Deterministic wireless connectivity is becoming essential for Industry 4.0 systems, in which sensing, communication, computation, and control must operate under strict timing and reliability constraints. Practical deployment remains challenging because of the ongoing reliance on fragmented protocol stacks and loosely integrated logic, which continue to hinder application interoperability and network-level determinism. This study presents a standards-aligned deterministic wireless middleware that bridges this gap by linking the application intent to cross-layer orchestration across heterogeneous sensing, wireless transport resources, and edge analytics. The framework is designed around middleware functions for device and service discovery, semantic normalization, publish-subscribe event brokering, deadline-aware policy enforcement, synchronization control, and resilience management, while remaining compatible with TSN, DetNet, OPC UA, DDS, and 3GPP time-sensitive communication principles. A real-time tennis training and match analytics testbed was used as a representative cyber-physical proxy for Industry 4.0 workloads involving multimodal sensing, synchronized fusion, low-latency analytics, and closed-loop feedback. This study is motivated by the need to move beyond application-specific wireless integration toward a middleware layer that can consistently translate real-time service intent into synchronized communication, deadline-aware forwarding, edge execution, and interoperable data exchange. Experimental evaluation across serve analysis, rally tracking, and coaching feedback showed that the proposed middleware reduced the mean end-to-end latency from 79.0 to 30.3 ms, lowered the jitter from 14.3 to 2.9 ms, increased the deadline satisfaction from 71.0% to 98.0%, and reduced the synchronization error from 7.3 to 3.2 ms relative to conventional wireless operations. The results demonstrate that standardized middleware can serve as the missing link between interoperable industrial applications and deterministic wireless connectivity in Industry 4.0 environments.
Efficient charging navigation has become a key service for Electric Vehicles (EVs) in Vehicle-to-Everything (V2X)-assisted urban transportation systems. In charging hotspot areas, Unmanned Aerial Vehicle (UAV)-assisted sensing can provide timely road-network information around charging stations, access roads, and nearby intersections. However, road-network conditions, charging-station states, and information freshness are not always jointly considered. As a result, an EV may spend a long time traveling to and waiting at a charging station. Outdated or unreliable data may further mislead charging navigation decisions. To address this problem, this paper proposes FA-JSPR, a Freshness-Aware Joint Station–Path Ranking method for EV charging navigation. FA-JSPR integrates EV states, UAV-sensed road-network information, and charging-station states, and constructs a freshness-aware charging-navigation state. Based on this state, candidate station–path pairs are ranked by considering road conditions, station service states, and information freshness. SUMO and Python simulations show that FA-JSPR reduces the travel time and queue waiting time before charging service. It also enables more EVs to successfully reach and access charging stations and achieves better robustness under communication delay and packet loss.
Deterministic wireless connectivity is essential for Industry 4.0 systems that combine real-time interaction, cyber-physical sensing, edge intelligence, and heterogeneous access networks. Deterministic capabilities are still dispersed among various standards families, such as IEEE 802.1 TSN, IETF DetNet, OPC UA, DDS, 3GPP URLLC/5GS, and ETSI MEC. This dispersion complicates coordination at the application level. This article presents a cross-standard 6G control plane middleware for deterministic time-synchronized music instruction, used as an intuitive but technically stringent cyber-physical training use case. The proposed middleware translates music-instruction intent into deterministic service classes, generates timing profiles, maps requirements to standard-level control functions, orchestrates edge execution, and updates policies at runtime under load, mobility, and synchronization stress. The architecture was evaluated through scenario-based experiments comparing best-effort delivery, conventional QoS-aware prioritization, and the proposed cross-standard middleware. The results indicate lower latency growth under increasing learner load, tighter jitter control across traffic classes, reduced multimodal synchronization error, higher deadline satisfaction under stress, faster packet-loss recovery after adaptive policy updates, and improved learner-facing QoE under the evaluated scenario conditions. This study highlights that deterministic wireless training systems require more than packet prioritization; they require standardized middleware abstractions that connect application intent, service-class semantics, cross-standard enforcement, edge orchestration, telemetry, security, and runtime adaptation.
