This paper presents a novel application for V2X communication, namely a Mountain-Bike-to-Hiker Warning System (or short MBHWS) which should ease the ongoing conflict between hikers and mountain bikers in many alpine regions where both use shared trails. Fast approaching and overtaking bikers can scare hikers, especially when the latter do not recognize the bikers early enough, and in consequence conflict-ridden or dangerous situations regularly occur in mountains. Building on recent technology advances and inspired by recent works on Vulnerable-Road-User protection, we analyze the MBHWS use-case and its requirements, derive and implement a system design to show the general feasibility, conduct technical tests of the system prototype in a realistic outdoor environment in the Italian Alps, and also perform a user-study to analyze user acceptance and preferences. We conclude that a MBHWS is technically feasible based on widely available consumer-grade equipment and our user study gives indications about high user-demand and preferences of different user groups.
The definition and deployment of next-generation mobile networks must incorporate considerations of sustainability and environmental impact. In this context, estimating the energy consumption of a mobile network deployment across a city or region is of utmost importance. Nationwide historical aggregate values are informative but do not allow to understand the underlying dynamics or to perform prospective studies. There is hence a need for bottom-up approaches to assess the energy consumption of mobile networks. However, this is a complex task, as it depends on numerous factors, including the number and spatial distribution of base stations, the underlying technologies and their configuration, as well as user demand patterns and their geographic distribution. This paper thus introduces a simulation framework aimed at estimating the energy consumption of 5G networks at urban, regional or national scale. The framework integrates radio propagation models operating in the 3.5 GHz band with publicly available datasets describing user and base station locations, as well as network traffic volumes. As a case study, we consider the 5G deployment in France and examine the spatial distribution of network load and the resulting energy consumption. The source code and datasets are publicly available, ensuring that the simulator is fully reproducible and easily adaptable to other use cases, countries, or regions.
Network slicing enables 5G and beyond networks to concurrently support diverse services, including emerging IoT applications, over a shared, softwarized infrastructure. Traditional resource management approaches are often reactive, resulting in suboptimal utilization and potential Service Level Agreement (SLA) violations under dynamic traffic conditions. We present Proactive Resource OPtimization for Heterogeneous nETwork Slicing (PROPHET), a framework for Proactive Resource Optimization in heterogeneous slice-enabled networks. PROPHET integrates an attention-based traffic forecasting model with a deep reinforcement learning (DRL) resource allocator based on Proximal Policy Optimization (PPO) to anticipate future slice demands and proactively adjust configurations. Evaluated using real LTE traffic traces representing enhanced Mobile Broadband (eMBB), ultra-Reliable and Low-Latency Communication (uRLLC)-like, and best-effort services, PROPHET improves SLA compliance and resource efficiency compared to reactive baselines. The proposed framework is equally applicable to IoTdriven slices, supporting massive connectivity and ultra-reliable low-latency requirements envisioned for next-generation smart environments.
Physical layer security (PLS) deals with implementing security mechanisms directly at the physical layer, potentially at the signal level. The recent advances on reconfigurable intelligent surfaces (RISs) make it possible to exploit their manipulation capabilities to make a signal decodable at specific locations and unintelligible at other ones, adding additional security and privacy mechanisms on top of existing ones. In previous work, we devised a mathematical framework to achieve multi-receiver PLS in the presence of multiple RISs, showing its effectiveness by means of spatial bit error rate (BER) maps. Our analyses, however, modeled each reception point in the BER map as affected by an independent Rice fading realization on top of path loss, making it impossible to study the size of the secured area around intended receivers. In this work, we extend our analyses by exploiting the Sionna ray tracing (RT) software which, by considering 3D scenarios, can properly estimate the effects of fading by considering realistic reflections and multi-path propagation. The results confirm the applicability of the methodology, but raise several interesting points regarding spatial correlation and undesired signal attenuation effects.
