

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 Internet of Things (IoT) for smart ocean is a promising paradigm that will support emerging applications in the areas of maritime transport, emergency search and rescue, security and border surveillance, environmental protection, etc. There has been a surging amount of data acquired from different maritime terminals, such as vessels, buoys, and offshore platforms. As a result, the demand for high-speed, ultrareliable, and low-latency maritime communications and data processing is proliferating. In this context, transmission and processing of maritime data have become a research hotspot. IoT technologies are expected to dramatically enhance the capacity, safety, and efficiency of connected vessels and other maritime terminals. Meanwhile, the unique characteristics of smart ocean applications create heterogeneous challenges in achieving viable, reliable, and secure communications and data processing. Addressing the challenges calls for novel approaches and consideration for the deployment of next-generation maritime communication networks. Therefore, it is essential to pursue research on new theories, architecture, and technologies to fully exploit the capability that is delivered by IoT for smart ocean to form efficient and intelligent maritime communication systems. This special issue aims to create a platform for researchers from both academia and industry to disseminate state-of-the-art results and to advance the applications of IoT for the smart ocean.

Inappropriate lane following and changing behaviors of connected and autonomous vehicles (CAVs) can result in accidents, such as rear-end collision and side collision. To remedy that, the use of deep reinforcement learning (DRL) for autonomous driving decisions is currently a widely used promising solution. In this case, the accuracy and effectiveness of such a machine learning (ML) model is quite essential for this artificial intelligence (AI)-enabled CAVs. This article proposes a blockchain-based collective learning (BCL) framework for autonomous lane-changing systems. Four key issues, namely, learning efficiency, data security, users' privacy, as well as communication burden, are addressed by applying collective learning, vehicular blockchain, and knowledge transfer. First, we model the lane-changing problem as a DRL process and learn the autonomous lane-changing strategy through the deep deterministic policy gradient (DDPG) algorithm. Second, a single CAV involves a limited number of driving scenarios, and the independent learning method has the problem of inefficiency. Therefore, we propose a collective learning framework to utilize the “collective intelligence” shared by CAVs. Third, a vehicular blockchain is then applied to ensure the security and privacy of the user and data. In addition, the introduction of the blockchain can incentivize more users to participate in collective learning. Finally, in order to accelerate the learning process and achieve higher level performance while further reducing the communication burden, we use the corresponding knowledge extracted from the ML model such as human learning, as privileged information for sharing instead of directly sharing local ML models. Extensive simulation results validate the effectiveness and efficiency of our proposal in terms of learning efficiency, driving safety, as well as system security and robustness.

Vehicular edge computing (VEC), which integrates mobile-edge computing (MEC) into vehicular networks, can provide more capability for executing resource-hungry applications and lower latency for connected vehicles. Distributing the result content to connected vehicles is vital for them to take proper actions based on computing results. However, the increasing number of connected vehicles and the limited communication resources make the content distribution a challenge. Besides, the diversity of connected vehicles and contents makes it more challenging for content distribution. To address this issue, in this article, we propose EdgeVCD, an intelligent algorithm-inspired content distribution scheme. Specifically, we first propose a dual-importance (DI) evaluation approach to reflect the relationship between the Priority of Vehicles (PoV) and the Priority of Contents (PoC). To make use of the limited communication resources, we then formulate an optimization problem to maximize the system utility for content distribution. To solve the complex optimization problem effectively, we first divide the road into small segments. Then, we propose a fuzzy-logic-based method to select the most proper content replica vehicle (CRV) for aiding content distribution and redefine the number of content request vehicles in each segment. Thereafter, the optimization problem is transformed into a nonlinear integer programming problem. Inspired by the artificial immune system, we propose an immune clone-based algorithm to solve it, which has a fast convergence to an optimal solution. Extensive simulations validate the effectiveness of our proposed EdgeVCD in terms of system utility, average utility, and convergence.

With the development of autonomous driving, the surging demand for data communications as well as computation offloading from connected and automated vehicles can be expected in the foreseeable future. With the limited capacity of both communication and computing, how to efficiently schedule the usage of resources in the network toward best utilization represents a fundamental research issue. In this article, we address the issue by jointly considering the communication and computation resources for data scheduling. Specifically, we investigate on the vehicular edge computing (VEC) in which edge computing-enabled roadside unit (RSU) is deployed along the road to provide data bandwidth and computation offloading to vehicles. In addition, vehicles can collaborate among each other with data relays and collaborative computing via vehicle-to-vehicle (V2V) communications. A unified framework with communication, computation, caching, and collaborative computing is then formulated, and a collaborative data scheduling scheme to minimize the system-wide data processing cost with ensured delay constraints of applications is developed. To derive the optimal strategy for data scheduling, we further model the data scheduling as a deep reinforcement learning problem which is solved by an enhanced deep $Q$ -network (DQN) algorithm with a separate target $Q$ -network. Using extensive simulations, we validate the effectiveness of the proposal.

As the extension of cloud computing and a foundation of IoT, fog computing is experiencing fast prosperity because of its potential to mitigate some troublesome issues, such as network congestion, latency, and local autonomy. However, privacy issues and the subsequent inefficiency are dragging down the performances of fog computing. The majority of existing works hardly consider a reasonable balance between them while suffering from poisoning attacks. To address the aforementioned issues, we propose a novel blockchain-enabled federated learning (FL-Block) scheme to close the gap. FL-Block allows local learning updates of end devices exchanges with a blockchain-based global learning model, which is verified by miners. Built upon this, FL-Block enables the autonomous machine learning without any centralized authority to maintain the global model and coordinates by using a Proof-of-Work consensus mechanism of the blockchain. Furthermore, we analyze the latency performance of FL-Block and further derive the optimal block generation rate by taking communication, consensus delays, and computation cost into consideration. Extensive evaluation results show the superior performances of FL-Block from the aspects of privacy protection, efficiency, and resistance to the poisoning attack.

