Papers

Misbehavior detection in vehicle-to-everything (V2X) systems is essential for ensuring the semantic correctness of exchanged messages and preventing the dissemination of falsified information. Existing data-centric misbehavior detection approaches largely rely on statistical validation or supervised machine learning models under the implicit assumption of fully observable V2X streams. In practice, however, vehicular environments are inherently partially observable due to hardware failures, intermittent connectivity, and environmental occlusions. Moreover, missingness itself can be strategically exploited by adversaries to evade detection. In this paper, we study misbehavior detection under incomplete V2X observations and propose a deep reinforcement learning (DRL)-based detection framework that learns adaptive policies with incomplete data. We further introduce an adversarial threat model in which attackers exploit or deliberately induce missingness to evade detection, including evasion via natural occlusions and adversarial feature suppression. Extensive experiments conducted on the VeReMi dataset under various missingness patterns demonstrate that DRL significantly outperforms a powerful XGBoost baseline under natural partial observability. However, results also reveal a critical vulnerability: DRL policies can be highly susceptible to evasion attacks that strategically exploit natural missingness. In contrast, DRL exhibits more gradual degradation under direct feature suppression compared to static tree-based models.

Knowledge distillation is the standard way to compress encrypted-traffic classifiers for the edge, and almost all such work judges students by accuracy alone. We ask what else a student inherits: unknown-traffic detection, calibration, shortcut reliance, and whether any survives a year of drift. Resemblance proves little on its own, since soft targets also regularise. We therefore distil one 101k-parameter student from two teachers of equal accuracy but different construction, a five-member ensemble and a single wider model, so that following one rather than the other is attributable to it. The design was pre-registered before any test result was seen. We tested ten hypotheses on CESNET-TLS-Year22, a year of real TLS traffic, across 18 test windows over 35 weeks. Two are supported: a student's per-flow unknown-scores shift toward its own teacher, but only at a conventional temperature, not the accuracy-optimal one; and a shortcut-reliant teacher passes its over-confidence to a student that never sees the feature. The drift prediction is reversed under both scores, the gap narrowing rather than widening and the student overtaking under the energy score in two of three replicates, as is the prediction that such a teacher harms its student's detection, which improves slightly. Shortcut reliance is set by model size, not distillation. Under the logit-based scores nothing else transfers: distillation beats neither a temperature-scaled direct student nor label smoothing. Exploratory analysis shows this turns on the scoring rule: with a feature-space detector the teacher detects unknown traffic 0.073 AUROC better than the direct student, where the energy score sees 0.000, and the conventional-temperature student inherits most of it. Label smoothing, with no teacher, recovers more. Distillation transfers the teacher's habits; what looks like an inherited ability is available without one.

The slowdown of Moore's law and the area limit of monolithic integration have made chiplet-based designs inevitable across many domains, including network ASICs. However, combining multiple network ASICs together poses a fundamental challenge: maintaining sufficient inter-ASIC bandwidth to match the performance of an idealistic single-ASIC design. Providing full bandwidth is prohibitively expensive as it requires valuable forwarding capacity, while reducing inter-ASIC bandwidth creates severe performance bottlenecks. We propose a novel multi-ASIC switch architecture that introduces a circuit-switched indirection layer in front of the ASICs. This layer flexibly remaps ingress ports across ASICs, localizing traffic and minimizing inter-ASIC communication based on observed patterns. Our system, Fastroute, combines packet and circuit switching to deliver performance comparable to a single-ASIC switch while reducing inter-ASIC bandwidth requirements. This frees up capacity for external network interfaces, allowing Fastroute to outperform traditional non-oversubscribed multi-ASIC designs. Our hardware prototype demonstrates the system's functional feasibility by evaluating it on an LLM training workload. By reducing bandwidth and power overhead, Fastroute bridges the gap between silicon fabrication limits and soaring application demands. It provides an efficient transition to multi-ASIC switches, enabling bandwidth and radix demand to be met without waiting for the next ASIC generation.