Beyond-5G and 6G networks must support diverse QoS, from ultra-reliable low latency communications to enhanced mobile broadband. This requires joint, efficient resource management that combines network slicing and dynamic beam management. This paper addresses the complexity of optimizing resource allocation across users and slices served by a set of beams. We propose SBQF, a high-performance technique that optimizes resource allocation at both the MAC and the physical layers. SBQF is designed to maximize the overall user utility and is in particular efficient in improving the performance of users requiring latency-critical services. Validated using real-world vehicular traces, SBQF significantly improves latency, user utility, and data rates over existing solutions.
6G testbeds bring together heterogeneous compute and radio resources to support cross-domain experimentation, including networking workloads that integrate AI functionality. Existing schedulers, such as backfilling, priority, and quota mechanisms, react locally to events and offer no guarantees of Pareto efficiency or preference satisfaction in non-monetary, multi-tenant environments. We adapt the Top Trading Cycles and Chains (TTCC) mechanism from matching theory to treat each resource release as a trigger for a global, preference-aware reallocation. Under declared QoS preferences, TTCC yields Pareto-efficient assignments while maintaining high utilization. Simulations across multiple cluster scales and load regimes show that TTCC, augmented with an EASY-style backfilling step when no further trades are possible, matches backfilling on utilization, improves preference-aware utility relative to nonpriority baselines, and preserves competitive P95/P99 bounded slowdown. TTCC therefore provides a principled, preferenceaware alternative to heuristic scheduling for multi-tenant $\mathbf{6 G}$ testbeds.
Urban Air Mobility (UAM) is envisioned as a key component of next-generation transportation systems, requiring highly reliable, low-latency, and high-throughput wireless connectivity to ensure safe and efficient aerial operations. Leveraging existing terrestrial mobile networks represents a cost-effective and scalable solution to support these demanding communication needs, offering flexible infrastructure, wide coverage, and seamless integration with future 6G technologies. This paper investigates the feasibility and advantages of employing mobile networks for UAM connectivity, with a particular focus on RAN-level optimization through vehicular-traffic awareness. By integrating information about planned trajectories and urban vehicle traffic conditions into the radio access decision process, the proposed approach aims to enhance link reliability and improve overall spectral efficiency, demonstrating the potential of mobile networks as a reliable enabler for large-scale and performance-critical UAM deployments.
Remote driving relies on continuous Vehicle-toSatellite (V2S) connectivity in areas lacking terrestrial coverage. While early research focuses on latency and reliability for teleoperation - often evaluated under static or simplified setups these metrics depend on specific channel models and access schemes. This paper instead provides a technology-agnostic analysis of connectivity dynamics that govern Handovers (HOs) in Low Earth Orbit (LEO) satellite networks. Using a high-fidelity simulator with satellite mobility, vehicle movement, and urban obstructions, we evaluate Line-of-Sight (LoS) availability and HO frequency under different strategies. Results show that obstruction topology and HO triggers significantly affect link continuity, with obstacle height having a non-linear impact on HO rates. A key takeaway is also that advance knowledge of satellite and vehicle trajectories can enable more intelligent HO strategies, but only if local obstruction geometry is also considered. These insights establish a baseline for future studies that integrate detailed channel models and assess end-to-end performance.
Cloud Gaming with Virtual Reality (VR) imposes strict requirements on bandwidth and latency to ensure Quality of Experience (QoE). However, current literature often focuses on traditional 2D gaming or older VR hardware. This paper presents a flow-level measurement study of Cloud VR traffic using a Meta Quest 3 headset and the Steam Link platform running a AAA title (Half-Life: Alyx). Our analysis reveals that the service utilizes a unified QUIC tunnel for both video and control data. We observed a distinct asymmetry in traffic patterns: the downlink behaves as a bursty flow driven by video fragmentation, while the uplink maintains a steady, periodic flow for telemetry and tracking. Furthermore, we systematically analyzed the impact of environment topology and player behavior on network load. Statistical results confirm that visual complexity (scenario) is the primary determinant of average throughput, whereas the player profile (exploratory vs. objective-oriented) significantly impacts flow variance and stability. These findings provide empirical guidelines for capacity planning and QoS strategies in immersive streaming services.