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.

Realizing the ultra-low latency and high-accuracy solutions for rear-end collision is still challenging, especially under the condition in which many uncertainties exist. This paper proposes an artificial intelligence-based warning algorithm for rear-end collision avoidance. Three key issues are addressed by applying the neural network approach, including noises in positioning, inaccurate risk assessment, and enhanced comfort level of passengers. First, to filter the noises in positioning, wireless vehicular communications are leveraged; accurate relative lane positioning can be achieved to justify when two vehicles are in the same lane. Second, an online neural network model is developed to assess the risk of collisions in real time while driving. The algorithm can converge fast to a globally optimal solution and adapt to different traffic environments. Third, to maximize the comfort of passengers during the braking process, a graded warning strategy is developed at the prerequisite of guaranteed safety. With the above schemes sewed in to one framework, our proposal can achieve rear-end warning with reduced missing alarm rate, accurate risk assessment and enhanced comfort to passengers. The extensive simulations validate the effectiveness and accuracy of our proposal in terms of relative lane positioning, risk assessment, and collision avoidance.

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.

There is a growing interest in the use of renewable energy sources to power fog networks in order to mitigate the detrimental effects of conventional energy production. However, renewable energy sources, such as solar and wind, are by nature unstable in their availability and capacity. The dynamics of energy supply hence impose new challenges for network planning and resource management. In this paper, the sustainable performance of a fog node powered by renewable energy sources is studied. We develop a generic analytical model to study the energy sustainability of fog nodes powered by renewable energy sources, by generalizing the leaky bucket model to shape and police traffic source for rate-based congestion control in high-speed fog networks. Based on the closed-form solutions of energy buffer analysis, i.e., the energy depletion probability and mean energy length, we study the energy sustainability in two special but real-happening scenarios. The experimental results show that with proper design the leaky bucket model effectively reflects the energy sustainability of data traffic in fog networks. Numerical results also reveal that the model performance is sensitive to certain traffic source characteristics in fog networks.

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.

With the surge service requirements from vehicular users, especially for automated driving, providing real-time high-rate wireless connections to fast-moving vehicles is ever demanding. This motivates the development of the emerging LTE-V network, a 5G cellular-based vehicular technology. However, note that the vehicular user group is typically of a very large scale, whereas the bandwidth spectrum available for vehicular communications is very limited. To efficiently allocate and utilize the slim spectrum resource to vehicle users are therefore important. This paper develops a service priority oriented spectrum allocation scheme in an LTE-V network, which explores the features of vehicular networks toward economic yet QoS guaranteed spectrum allocation. Specifically, the work exploits two features of the vehicular networks. First, vehicles in the proximity typically have similar information requirements, e.g., road conditions. As a result, the location-based multicast (i.e., geocast) could be applied to save the spectrum. Second, different types of vehicles, e.g., ambulances, buses, and private cars, are of different bandwidth and service requirements. Therefore, differential services and spectrum allocations should be applied. By jointly considering the above features, we develop a 2-D service importance oriented framework for LTE-V network spectrum allocations. The spectrum allocation issue is finally modeled as a mixed integer programming problem to maximize the system utility, and solved using an immune clonal based algorithm. The convergence of the proposed algorithm is proved, and using numerical results, we show that our proposal can outperform the typical heuristics-based spectrum resource allocation in terms of convergence and average delay.

Very recent studies showed that the area spectral efficiency (ASE) of downlink cellular networks will continuously decrease and finally crash to zero as the base station (BS) density increases toward infinity if the absolute height difference between BS antenna and user equipment antenna is larger than zero. Such a phenomenon is referred to as the ASE crash. We revisit this issue by considering optimizing the BS antenna downtilt in cellular networks. It is common to adjust antenna pattern to tune the direction of the vertical beamforming and thus increasing received signal power and/or reducing inter-cell interference power to improve network performance. This paper focuses on investigating the relationship between the BS antenna downtilt and the downlink network performance in terms of the coverage probability and the ASE. Our results reveal an interesting find that there exists an optimal antenna downtilt to achieve the maximum coverage probability for each BS density. Numerically solvable expressions are derived for such optimal antenna downtilt, which is a function of the BS density. Our numerical results show that after applying the optimal antenna downtilt, the network performance can be significantly improved, and hence the ASE crash can be delayed by nearly one order of magnitude in terms of the BS density. Our results also give guidance on setting the optimum downtilt angle to maximize network performance given a fixed BS density.

Recently, it has been standardized by the 3rd Generation Partnership Project (3GPP) [1] that device-to-device (D2D) communications should use uplink resources when coexisting with conventional cellular communications. With uplink resource sharing, both cellular and D2D links cause significant co-channel interference. In this paper, we consider a D2D mode selection criterion based on the maximum received signal strength (MRSS) for each user equipment (UE) to control the D2D-to-cellular interference. Specifically, a UE will operate in a cellular mode, if its received signal strength from the strongest base station (BS) is larger than a threshold β; otherwise, it will operate in a D2D mode. Furthermore, in our study, cellular UEs, D2D transmit UEs and D2D receiver UEs constitute the entire UE set, which is a more practical assumption than dropping more UEs for D2D reception only in existing works. The coverage probability and the area spectral efficiency (ASE) are derived for both the cellular network and the D2D one. Through our theoretical and numerical analyses, we quantify the performance gains brought by D2D communications and provide guidelines for selecting the parameters for network operations.