Application developers of distributed learning services face challenges that a typical federated learning loop does not address. Specifically, the model updates can still leak private data, devices might not be able to participate in the training due to limited resources, a single aggregator might not be able to scale, and the transmissions of model weights induce a considerable bandwidth cost. This paper demonstrates DLaaS (Distributed Learning as a Service) from the developer's vantage point. Using a single admin dashboard, the developer initiates a distributed/federated learning job and is able to activate Differential Privacy (DP), Split Learning (SL), Hierarchical Aggregation (HA), and Knowledge Distillation (KD) as declarative options, with no change to the clients' code. We demonstrate the complete service lifecycle on an industrial smart-home Wake-up Word (WuW) task, using the "Ok Aura" dataset. Once the developer initiates a distributed learning job by toggling DP, SL, HA, and KD in the admin dashboard, the system dispatches the job to a set of Android clients and Dockerized helper aggregators. In the demonstration, these mechanisms run live across configurations. Then, the clients train the model locally and return their updates. The trained model is served to a consumer-side Android application that performs on-device WuW detection on a live microphone stream. In particular, the conference attendees will be invited to speak the trigger phrase and monitor in real time the per-class confidence and inference latency. Finally, we release the source code and short video walkthroughs of these configurations.

In the context of 6G ubiquitous connectivity, the space-air-ground-sea integrated network (SAGSIN) emerges as a new paradigm for pervasive service provisioning. To support expanding maritime activities in infrastructure-scarce ocean areas, we propose an innovative dynamic task and resource scheduling approach for SAGSIN to deliver computing services for vessels. It integrates broad-coverage satellites, relay-capable high-altitude platform (HAP), energy-sufficient coastal base station (BS), and flexibly deployed uncrewed aerial vehicles (UAVs) to accommodate wide-area, highly mobile, and sustained maritime services. To address the challenge of task scheduling across four layers, a dynamic task offloading algorithm is developed. It steers task flows toward servers with light loads, strong computing capabilities, and high-rate links based on real-time system states to reduce task execution delay, integrating an anticipatory satellite handover strategy to mitigate post-handover congestion and improving satellite resource utilization. Considering the limited endurance of UAVs, we impose residual energy constraints to ensure task backlog handover and safe return. Furthermore, the UAV-BS bandwidth allocation, UAV trajectories, and computing resource allocation are jointly optimized to enhance the connectivity among low-altitude devices and accelerate task completion. Simulation results validate the proposed method's superior adaptability to system resource variations during task execution in complex maritime environments, achieving at least a 23% reduction in average task delay over benchmarks.

This paper examines input/output buffered crossbar switches under combined packet and cell data. Our switch architecture uses input buffering with Virtual Output Queues to avoid Head of Line Blocking. The switch fabric is a crossbar with no speedup. We use a modified iSlip [McKeown] crossbar scheduler geared towards packet data, called piSlip. Cell ports are largely unmodified from standard iSlip behavior. For packet output ports, we introduce changes to the grant pointer which minimizes output latency caused by packet reassembly. Our model uses several output states, including packet cut-through. From simulation results, we show that piSlip with virtual cut-through offers latency characteristics significantly better than unmodified iSlip with similar packet port interfaces. Simulation further shows that piSlip and iSlip have similar maximum and average buffering requirements.

Energy imbalance remains a key challenge in Wireless Sensor Networks (WSNs), as nodes near the base station deplete their energy faster due to heavy forwarding loads. While mobile agents (MAs) have been employed for either data collection or sensor charging, existing approaches lack adaptability and fail to integrate both functions under realistic hardware constraints. This paper introduces a unified mobile agent framework that performs both data collection and wireless charging sequentially under single-antenna limitations. The agent's decision-making is formulated as a two-layer Hierarchical Reinforcement Learning (HRL) problem, where the upper layer optimizes movement planning and the lower layer determines the appropriate service based on real-time network states. This hierarchical structure enables the agent to learn adaptive task scheduling policies without predefined rules. Extensive simulations demonstrate that the proposed method achieves up to 15% longer network lifetime and more balanced energy distribution compared with state-of-the-art mobile agent and deep RL approaches.