Tire Pressure Monitoring System (TPMS) transmissions of modern cars are sent over the air in clear text and entail a unique identifier that does not change over very long periods of time. In this work, we investigate the privacy implications for car owners of this design choice by collecting and analyzing TPMS transmissions from a network of lowcost spectrum receivers that we deploy along the road over a period of 10 weeks. Our measurement study comprises data from 12 verified cars, but malicious actors could easily scale their efforts to track several thousands of cars, given that we observed at least 20k cars during our measurements. Our results show that TPMS transmissions can be used to systematically infer potentially sensitive information such as the presence, type, weight, or driving pattern of the driver. The affordability of the equipment to cause these threats, as low as $100 per receiver, urges policymakers and car manufacturers to design a more secure and privacy-preserving TPMS for future cars.
The latest generations of wireless networks are focusing on the integration of Joint Communication and Sensing capabilities to improve environment awareness and thus enhance services and user experience. One of the elements commonly used for sensing tasks is the Channel State Information (CSI) extracted at the receiver. Most studies exploit Artificial Intelligence (AI) to perform CSI-based sensing tasks, from localization to activity recognition and more. These studies have shown, at least in small, controlled experiments, exceptional capabilities, but have hardly ever offered interpretative models, insight on the sensing capabilities of the methods or fundamental results and bounds. Fortunately, studies are starting to appear that explore an analytic characterization of the CSI. This work falls into this latter category, proposing a quantitative method to measure the relationship between different CSI's. The approach leverages a quantized representation of the CSI amplitude and a reduced-complexity matrix representation that allows to compute a form of Mutual Information (MI) between CSI's. The MI is then used as a compact indicator of the similarity of two sets of CSI, potentially collected in different environments. The proposed method aims at providing a complement or, possibly, an alternative to AI-based sensing, improving the understanding of the CSI features exploited by AI. Results, encouraging though preliminary, are obtained on real-life situations and experiments that overall cover more than six hours of data collection, spanning across several months and amounting to over 800000 CSI's.
The control of climate conditions in Controlled Environment Agriculture (CEA) results in increasing use of sensors and Internet of Things (IoT) devices. They generate vast amounts of redundant environmental data, often overwhelming storage, transmission, and decision-making systems. This paper introduces a correlation-driven approach to IoT data filtering which allows detecting meaningful environmental changes and reduces data flow. This translates into saving bandwidth and reducing energy consumption of battery-equipped IoT devices. By applying the statistical process control method CUSUM (Cumulative Sum) to IoT datasets collected in an experimental greenhouse in Luxembourg, this paper focuses on distinguishing drifted data from stable data across specific time periods in different seasons, based on the specific fluctuation patterns exhibited before or after an event occurred in the greenhouse (e.g., opening/closure of vents). With data statistics extracted from the collected IoT dataset - including temperature T, humidity H, among other parameters - our work estimates the change point position and focuses on observing a unique fluctuation pattern within each assigned data observation window, which ensures independent data analysis over the event and avoids inaccurate detections triggered by cross-event observations. Furthermore, with the two most correlated features identified ($T$, and $H$), the data filtering decisions are made based on their shared drift positions, thereby enhancing the filtering ratio of redundant data. As a result, the proposed correlation-based CUSUM data filtering scheme increases the filtering ratio from 15.2 % for temperature and $\mathbf{5 2. 6 \%}$ for humidity as individual features to $\mathbf{6 0. 5 \%}$ for the most correlated feature pair.
The embedded SIM (eSIM) is rapidly reshaping how mobile devices connect to networks, enabling seamless activation and provisioning without the need for physical SIM cards. Unlike traditional SIMs, the eSIM ecosystem introduces a significantly more complex landscape, where the direct relationship between customers and Mobile Network Operators is mediated by multiple intermediate stakeholders and infrastructures. In this work, our objective is to examine this landscape and shed light on the actors involved in the eSIM provisioning process, exploring how they interact and how their implementations may be related. To this end, we performed an Internet-wide empirical analysis of eSIM provisioning infrastructures, focusing on fingerprinting real-world implementations of Subscription Manager Data Preparation Plus servers. By analyzing certificates, endpoint behaviors, and implementation patterns, we investigate whether server implementations conform to standard specifications or deviate from them, and whether credentials and infrastructures are reused across different actors in the ecosystem. Our preliminary findings suggest that 50% of online servers rely on only six shared implementations and certificates, offering early insights into the consolidation of the eSIM provisioning landscape and laying the ground for a deeper understanding of its actors and relationships.