In vehicular ad hoc networks (VANETs), three-dimensional scenarios are always ignored, even though they are attractive for their effectiveness in land use. Focusing on those scenarios, we propose and prove their severe impacts on the performance of a vehicular network. We first conduct a transmission experiment. The results prove that the existence of those scenarios induces the inter-layer communication, and then significantly reduces the transmission range. Furthermore, we demonstrate that the variation of the transmission range makes an enormous difference in the neighbor number, which severely affects the network performance. At last, our extensive simulations show that the aforementioned analysis are in fact quite accurate.

© 2018 IEEE. Neighbor discovery, providing the neighbor information by broadcasting discovery messages, is a promising solution for cooperative perception of Intelligent Vehicles (IVs). However, the high vehicle mobility and severe channel randomness of IV environments call for a frequent discovery, which results in a superabundant overhead. In this paper, we propose a new framework for cooperative perception of IVs by novelly introducing an improved neighbor discovery method. We first establish an analytical framework to capture the quantitive relation between the hitting probability of neighbor discovery with the vehicle mobility and channel randomness using a closed-form expression. Based on the analysis, an adaptive neighbor discovery method is developed to adaptively make tradeoff between the discovery accuracy and overhead at varying driving status of IVs. Applying the improved neighbor discovery, the process of cooperative perception is discussed. Accordingly, simulations in three IV scenarios are conducted whose results are consistent with our analysis.

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.

Accurate malware propagation modeling in wireless ad hoc networks (WANETs) represents a fundamental and open research issue which shows distinguished challenges due to complicated access competition, severe channel interference, and dynamic connectivity. As an effort towards the issue, in this paper, we investigate the malware propagation under two spread schemes including Unicast and Broadcast, in Spread Mode and Communication Mode, respectively. We highlight our contributions in three-fold in the light of previous literature works. First, a bound of malware infection rate for each scheme is provided by applying the wireless network capacity theories. Second, the impact of mobility on malware propagations has been studied. Third, discussion of the relationship between different schemes and practical applications is provided. Numerical simulations and detailed performance analysis show that the Broadcast Scheme with Spread Mode is most dangerous in the sense of malware propagation speed in WANETs, and mobility will greatly increase the risk further. The results achieved in this paper not only provide insights on the malware propagation characteristics in WANETs, but also serve as fundamental guidelines on designing defense schemes.

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.

Effective file transfer between vehicles is fundamental to many emerging vehicular infotainment applications in the highway Vehicular Ad Hoc Networks (VANETs), such as content distribution and social networking. However, due to fast mobility, the connection between vehicles tends to be short-lived and lossy, which makes intact file transfer extremely challenging. To tackle this problem, we presents a novel Cluster-based File Transfer (CFT) scheme for highway VANETs in this paper. With CFT, when a vehicle requests a file, the transmission capacity between the resource vehicle and the destination vehicle is evaluated. If the requested file can be successfully transferred over the direct Vehicular-to-Vehicular (V2V) connection, the file transfer will be completed by the resource and the destination themselves. Otherwise, a cluster will be formed to help the file transfer. As a fully-distributed scheme that relies on the collaboration of cluster members, CFT does not require any assistance from roadside units or access points. Our experimental results indicate that CFT outperforms the existing file transfer schemes for highway VANETs.

The Intelligent Transportation System (ITS) targets to a coordinated traffic system by applying the advanced wireless communication technologies for road traffic scheduling. Towards an accurate road traffic control, the short-term traffic forecasting which predicts the road traffic at the particular site in a short period is often useful and important. In existing works, Seasonal Autoregressive Integrated Moving Average (SARIMA) model is a popular approach. The scheme however encounters two challenges: (1) the analysis on related data is insufficient whereas some important features of data may be neglected; and (2) with data presenting different features, it is unlikely to have one predictive model that can fit all situations. To tackle above issues, in this work, we develop a hybrid model to improve accuracy of SARIMA. In specific, we first explore the autocorrelation and distribution features existed in traffic flow to amend structure of the time series model. Based on the Gaussian distribution of traffic flow, a hybrid model with a Bayesian learning algorithm is developed which can effectively expand the application scenarios of SARIMA. We show the efficiency and accuracy of our proposal using both analysis and experimental studies. Using the real-world trace data, we show that the proposed predicting approach can achieve satisfactory performance in practice.

This paper gives models and analytic techniques for studying shaping and policing data traffic in fog networks. The traffic in these networks is expected to be highly diverse and bursty, and regulation will be required as an integral part of congestion control. We generalize the Leaky Bucket model to shape and police traffic source for rate-based congestion control in high-speed fog networks. In particular, the Markov modulated fluid sources reflect the bursty characteristics of data traffic. To measure the performance of the model in shaping and policing traffic, we derive four performance metrics. The experimental results show that with proper design the Leaky Bucket model effectively controls a 4-way trade-off between throughput, loss probability, delay and burstiness of data traffic. Numerical results also reveal that the model performance is sensitive to certain traffic source characteristics.

