Search Results (293)

In smart cities, crowding in transportation hubs, large event venues, and commercial/tourist districts can rapidly escalate from service congestion to public-safety incidents. Real-world operations are constrained by heterogeneous sensing coverage, delayed statistics, privacy requirements, and the need for accountable multi-agency decisions. Following a rigorous PRISMA protocol, we synthesized 107 primary empirical studies (2020–2026) to systematically review context-aware crowd technologies. Moving beyond isolated algorithmic benchmarks, we organized these advances into a mathematically formalized closed-loop framework (Sensing–Prediction–Intervention–Feedback). Crowd sensing has evolved toward edge-based computer vision, passive mobile signaling, and multimodal fusion to balance operational trade-offs among density applicability, environmental robustness, privacy burdens, and end-to-end latency. Prediction architectures—converging on Spatiotemporal Graph Neural Networks (ST-GNNs) and simulation-augmented digital twins—are critically evaluated against constraints in predictive horizon, computational overhead, and explainability. To bridge theory and practical deployment, we deduce a multidimensional evaluation taxonomy and a hierarchical trigger-and-escalation matrix, tailoring control philosophies (e.g., spatiotemporal capacity synchronization and dynamic demand reshaping) to the three heterogeneous scenarios. Finally, we propose four strategic directions to chart a deployment-oriented roadmap for the integrated Urban Brain: edge-based privacy-preserving fusion, cross-scenario generalization, accountable Explainable Artificial Intelligence (XAI) with human-in-the-loop synergy, and end-to-end outcome-oriented empirical validation. Full article

Background/Objectives: This scoping review examined recent evidence on the use of transformer-based language models, encompassing encoder-only architectures (e.g., BERT and its clinical variants) and generative large language models (LLMs; e.g., GPT-4 and Llama), to support clinical decision making from unstructured clinical notes, with implications for behavioral-health services where narrative documentation is central. Methods: Following PRISMA-ScR guidelines, PubMed, PsycINFO, and Web of Science were searched for peer-reviewed studies published between 1 January 2023, and 5 August 2025. Studies applying transformer-based language models to clinical narratives for healthcare tasks and reporting evaluative outcomes were included. We extracted data on clinical tasks, model architectures, enhancement strategies, and evaluation metrics; mapped each study by primary purpose, care setting, and primary model approach; and charted reported validation design, direct human comparison, fairness assessment, workflow evaluation, and clinical deployment. Results: Thirty-six studies were included. Information extraction/de-identification and classification/prediction predominated, whereas summarization/generation was less commonly represented. Model approaches appeared to align with task characteristics: encoder-only and decoder-only systems were frequently used for extraction, encoder–decoder systems for generation, and hybrid or pipeline-based approaches for classification and prediction. Standard task-specific metrics (e.g., F1 and AUROC) predominated, whereas evidence beyond retrospective task performance, including direct human comparison, fairness assessment, workflow evaluation, clinical deployment, and temporal or external validation, was rare. No included study evaluated a transformer-based language model application within a behavioral-health service or behavioral-health workflow. Conclusions: Transformer-based language models have been applied across diverse clinical-note tasks, but the evidence base more strongly supports retrospective task feasibility than transportability, equitable performance, workflow benefit, or safe clinical deployment. Future research should prioritize transparent reference standards, external and prospective validation, clinically meaningful human comparison, and equity-focused evaluation, including direct studies in behavioral-health services. Full article