High-altitude platforms (HAPs) are key enablers of next-generation non-terrestrial networks (NTNs), offering wide coverage, long endurance, and rapid deployment. Despite these advantages, current NTN designs remain satellite-centric and rely on terrestrial cellular assumptions, limiting flexibility and scalability. To overcome these limitations, this article proposes a multi-layer HAP-centric flying ad-hoc network (FANET). In this framework, HAPs are integrated with distributed uncrewed aerial vehicles (UAVs) to form a standalone, cell-free (CF) non-terrestrial system capable of autonomous operation. The layered architecture consists of an inter-HAP ad-hoc layer, a HAP-to-UAV cooperative layer, and a UAV-to-ground access layer, collectively enabling aerial connectivity, adaptive coverage, and interference-aware user access. Unique challenges for each layer are analyzed, including inter-HAP connectivity, FANET co-existence with terrestrial networks (TNs), and user access under heterogeneous conditions. Moreover, the article introduces enabling strategies such as fast beam alignment for high data rate connectivity, uncoordinated FANET/TN co-existence, and user localization and environment classification. Validated by three case studies, the discussion also outlines standardization pathways. The results highlight HAP-centric FANETs as a foundation for resilient, scalable, and application-oriented 6G NTN deployments.
The convergence of quantum computing threats and resource-constrained Internet of Things (IoT) ecosystems presents a fundamental security challenge for emerging 6G networks. Post-Quantum cryptography (PQC) has been designated as a key research priority by several leading institutions seeking advances in encryption techniques that will drive a paradigm shift. Post-quantum cryptography (PQC) can resist quantum attacks, but direct deployment on constrained IoT hardware often incurs high computation, memory, and energy costs. This paper presents QSAFE, a lightweight hybrid cryptographic framework that combines established classical primitives with NIST-recommended PQC algorithms to provide defense-in-depth without compromising operational feasibility on varied IoT platforms. QSAFE is positioned as a systems and engineering framework for quantum-resilient 6G IoT rather than a new cryptographic primitive. Comparative experiments show that QSAFE achieves higher performance than selected pure PQC baselines while incurring only 6–8% overhead relative to classical-only protocols under our evaluated configuration. Security analysis demonstrates that QSAFE complies with NIST Security Level 1 (SL1) through an AND-composition defense-in-depth architecture. Future work includes hardware acceleration, adaptive platform security mechanisms, and standardization in collaboration with the Internet Engineering Task Force (IETF) and the National Institute of Standards and Technology (NIST).
Semantic communication is a promising candidate for enabling native AI capabilities in 6G networks. It has the potential to reduce latency and bandwidth consumption in machine-to-machine communication by conveying semantic meaning rather than raw data. However, this paradigm shift also poses significant challenges for post-incident forensic analysis. Operators can no longer rely on traditional packet captures to determine what was actually said, which models were used, the prevailing channel conditions, or whether the receiver had granted consent. To address these challenges, we propose a forensic auditing architecture that provides engineers and researchers with a reliable framework for investigating incidents in semantic communication systems. The proposed design involves lightweight signed witnesses at each encoder, decoder, and channel monitor. These records are organized as per-agent hash chains anchored in a transparent public log for integrity and traceability. We demonstrate its efficacy through a vehicle-to-everything (V2X) case study in which the witness layer reconstructs attack provenance that traditional channel-and signature-based baselines miss. Evaluation results obtained in a controlled software-emulation environment indicate that the witness layer consumes less than 1% of a CPU core at 100 records per second and approximately 300 bytes per event, suggesting that the proposed architecture is sufficiently lightweight for practical deployment in operational environments.
3GPP Release 19 intentionally leaves satellite power amplifier linearization unspecified, allowing vendors to choose optimization strategies. This flexibility is essential for 6G Non-Terrestrial Networks, where satellite energy efficiency affects constellation sustainability and lifespan. This article examines satellite linearization evolution across three eras: Traveling Wave Tube Amplifiers with analog predistortion, GaAs solid-state amplifiers with ground-based digital predistortion, and regenerative payloads with adaptive digital predistortion. We identify a critical challenge: onboard adaptive digital predistortion poorly scales to hundreds of antenna elements due to computational burden on power-limited satellites. A Joint Satellite–Ground Digital Predistortion (JSG-DPD) is proposed, utilising a distributed architecture where ground stations compute predistortion coefficients, which are then applied by satellites through lookup tables. This eliminates the need for continuous onboard adaptive DPD while preserving linearization performance. JSG-DPD addresses practical issues: sub-array grouping for massive arrays, predictive coefficient families for Doppler dynamics, temperature-indexed sets for thermal variation, and canonical management for constellation coherence. JSG-DPD significantly reduces satellite power consumption while achieving comparable linearization. This solution provides practical implementation guidance and support for sustainable 6G satellite systems.
