Search Results (188)

The Internet of Vehicles (IoV) is reshaping intelligent transportation through pervasive connectivity, real-time data exchange, cooperative perception, and vehicle–edge–cloud services, while also expanding cybersecurity and privacy risks across heterogeneous cyber–physical environments. This paper presents a PRISMA 2020-informed systematic review of IoV security and privacy protection research. A cross-layer and lifecycle-oriented analytical framework is developed by integrating a four-layer IoV architecture—sensing layer, network access layer, coordinative computing layer, and application layer—with a five-stage data lifecycle covering data collection, transmission, storage, usage, and disposal. Based on this framework, the paper examines representative threat surfaces, vehicle-to-everything (V2X) communication security, public key infrastructure (PKI) based authentication, trust management, privacy-preserving data sharing, intrusion detection, active defense, and AI-assisted security analytics. Privacy-preserving mechanisms, including differential privacy, federated learning, blockchain, homomorphic encryption, and secure multi-party computation, are further compared in terms of deployment layer, lifecycle stage, real-time suitability, and representative performance evidence. In addition, the review discusses the engineering relevance of UNECE WP.29 R155/R156, ISO/SAE 21434, and related national standards, with emphasis on compliance evidence, over-the-air (OTA) governance, supply-chain coordination, and lifecycle cybersecurity management. The review shows that no single protection mechanism can simultaneously satisfy the requirements of real-time performance, scalability, privacy preservation, trustworthiness, and regulatory compliance in dynamic IoV environments. Future research should emphasize lightweight and adaptive protection, cross-layer trust coordination, privacy–utility co-optimization, trustworthy AI-assisted security operations, and evidence-based lifecycle governance. This review provides a structured reference for researchers and a practical basis for secure and privacy-aware IoV system design. Full article

Resource allocation for cognitive Vehicle-to-Everything (V2X) networks is challenging due to dynamic spectrum sharing, strong interference coupling, and stringent latency constraints for safety-critical Vehicle-to-Vehicle (V2V) traffic. Although recent Multi-Agent Reinforcement Learning (MARL) approaches report promising gains, many evaluations are conducted at limited and fixed network scales, which restricts insights into scalability under dense spectrum reuse. This paper investigates cooperative multi-agent learning for interference-aware and deadline-constrained V2X resource management. We propose a Q-value Transformation (QTRAN)-based value decomposition framework under centralized training with decentralized execution (CTDE) for joint resource-block and power allocation among V2V agents. The proposed approach is implemented in a realistic V2V/V2I simulator incorporating Manhattan grid mobility, fast fading, explicit cross-tier and co-channel interference, and per-link payload/deadline dynamics. Beyond communication-level performance, improved timely delivery of V2V safety messages can support cooperative maneuvering, collision avoidance, platooning, and infrastructure-assisted traffic management. Extensive simulations across varying numbers of V2V agents benchmark QTRAN against independent learning baselines including MARL and centralized single-agent learning (SARL). Results show that QTRAN improves performance compared with the selected learning baselines and enhances the throughput–reliability trade-off under interference-coupled spectrum reuse. For instance, at $N_{\mathrm{V}2\mathrm{V}}=20$, QTRAN achieves a V2V rate of $0.194\pm 0.004$ and a V2I rate of $9.117\pm 0.213$, while reaching a V2V success rate of $0.812\pm 0.017$ with a low Deadline Miss Ratio of $0.001\pm 0.000$. At higher density ($N_{\mathrm{V}2\mathrm{V}}=50$), QTRAN sustains strong reliability (V2V success rate of $0.719\pm 0.006$ and Completion Ratio of $0.716\pm 0.006$) while maintaining competitive infrastructure throughput (V2I rate of $9.251\pm 0.114$). These results indicate that QTRAN effectively captures non-linear interference interactions, enabling coordinated decentralized spectrum and power decisions under the adopted density-based evaluation setting, thereby enhancing V2V reliability and throughput in cognitive Internet of Vehicles. Full article