Urban Rail Transit (URT) system has been one of the major trip modes in cities worldwide. As the passengers arrive at variable rates in different time slots, e.g., rush and non-rush hours, the service frequency at a site directly relates to perceived service quality of passengers; the high service frequency, however, incurs increased operation cost to the running of URT. Therefore, a tradeoff between the interest of railway operator and the service quality of passengers needs to be addressed. In this paper, we develop a model on the method of train operation scheduling using a Stackelberg game model. The railway operator is modeled as the game leader and the passengers as the game follower; an optimal service frequency can be determined according to strike the tradeoff between passengers' service quality and the operation cost of URT. Numerical experiments based on the operation data from Nanjing transit subway at China are presented and the results demonstrate that the proposed model can significantly improve the traffic efficiency.

In the process of cities informatization, the smart cities use of information technology and service system to handle of urban problems, improving people's lives. We study the problem of indoor signal coverage in smart cities. Indoor signal coverage inside large buildings is important to provide guaranteed communication quality for indoor mobile users. Specifically, traditional cellular coverage is deficit for indoor communications in the following three aspects: the emergence of the mobile signals are weak, or even blind at indoor environment, due to the complicated interior building structure; in some area with high traffic, traditional network is far from sufficient to meet the capacity needs of users; radio frequency interference problem between floors seriously a2642ects the stability of mobile signals and communication quality. Previous research mainly focuses on the outdoor macro cell coverage, and fails to meet the surge demand of indoor communication demand. Indoor coverage system is therefore demanded to solve the issue. In this paper, we investigate on the planning of an indoor distribution system. Based on the analysis of an indoor coverage system, an TD-LTE system is developed to provide indoor signal coverage. We implement our design in a real-world scenario. Using realworld experiments, we verifies the performance of the proposed system.

Wireless Body Area Networks (WBANs) have attracted significant attentions because of their important role in medical applications with the development of requirements in health monitoring and diagnosis. IEEE 802.15.6, as the international standard for WBAN, supports network in operating on, in or around human body. Owing to the special propagation characteristics as affected by human body, WBAN needs reliable access mechanism to guarantee the stability of nodes access and information transmission. To achieve the high slot utilization rate and low average packet delay, we propose a gated scheduled access mechanism based on IEEE 802.15.6 and study the impact of allocation slot length on network performance. To examine the performance of our proposal, we conduct hardware experiment through a novel prototype system based on IEEE 802.15.6 standard. The experiment results show that the obtained optimal allocation slot length under the gated scheduled access mechanism can well satisfy the Quality of Service (QoS) requirements in different data rates, which is consistent with the results of theoretical analysis. The comparison results also show that our prototype system can be a practical reference in the future study of wireless body area network.

The demands for vehicular Internet access are proliferating. To enable vehicular communications, roadside units (RSUs) can be deployed along the roadside to provide wireless coverage and network access for driving‐thru vehicles and the performance of vehicle to RSU communications have been studied in multiple contexts. However, there is not still an efficient media access control (MAC) scheme specific for the sparse highway environment. In this study, the authors investigate the MAC scheme of drive‐thru Internet in a sparse highway environment by a Markov chain encountering model. The analytical model incorporates the high‐node mobility with the modelling of distributed coordination function (DCF) and unveils the impacts of mobility velocity and number of vehicles on the throughput. On the basis of the model, they develop a new MAC scheme and show that when vehicle number is small the proposed MAC scheme can obtain higher throughput and mitigate the impacts of vehicle mobility on the system throughput, which is desirable for the sparse highway environment. Using extensive simulations, they validate the accuracy of the analytical model and effectiveness of the proposed MAC scheme.

Fog computing, known as “cloud closed to ground,” deploys light-weight compute facility, called Fog servers, at the proximity of mobile users. By precatching contents in the Fog servers, an important application of Fog computing is to provide high-quality low-cost data distributions to proximity mobile users, e.g., video/live streaming and ads dissemination, using the single-hop low-latency wireless links. A Fog computing system is of a three tier Mobile–Fog–Cloud structure; mobile user gets service from Fog servers using local wireless connections, and Fog servers update their contents from Cloud using the cellular or wired networks. This, however, may incur high content update cost when the bandwidth between the Fog and Cloud servers is expensive, e.g., using the cellular network, and is therefore inefficient for nonurgent, high volume contents. How to economically utilize the Fog–Cloud bandwidth with guaranteed download performance of users thus represents a fundamental issue in Fog computing. In this paper, we address the issue by proposing a hybrid data dissemination framework which applies software-defined network and delay-tolerable network (DTN) approaches in Fog computing. Specifically, we decompose the Fog computing network with two planes, where the cloud is a control plane to process content update queries and organize data flows, and the geometrically distributed Fog servers form a data plane to disseminate data among Fog servers with a DTN technique. Using extensive simulations, we show that the proposed framework is efficient in terms of data-dissemination success ratio and content convergence time among Fog servers.

In recent years, various researches have been conducted in order to find ways to cover a target or groups of targets with priority-based target coverage and sensor deployment mechanisms taking the front seats. However, with these researches, effective target coverage has been a recurrent issue due to various factors like conflict between sensors and excessive waiting time for targets to be covered. In this paper, we proposed an algorithm based on queuing theory in tandem with mobile crowd sensing to tackle these issues. To do this, first, we develop some models which are based on the birth-and-death mechanism (one of the tools in queuing theory) to determine how long a target has to wait, the mean busy period of sensors and mean idle period of sensors. While developing these models, we consider cases where there exist a single sensor and n-sensors in the system. Based on these models, we develop the required algorithm. The simulation result shows that as the number of sensors increases relative to the number of targets, an average time before a target gets discovered is 0.2 s and sensor utilization decreasing toward zero as the number of sensors increases.