Smart manufacturing needs to move sensor data off the plant floor, react to it, and feed decisions back to actuators within bounded time. Programmable logic controllers (PLCs) handle fast, deterministic, safety-critical actuation, but they are not designed for the higher-level functions required by Industry 4.0, such as predictive maintenance, machine learning inference, and cross-facility analytics. These functions need a scalable, durable, and observable communication substrate. We present the communication architecture of a production system that provides this substrate and closes the loop back to the plant in near real time. The design is built from industry-standard components: Apache Kafka as the streaming backbone, OPC-UA for PLC connectivity, a relational time-series database for persistence, and JSON for serialization. The novelty is architectural. We show how these standards are integrated for closed-loop industrial control through four design choices: a single event stream per production line serves control, monitoring, machine learning, and durable recording, allowing one producer to serve many independent consumers; a protocol bridge converts polled OPC-UA traffic into publish/subscribe streams, aligns per-signal timestamps to a common time base to remove cross-signal jitter, and provides a symmetric actuation path; a transport technique carries sub-second process dynamics at a coarser publication cadence by packing timestamped samples into fixed-order arrays; and an edge-to-cloud replication scheme keeps the edge authoritative, so local control continues during wide-area network outages while cloud analytics operate on replicated data. We describe the loop latency budget, report measured broker transport latency, and discuss operational experience. The system provides soft, near-real-time behavior rather than hard real-time guarantees.

Wireless local area network (WLAN) performance is jointly shaped by medium contention, propagation, Wi-Fi configuration, and transport-layer behavior. This study evaluates IEEE 802.11g and IEEE 802.11ax in a single-access-point uplink topology, comparing UDP and TCP under Friis, LogDistance, and cascaded Friis-Nakagami propagation models. The factorial design spans 5 to 50 stations, six access-point distances from 5 m to 50 m, and three random seeds. Of 1,080 scheduled executions, 1,073 produced valid CSV outputs; seven runs terminated with internal ns-3 and cppyy segmentation errors and were excluded. Across the global scenario summaries, TCP FlowMonitor packet delivery ratios (PDRs) range from 0.994 to 0.997, whereas UDP PDRs range from 0.298 to 0.731. The highest mean aggregate throughput is 2.619 Mb/s for TCP under the configured IEEE 802.11ax mode with Friis propagation; UDP reaches 25.854 Mb/s in the same topology. These results show that packet delivery ratio, throughput, delay, and fairness must be considered together. FlowMonitor metrics are IP-flow observations, so the study does not establish application-level delivery or isolate optional IEEE 802.11ax multi-user mechanisms.

Many cloud-based applications are organized as loosely coupled microservices, where invoking a service's API triggers a cascade of APIs across many services and leads to inter-service exchange of API parameters and output responses. Current tools for monitoring microservice safety properties have limited expressiveness for properties that describe the flow of data through API calls. To this end, we present SafeNom, a specification and monitoring framework for microservices based on nominal languages. SafeNom policies can express both the desired order of API calls and how the data carried in requests and responses should or should not flow between the APIs. Policies are enforced using a nominal automaton-based distributed runtime monitor which can be applied in a blackbox and non-invasive manner, without access to the service implementation and without making changes to the service implementation. Our experiments show that our monitor can efficiently enforce rich data-aware properties while incurring minimal latency overhead, on the order of a few milliseconds.

Mobile emergency networks can experience independently changing intermediate wireless links on timescales shorter than endpoint feedback can track. When an egress changes after packet emission, feedback affects only later source data, while the on-path node observes the current condition with the affected packet still mutable. This paper presents DINA, a packet-level in-network semantic adaptation method. An image is divided into self-describing spatial packets carrying coordinates, a current representation identifier, and payload. At each eligible node, an offline-trained frozen selector scores compatible operators, immediately transforms the packet, and forwards it without image reconstruction or cross-packet adaptation state. Later nodes can retain or further compact the packet through the same typed compatibility contract. The receiver places available packets by coordinate, fills missing regions with black, and runs a fixed machine task. We realize DINA in a 24-node UAV environment using XDP and AF_XDP. In the primary forest-fire trace, DINA raises deadline tile coverage from 40.4% to 72.6% and classification accuracy from 77.5% to 95.0% relative to forwarding. In an independently trained RescueNet segmentation case, it raises coverage from 65.6% to 91.7% and foreground mIoU from 0.486 to 0.541. Sufficient- and extreme-capacity profiles expose a no-gain boundary and a common task-failure boundary, respectively.