Vehicle-to-Everything (V2X) communication is a critical enabler of autonomous driving, supporting real-time information exchange among vehicles, roadside infrastructure, pedestrians, and cloud services. However, the security of current V2X systems largely relies on classical cryptographic mechanisms, which are expected to become vulnerable in the presence of large-scale quantum computers. Given the long operational lifespan and stringent safety requirements of autonomous vehicular networks, the transition toward quantum-resistant authentication and key management mechanisms has become increasingly important. This paper presents a comprehensive survey of post-quantum Authentication and Key Agreement (AKA) protocols for secure V2X communications. The survey systematically reviews V2X communication architectures, security and privacy requirements, existing authentication frameworks, and emerging post-quantum cryptographic approaches. Representative AKA schemes and NIST-standardized post-quantum algorithms are comparatively analyzed in terms of security strength, computational complexity, communication overhead, storage requirements, scalability, and deployment suitability for resource-constrained vehicular environments. The survey further examines practical implementation challenges, including latency constraints, bandwidth limitations, signature size expansion, memory consumption, and hardware resource requirements. The analysis reveals that achieving quantum-resistant security in V2X networks requires balancing strong cryptographic protection with the stringent performance demands of safety-critical vehicular applications. While recent post-quantum approaches offer promising security guarantees against quantum adversaries, their practical deployment remains constrained by computational and communication overhead. Finally, this survey identifies key research gaps and outlines future directions for the development of lightweight, scalable, and quantum-resilient AKA frameworks capable of supporting next-generation autonomous transportation systems. The findings provide researchers and practitioners with a structured understanding of the opportunities, limitations, and challenges associated with securing future V2X communications in the quantum era. Full article

To further enhance the covert communication capability of satellite–ground-integrated sensor networks, a dual-polarization constellation joint modulation scheme based on frequency-hopping double-layer multi-parameter weighted fractional Fourier transform (FH-DL-MPWFRFT) is proposed from the perspective of physical layer security. The proposed scheme integrates the constellation confusion property of weighted fractional Fourier transform (WFRFT) with the anti-interception capability of frequency-hopping (FH) phase scrambling. Specifically, the weighted parameters of conventional 4-WFRFT are extended to construct a multi-parameter and multi-layer signal representation, and FH phase scrambling is introduced to realize dynamic constellation rotation and phase-domain encryption. Furthermore, a secure transmission model for satellite–ground-integrated sensor networks is established, revealing the constellation optimization principle and the fission-fusion mechanism of dual-polarization signals. Simulation results show that, compared with the non-FH benchmark, the proposed scheme significantly improves waveform-level anti-interception performance; even when eavesdropper obtains the modulation scheme and partial transform parameters, the symbol error rate (SER) of quadrature phase shift keying (QPSK) and four-phase modulation (4PM) signals remains around 0.4 to 0.5 under parameter mismatch, indicating that effective demodulation is difficult to achieve. Full article

The Internet of things (IoT) is rapidly pervading daily life and linking everything. Although higher connectivity offers many benefits, including higher productivity, robotic processes, and decision-making guided by data, it also poses a number of security dangers. Modern risks to data authenticity and confidence are getting harder to handle through typical central safety solutions. In this paper, we present a detailed investigation of the latest innovations and approaches for assessing reputation and confidence in the blockchain-empowered Internet of Things (BIoT) area. A comprehensive literature search was conducted across major electronic databases, including IEEE, Springer, Elsevier, Wiley, MDPI, and top indexed conference proceedings. The publication year was restricted to the period from 2018 to 2025. The methodological quality of a total of 122 studies met the inclusion criteria assessed using predefined quality measures. We figure out existing flaws at each layer of IoT architecture, illustrating how autonomous, transparent, and impenetrable blockchain ledgers address these flaws. Plus, we analytically compare public, private, consortium, and hybrid blockchain networking architectures to emphasize the underlying compromises among security, reliability, and decentralization. We also assess how reputation evaluation techniques evolved over time, moving from classical fuzzy logic and weighted average models to modern mature game theory and machine learning (ML) models, addressing their limitations in terms of computational overhead, scalability, adaptability, and deployment feasibility in IoT systems. Additionally, we outline future directions for BIoT system trust assessment and identify research limitations and potential solutions. Our research indicates that although ML-driven models offer more accurate predictions for identifying illicit node activities, they are still constrained by limited unbalanced data and high processing overhead. Full article