Future 6G systems must operate as intelligent, sustainable, and self-optimizing infrastructures that integrate heterogeneous communication and power domains. We present an energy-aware, multi-domain orchestration architecture for 6G networks, built on a hierarchical control model comprising an Inter-Domain Management and Orchestration (IDMO) layer, domain-level Management and Orchestration (DMO) entities, and Infrastructure Domain Managers (IDMs), interconnected through a Service-Based Management Architecture. Furthermore, the architecture incorporates Virtual Power Plants (VPPs) and an inter-domain Energy Management System (EMS) that jointly interface (physically and logically) with the network infrastructure while respecting the operational autonomy of local grids. In this model, the VPP exposes unified, virtualized interfaces for energy providers and domain-embedded sources at network elements (e.g., photovoltaics and storage) for network-level coordination without interfering with local grid control. The inter-domain Energy Management System (EMS) ingests standardized, multi-domain energy telemetry and forecasts (e.g., generation potential, carbon, and consumption intensity) and applies predictive modeling to produce localityaware reports for the IDMO. Guided by this intelligence, the IDMO coordinates cross-domain service decomposition, placement, and reconfiguration. Each DMO can locally promote IDMs whose energy mix satisfies sustainability and performance targets. We aim to validate the approach in a cross-domain proof-of-concept spanning two sites: a baseline site and a sustainable site, to demonstrate that our proposed architecture allows to balance orchestration latency and energy savings.
Demand for time-sensitive, resilient wireless networking is rapidly growing for critical systems and applications. Addressing this demand, Wi-Fi 7 introduces new features, including multi-link operation (MLO) and restricted target wake time (R-TWT). While MLO enables simultaneous use of multiple channels across 2.4, 5, and 6 GHz bands for increased reliability and spectral efficiency, R-TWT helps coordinate Wi-Fi 7 networks for timely channel access and improved energy efficiency. Although individual benefits of these features have been demonstrated in the literature, their combined use remains largely unexplored. In this paper, we propose scheduling heuristics to coordinate R-TWT service periods across multiple links in MLO devices. Moreover, we present an open-source multi-link R-TWT simulation framework implemented in OMNeT++. The experiments in this framework demonstrate that our heuristics achieve predictable latency and low jitter in various scenarios, and significantly reduce energy consumption compared to the non-scheduled approach.
Reliable multi-hop communication in 5G New Radio (NR) Vehicle-to-Everything (V2X) communications remains challenging, especially in dynamic, infrastructure-less scenarios. We present a joint framework for distributed scheduling and multi-hop relaying over the Sidelink interface, tailored to dynamic formations such as virtually coupled trains. Motivated by the railway requirement to operate strictly at Layer 2 (i.e., without the ETSI ITS stack), our design integrates sensing based on inter-UE coordination with a proactive routing protocol inspired by B.A.T.M.A.N. that fits the Sidelink MAC model. Unlike approaches that treat routing and resource selection independently, our cross-layer strategy couples the two via a hybrid next-hop metric - a weighted combination of Signal-to-Interference-plus-Noise Ratio (SINR) and available resource blocks - so that paths can avoid weak links and relays with limited resources. Simulations in a virtual train coupling scenario show high delivery ratios, reduced hidden-node collisions, and robust performance across densities, indicating the suitability of the proposed Layer 2 framework for V2X-enabled train automation and future cooperative mobility systems.
In this paper, we propose a model to improve the reliability of a mesh-based last-mile replacement for broadband connectivity in rural and underserved areas. Using open data from ten such regions, we analyze representative demand scenarios and focus on enhancing the robustness of the corresponding wireless backhaul design. We introduce graph-partitioning methodologies to construct mesh clusters with multiple gateways and increased vertex-connectivity, ensuring stronger resilience to radio and node failures. To capture the stochastic behavior of device outages, we model radio failures through a Markov-chain formulation and derive metrics such as failure probability and expected time to disconnection. Through numerical evaluation, we show that incorporating multi-gateway structures and reliabilityaware topology constraints can significantly improve the resilience of rural mesh networks with only modest additional infrastructure requirements.