Mobile crowdsensing (MCS) is a promising enabler of Sensing-as-a-Service (SaaS) for next generation networks (NGNs), where sensing, communication, and computing are jointly considered as on-demand services. In MCS, mobile units (MUs) collect and deliver sensing data to data requesters (DRs) via a mobile crowdsensing platform (MCSP) in exchange for monetary incentives. After sensing tasks are announced, MUs strategically select tasks to maximize their long-term utility while accounting for energy and time costs, whereas the MCSP assigns tasks to maximize its own service revenue and data quality. A fundamental challenge arises from the lack of prior knowledge of MUs' sensing qualities and task efforts, as well as the energy limitations of battery-powered devices, which directly impacts service availability and reliability in SaaS for NGNs. To address these challenges, we formulate the interaction between MUs and the MCSP as a dynamic two-sided matching game under incomplete information, explicitly incorporating energy constraints. We propose Energy-aware Two-Sided Learning (ETSL), a fully decentralized and lightweight learning framework in which MUs locally learn task proposal strategies, while the MCSP learns the data quality of participating MUs to devise task assignment strategy. ETSL jointly enables MUs' energy-aware task proposals and MCSP's adaptive task assignment, considering their individual preferences to maximize their net revenues. Simulation results demonstrate that ETSL significantly improves MU and MCSP profits and overall energy efficiency, highlighting its effectiveness as a scalable and sustainable SaaS solution for NGN.

Reducing communication in derivative-free decentralized learning requires controlling the disagreement accumulated over multiple local updates. This paper develops SPADE-DFL, a primal–dual method that allows the number of local function-value updates between neighbor exchanges to grow with the computation budget while preserving the nonprivate convergence order. For smooth nonconvex objectives under uniform query-moment bounds, the prescribed nonprivate schedule achieves a time-averaged stationarity and consensus bound of $\mathcal{O}(T^{-1/3})$ using only $\Theta(T^{2/3})$ communication rounds, where $T$ is the number of local updates per client. For private training, the accumulated data-dependent increment is isolated from the graph correction, allowing one protected state per client and round to generate all outgoing messages. We prove client-level differential privacy for the full interactive transcript and quantify the resulting optimization error over a finite horizon. Experiments on four classification tasks show that SPADE-DFL achieves higher mean test accuracy than existing decentralized learning methods.

Speculative decoding accelerates Large Language Model (LLM) inference by using a lightweight draft model to propose candidate tokens for parallel verification by a target model. Drafting and verification, however, exhibit different service characteristics and favor different batch configurations, making fixed draft-target coupling inefficient under concurrent workloads. Existing distributed designs can physically separate the two stages, but often retain request or batch affinities that prevent their capacities from being shared globally. We present NebulaSD, a many-for-many, or M-for-N, speculative decoding system that organizes draft and target workers into independently schedulable resource pools and dynamically reconstructs stage-specific batches from shared request pools. Such dynamic reassignment removes fixed worker locality, requiring request states to be made available at newly selected workers without introducing migration stalls. NebulaSD addresses this challenge through worker-triggered batch reconstruction and asynchronous KV-state preparation overlapped with model execution. We evaluate NebulaSD from both system and scaling perspectives, showing that dynamic pooling improves request-round processing rate by 50.4% over a physically disaggregated baseline and 72.6% over co-located execution on a four-GPU deployment while substantially increasing effective GPU utilization. Profile-driven simulations further show approximately proportional compute-side capacity scaling under idealized state movement.

Identifying the set of monitored websites in mixed encrypted traffic is challenging because an individual flow often provides only partial evidence of website identity. To address this challenge, we propose FlowAtom, which constructs shared prototypes, called Atoms, from flow representations without website labels. Specifically, FlowAtom pretrains a flow encoder on external unlabeled traffic and aggregates Atom responses across flows within each observation window into a fixed-dimensional, permutation-invariant representation for monitored website-set prediction. Across Direct HTTPS, Trojan, and VMess, FlowAtom achieves micro-F1 scores of 97.82%, 94.43%, and 93.92% in closed-world evaluation, respectively, and consistently outperforms the evaluated baselines in open-world evaluation on windows containing monitored visits. The code is available at https://github.com/aimafan123/FlowAtom.