The rapid deployment of sixth-generation (6G) Internet of Things (IoT) networks demands lightweight authentication mechanisms that provide low latency, high scalability, and robust security for resource-constrained devices operating in dynamic wireless environments. Conventional authentication approaches based on Transport Layer Security (TLS) and Datagram Transport Layer Security (DTLS), blockchain-assisted architectures, and Generic Bootstrapping Architecture (GBA)-based schemes introduce significant computational complexity, communication overhead, synchronization delays, and infrastructure dependencies, limiting their suitability for large-scale edge-assisted IoT environments. This paper proposes Lightweight Timed Efficient Stream Loss-Tolerant Authentication over the Constrained Application Protocol (L-TESLA-CoAP), a lightweight and infrastructure-independent authentication framework that integrates adaptive TESLA delayed-key authentication, CoAP communication, edge-assisted synchronization, replay-aware synchronization, SHA3-HMAC-based symmetric authentication, and rotating pseudonym identities to provide continuous packet-level authentication. The proposed framework was implemented and evaluated using a Python-based simulation environment under constrained 6G IoT communication scenarios with network sizes ranging from 50 to 1000 IoT devices. The comparative evaluation against CoAP, DTLS, TLS, Blockchain-CoAP, and GBA-Hybrid TESLA shows that the proposed framework achieves low authentication latency (approximately 0.8–1.3 s) and low energy consumption (approximately 60–75 mJ) while maintaining packet-loss recovery capability, reduced communication overhead, reduced computation time, low memory consumption, and authentication throughput. Furthermore, the proposed framework provides resilience against replay, packet injection, impersonation, synchronization manipulation, and denial-of-service attacks through adaptive synchronization and delayed key disclosure. These results indicate that L-TESLA-CoAP provides an efficient, scalable, and lightweight authentication solution suitable for next-generation edge-assisted 6G IoT applications. Full article

Service-Based Architectures (SBAs) in 5G core and cloud-native deployments require differentiated treatment for service classes with heterogeneous latency, reliability, and throughput expectations. Although HTTP/3 over QUIC is an important evolution of the HTTP ecosystem, HTTP/2 remains operationally relevant in SBA environments where TCP/TLS-based infrastructures and 3GPP service-based interfaces continue to rely on HTTP/2 communication. This paper therefore focuses on HTTP/2 priority signaling and the problem of translating application-level Service Level Agreement (SLA) policies into protocol-level priority metadata. To address this problem, the paper presents an SLA-aware priority management system built around the RFC 9218 extensible prioritization scheme, specifically its urgency and incremental parameters. The system integrates three coordinated subsystems: a rule-based Priority Classification Engine (PCE), a feedback-driven Dynamic Priority Mapping Algorithm (DPMA), and a runtime priority-update manager that applies bounded priority adjustments under changing network and load conditions. The revised evaluation reports a 7200-observation baseline campaign covering four operating modes, ten service classes, nine network profiles, and twenty repetitions per service–profile–mode combination, together with a 14,880-observation scalability and overhead campaign across increasing concurrent-stream levels. Compared with the unmanaged HTTP/2 baseline, DPMA reduced mean latency by 24.8%, P95 latency by 35.1%, P99 latency by 38.0%, and SLA violations by 19.9 percentage points. Compared with the legacy RFC 7540 baseline, DPMA reduced mean latency by 39.0%, P95 latency by 49.3%, P99 latency by 49.9%, and SLA violations by 21.1 percentage points. Compared with the static RFC 9218 baseline, DPMA reduced mean latency by 38.7%, P95 latency by 48.1%, P99 latency by 50.6%, and SLA violations by 21.4 percentage points. The scalability analysis shows that DPMA maintained P95 latency between 126.8 ms and 128.2 ms over the tested 1–100 concurrent-stream range, with priority-update decision overhead below 0.004 ms per request. The results indicate that SLA-aware use of RFC 9218 priority metadata can improve latency and SLA-compliance behavior in controlled SBA-like HTTP/2 environments while preserving a transparent and auditable prioritization policy. Full article

The evolution of wireless networks toward 6G and Open Radio Access Network (O-RAN) architectures brings unprecedented demands for flexible and energy-efficient resource allocation mechanisms. A key challenge is to allocate radio resources effectively among heterogeneous units while satisfying diverse quality-of-service (QoS) requirements. Traditional allocation methods often fail to capture energy efficiency considerations or lack adaptability in highly dynamic and decentralized environments. To address this, we formulate the resource allocation problem as a non-cooperative game among SDN-enabled Central Units (CUs) and Distributed Units (DUs), where each player’s utility captures a trade-off between throughput gains and resource costs under threshold-based SINR QoS constraints. We show that the game admits an exact generalized potential function, guaranteeing the existence of a pure-strategy Nash equilibrium and convergence under sequential best response dynamics. The SDN controller supervises the network by adjusting system-level parameters, such as the resource price, to guide the network toward efficient and fair allocations. This formulation provides a rigorous and tractable framework for distributed spectrum sharing in 6G O-RAN systems, with the potential to support intelligent and adaptive control in future wireless networks. The proposed framework is evaluated against both classical resource allocation strategies (equal and greedy allocation) and advanced optimization-based and game-theoretic baselines, including convex optimization, proportional fairness, water-filling, and Stackelberg formulations, and shows competitive performance. Full article