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.

Mobile users typically have high demand on localized and location-based information services. To always retrieve the localized data from the remote cloud, however, tends to be inefficient, which motivates fog computing. The fog computing, also known as edge computing, extends cloud computing by deploying localized computing facilities at the premise of users, which prestores cloud data and distributes to mobile users with fast-rate local connections. As such, fog computing introduces an intermediate fog layer between mobile users and cloud, and complements cloud computing toward low-latency high-rate services to mobile users. In this fundamental framework, it is important to study the interplay and cooperation between the edge (fog) and the core (cloud). In this paper, the tradeoff between power consumption and transmission delay in the fog-cloud computing system is investigated. We formulate a workload allocation problem which suggests the optimal workload allocations between fog and cloud toward the minimal power consumption with the constrained service delay. The problem is then tackled using an approximate approach by decomposing the primal problem into three subproblems of corresponding subsystems, which can be, respectively, solved. Finally, based on simulations and numerical results, we show that by sacrificing modest computation resources to save communication bandwidth and reduce transmission latency, fog computing can significantly improve the performance of cloud computing.

Using cloud computing, individuals can store their data on remote servers and allow data access to public users through the cloud servers. As the outsourced data are likely to contain sensitive privacy information, they are typically encrypted before uploaded to the cloud. This, however, significantly limits the usability of outsourced data due to the difficulty of searching over the encrypted data. In this paper, we address this issue by developing the fine-grained multi-keyword search schemes over encrypted cloud data. Our original contributions are three-fold. First, we introduce the relevance scores and preference factors upon keywords which enable the precise keyword search and personalized user experience. Second, we develop a practical and very efficient multi-keyword search scheme. The proposed scheme can support complicated logic search the mixed “AND”, “OR” and “NO” operations of keywords. Third, we further employ the classified sub-dictionaries technique to achieve better efficiency on index building, trapdoor generating and query. Lastly, we analyze the security of the proposed schemes in terms of confidentiality of documents, privacy protection of index and trapdoor, and unlinkability of trapdoor. Through extensive experiments using the real-world dataset, we validate the performance of the proposed schemes. Both the security analysis and experimental results demonstrate that the proposed schemes can achieve the same security level comparing to the existing ones and better performance in terms of functionality, query complexity and efficiency.

Fog computing, characterized by extending cloud computing to the edge of the network, has recently received considerable attention. The fog is not a substitute but a powerful complement to the cloud. It is worthy of studying the interplay and cooperation between the edge (fog) and the core (cloud). To address this issue, we study the tradeoff between power consumption and delay in a cloud-fog computing system. Specifically, we first mathematically formulate the workload allocation problem. After that, we develop an approximate solution to decompose the primal problem into three subproblems of corresponding subsystems, which can be independently solved. Finally, based on extensive simulations and numerical results, we show that by sacrificing modest computation resources to save communication bandwidth and reduce transmission latency, fog computing can significantly improve the performance of cloud computing.

Driven by the ever-growing expectation of ubiquitous connectivity and the widespread adoption of IEEE 802.11 networks, it is not only highly demanded but also entirely possible for in-motion vehicles to establish convenient Internet access to roadside WiFi access points (APs) than ever before, which is referred to as Drive-Thru Internet. The performance of Drive-Thru Internet, however, would suffer from the high vehicle mobility, severe channel contentions, and instinct issues of the IEEE 802.11 MAC as it was originally designed for static scenarios. As an effort to address these problems, in this paper, we develop a unified analytical framework to evaluate the performance of Drive-Thru Internet, which can accommodate various vehicular traffic flow states, and to be compatible with IEEE 802.11a/b/g networks with a distributed coordination function (DCF). We first develop the mathematical analysis to evaluate the mean saturated throughput of vehicles and the transmitted data volume of a vehicle per drive-thru. We show that the throughput performance of Drive-Thru Internet can be enhanced by selecting an optimal transmission region within an AP's coverage for the coordinated medium sharing of all vehicles. We then develop a spatial access control management approach accordingly, which ensures the airtime fairness for medium sharing and boosts the throughput performance of Drive-Thru Internet in a practical, efficient, and distributed manner. Simulation results show that our optimal access control management approach can efficiently work in IEEE 802.11b and 802.11g networks. The maximal transmitted data volume per drive-thru can be enhanced by 113.1% and 59.5% for IEEE 802.11b and IEEE 802.11g networks with a DCF, respectively, compared with the normal IEEE 802.11 medium access with a DCF.

Wireless Sensor Networks are vulnerable to node replication attacks due to deployment in unattended environments and the lack of physical tamper-resistance. An adversary can easily capture and compromise sensor nodes and after replicating them, he inserts arbitrary number of replicas into the network to mount a wide variety of internal attacks. In this paper we propose a novel distributed solution (RAND) for the detection of node replication attack in static WSNs which combines random walks with network division and works in two phases. In the first phase called network configuration phase, the entire network is divided into different areas. In the second phase called replica detection phase, the clone is detected by following a claimer-reporter-witness framework and a random walk is employed within each area for the selection of witness nodes. Simulation results show that our scheme outperforms the existing witness node based strategies with moderate communication and memory overhead.