This work introduces an indoor Battery-Free Wireless Sensing Node powered through radiative Wireless Power Transfer (WPT). The proposed platform targets secure, energyefficient active sensing and overcomes key limitations of many prior battery-free approaches, which commonly provide neither on-node computation nor cryptographic protection. The node combines temperature, humidity, pressure and Volatile Organic Compound (VOC) measurements with a low-power microcontroller that executes sensor calibration, derives a VOC index, formats the payload, and applies AES-128 encryption before wireless transmission. Energy harvesting and communication are enabled by a 1-bit controlled Backscatter Rectenna (BR), which both scavenges incident RF power and produces an orthogonally polarized backscattered signal for robust polarimetric operation. Experimental results validate reliable multi-sensor readout and encrypted data transfer, while maintaining a very low energy budget for the complete sense-compute-encrypt-transmit cycle.

This paper presents a lightweight and protocolindependent security mechanism for battery-free Bluetooth Low Energy (BLE) sensor nodes operating in Simultaneous Wireless Information and Power Transfer (SWIPT) architecture. The proposed approach exploits polarization-shift backscattering of the wireless power wave to transmit an encrypted device identification prior to data communication. A fail-safe RF switch and orthogonally polarized antennas are integrated as an external add-on module, enabling controlled backscatter without modifying the original energy-harvesting rectifier. The identification payload is encrypted using AES-128 and transmitted with minimal energy overhead. Experimental validation on a battery-free BLE sensor node demonstrates reliable extraction of the backscattered identification signal, seamless coexistence with BLE advertising, and improved RF-to-DC harvesting efficiency compared to rectifier-based backscatter solutions. The results confirm that polarization-shift backscatter identification provides an effective and practical security for battery-free BLE sensing systems.

Today's digital healthcare services rely on various applications and functions that must be continuously accessible. Cloud computing enables global access to these services through public networks, which often also introduce increased latency, higher bandwidth consumption, and additional security risks compared to local operation. Edge computing mitigates these challenges by deploying cloud services closer to the end users and data sources, thereby improving resilience to network disruptions and reducing latency, bandwidth usage, and exposure to security threats. However, service deployment typically relies on the availability of centralized registry servers. Consequently, if the network connection or the registry server itself becomes unavailable, service deployment at the target edge node may fail. To address this, we propose a three-tier registry architecture to enhance deployment reliability and service availability, considering DockerHub as a remote public registry, an MEC-based off-premises registry as a remote private registry, and a LAN-based on-premises registry as a local private registry. The performance and efficiency are analyzed through measurements related to the estimated deployment time, inflicted network and computational load, and energy consumption, while the required nanoservices are deployed from different tiers. The experimental results indicate that alongside the improved tolerance to network disruptions, the local private and remote private registries also outperform the remote public registry in the deployment performance. The findings highlight the effectiveness of proximity-aware service distribution in improving the resilience, performance and efficiency of service deployment.

The wireless physical (PHY) layer has enabled successive generations of cellular systems through advances in modulation, coding, waveforms, and multiple-input multiple-output (MIMO) transmission. This article assesses whether these techniques are now approaching maturity and where substantial further gains remain possible. Field measurements and quantitative evaluations indicate that many link-level refinements, including constellation shaping, channel-code evolution, reduced-complexity receivers, and waveform enhancements, remain valuable but typically provide bounded gains that must be balanced against implementation complexity and overhead. In contrast, massive MIMO and distributed MIMO offer a more scalable system-level opportunity by increasing the number and quality of usable spatial channels. Their effectiveness relies on time-division duplex reciprocity for scalable channel state information (CSI) acquisition, while practical limitations include calibration, pilot reuse, channel aging, weak pilot reception from cell-edge users, and the fronthaul and synchronization requirements of coherent distributed operation. Artificial intelligence and machine learning (AI/ML) provide complementary opportunities for further PHY layer innovations. The PHY layer is therefore not dead; its most consequential advances will come from deployable spatial processing and CSI acquisition, complemented by targeted link-level and AI/ML-based refinements.