With the rapid growth of electric vehicles (EVs) and renewable energy generation, Vehicle-to-Grid (V2G) technology has emerged as a promising approach for transforming EVs from passive charging loads into flexible distributed energy storage resources. By enabling bidirectional power exchange between EV batteries and the power grid, V2G can support renewable energy accommodation, peak shaving, demand response, ancillary services, and local grid balancing. This review provides a systematic synthesis of recent advances in V2G systems for renewable energy integration, with particular emphasis on coordinated scheduling, economic mechanisms, battery degradation, and user engagement. First, the technical foundations of V2G are introduced, including Vehicle-to-Everything operating modes, bidirectional charging architecture, aggregation mechanisms, grid-support services, and renewable accommodation pathways. Second, major scheduling strategies are reviewed, including price-based, load-based, renewable-forecast-driven, centralized, distributed, and hybrid approaches. Third, the economic feasibility of V2G is examined from the perspectives of revenue streams, pricing mechanisms, business models, battery aging costs, and compensation schemes. In addition, user participation barriers, such as range anxiety, battery lifetime concerns, loss of control, uncertain financial returns, and data privacy, are discussed. Key challenges related to communication standards, interoperability, cybersecurity, market access, policy design, and pilot-scale validation are also summarized. Finally, future development directions are identified, including AI-based scheduling, aggregator platforms, fleet-scale V2G, degradation-aware optimization, carbon-aware electricity markets, and user-centered participation mechanisms. This review highlights that large-scale V2G deployment requires the integrated coordination of technical scheduling, economic incentives, battery health protection, and user acceptance in renewable-rich power systems. Full article

To address the stringent demands of intelligent connected vehicles for high bandwidth, low latency, and highly reliable communication, this paper systematically summarizes the semantic communication technology of the Internet of Vehicles (IoV) based on information “meaning” transmission, covering basic theory, key technologies, application practice and challenge and trends. First, the paper expounds the knowledge driven and task oriented paradigm characteristics of semantic communication and its efficiency advantages in the IoV. Second, in terms of key technologies, semantic extraction achieves efficient feature compression through multimodal fusion and Generative Artificial Intelligence (GAI); semantic coding employs hierarchical codebooks and adaptive strategies to optimize transmission efficiency; semantic transmission leverages deep reinforcement learning for the joint scheduling of resources such as spectrum and power; and semantic decoding utilizes reconstruction networks and GAI to enhance resilience against impairments. Application practices demonstrate that semantic communication can significantly compress image data transmission volume for autonomous driving collaborative perception while maintaining high-fidelity reconstruction under adverse channel conditions. It significantly reduces the communication load and improves the system utility in vehicle-to-infrastructure coordination and in-vehicle service. Despite facing technical challenges such as semantic consistency, dynamic adaptability, and security trustworthiness, future semantic communication will evolve towards deep integration with distributed collaborative knowledge networks, lightweight real-time decision-making agents, and integrated “communication, sensing, and computing” architectures, positioning itself as a key enabling technology for empowering Sixth Generation mobile communication (6G) of intelligent vehicular networks. Full article

The rapid proliferation of Internet of Things (IoT) devices within 5G-enabled smart city environments has introduced unprecedented challenges in communication protocol management across heterogeneous network architectures. With connected IoT devices projected to reach 21.1 billion by the end of 2025 and approximately 39 billion by 2030, existing static protocol selection mechanisms are unable to accommodate the dynamic Quality of Service (QoS) requirements of different smart city applications, such as enhanced Mobile Broadband (eMBB), Ultra-Reliable Low-Latency Communication (URLLC), and massive Machine-Type Communication (mMTC). This paper presents APO-DRL (Adaptive Protocol Optimization using Deep Reinforcement Learning), a framework that utilizes a Dueling Double Deep Q-Network (D3QN) combined with a Prioritized Experience Replay mechanism for intelligent, real-time communication protocol selection and parameter optimization in heterogeneous IoT networks. The proposed framework formulates the protocol optimization problem as a Markov Decision Process (MDP), wherein the DRL agent dynamically selects the optimal communication protocol (NB-IoT, LTE-M, LTE Cat-1, or 5G NR) and adaptively tunes transmission parameters based on real-time network conditions. Experimental evaluation in a 3GPP TR 38.901 Urban Macro simulation environment with N = 30 devices demonstrates that APO-DRL achieves a 138.9% improvement in average throughput compared to Static Allocation (60.00 vs. 25.12 Mbps), while simultaneously achieving the highest QoS satisfaction (83.38%) across all methods, albeit with higher energy consumption and packet loss than Static Allocation. Relative to D3QN+PER, APO-DRL exhibits substantially lower cross-seed throughput variance (±0.88 vs. ±11.03 Mbps), confirming that QA-PER produces a more stable and reproducible learned policy. Full article