Environmental monitoring applications using IoT sensors play a crucial role across diverse domains such as agriculture, industrial facilities, and urban air quality monitoring. These systems typically rely on multiple heterogeneous sensors to collect environmental data and support informed decisionmaking. However, since many IoT devices are battery-powered with limited energy capacity, sustaining long-term operation becomes challenging, particularly when energy-intensive sensors are involved. To address these energy constraints, we propose a resource-efficient virtual sensing framework for real-time operation in multi-sensor IoT environments. Sensors or edge devices can be temporarily deactivated to reduce energy consumption while maintaining continuous data streams. Readings from deactivated sensors are reconstructed using a recursive prediction mechanism: each prediction uses the two most recent values from the sensor's stream, which may themselves be predicted, along with recent and current measurements from spatially correlated active sensors. The predicted values are continuously integrated into the data stream, enabling extended deactivation periods and strategic cycling of sensors between active and inactive states. We also propose a calibration phase that estimates the maximum safe deactivation period for each sensor to ensure reconstruction accuracy remains within acceptable bounds. We evaluate our method on real-world sensor datasets, demonstrating accurate reconstruction with low computational overhead. We showed that the model's lightweight design enables efficient edge deployment via TinyML implementation, reducing overall energy consumption without compromising data quality.
Machine learning (ML) models are widely used for detecting malicious traffic in networked systems, including Internet of Things (IoT) environments. Recent work has shown that accuracy alone is insufficient, as models must also provide reliable and well-calibrated confidence estimates to avoid overconfident errors and unstable behavior in practical deployments. Improving model calibration typically relies on post-hoc techniques or additional training data, but both can increase model complexity and energy demand, which is in turn an important limitation for resource-constrained edge deployments. While the trade-off between predictive accuracy and energy consumption has been explored in prior work, the joint trade-off involving prediction reliability has received far less attention despite its critical importance for operational Intrusion Detection Systems (IDS). In this paper, we address this gap by investigating the relationship between predictive performance, reliability, and energy consumption in ML-based IDS for IoT networks. To this end, we develop an evaluation framework that applies post-hoc calibration techniques, namely, Platt scaling and isotonic regression, to ML classifiers and systematically quantifies their impact. Using real IoT traffic data, we evaluate models under varying training data sizes and measure predictive accuracy, calibration quality, and computational energy requirements. Experimental results show that post-hoc calibration improves confidence reliability considerably, with isotonic regression reducing calibration error (ECE) by over 95% and Platt scaling by 83%, while increasing inference energy by only $\mathbf{2}-\mathbf{3} {\%}$, demonstrating that reliability does not come at meaningful computational cost. Results further highlight that calibrated models achieve the same levels of predictive performance and reliability using up to $60-90 {\%}$ less training data, reducing both data needs and energy demand.
This paper presents Plaza6G, the first operational Experiment-as-a-Service (ExaS) platform unifying cloud resources with next-generation wireless infrastructure. Developed at CTTC in Barcelona, Plaza6G integrates GPU-accelerated compute clusters, multiple 5G cores, both open-source (e.g., Free5GC) and commercial (e.g., Cumucore), programmable RANs, and physical or emulated user equipment under unified orchestration. In Plaza6G, the experiment design requires minimal expertise as it is expressed in natural language via a web portal or a REST API. The web portal and REST API are enhanced with a Large Language Model (LLM)-based assistant, which employs retrieval-augmented generation (RAG) for up-to-date experiment knowledge and Low-Rank Adaptation (LoRA) for continuous domain fine-tuning. Over-the-air (OTA) trials leverage a four-chamber anechoic facility and a dual-site outdoor 5G network operating in sub-6 GHz and mmWave bands. Demonstrations include automated CI/CD integration with sub-ten-minute setup and interactive OTA testing under programmable propagation conditions. Machine-readable experiment descriptors ensure reproducibility, while future work targets policy-aware orchestration, safety validation, and federated testbed integration toward open, reproducible wireless experimentation.