There is a growing interest in the use of renewable energy sources to power wireless networks in order to mitigate the detrimental effects of conventional energy production or to enable deployment in off-grid locations. However, renewable energy sources, such as solar and wind, are by nature unstable in their availability and capacity. The dynamics of energy supply hence impose new challenges for network planning and resource management. In this paper, the sustainable performance of a wireless mesh network powered by renewable energy sources is studied. To address the intermittently available capacity of the energy supply, adaptive resource management and admission control schemes are proposed. Specifically, the goal is to maximize the energy sustainability of the network, or equivalently, to minimize the failure probability that the mesh access points (APs) deplete their energy and go out of service due to the unreliable energy supply. To this end, the energy buffer of a mesh AP is modeled as a G/G/1(/N) queue with arbitrary patterns of energy charging and discharging. Diffusion approximation is applied to analyze the transient evolution of the queue length and the energy depletion duration. Based on the analysis, an adaptive resource management scheme is proposed to balance traffic loads across the mesh network according to the energy adequacy at different mesh APs. A distributed admission control strategy to guarantee high resource utilization and to improve energy sustainability is presented. By considering the first and second order statistics of the energy charging and discharging processes at each mesh AP, it is demonstrated that the proposed schemes outperform some existing state-of-the-art solutions.

The recent advances in wireless communication techniques have made it possible for fast-moving vehicles to download data from the roadside communications infrastructure [e.g., IEEE 802.11b Access Point (AP)], namely, Drive-thru Internet. However, due to the high mobility, harsh, and intermittent wireless channels, the data download volume of individual vehicle per drive-thru is quite limited, as observed in real-world tests. This would severely restrict the service quality of upper layer applications, such as file download and video streaming. On addressing this issue, in this paper, we propose ChainCluster, a cooperative Drive-thru Internet scheme. ChainCluster selects appropriate vehicles to form a linear cluster on the highway. The cluster members then cooperatively download the same content file, with each member retrieving one portion of the file, from the roadside infrastructure. With cluster members consecutively driving through the roadside infrastructure, the download of a single vehicle is virtually extended to that of a tandem of vehicles, which accordingly enhances the probability of successful file download significantly. With a delicate linear cluster formation scheme proposed and applied, in this paper, we first develop an analytical framework to evaluate the data volume that can be downloaded using cooperative drive-thru. Using simulations, we then verify the performance of ChainCluster and show that our analysis can match the simulations well. Finally, we show that ChainCluster can outperform the typical studied clustering schemes and provide general guidance for cooperative content distribution in highway vehicular communications.

In this paper, we investigate the asymptotic performance limits (throughput capacity and average packet delay) of social-proximity vehicular networks. The considered network involves N vehicles moving and communicating on a scalable grid-like street layout following the social-proximity model: Each vehicle has a restricted mobility region around a specific social spot and transmits via a unicast flow to a destination vehicle that is associated with the same social spot. Moreover, the spatial distribution of the vehicle decays following a power-law distribution from the central social spot toward the border of the mobility region. With vehicles communicating using a variant of the two-hop relay scheme, the asymptotic bounds of throughput capacity and average packet delay are derived in terms of the number of social spots, the size of the mobility region, and the decay factor of the power-law distribution. By identifying these key impact factors of performance mathematically, we find three possible regimes for the performance limits. Our results can be applied to predict the network performance of real-world scenarios and provide insight on the design and deployment of future vehicular networks.

The packet loss burstiness over wireless channels is commonly acknowledged as a key impacting factor on the performance of networking protocols.An accurate evaluation of the packet loss burstiness, which reveals the characteristics and performance of the wireless channels, is crucial to the design of wireless systems and the quality-of-service provisioning to end users.In this paper, a simple yet accurate analytical framework is developed to dimension the packet loss burstiness over generic wireless channels.In specific, we first propose a novel and effective metric to characterize the packet loss burstiness, which is shown to be more compact, effective, and accurate than the metrics proposed in existing literature for the same purpose.With this metric, we then develop an analytical framework and derive the closed-form solutions of the packet loss performance, including the packet loss rate and the loss-burst/loss-gap length distributions.Lastly, as an example to show how the derived results can be applied to the design of wireless systems, we apply the analytical results to devise an adaptive packetization scheme.The proposed packetization scheme adaptively adjusts the packet length of transmissions based on the prediction of the packet loss rate and loss-burst/loss-gap lengths of the wireless channel.Via extensive simulations, we show that with the proposed packetization scheme, the channel throughput can be enhanced by more than 10% than the traditional scheme.

The recent advances in wireless communication techniques have made possible for vehicles to download from the roadside communications infrastructure, namely drive-thru Internet. However, due to the fast-motions, harsh and intermittent wireless channels, the download volume of individual vehicles per drive-thru is quite limited as observed in real-world tests. This severely restricts the service quality of upper-layer applications, such as file download and video streaming. To address this issue, we take a historical approach by evaluating the integrated download throughput of a cooperative vehicle group in the highway environment. In specific, we first introduce a practical microscopic vehicular mobility model, which takes the randomness of speed update and safety distance requirement into account. Then, we analyze and formulate the number of contending vehicles within the coverage range of access point (AP) in the single-lane highways scenario, which can also be easily extended into the multi-lane highways scenario. Furthermore, we derive the data download volume by a vehicle per drive-thru, and analyze the relationship between the mobility speed and the data download volume. Finally, we derive the number of cooperative vehicles required for completing a download task in our investigated highways drive-thru Internet. The analytical model and evaluation results provide general guidance for cooperative content distribution and protocol design in drive-thru Internet.