The “Dark Fleet”—vessels that disable their Automatic Identification Systems (AIS) to engage in illegal activities—poses a serious challenge to global maritime security. Traditional satellite surveillance is limited by persistent cloud cover and orbital revisit times. This article explores how High-Altitude Platform Systems (HAPS), operating in the stratosphere, can serve as opportunistic “sniffers.” By intercepting the uplink signals transmitted from vessel terminals to Low Earth Orbit (LEO) satellites, HAPS can analyze the traffic patterns of these “dark” targets. We show that this non-cooperative passive stratospheric approach achieves a vessel classification accuracy of 80.3%, outperforming cooperative satellite-based baselines by exploiting more favorable signal geometry.
Future Sixth-Generation (6G) networks are expected to provide global, secure, and resilient connectivity by tightly integrating terrestrial and Non-Terrestrial Networks (NTNs). Beyond high-data-rate classical communications, this new generation of mobile communication systems opens the door to the deployment of quantum services at a global scale, leveraging space and aerial network elements as native components of the 6G ecosystem. This article explores the joint provision of classical and quantum communications over Free Space Optical (FSO) links in 6G NTNs, with a focus on Low Earth Orbit (LEO) satellite constellations. It also discusses how quantum communication services, as illustrated through Quantum Key Distribution (QKD), can be integrated with classical feeder and inter-satellite links when FSO payloads on LEO satellites become available. Key physical-layer challenges arising from the coexistence of classical and quantum optical channels are analyzed, including atmospheric effects, crosstalk, filtering, Doppler shifts, and pointing constraints. At the network level, the architectural trade-offs related to satellite orbits, inter-satellite links, and optical ground station deployment are discussed, highlighting open challenges and opportunities toward scalable quantum-enabled 6G NTNs.
Intelligent Transportation Systems (ITS) aim to improve traffic efficiency, management, driver comfort, and safety. It comprises various components, including vehicles, sensors, base stations, and road infrastructure. In the near future, ITS will need to support multi-modal transportation schemes, including aerial vehicles. Therefore, ITS must be integrated with Unmanned Aircraft Systems (UAS) and rely on 3-D connectivity provided by Non-Terrestrial Networks (NTNs) to achieve this support. In other words, various Unmanned Aerial Vehicles (UAVs) will become integral parts of future ITS due to their mobility, autonomous operation, and communication/processing capabilities. This article presents our view on next-generation 3-D ITS, its benefits over existing ITS, enabling technologies, and key challenges. As a case study, we developed and demonstrated the need and advantages of having separate models for pedestrians and vehicular users.
Industry 4.0 applications increasingly depend on deterministic wireless connectivity to support predictable latency, synchronized communication, reliable continuity, and the delivery of interoperable services across heterogeneous industrial environments. Achieving end-to-end determinism is challenging because current standards, such as IEEE 802.1 TSN, IETF DetNet, OPC UA, DDS, and 3GPP URLLC/6G, are typically implemented as distinct domains, lacking a cohesive middleware layer. This study proposes a standards-compliant deterministic middleware framework for time-sensitive 6G-enabled education management in Industry 4.0. The framework treats industrial education management as a cyber-physical orchestration problem involving immersive XR training, instructor analytics and feedback, digital twin interaction, session control, and industrial endpoint coordination. A layered architecture is proposed to translate application intent into standards-based service-to-network mapping, deterministic orchestration, resource control, and closed-loop conformance validation across wireless, backbone, edge, and cloud domains. A representative deployment scenario and comparative engineering performance interpretation show that the proposed middleware provides superior latency predictability, synchronization stability, service continuity, interoperability effectiveness, adaptive reliability, and edge network coordination compared with baseline alternatives. The results highlight the significance of standardizing deterministic middleware as the missing bridge between application-level service semantics and cross-domain deterministic execution in future 6G-enabled Industry 4.0 systems.