Network functions virtualization (NFV) is an emerging technology that enables flexible service deployment for supporting the Beyond 5G/6G network. NFV transforms physical network devices into virtual network functions (VNF) over Edge Computing capabilities, thereby facilitating the agility of network services and reducing management costs. To effectively monitor Internet of Things (IoT) network resources, service function chaining (SFC) is used for its virtualizations to ensure the multi-service requirements are sufficiently in capability, scalability, and flexibility for computation workloads alignments. However, to satisfy the resource availability requirements and efficiency under several conditions, SFC reconfiguration methods face the challenges in meeting significant latency requirement of delay-sensitive applications while reaching the importance of energy saving on orchestration timespan. In this paper, we propose task management-aware SFC and orchestrating schemes, namely GNN-PPO. In this framework, we utilize the Graph Neural Network (GNN), which relies on the message-passing neural network (MPNN), to capture all the abstraction of physical resource nodes and link capabilities over MEC node states. In particularly, GNN is divided construction into two phrases: (1) GNN represents nodes for all the Mobile edge computing (MEC) nodes, which have a global view on resources of computation and communicational capabilities that could serve as carriers; (2) VNFs are transferred into graph networks by using feature-extraction MPNN to manage each VIM that seeks an optimal and reliable analysis of traffic fluctuations. Lastly, Deep Reinforcement Learning (DRL) is used to embrace the network determination in policy strategy, which utilizes a Proximal Policy Gradient (PPO). On the other hand, we propose a novel network architecture based on PPO to perform the design for the optimization of resource utilization and facilitate energy consumption on MEC servers under diverse setting scenarios, which enables continuous policy enforcement for our system. With the experimental results, we compare our proposed solution with reference schemes in terms of rewards with learning rate and batch size, average request acceptance, SFC success, packet delivery, throughput, and resource utilization ratio that confirm the scheme’s scalability and practical suitability for IoT network deployment. Full article

Existing PLC security solutions face a fundamental conflict between stringent real-time requirements and robust protection: traditional IT security mechanisms (e.g., encryption, authentication) introduce unacceptable latency, while software-based redundancy schemes operate at millisecond scale and remain vulnerable to common-cause failures. To bridge this gap, this study proposes MimicPLC v1.0, a dynamic defense mechanism based on a heterogeneous multi-core architecture that integrates threat perception, dynamic fault tolerance, and rapid recovery within a single chip, thereby reconciling real-time determinism with proactive security in industrial control systems. The architecture integrates three distinct CPU cores (MIPS, ARM, and RISC-V) within a single system-on-chip (ESC0830), coordinated by a dedicated hardware-based mimic scheduling subsystem. This subsystem performs real-time, loosely coupled, transaction-level consistency checks on the AHB-Lite bus operations of the heterogeneous processors, achieving nanosecond-scale arbitration latency for threat detection. We evaluate the proposed design using an industrial-strength testbed, incorporating a custom development board and the Synopsys Verdi simulation environment, under critical attack scenarios including Denial-of-Service (DoS), replay, code injection, and parameter overwrite attacks. The system maintains continuous operation through adaptive redundancy, demonstrating attack perception within 73 clock cycles and leveraging instruction-set asymmetry for effective threat containment. Rigorous validation, including 100 consecutive parameter override attacks, confirms a 100% interception rate within our tested attack scenarios, with zero false positives observed. The design complies with the IEC 61131-3 real-time standard, exhibiting a worst-case recovery duration of 9.3 ms and a 95% confidence interval for recovery latency of [4.0354, 4.0363] ms. This work pioneers a paradigm of rapid-detection endogenous security with nanosecond-scale arbitration for next-generation industrial control systems. Full article