This paper presents a unified perspective on Orthogonal Frequency-Division Multiplexing (OFDM)-based joint radar–communication (JRC) sensing, focusing on the efficient reuse of time and frequency resources in range–Doppler estimation and imaging scenarios. By leveraging OFDM’s inherent subcarrier orthogonality, noise-like temporal properties, and minor carrier frequency offsets, these systems can support concurrent transmissions over the same spectral and temporal resources while maintaining interference resilience. Experimental and simulation-based insights demonstrate the feasibility of simultaneous sensing across users and antennas, even in dense Radio Frequency (RF) environments. We analyze trade-offs, implementation considerations, and system-level implications to provide a consolidated foundation for designing future OFDM-based JRC systems. The feasibility of an Orthogonal Time Frequency Space (OTFS) waveform for the proposed method is also investigated. The review highlights the potential of such architectures in spectrum and time-congested applications such as Vehicle-to-Everything (V2X), indoor localization, Internet of Things (IoT), and beyond fifth-generation (5G) networks. Full article

The convergence of the Internet of Everything (IoE) and healthcare requires ultra-reliable, low-latency, and intelligent communication systems. Sixth-generation (6G) wireless networks, coupled with digital twin (DT) models and large AI models (LAMs), are envisioned to promise substantial and practically meaningful improvements in smart healthcare by enabling real-time monitoring, diagnosis, and personalized treatment. In this article, we propose an LAM-enhanced DT-driven network slicing framework for healthcare applications. The framework leverages large models to provide predictive insights and adaptive orchestration by creating virtual replicas of patients and medical devices that guide dynamic slice allocation. Reinforcement learning (RL) techniques are employed to optimize slice orchestration under uncertain traffic conditions, with LAMs augmenting decision-making through cognitive-level reasoning. Numerical results show that the proposed LAM–DT–RL framework reduces service-level agreement (SLA) violations by approximately 42–43% compared to a reinforcement-learning-only slicing strategy, while improving spectral efficiency and fairness among heterogeneous healthcare services. Finally, we outline open challenges and future research opportunities in integrating LAMs, DTs, and 6G for resilient healthcare IoE systems. Full article

The anticipated transition from 5G to 6G is driven not by incremental performance demands but by a widening mismatch between emerging application requirements and the capabilities of existing cellular systems. Despite rapid progress across 3GPP Releases 15–20, the current literature lacks a unified analysis that connects these standardization milestones to the concrete technical gaps that 6G must resolve. This study addresses this omission through a cross-release, application-driven review that traces how the evolution from enhanced mobile broadband to intelligent, sensing integrated networks lays the foundation for three core 6G service pillars: immersive communication (IC), everything connected (EC), and high-precision positioning. By examining use cases such as holographic telepresence, cooperative drone swarms, and large-scale Extended Reality (XR) ecosystems, this study exposes the limitations of today’s spectrum strategies, network architectures, and device capabilities and identifies the performance thresholds of Tbps-level throughput, sub-10 cm localization, sub-ms latency, and 10 M/km² device density that next-generation systems must achieve. The novelty of this review lies in its synthesis of 3GPP advancements in XR, the non-terrestrial network (NTN), RedCap, ambient Internet of Things (IoT), and consideration of sustainability into a cohesive key performance indicator (KPI) framework that links future services to the required architectural and protocol innovations, including AI-native design and sub-THz operation. Positioned against global initiatives such as Hexa-X and the Next G Alliance, this paper argues that 6G represents a fundamental redesign of wireless communication advancement in 5G, driven by intelligence, adaptability, and long-term energy efficiency to satisfy diverse uses cases and requirements. Full article

Symmetric cryptographic schemes such as RSA and ECDSA (Elliptic Curve Digital Signature Algorithm), used for digital signatures in protocols like TLS, DTLS, and secure messaging, are computationally intensive. This makes them unsuitable for constrained environments, such as the Internet of Things (IoT) and the Internet of Vehicles (IoV). This study introduces a blockchain-based framework that utilizes the Ethereum network to store and verify public keys associated with digital certificates. By replacing signature decryption with blockchain-based public key verification, the solution significantly reduces cryptographic overhead and latency in V2V messages. It supports various certificate formats, including Public Key Infrastructure (PKI)/Certificate Authority (CA) certificates such as X.509 and L-ECQV, as well as self-signed certificates. Applications include secure communication protocols like Datagram Transport Layer Security (DTLS)/Transport Layer Security (TLS), V2V mutual authentication in V2X messaging, and lightweight certificate management within IoT ecosystems. Empirical results show that the DTLS handshake with this scheme is reduced from 12 s to less than 6 s. Additionally, it enables vehicles and IoT devices to perform one-time signature verification with minimal latency in V2V messaging, demonstrating significant performance improvements for high-density deployments involving mutual authentication between IoT devices and V2V communication. Full article