Managing radio resources reliably in networks that combine ground-based and satellite segments is an increasing challenge for next-generation mobile systems. AI-based traffic forecasting tools can predict future load, but they rarely indicate how much operators should trust a given prediction or what actions to take when different models disagree. This paper addresses that gap by introducing a lightweight trust layer that sits on top of existing forecasting models and combines their outputs with live network measurements to produce three simple indicators: how confident we are that a problem is coming (belief), how possible it is that things are still fine (plausibility), and how much the models disagree with each other (conflict). These three signals are then used to drive four practical actions: protecting capacity, maintaining the current state, hedging under uncertainty, and safely releasing unused resources, in a way that any network operator can inspect and adjust. Tested on real satellite traffic data, the approach eliminates service quality violations that occur with individual models, reduces unnecessary resource over-allocation from about 44% to just 5%, and lowers operational costs by roughly 87% compared to the best individual model alone. We also discuss how these ideas fit within current and future open radio network standards, and what open challenges remain before such a framework can be widely deployed.
Integration of non-terrestrial networks (NTNs) into fifth- and sixth-generation cellular networks has become a key item in 3GPP’s standardization agenda – so much so, that we speak today of the “6G from the Sky” vision, extending connectivity beyond terrestrial infrastructure and enabling direct-to-device satellite services on a global scale. This paper reports on the research efforts that are being carried out worldwide on a specific technology that is seen as one of the main enablers for this vision: satellite swarms, as opposite and/or complementary to more conventional (mega)constellations. A satellite swarm is a precious resource that can be used either as a distributed, single-point mega-antenna to instantiate a multibeam, multi-frequency satellite network, or as a set of multiple access-points-in-the-sky to realize a user-centric cooperative distributed network, much like what is envisaged in terrestrial 6G to implement distributed multiple input multiple output networks. The role of satellite swarms is revised in a standards-oriented perspective, focusing on the critical aspects of the technology (such as inter-satellite synchronization, communication and resource management, just to mention a few), highlighting the benefits of adopting geostationary Earth orbit or low Earth orbit locations in terms of spectral and area efficiency, and discussing the potential applicability to future network generations.
In integrated terrestrial network (TN) and non-terrestrial network (NTN) systems, balancing information freshness and communication security is particularly challenging, especially with dynamic and potentially malicious nodes in the network. This work explores the trade-off between age of information (AoI) and age of leaked information (AoLI) in reconfigurable intelligent surface (RIS)-assisted wireless networks. To address this, a collaborative intelligence-based framework is developed, where hierarchical learning supports the joint optimization of RIS configuration and power allocation under changing network conditions. The proposed approach is evaluated against both conventional optimization methods and modern machine learning (ML)-based techniques, showing clear improvements in managing the AoI-AoLI trade-off. The results also point to the value of adaptive and distributed decision-making in handling the complexity of TN-NTN environments. Finally, the study discusses open challenges and future directions, including the use of integrated sensing to improve network awareness, which could further strengthen the design of secure and intelligent 6G communication systems.
Sixth-generation (6G) wireless networks are expected to deliver transformative capabilities including ultra-reliable low-latency communications and massive connectivity. Reconfigurable intelligent surfaces (RISs) have emerged as a key enabling technology by dynamically manipulating wireless propagation through programmable reflecting elements. However, accurate channel estimation in RIS-assisted networks faces significant challenges from cascaded channel structures, passive elements, and high-dimensional parameter spaces. Quantum machine learning (QML) offers a fundamentally different computational paradigm that may address these challenges through quantum properties. This article provides a tutorial overview of QML for channel estimation in RIS-assisted 6G networks. We present RIS-assisted system architectures and channel estimation challenges, introduce quantum computing fundamentals, and survey QML architectures. As a use case, we consider a hybrid quantum-classical framework, e.g., convolutional neural network (CNN)-quantum long short-term memory (QLSTM), for channel estimation in RIS-assisted non-orthogonal multiple access (NOMA) systems. This case study shows consistent accuracy improvements compared to quantum and classical benchmarks. Finally, we discuss implementation challenges and future research directions for practical deployment of quantum-enhanced wireless systems.