The increasing penetration of renewable energy sources and converter-interfaced loads has intensified the need for fast and reliable grid-support services. Although electric vehicle (EV) battery chargers have emerged as promising resources for Vehicle-to-Grid (V2G) applications, existing solutions typically focus on individual services such as virtual inertia or frequency regulation, while limited attention has been given to the coordinated provision of multiple ancillary services within a unified framework. Furthermore, the use of batteries alone for fast frequency support may accelerate battery degradation due to frequent high-power transients. To address these challenges, this paper proposes a hybrid energy storage-based EV battery charger architecture and a coordinated multi-timescale control strategy capable of simultaneously providing virtual inertia support, long-term frequency regulation, reactive power compensation, and harmonic mitigation. The proposed approach utilizes a DC-link capacitor to deliver fast inertial response while the battery supplies sustained frequency support, thereby reducing battery stress and improving energy management efficiency. An enhanced frequency estimation method based on a phase-locked loop combined with a low-pass filter is also introduced to improve dynamic performance. Simulation results demonstrate the effectiveness of the proposed strategy under various grid disturbances. The system achieves an equivalent virtual inertia constant of approximately 1.85 s and delivers up to 786 W of transient inertial support within 80 ms during frequency events. The enhanced frequency estimation method significantly reduces transient overshoot, while harmonic compensation limits the grid current and voltage total harmonic distortion to 1.50% and 3.23%, respectively. In addition, the controller provides up to 400 VAR of reactive power support during voltage disturbances while maintaining stable battery operation. These results demonstrate that the proposed EV battery charger can function as a multifunctional grid-support resource, enhancing frequency stability, voltage regulation, power quality, and overall V2G capability in future smart grids. Full article

The increasing interconnectivity and digital transformation of Communication, Navigation, and Surveillance (CNS) systems have expanded their attack surface, rendering traditional perimeter-based security models inadequate for protecting these critical infrastructures. Zero Trust Architecture (ZTA), founded on the principle of “never trust, always verify,” offers a paradigm shift towards continuous, context-aware security. This paper presents a literature review investigating the application of ZTA principles to secure modern CNS ecosystems, following the guidelines of the International Civil Aviation Organization (ICAO) through its Cybersecurity Strategy and Plan. We analyze the alignment of ZTA core tenets—such as least-privilege access, micro-segmentation, and continuous authentication—with the unique operational requirements of CNS systems. This paper also presents a cybersecurity framework, under development within the Future Communications Digital Infrastructure (FCDI) project of the SESAR JU program, which aims to assist CNS stakeholders in collaboratively identifying cybersecurity threats within their scope of responsibility. The review critically examines implementation challenges for specific CNS subsystems: secure aeronautical communications (e.g., LDACS), resilient PNT (Positioning, Navigation, and Timing) services, and integrated surveillance networks (e.g., ADS-B, multilateration). Furthermore, we identify and evaluate domain-specific challenges, including integration with legacy avionics and ground systems, managing stringent latency and reliability constraints, and protecting against sophisticated threats targeting supply chains and data fusion processes. By synthesizing current research and practical deployment insights, this review aims to provide a foundational reference for aerospace engineers, cybersecurity specialists, and policymakers, offering a roadmap to enhance the cyber-resilience of vital CNS infrastructure in an era of evolving digital threats. Full article

Converging millimetre-wave (mmWave) radio access with passive optical network (PON) fronthaul under the Open RAN (O-RAN) architecture promises unprecedented capacity for beyond-5G and 6G systems. Yet today, dynamic bandwidth allocation (DBA) in the PON and physical resource block (PRB) scheduling in the mmWave RAN operate independently, a critical design flaw that causes severe latency accumulation, resource fragmentation, and consistent failure to meet the divergent quality-of-service requirements of network slices. This paper breaks that deadlock by introducing the first slice-aware, computationally efficient orchestration framework that jointly optimises DBA and PRB allocation in a converged mmWave-PON O-RAN. We formulate the problem as a constrained Markov decision process (CMDP) with explicit latency, reliability, and throughput constraints for URLLC, eMBB, and mMTC slices. The core technical advance is a reward-shaped proximal policy optimisation (RS-PPO) algorithm whose potential-based shaping function directly penalises DBA–PRB misalignment and dense feedback on queue build-up, accelerating learning without compromising optimality. To make this work in near-real time on the O-RAN RIC, we embed three complementary efficiency engines: graph convolutional network (GCN) state abstraction, action masking, and prioritised N-step replay. Extensive 3GPP-compliant simulations show that RS-PPO slashes URLLC end-to-end latency by 37% (from 1.38 ms to 0.87 ms), boosts PRB utilisation by 28% (from 68% to 87%), and delivers 99.999% reliability, all while converging 45% faster and cutting inference time by 45% (to just 2.3 ms). The result is a sub-5 ms control cycle, compatible with O-RAN specifications and deployable as an xApp on the near-RT RIC. Our framework closes a long-standing coordination gap left unresolved by prior art, enabling true slice-aware convergence between the optical and wireless domains. Full article

