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arXiv2026

Can NR-V2X Sidelink support A2A links?

In the context of 5G, 3GPP introduced New Radio vehicle to everything (NR-V2X) for direct vehicle-to-vehicle communication. However, starting from Release 18 the focus of the standard has been expanded from vehicles to any device and use case that can benefit from direct communication. In 3GPP terminology, the standard is now referred to simply as Sidelink communication. This standard allows direct communication between devices based on synchronous resource scheduling. Users can rely on controlled scheduling when in network coverage or, in the absence of coverage, autonomously select resources for transmission via a distributed resource allocation mechanism. Focusing on the autonomous resource allocation, this paper investigates the possibility of applying Release 18 Sidelink communication to Air-to-Air (A2A) links between airborne entities. The paper outlines the main challenges and required modifications to adapt the current standard for longer links in the order of kilometres. The analysis identifies the propagation delay as a critical limitation. Communications at distances over 42.4 km require a restriction of the user's transmitting opportunities. However, sidelink communication remains feasible for distances below this threshold without modifications to the standard.

cs.NI↗

arXiv2026

A Scan-Based Analysis of Internet-Exposed IoT Devices Using Shodan Data

An open measurement problem in IoT security is whether scan-observable network configurations encode population-level exposure risk beyond individual devices. An analysis of internet-exposed IoT endpoints using a controlled multi-country sample from Shodan Search and Shodan InternetDB, selecting 100 hosts identified via TCP port 7547 (TR-069/CWMP) and evenly distributed across the ten most represented countries. Hosts are enriched with scan-derived metadata and analyzed using feature-relevance assessment, cross-country comparisons of open and risky port exposure, and supervised classification of higher-risk exposure profiles. The analysis reveals consistent cross-country differences in exposure structure, with mean risky-port counts ranging from 0.4 to 1.0 per host, and achieves balanced accuracy of approximately 0.61 when classifying higher-risk exposure profiles.

cs.CR↗

arXiv2026

Indoor Neutral-Host Networks Over Shared Spectrum and Shared Infrastructure: A Comparison Study of Real-World Deployments

Indoor high-capacity connectivity is frequently constrained by significant building penetration loss and the inherent uplink power limitations of a typical outdoor macro-cell deployment. While Mobile Network Operators (MNOs) must optimize spectrum across low-band (<1 GHz) and mid-band (1-7 GHz) frequencies, uplink performance remains disproportionately degraded due to link budget asymmetry. Neutral-host (NH) networking provides a scalable alternative by transparently offloading MNO subscribers via spectrum sharing and shared infrastructure. We present a multi-site measurement study comparing Citizens Broadband Radio Service (CBRS)-enabled NH networks against public MNO 4G/5G macro deployments and Wi-Fi. Our results show: (i) significant building penetration loss with up to 15.5 dB in low-bands and 17.9 dB in mid-bands, resulting in a ~10 dB RSRP deficit for MNO mid-bands compared to low-bands; (ii) NH networks provide a 30 dB higher median indoor RSRP with indoor NH normalized downlink throughput matches MNO outdoor performance, while its uplink performance exceeds MNO levels in both indoor and outdoor settings; (iii) NH proximity enables superior uplink efficiency, utilizing 64-QAM for 56% of transmissions (versus <6% for MNOs) and reducing median UE transmit power by 5 dB; (iv) MNOs rely on low-band spectrum for indoor uplink transmissions, while the NH deployment maintains high-performance mid-band connectivity; and (v) NH outperforms MNOs in end-to-end throughput but trails Wi-Fi in uplink throughput and latency due to packet routing overhead to the MNO core.

cs.NI↗

arXiv2025

SPARQ: An Optimization Framework for the Distribution of AI-Intensive Applications under Non-Linear Delay Constraints

Next-generation real-time compute-intensive applications, such as extended reality, multi-user gaming, and autonomous transportation, are increasingly composed of heterogeneous AI-intensive functions with diverse resource requirements and stringent latency constraints. While recent advances have enabled very efficient algorithms for joint service placement, routing, and resource allocation for increasingly complex applications, current models fail to capture the non-linear relationship between delay and resource usage that becomes especially relevant in AI-intensive workloads. In this paper, we extend the cloud network flow optimization framework to support queuing-delay-aware orchestration of distributed AI applications over edge-cloud infrastructures. We introduce two execution models, Guaranteed-Resource (GR) and Shared-Resource (SR), that more accurately capture how computation and communication delays emerge from system-level resource constraints. These models incorporate M/M/1 and M/G/1 queue dynamics to represent dedicated and shared resource usage, respectively. The resulting optimization problem is non-convex due to the non-linear delay terms. To overcome this, we develop SPARQ, an iterative approximation algorithm that decomposes the problem into two convex sub-problems, enabling joint optimization of service placement, routing, and resource allocation under nonlinear delay constraints. Simulation results demonstrate that the SPARQ not only offers a more faithful representation of system delays, but also substantially improves resource efficiency and the overall cost-delay tradeoff compared to existing state-of-the-art methods.