Machine learning-based Network Intrusion Detection Systems often report near-perfect performance on IoT benchmarks. However, whether these models learn generalizable attack behavior or exploit spurious dataset shortcuts- such as static testbed IP/MAC addresses and chronological recording artifacts-remains an important question. We evaluate the CyberFlowIoT-GICAP benchmark, containing 3,617,388 flow records across 126 PCAP sessions with 849,395 benign flows. Four learning paradigms are evaluated across four feature configurations using PCAP-disjoint splits; LightGBM is additionally evaluated using conventional random-flow splitting. When only statistical flow behavior is used (Fbehav), LightGBM (92.58% +/- 8.18%), Random Forest (92.59% +/- 8.18%), and Deep MLP (92.55% +/- 8.18%) achieve nearly identical Macro-F1, indicating that performance is constrained by feature representation rather than model complexity. With raw timestamps (Ftstamp), tree-based models reach 99.28% Macro-F1, while the linear model remains at 90.62%, showing that nonlinear models can exploit dataset-specific temporal structure. Attack detectability is highly asymmetric: high-rate and active attacks maintain >99.8% recall from flow behavior alone in nonlinear models, whereas the DNS Beaconing drops from 27.78% to 0.00% recall when contextual features are removed. Conventional random-flow splitting increases attack recall by up to 14.00%, highlighting the effect of placing flows from the same sessions in both training and test sets. We conclude with a 4-point protocol checklist for realistic IoT NIDS evaluation.

We consider the problem of load balancing in a system with one dispatcher and $N$ parallel servers. The dispatcher must select one server to dispatch new jobs at every time-step and each server buffers incoming jobs in a queue. However, the dispatcher does not know the servers' backlogs and must make dispatching decisions based on previous observations. The dispatcher's objective is to dispatch jobs to the shortest queue. This problem can be formulated as a restless multi-arm bandit (RMAB) problem where each arm's state is its corresponding belief vector. Our goal is to verify Whittle indexability for this problem and derive a low complexity Whittle index policy. Previous Whittle indexability results cannot be directly applied due to the multi-dimensional nature of the belief vector. To overcome this issue, we define the RMAB state as the tuple of the most recent backlog observation and the time since this observation. We consider two model variations, a standard finite queue model and a blocking queue model. We show that the single-arm decoupled problems of both these models have threshold optimal solutions under some assumptions. For the standard finite queue model, we prove indexability and derive the Whittle index policy in closed form. For the blocking queue model, we derive a sufficient condition for indexability under threshold optimality and use it to show indexability for some special cases.

Vehicle-to-Everything (V2X) communications impose very demanding requirements in terms of latency and reliability, which must be met in scenarios where multiple services with diverse performance targets coexist. In such scenarios, traffic-intensive services compete for limited radio resources, complicating the fulfillment of V2X service demands. Within this context, Network Slicing (NS) emerges as a key factor that enables the creation of multiple slices and the allocation of resources among them to satisfy heterogeneous service requirements. In particular, this work addresses the Radio Access Network (RAN) slicing problem from the perspective of Physical Resource Block (PRB) partitioning under high traffic demand conditions. To this end, a reinforcement learning approach based on Proximal Policy Optimization (PPO) is proposed to determine PRB allocations that satisfy the strict latency and reliability requirements of V2X services, while improving resource utilization efficiency and minimizing performance degradation of enhanced Mobile BroadBand (eMBB) services. The proposed solution is evaluated through simulation-based experiments under various traffic loads and different V2X service requirements, demonstrating its ability to adapt resource partitioning to network conditions and service demands.

The increasing adoption of software defined networking and network function virtualization, combined with rapid advances in Machine Learning (ML), is driving the evolution toward intelligent network softwarization across cloud, edge, and distributed environments. However, deploying complex learning models in such heterogeneous environments introduces challenges in latency, computation, and energy consumption. Knowledge Distillation (KD) has emerged as a promising approach to enable lightweight and efficient intelligence by transferring knowledge from high-capacity teacher models to compact student models. Despite its extensive study in general ML domains, the integration of KD into intelligent softwarized networks remains fragmented and underexplored. In this paper, we review and classify recent efforts that incorporate KD within softwarized networks, analyze current trends and limitations, and outline open challenges and future directions toward scalable, adaptive, and energy-aware distillation mechanisms.