The effective inter-vehicle transmission of content files, e.g., images, music and video clips, is the basis of media communications in vehicular networks, such as social communications and video sharing. However, due to the presence of diverse node velocities, severe channel fadings and intensive mutual interferences among vehicles, the inter-vehicle or vehicle-to-vehicle (V2V) communications tend to be transient and highly dynamic. Content transmissions among vehicles over the volatile and spotty V2V channels are thus susceptible to frequent interruptions and failures, resulting in many fragment content transmissions which are unable to finish during the connection time and unusable by on-top media applications. The interruptions of content transmissions not only lead to the failure of media presentations to users, but the transmission of the invalid fragment contents would also result in the significant waste of precious vehicular bandwidth. On addressing this issue, in this work we target on provisioning the integrity-oriented inter-vehicle content transmissions. Given the initial distance and mobility statistics of vehicles, we develop an analytical framework to evaluate the data volume that can be transmitted upon the short-lived and spotty V2V connection from the source to the destination vehicle. Provided the content file size, we are able to evaluate the likelihood of successful content transmissions through the model. Based upon this analysis, we propose an admission control scheme at the transmitters, that filters the suspicious content transmission requests which are unlikely to be accomplished over the transient inter-vehicle links. Using extensive simulations, we demonstrate the accuracy of the developed analytical model, and the effectiveness of the proposed admission control scheme. In the simulated scenario, with the proposed admission control scheme applied, it is observed that about 30% of the network bandwidth can be saved for effective content transmissions.

Without direct path, information delivery in sparse delay tolerant networks (DTNs) typically relies on intermittent relays, making the transmission not only unreliable but also time consuming. To make the matter even worse, the source nodes may transmit some encrypted “junk” information, similar as the spam emails in current mail systems, to the destinations; without effective control, the delivery of encrypted junk information would significantly consume the precious resource of DTN and accordingly throttle the network efficiency. To address this challenging issue, we propose PReFilter, an efficient privacy-preserving relay filter scheme to prevent the relay of encrypted junk information early in DTNs. In PReFilter, each node maintains a specific filtering policy based on its interests, and distributes this policy to a group of “friends” in the network in advance. By applying the filtering policy, the friends can filter the junk packets which are heading to the node during the relay. Note that the keywords in the filtering policy may disclose the node's interest/preference to some extent, harming the privacy of nodes, a privacy-preserving filtering policy distribution technique is introduced, which will keep the sensitive keywords secret in the filtering policy. Through detailed security analysis, we demonstrate that PReFilter can prevent strong privacy-curious adversaries from learning the filtering keywords, and discourage a weak privacy-curious friend to guess the filtering keywords from the filtering policy. In addition, with extensive simulations, we show that PReFilter is not only effective in the filtering of junk packets but also significantly improve the network performance with the dramatically reduced delivery cost due to the junk packets.

In this paper, the asymptotic capacity and delay performance of social-proximity urban vehicular networks with inhomogeneous vehicle density are analyzed. Specifically, we investigate the case of N vehicles in a grid-like street layout while the number of road segments increases linearly with the population of vehicles. Each vehicle moves in a localized mobility region centered at a fixed social spot and communicates to a destination vehicle in the same mobility region via a unicast flow. With a variant of the two-hop relay scheme applied, we show that social-proximity urban networks are scalable: a constant average per-vehicle throughput can be achieved with high probability. Furthermore, although the throughput and delay of a unicast flow may degrade in a high density area, almost constant per-vehicle throughput Ω(1/log (N)) and almost constant delay O(log2(N)) (except for the polylogarithmic factor) are still achievable with high probability. By identifying the key impact factors of performance mathematically, our results should provide insight on the design and deployment of future vehicular networks.

The pervasive adoption of IEEE 802.11 radios in the past decade has made possible for the easy Internet access from a vehicle, notably drive-thru Internet. Originally designed for the static indoor applications, the throughput performance of IEEE 802.11 in the outdoor vehicular environment is, however, still unclear especially when a large number of fast-moving users transmitting simultaneously. In this paper, we investigate the performance of IEEE 802.11 DCF in the highly mobile vehicular networks. We first propose a simple yet accurate analytical model to evaluate the throughput of DCF in the large scale drive-thru Internet scenario. Our model incorporates the high-node mobility with the modeling of DCF and unveils the impacts of mobility (characterized by node velocity and moving directions) on the resultant throughput. Based on the model, we show that the throughput of DCF will be reduced with increasing node velocity due to the mismatch between the MAC and the transient high-throughput connectivity of vehicles. We then propose several enhancement schemes to adaptively adjust the MAC in tune with the node mobility. Extensive simulations are carried out to validate the accuracy of the developed analytical model and the effectiveness of the proposed enhancement schemes.

Next generation communication networks are anticipated to make use of renewable energy sources, e.g., solar and wind power, to reduce carbon footprints and achieve an environmentally sustainable system. However, renewable energy sources have the limitation of unstable availability and capacity, which introduces new challenges for network planning and resource management. In this paper, adaptive resource management is introduced for wireless mesh networks that are powered by sustainable energy sources. The objective is to address the unreliability of the energy supply and to maximize the energy sustainability of the network, or equivalently, minimize the probability that mesh access points (APs) deplete their energy and go out of service. Specifically, the energy buffer of a mesh AP is modeled as a G/G/1 queue and a diffusion approximation is applied to analyze the transient evolution of the queue length and energy depletion duration. Based on the analysis, a resource management scheme is proposed to adaptively distribute traffic over various relay paths across the network and a distributed admission control strategy is applied to further guarantee high resource utilization under the energy sustainability constraint. By considering the first and second order statistics of the energy charging and discharging processes, it is demonstrated that the proposed scheme outperforms some existing state-of- the-art solutions.