cs.NI↗

arXiv2025

PRATA: A Framework to Enable Predictive QoS in Vehicular Networks via Artificial Intelligence

Predictive Quality of Service (PQoS) makes it possible to anticipate QoS changes, e.g., in wireless networks, and trigger appropriate countermeasures to avoid performance degradation. Hence, PQoS is extremely useful for automotive applications such as teleoperated driving, which poses strict constraints in terms of latency and reliability. A promising tool for PQoS is given by Reinforcement Learning (RL), a methodology that enables the design of decision-making strategies for stochastic optimization. In this manuscript, we present PRATA, a new simulation framework to enable PRedictive QoS based on AI for Teleoperated driving Applications. PRATA consists of a modular pipeline that includes (i) an end-to-end protocol stack to simulate the 5G Radio Access Network (RAN), (ii) a tool for generating automotive data, and (iii) an Artificial Intelligence (AI) unit to optimize PQoS decisions. To prove its utility, we use PRATA to design an RL unit, named RAN-AI, to optimize the segmentation level of teleoperated driving data in the event of resource saturation or channel degradation. Hence, we show that the RAN-AI entity efficiently balances the trade-off between QoS and Quality of Experience (QoE) that characterize teleoperated driving applications, almost doubling the system performance compared to baseline approaches. In addition, by varying the learning settings of the RAN-AI entity, we investigate the impact of the state space and the relative cost of acquiring network data that are necessary for the implementation of RL.

cs.NI↗

arXiv2025

Motor Rotation Speed Estimation based on Magnetic Inductive Sensing

Rotation speed is a key metric for many applications, such as calibrating electric motors in a factory, monitoring a car's engine health, detecting faults in electrical appliances, and more. However, existing measurement techniques have several drawbacks, including the need for line-of-sight visibility, intrusive machine modification, fixed sensor deployment, or short sensing ranges. In this paper, we introduce MagTach, a handheld hardware-software system for rotation speed measurement that leverages electromagnetic field sensing to provide highly accurate and convenient readings.

cs.NI↗

arXiv2025

When Next-Gen Sensing Meets Legacy Wi-Fi: Performance Analyses of IEEE 802.11bf and IEEE 802.11ax Coexistence

Sensing is emerging as a vital future service in next-generation wireless networks, enabling applications such as object localization and activity recognition. The IEEE 802.11bf standard extends Wi-Fi capabilities to incorporate these sensing functionalities. However, coexistence with legacy Wi-Fi in densely populated networks poses challenges, as contention for channels can impair both sensing and communication quality. This paper develops an analytical framework and a system-level simulation in ns-3 to evaluate the coexistence of IEEE 802.11bf and legacy 802.11ax in terms of sensing delay and communication throughput. Forthis purpose, we have developed a dedicated ns-3 module forIEEE 802.11bf, which is made publicly available as open-source. We provide the first coexistence analysis between IEEE 802.11bfand IEEE 802.11ax, supported by link-level simulation in ns-3to assess the impact on sensing delay and network performance. Key parameters, including sensing intervals, access categories, network densities, and antenna configurations, are systematically analyzed to understand their influence on the sensing delay and aggregated network throughput. The evaluation is further extended to a realistic indoor office environment modeled after the 3GPP TR 38.901 standard. Our findings reveal key trade-offs between sensing intervals and throughput and the need for balanced sensing parameters to ensure effective coexistence in Wi-Fi networks.

cs.NI↗

arXiv2023

An End-To-End Encrypted Cache System with Time-Dependent Access Control

Due to the increasing use of encrypted communication, such as Transport Layer Security (TLS), encrypted cache systems are a promising approach for providing communication efficiency and privacy. Cache-22 is an encrypted cache system (Emura et al. ISITA 2020) that makes it possible to significantly reduce communication between a cache server and a service provider. In the final procedure of Cache-22, the service provider sends the corresponding decryption key to the user via TLS and this procedure allows the service provider to control which users can access the contents. For example, if a user has downloaded ciphertexts of several episodes of a show, the service provider can decide to provide some of the contents (e.g., the first episode) available for free while requiring a fee for the remaining contents. However, no concrete access control method has been implemented in the original Cache-22 system. In this paper, we add a scalable access control protocol to Cache-22. Specifically, we propose a time-dependent access control that requires a communication cost of $O(\log T_{\sf max})$ where $T_{\sf max}$ is the maximum time period. Although the protocol is stateful, we can provide time-dependent access control with scalability at the expense of this key management. We present experimental results and demonstrate that the modified system is effective for controlling access rights. We also observe a relationship between cache capacity and network traffic because the number of duplicated contents is higher than that in the original Cache-22 system, due to time-dependent access control.

cs.CR↗