Algorithms in multi-agent systems such as federated learning, mobile robotic swarming, and consensus control can be designed and analyzed as distributed stochastic approximation algorithms. Such algorithms involve information exchanges between agents for various computations. The freshness of the information can be quantified using the Age of Information (AoI) metric. Consider robotic teams operating in highly obstructed geographical settings, such as subterranean or dense urban environments. Because of spatial disconnections, AoI has empirically been observed to be heavy-tailed with unbounded moments. However, most analyses assume AoI with bounded moments, creating a gap between theory and practice. To the best of our knowledge, ours is the first analysis under general heavy-tailed AoI with potentially infinite mean. We study the stability (almost sure boundedness of the distributed iterates) and convergence of multi-agent systems that are strictly dissipative in the scaling limit (system at “infinity”). Examples include most gradient-based and consensus algorithms under the Robbins-Monro step-size regime.

Intent-based networking decouples desired outcomes from device-level configuration, but most systems still map intents to parameters of an algorithm selected in advance. Large language models (LLMs) create an opportunity to automate algorithm design, yet unrestricted generated code is unsuitable for transport-network control because feasibility, reproducibility, and robustness must be enforced independently of the model. We present VERA-TN, a verification-guided framework that compiles a network intent into a bounded algorithm-design specification. The target architecture uses an LLM as a semantic variation operator over typed request-ordering and path-ranking programs; generated logic remains separated from a trusted allocator that enforces path validity, latency, capacity, and single-path constraints. We prove feasibility preservation under explicit assumptions and establish a sufficient bound for the lexicographic latency tie-break in the exact reference model. The released proof-of-concept instantiates the same interface with a bounded ten-parameter numerical candidate and deterministic replay, rather than a completed live-LLM/AST study. Across 150 certified held-out cases on a 28-node TEFNET24-derived hierarchy, evolutionary search reaches a mean priority-utility ratio of 0.958, compared with 0.952 for equal-budget random search and 0.940 for priority-greedy routing. The gain over random search is small but statistically detectable (Holm- adjusted p = 0.0083). The candidate does not improve congestion relative to MILP-C, and the effect of failure-aware training is inconclusive at the 0.05 level (p = 0.051). Eight discovery runs on the official national topology and replay on 12 unseen metro-regional topologies show no stable intent-specific specialization. These results support the trust-boundary and numerical-evolution claims but do not establish a benefit from LLM generation.

This article provides an overview of potential alternatives for providing wireless backhaul in regions that suffer from the lack of fiber optic-connectivity to the core network. These regions can be rural and remote locations, low-income neighborhoods in urban and suburban regions, and post-disaster locations suffering from the destruction of cellular infrastructure. For these scenarios, extending fiber optic cables to such locations might be extremely expensive, impractical, or simply not feasible. Hence, in order to enhance the backhaul connectivity in these scenarios, we study the potential and applicability of the integrated access and backhaul (IAB) technique, and a hybrid combination of IAB and non-terrestrial networks (NTN) that includes high/low altitude platforms (HAPs/LAPs) and low earth orbit (LEO) satellites. We conclude this article by discussing the design considerations and potential research problems that would enable efficient deployment of such solutions.

The increasing number of Internet of Things (IoT) devices and applications leads to severe access congestion in conventional base station (BS) networks. Meanwhile, the low transmit power of IoT devices requires larger diversity gains from the system design. Therefore, low-cost traffic management and signal enhancement mechanisms become essential. Reconfigurable intelligent surfaces (RISs) are increasingly significant due to their flexibility and efficiency. Using reflection, refraction, and amplification, RISs can act as innovative relay nodes to overcome spatial limitations caused by blockages, and improve the coverage range of existing infrastructure. However, more extensive applications of RISs remain to be explored. In this paper, we propose to deploy co-sited RISs on BSs and use the integrated access and relay (IAR) architecture to enable wave-domain task-offloading between BSs, without requiring extra infrastructure or incurring extra decoding latency. Considering the fixed and dynamic sub-carrier schemes, we show the performance improvement in cell-free networks and cellular networks. In addition, we investigate the effect of RIS elements allocated to each IoT device in both cell-free and cellular networks. Finally, we discuss the opportunities and challenges of IAR BSs for future communications design.