Location privacy is one of the Quality of Privacies (QoP) in vehicular ad hoc network (VANET) and imperative for the VANET's full flourish. Frequent pseudonym changing can provide a promising solution to achieve location privacy, however if the pseudonyms are changed in an improper occasion, the solution is ineffective. In this paper, to improve the effectiveness of this kind of solution, we first introduce the social spot where many vehicles could aggregate, e.g., a road intersection when the traffic light is red or a free parking lot near a shopping mall. We then propose a social spot based pseudonyms changing technique to achieve the location privacy. By taking the anonymity set size as the privacy metric, we develop two anonymity analytic models to quantitatively investigate the location privacy achieved in the technique. The analytical results show that better location privacy can be achieved when a vehicle changes its pseudonyms at some highly social spots, and as a result, the proposed models can be used to assist vehicles to change their pseudonyms for better location privacy at the right moment and place.

The copious social and user generated contents, like Facebook and Youtube, are re-shaping the way people share, access, and digest information. Although flourishing in Internet, content sharing services are still considered expensive and not ready for mobile users of vehicular networks. In this paper, we propose VTube, an autonomous and cost-effective infrastructure, to facilitate the localized content publish/subscribe in an urban area. VTube relies on the distributed low-cost light-weight storage buffers, namely roadside buffer, installed in the city facilities, such as stores, museums, cafeteria, etc., to cache and publish contents for mobile users. The contents at different storage buffers are then transported to different locations by moving vehicles and cached collaboratively in both vehicles and storage buffers across the city. In this work, we unfold the design of VTube by first presenting the detailed design principles and practices of VTube. Given the content availability and capacity of the buffer storage, we then develop a mathematical model to evaluate the mean download delay of mobile users. Using the delay as an input, we formulate the content replication problem in roadside buffers as a stochastic programming problem to attain the mean system-wide minimum download delay. Finally, we propose a fully distributed random walk based algorithm to solve the optimization problem. Extensive simulations demonstrate that VTube can minimize the download delay of users because of the exploitation of vehicle mobility and distributed buffer storage at different locations.

To address the impact of the network dynamics on video streaming, the playout buffer is typically deployed at the receiver. With different buffer storage, users thus have different tolerance to the network dynamics. In this paper, we exploit this feature for channel allocation in cognitive radio (CR) networks. We first model the channel availability as an on-off process which is stochastically known. Based on the bandwidth capacity and the specific buffer storage of users, we then intelligently allocate the channels to maximize the overall network throughput while providing users with the smooth video playback, which is formulated as an optimization framework. Given the channel conditions and the video packet storage in the playout buffer, we propose a centralized scheme for provisioning the superior video service to users. Simulation results confirm that by exploiting the playout buffer of users, the proposed channel allocation scheme is robust against intense network dynamics and provides users with the elongated smooth video playback.

For peer-to-peer (P2P) networks continually to flourish, QoS provision is critical. However, the P2P networks are notoriously dynamic and heterogeneous. As a result, QoS provision in P2P networks is a challenging task with nodes of the varying and intermittent throughput. This raises a fundamental problem: is stable and delicate QoS provision achievable in the highly dynamic and heterogeneous P2P networks? In this work, we investigate BitTorrent (BT) with the particular interest in its QoS performance in the highly dynamic and heterogeneous network. Our contributions are two-fold. First, we develop an analytical model to examine a randomly selected BT node under a microscope. Based on the model, we study the mean and variance of nodal download rate in the dynamic network and the performance of BT in QoS provision under different levels of peer churns. Our analysis unveils that although BT strives to provide nodes with guaranteed throughput, due to the network dynamics, the download rates of the peers oscillate extraordinarily and can hardly converge to the target QoS as proposed in previous literature. Second, to improve the QoS provision, we propose an enhanced protocol incorporating with BT. The proposed protocol enables nodes to quickly and elaborately search their uploaders, and as a result, achieve guaranteed and stable QoS in the dynamic networks. Using both analysis and simulations, we validate the effectiveness of the proposed protocol in comparisons with the original BT.

The newly emerged vehicular ad hoc network adopts the contention based IEEE 802.11 DCF as its MAC. While it has been extensively studied in the stationary indoor environment (e.g., WLAN), the performance of DCF in the highly mobile vehicular environment is still unclear. On addressing this issue, we propose a simple but accurate analytical model to evaluate the throughput performance of DCF in the high speed vehicle-to-infrastructure (V2I) communications. We unveil the impacts of nodes mobility (velocity and moving directions) on the system throughput. Particularly, we show that with node velocity increasing, the throughput of DCF decreases monotonically, which demonstrates the inefficiency of DCF in the highly mobile environment. Via extensive simulations, we validate the accuracy of the developed analytical model and finally discuss the method to optimize DCF towards the maximal throughput.

We develop an analytical framework to investigate the impacts of network dynamics on the user perceived video quality. Our investigation stands from the end user's perspective by analyzing the receiver playout buffer. In specific, we model the playback buffer at the receiver by a$G/G/1/\infty$and$G/G/1/N$queue, respectively, with arbitrary patterns of packet arrival and playback. We then examine the transient queue length of the buffer using the diffusion approximation. We obtain the closed-form expressions of the video quality in terms of the start-up delay, fluency of video playback and packet loss, and represent them by the network statistics, i.e., the average network throughput and delay jitter. Based on the analytical framework, we propose adaptive playout buffer management schemes to optimally manage the threshold of video playback towards the maximal user utility, according to different quality-of-service requirements of end users. The proposed framework is validated by extensive simulations.