With the rapid proliferation in communication devices and the expansion of applications, future sixth-generation (6G) networks are expected to enable a truly connected world. They will allow large-scale use cases, such as the Internet of Things (IoT) and unmanned aerial vehicles (UAVs), providing significantly faster and more innovative services ubiquitously. However, challenges remain, particularly in security. The growing number of devices and increased connectivity may lead to a larger attack surface. Many emerging technologies are actively addressing these security and privacy concerns, ensuring that we can benefit from the advantages of 6G networks and applications without falling victim to malicious attacks. In this paper, we conduct a comprehensive literature review of emerging technologies for secure communication in 6G networks, including artificial intelligence (AI) and machine learning (ML), blockchain technology, quantum-safe communication, and physical-layer security. First, we discuss the architecture of 6G networks from a security perspective. Second, we review existing surveys on 6G security issues and provide a quantitative analysis to identify research gaps, including technology-driven silos and domain fragmentation. Third, we develop a hierarchical taxonomy of security challenges and attacks in 6G networks, covering physical-layer attacks, network-level threats, device vulnerabilities, data privacy concerns, and emerging application-specific risks. We then examine the roles of key enabling technologies and present a mapping between security threats and corresponding technological solutions, along with a unified evaluation framework to facilitate cross-technology comparison. Furthermore, we propose an integrated multi-technology security framework and discuss practical deployment challenges by bridging the gap between simulation-based studies and real-world implementations. Finally, we outline concrete future research directions for advancing secure 6G communication systems. Full article

Future 6G networks require higher performance and wider service coverage. Multi-domain Service Function Chain (SFC) deployment enables service provisioning across multiple network domains to meet these demands. However, when collaboration occurs among different network operators, privacy-preserving mechanisms are required to protect sensitive information such as internal topology and resource availability. Existing SIRM-based mechanisms, such as the Privacy-Preserving Deployment Mechanism (PPDM), address this challenge but suffer from structural limitations: PPDM performs whole-chain feasibility evaluation with extensive virtual occupation. This paper proposes a $\mathcal{B}$-Batch Sequential Deployment mechanism for privacy-preserving multi-domain SFC deployment. Instead of evaluating whole-chain feasibility at once, the proposed $\mathcal{B}$-Batch mechanism partitions each incoming SFC into fixed-size VNF batches and constructs a batch-level SIRM. This design confines virtual occupation to the current batch and reduces both its magnitude and duration while remaining fully compatible with the SIRM privacy model and the hierarchical multi-domain control architecture. A Deep Q-Network (DQN) is employed to learn substrate node selection policies based solely on SIRM-based state information, without exposing domain-internal topology or resource details. Simulation results on a three-domain AARNET substrate topology demonstrate that the proposed mechanism consistently improves deployment robustness under varying traffic intensities and SFC lengths, including short (3–6 VNFs), medium (6–9 VNFs), and long (9–12 VNFs) service chains. Compared with PPDM, the proposed $\mathcal{B}$-Batch mechanism achieves higher acceptance ratios under moderate-to-heavy traffic while reducing end-to-end delay and improving average substrate resource utilization. Node selection analysis further shows that smaller batch sizes preserve feasibility through compact node reuse, whereas larger batch sizes encourage broader substrate exploration. Overall, the proposed $\mathcal{B}$-Batch mechanism enhances feasibility preservation and deployment robustness in privacy-preserving multi-domain SFC orchestration. Full article