In this paper, we introduce Gnomon, a physics-informed system that forecasts user-perceived low-Earth-orbit (LEO) downlink throughput, uplink throughput, and round-trip time (RTT) under different levels of trace availability. Gnomon's physics layer reconstructs the serving geometry and four-leg bent-pipe attenuation from public weather, orbital, routing, and licensing data. Based on what is available, Gnomon conditions on the target terminal's own history (Mode 1), measurements from nearby publicly reachable dishes (Mode 2), or the physical covariates alone (Mode 3) to predict the link state: Modes 1 and 2 share a fine-tuned time-series foundation model, while Mode 3 uses a compact boosted-tree estimator. All three modes expose a common output interface and can be selected without retraining. We evaluate Gnomon using 8,260 minutes of 1 Hz measurements collected at nine sites across five states in the U.S. We train on three sites and hold out the remaining six sites and their serving beams. On these unseen sites, the own-trace mode reduces downlink-throughput and RTT prediction error by 17% and 11% relative to the strongest published baseline and, to our knowledge, provides the first LEO uplink forecasts. The neighbor-trace mode requires no on-site hardware, while the covariate-only mode reduces downlink-throughput and RTT error by 24.6% and 78.8% relative to the only prior covariate-only forecaster. Moreover, experiments show that Gnomon provides calibrated quantile bands and improves adaptive-bitrate streaming driven over real TCP flows on replayed Starlink links.

Ensuring safety and scalability in Collective Perception Service (CPS) remains a key challenge for Cooperative Intelligent Transport Systems (C-ITS). Conventional CPS enhances perception by broadcasting Collective Perception Messages (CPMs). However, its reliance on transmitting a potentially large volume of context-irrelevant information at high frequency leads to network congestion, processing delays, and poor scalability. We propose a Context-Aware Adaptive Filter that dynamically adjusts CPM content and transmission frequency based on contextual relevance and situational criticality. By prioritizing safety-critical objects and interactions, the proposed approach prevents information overload while preserving timely updates for decision-making. An end-to-end SUMO–Artery simulation evaluates safety, decision-making efficiency, and communication cost under different computational capacity tiers and transmission rates. Results show that the proposed adaptive filtering mechanism achieves safety performance comparable to conventional CPMs transmitted at the maximum allowed frequency (10~Hz), while reducing communication volume by over 93% and preventing queue saturation. This demonstrates that context-aware adaptivity enables CPS to remain both scalable and safety-compliant across heterogeneous computing platforms.

Private 5G networks are a key enabler for flexible industrial automation, especially when used in conjunction with Time-Sensitive Networking (TSN) technology. In this context, radio schedulers must multiplex safety-critical control traffic with bandwidth-hungry sensing streams over a fixed spectrum allocation. This paper proposes a Head-of-Line (HoL) Enhanced Hybrid Lyapunov scheduler for 5G-TSN networks that augments a drift-plus-penalty queue-stability core with an explicit head-of-line delay term and a class-isolation mechanism. The scheduler is evaluated in a 3GPP Indoor Factory scenario with standardized 3GPP fading, spatial consistency, and clutter blockage, using Automated Guided Vehicles (AGVs) generating concurrent URLLC, eMBB, and mMTC flows mapped to dedicated QoS-flow bearers. A fleet-size sweep of 5–30 AGVs on a fixed 20\,MHz carrier reveals a scheduler-independent capacity threshold at approximately 12 vehicles, verified by resource-block saturation. Below the threshold, the proposed scheduler is competitive with the strongest delay-aware baselines and its head-of-line term halves the URLLC deadline-miss ratio relative to the plain Lyapunov formulation. Beyond the threshold, it degrades selectively where the baselines collapse: at $2.5\times$ overload it delivers $1.8\times$ more URLLC traffic than the proportional-fair and delay-budget-aware baselines with a $\approx 4$–$7\times$ shorter 99th-percentile latency, resolving the capacity shortfall in favour of the critical classes instead of spreading it across the traffic mix, at a quantified cost in aggregate cell throughput. The results position Lyapunov-based scheduling as an attractive overload-robustness mechanism for industrial 5G deployments that must remain dependable under unexpected load conditions.