In the era of the ‘Internet of Everything’ and amid growing demands for high-quality economic development, public data has emerged as a new core factor of production, establishing itself as a pivotal force behind regional economic growth. However, existing research rarely clarifies the multi-dimensional impact and influence mechanism of public data openness on regional development, and there are still deficiencies in the research on transforming the advantages of data elements into sustainable economic driving forces. This study, in conjunction with the interpretation of data elements, employed a fixed-effects model to empirically investigate the impact and path of public data opening on the high-quality development of regional economies, using panel data from 31 provincial regions in China from 2017 to 2024. Empirical findings provide clear evidence that public data openness acts as a significant catalyst for high-quality economic development, thereby solidifying its role as an indispensable engine for sustainable growth in the digital era. Analysis of the underlying mechanisms reveals two primary channels: business environment optimization and improved factor allocation efficiency, with the latter proving to be the more significant driver. Furthermore, heterogeneity analysis reveals that the positive effects are most pronounced in fostering economic structural optimization, advancing the low-carbon environment and expanding shared public welfare, while their influence on innovation dynamism remains comparatively modest. The research results support the government in increasing the openness of public data, establishing and improving a data opening mechanism oriented towards the business environment, and deepening the integration and application of data to enhance the efficiency of factor allocation. Full article

The growing demand for internet-of-vehicles (IoV) communication requires compact antennas capable of supporting multiple frequency bands while maintaining stable radiation characteristics. This paper presents the design and validation of a multilayer microstrip patch antenna that achieves dual-band operation through the integration of shorting vias, a coupled ring, and an embedded parasitic patch. Parametric studies confirm that the adopted techniques yield impedance bandwidths of 28% at 1.8 GHz and 6.4% at 2.4 GHz, with a low-profile structure of $0.055{\lambda }_0$. Measured results demonstrate omnidirectional radiation patterns across the intended bands with a maximum gain of 4.46 dBi at 2.57 GHz. Beyond simulated and laboratory verification, field tests were conducted using LTE communication to evaluate the antenna’s quality of service (QoS) under realistic vehicular conditions. To reduce system cost and simplify testing, a low-cost in-house signal meter based on a Raspberry Pi microcontroller was developed and employed to compare the proposed antenna with a commercial monopole. The results confirm that the multilayer patch antenna provides improved bandwidth, gain, and radiation stability, making it a compact and cost-effective candidate for multiband IoV and V2X communication systems. Full article

The Internet of Vehicles (IoV) relies on Vehicle-to-Everything (V2X) communications to enable cooperative perception among vehicles, infrastructures, and devices, where Vehicle-to-Infrastructure (V2I) links are crucial for reliable transmission. However, the openness of wireless channels exposes IoV to eavesdropping, threatening privacy and security. This paper investigates an Intelligent Reflecting Surface (IRS)-assisted interweave Cognitive IoV (CIoV) network to enhance physical layer security in V2I communications. A non-convex joint optimization problem involving spectrum allocation, transmit power for Vehicle Users (VUs), and IRS phase shifts is formulated. To address this challenge, a heterogeneous multi-agent (HMA) Mamba RainbowDQN algorithm is proposed, where homogeneous VUs and a heterogeneous secondary base station (SBS) act as distinct agents to simplify decision-making. Simulation results show that the proposed method significantly outperform benchmark schemes, achieving a 13.29% improvement in secrecy rate and a 54.2% reduction in secrecy outage probability (SOP). These results confirm the effectiveness of integrating IRS and deep reinforcement learning (DRL) for secure and efficient V2I communications in CIoV networks. Full article