Blockchain technology has emerged as a foundational enabler for trusted data management, offering infrastructural security and integrity across industries. In e-healthcare, however, reliability, scalability, privacy, and cross-domain interoperability remain persistent challenges. Fragmented data silos, security vulnerabilities, and the absence of standardized processes hinder seamless operations and affect both consumers and providers. Motivated by the toward 6G networks and zero-touch operation, this paper presents a standardized blockchain-enabled framework for e-healthcare interoperability built on Hyperledger Sawtooth. The framework preserves both patient-centric and provider-centric data integrity through a hierarchical model and protects privacy using cryptographic hashing and access control policies together with consensus-backed validation. It integrates NuCypher Threshold Hash Re-Encryption for policy-based key management, the InterPlanetary File System (IPFS) for large medical objects, and a customized Proof of Elapsed Time (PoET) consensus for efficient transaction validation. The standardized interfaces of the proposed framework adhere to the HL7 FHIR specification for healthcare data exchange and the IEEE P2418.1 guideline for blockchain interoperability, ensuring syntactic and semantic consistency across heterogeneous systems. In experiments, the framework connected 30 nodes with a data transmission time of 2.5 seconds per node and enabled 150 nodes to complete transmission within 41 seconds. Compared with existing Hyperledger-based implementations, it reduced latency from 1.87% to 3.83% and increased throughput from 1.77% to 3.09% while lowering computational power consumption from 1.43% to 3.36%. The customized PoET achieved 267 protocol runs over 40,000 seconds, supporting efficient block validation. Security evaluation shows that NuCypher Threshold Hash Re-Encryption protected 261 transactions over the same period, reinforcing data privacy and integrity. These results indicate that the framework enhances e-healthcare interoperability while reducing computational overhead and ensuring secure and scalable data exchange. The approach contributes to reliable services that can operate with minimal manual intervention under zero-touch principles. By emphasizing standardized interfaces compliant with HL7 FHIR, IEEE P2418.1, and privacy regulations such as HIPAA and GDPR, the framework is positioned for 6G-ready healthcare ecosystems that demand accessibility, security, and interoperability.
Non-terrestrial networks (NTN) based on Low Earth Orbit (LEO) satellite constellations are positioned as a foundational component of 6G systems, yet their integration with terrestrial infrastructure presents significant standardization challenges. The 3GPP Network Slice Admission Control Function (NSACF), specified in TS 29.536, currently employs static threshold-based policies that cannot accommodate the dynamic energy constraints inherent to LEO satellite deployments with approximately 90-minute orbital periods and continuous eclipse/sunlight transitions. This paper proposes a QoS-Aware Predictive Admission Control (QAPAC) algorithm that enables AI-native NSACF operation by replacing static threshold-based admission control with predictive admission control using ML-based power consumption forecasting and QoS-driven dynamic threshold adaptation. Experimental evaluation on an emulated 6G NTN testbed demonstrates that QAPAC achieves 100% mission-critical service acceptance compared to 75–76% for baseline methods, with up to 19 percentage point improvement in service availability with high reliability and low latency. These results provide quantitative evidence supporting the evolution of NSACF specifications toward AI-native admission control for 6G systems.
As the 5G-Advanced landscape transitions toward 6G, mitigating severe path loss in mid-to-high frequency bands and expanding non-terrestrial network (NTN) coverage have become pivotal challenges. Network-Controlled Repeaters (NCR) and Reconfigurable Intelligent Surfaces (RIS) have emerged as flagship technologies for coverage enhancement; however, their technical boundaries and long-term standardization trajectories remain fragmented. This paper proposes a unified framework–”Homology, Heterogeneity, and Evolution” –to harmonize these two paradigms from the perspective of generalized electromagnetic radio frequency (RF) domain regulation. First, we establish the generalized architectural homology and unified RF-domain abstraction among NCR, passive RIS, and active RIS, identifying their shared architectural essence characterized by baseband-less RF processing under a unified cascaded channel model. Second, we characterize their structural heterogeneity along with their spatial degrees of freedom (DoF). Third, we investigate the multifaceted standardization and engineering challenges during the transition from NCR to RIS, offering potential solutions to overcome these barriers. Finally, we conceptualize RIS under an NCR-inspired architectural perspective–termed “NCR Type-II (RIS)” –and propose a pragmatic standardization roadmap. By leveraging and extending the existing side control information (SCI) framework from 3GPP 5G Release 18 (Rel-18), we provide a pathway for commercial deployment in 5G networks and the seamless integration of RIS into 6G architectures, supporting the broader vision of communication-sensing-computing integration.