Continuous and near-real-time monitoring of greenhouse gases (GHGs) is critical for achieving Net Zero emissions, ensuring early detection, compliance, accountability, and adaptive management. To this end, there is an increasing need to monitor GHGs at higher temporal resolutions, greater spatial resolutions, and larger coverage scales. However, spatial resolution and coverage remain significant challenges due to limited sensor network coverage and power sources for sensor nodes, even in urban areas. LoRaWAN, a cost-effective solution that provides long-range and high-penetration wireless connectivity with a low energy consumption, is an ideal choice for this application. Despite its promise, LoRaWAN faces several challenges, including a low data rate, low packet transmission rate, and low packet delivery success ratio, especially when the node density or environment variability is high. This paper presents a simulation-based analysis of a large-scale urban LoRaWAN sensor network operating in the CN470–510 MHz band, which is the only frequency band officially designated for low-power wide-area (LPWA) technologies such as LoRaWAN in China. This study investigates how the node density, sensor measurement update rate (i.e., update interval), and sensor measurement payload size affect two primary performance metrics: the sensor update delivery ratio (DR) and the radio frequency (RF) energy consumption (RFEC) per successful update. The performances of several enhanced adaptive data transmission algorithms in comparison to the conventional ADR+ algorithms are also analysed. The results indicate that both DR and RFEC are significantly influenced by the node density, sensor update rate, and payload size, with the effects being particularly significant under high-node-density and high-update-rate conditions. The analysis further reveals that the ADR-NODE-KAMA algorithm consistently achieves the best performance across most scenarios, providing up to a 2% improvement in DR and a reduction of 10–15 mJ in RFEC per successful sensor measurement update. Additionally, the sensor measurement payload size is shown to have a substantial impact on network performance, with each added sensor measurement contributing to a DR reduction of up to 2.24% and an increase in RFEC of approximately 80 mJ. LoRaWAN network operators can gain practical insights from these findings to optimize the performance and efficiency of large-scale GHG monitoring deployments. Full article

The increasing number of intelligent connected vehicles (ICVs) is leading to a growing scarcity of spectrum resources for the Internet of Vehicles (IoV), which has created an urgent need for the use of full-duplex non-orthogonal multiple access (FD-NOMA) techniques in vehicle-to-everything (V2X) communications. Meanwhile, for the flexibility of autonomous aerial vehicles (AAVs), V2X communications assisted by AAVs are regarded as a potential solution to achieve reliable communication between ICVs. However, if the integration of FD-NOMA and AAVs can satisfy the requirements of V2X communications, then quickly and accurately analyzing the total achievable rate becomes a challenge. Motivated by the above, an accurate analytical expression for the total achievable rate over Rician fading channels is proposed to evaluate the transmission performance of NOMA-enhanced AAV-assisted IoV with imperfect channel state information (CSI). Then, we derive an approximate expression with the truncated error, based on which the closed-form expression for the approximate error is theoretically provided. Finally, the simulation results demonstrate the accuracy of the obtained approximate results, where the maximum approximate error does not exceed 0.5%. Moreover, the use of the FD-NOMA technique in AAV-assisted IoV can significantly improve the total achievable rate compared to existing work. Furthermore, the influence of key network parameters (e.g., the speed and Rician factor) on achievable rate is thoroughly discussed. Full article

Ontology technology addresses data heterogeneity challenges in Internet of Everything (IoE) systems enabled by Cyber Twin and 6G, yet the subjective nature of ontology engineering often leads to differing definitions of the same concept across ontologies, resulting in ontology heterogeneity. To solve this problem, this study introduces a hybrid ontology matching method that integrates a Recurrent Neural Network (RNN) with syntax-based analysis. The method first extracts representative entities by leveraging in-degree and out-degree information from ontological tree structures, which reduces training noise and improves model generalization. Next, a matching framework combining RNN and N-gram is designed: the RNN captures medium-distance dependencies and complex sequential patterns, supporting the dynamic optimization of embedding parameters and semantic feature extraction; the N-gram module further captures local information and relationships between adjacent characters, improving the coverage of matched entities. The experiments were conducted on the OAEI benchmark dataset, where the proposed method was compared with representative baseline methods from OAEI as well as a Transformer-based method. The results demonstrate that the proposed method achieved an 18.18% improvement in F-measure over the best-performing baseline. This improvement was statistically significant, as validated by the Friedman and Holm tests. Moreover, the proposed method achieves the shortest runtime among all the compared methods. Compared to other RNN-based hybrid frameworks that adopt classical structure-based and semantics-based similarity measures, the proposed method further improved the F-measure by 18.46%. Furthermore, a comparison of time and space complexity with the standalone RNN model and its variants demonstrated that the proposed method achieved high performance while maintaining favorable computational efficiency. These findings confirm the effectiveness and efficiency of the method in addressing ontology heterogeneity in complex IoE environments. Full article