5G mobile network defines a set of use cases with very distinct requirements with regard to throughput, low latency, and high reliability. Network slicing is essential for addressing these end-to-end needs, breaking away from the monolithic architectures characteristic of previous mobile network generations. The Open RAN movement promotes the disaggregation of infrastructure components, allowing for contributions from various vendors and enhancing flexibility and openness within the ecosystem. On the other hand, this paradigm shift introduces potential security threats and vulnerabilities that challenges Slice reliability in Open RAN domains. This work presents a comprehensive investigation through a systematic literature review, examining the current research landscape regarding the security aspects of Open RAN slicing based on O-RAN standards, including studies focused on security metrics and indicators. Additionally, it evaluates the effectiveness of proposed solutions and identifies critical gaps that require further research. By providing a detailed overview of existing research and potential security strategies, this work aims to contribute to the development of secure and robust 5 G networks.
As Artificial Intelligence (AI) adoption accelerates across industries, the need for robust real-time cybersecurity solutions becomes increasingly critical. Traditional security frameworks that rely on static rules, signature-based detection, and threshold alerts struggle to detect sophisticated identity-based attacks that exploit behavioral anomalies rather than explicit policy violations. These limitations, combined with the growing volume of telemetry data and the latency of conventional detection methods, leave organizations vulnerable to prolonged breaches and data exfiltration. This paper presents a use case with a scalable, AI-driven approach to sensitive data leak detection using NVIDIA Morpheus, a GPU-accelerated cybersecurity framework. We demonstrate how Morpheus, in conjunction with NVIDIA BlueField DPUs and DOCA, enables high-throughput, low-latency inspection of real-time network telemetry without impacting system performance. The solution leverages a pre-trained NLP-based Sensitive Information Detection model capable of identifying ten categories of sensitive data within packet payloads, including credentials, PII, and cryptographic keys. Network-captured PCAP data serialized in JSONlines format passes through a linear, feed-forward Morpheus pipeline, configured via CLI and powered by Triton Inference Server with dynamic batching for optimized inference. The final output provides binary classification results that indicate the presence or absence of sensitive information. This case study illustrates how AI-enhanced telemetry analysis can significantly reduce detection and response times, offering a proactive defense mechanism against modern cyber threats.
Wi-Fi mesh networks are an excellent complementary solution to extend the reach of 5G infrastructure, particularly by providing local coverage or backhaul connectivity in hard-to-reach areas. However, the deployment of audio and video services in mesh network is challenging due to unstable wireless link quality. In this context, the choice of transport protocol can play an important role in the resulting Quality-of-Service (QoS) of applications and the perceived Quality-of-Experience (QoE) of users. In this paper, we evaluate video traffic performance over the UDP and TCP transport protocols and the audio traffic over UDP. We also examine the impact of competing traffic on audio performance in a tactical mesh network deployment for the scenario of firefighting intervention. The experimental results show that in small networks, where TCP can quickly retransmit lost packets, TCP-based video streaming can greatly outperform UDP. The competing video traffic can decrease the instantaneous audio similarity score.
This work evaluates the use of Wi-Fi 7 with Multi-Link Operation (MLO) for indoor positioning based on RSSI, seeking to reduce the density of Reference Points (RPs) and simplify the infrastructure. Using only 0.7656 RPs/m(2) and a single access point operating simultaneously in the 2.4 GHz, 5 GHz and 6 GHz bands, simulations were performed in GNU Octave. The results demonstrate that the SVM model achieved 92% accuracy with errors below 3 meters, while the MLP model attained 65% accuracy with errors below 1.71 m. The Random Forest model excelled in stability across varying RP densities, achieving 88% accuracy with errors below 3 m and 64% accuracy with errors below 1.71 m These findings highlight the efficient use of MLO in maintaining high accuracy with reduced infrastructure, positioning Wi-Fi 7 as a scalable and cost-effective solution for indoor positioning.
We propose a correlation-based detection method using Support Vector Machine (SVM) for ambient backscatter based tags. The proposed detector is evaluated against the Minimum Mean Square Error (MMSE) detector in various scenarios, including low Signal-to-Noise Ratio (SNR) conditions and multi-transmitter environments. Moreover, we compare our approach to an energy detector in terms of Bit Error Rate (BER) performance. The reader employs beamforming to steer the beams towards tags and RF source, improving the strength of the tag signal, attenuating the interferences and improving the estimation of the source signal, which is used in the correlation technique to build features for the SVM detector. Simulation results show that our correlation-based detector consistently out-performs the MMSE and energy detection benchmarks, especially in low SNR conditions and can support detection in the presence of multiple RF sources.
The rapid growth of the Internet of Things in urban environments has intensified the need for effective strategies for computational resource allocation in architectures that integrate Edge Computing and Cloud Computing. Resource allocation to meet IoT device demands is an NP-hard optimization problem, which restricts the applicability of exact algorithms in largescale scenarios. To address this challenge, this article proposes an exact algorithm based on an Integer Linear Programming (ILP) model, which serves as a reference bound for comparison, along with heuristic and metaheuristic approaches designed to enhance scalability and minimize costs. The heuristic strategies include a greedy algorithm, which employs a cost function to prioritize allocations, and a randomized algorithm, which explores solution space through random selection. The metaheuristic strategy is based on the Simulated Annealing (SA) technique. Experiments conducted in realistic Smart City scenarios demonstrate that the randomized algorithm may lead to poor allocation choices, resulting in higher costs and lower acceptance rates. On the other hand, the greedy algorithm achieved acceptance rates of up to 71 % with reduced costs, while SA further enhanced the solution quality, reaching a 96 % acceptance rate and reducing operational costs by up to approx. 40.3 % compared to the randomized algorithm. Additionally, both the greedy and the SA-based algorithms achieved runtime reductions greater than approx. 99 % compared to the exact algorithm based on the ILP model, enabling scalable and near real-time decision-making in large-scale IoT environments.
Dense wireless network (DWN) scenarios are more likely to suffer from the problem of hidden terminals and consequently an increase in collisions as they support a large number of stations in a limited geographic area. While the Restricted Access Window (RAW) mechanism introduced by IEEE 802.11ah reduces the levels of contention by separating nodes into groups with exclusive transmission slots, it does not take hidden terminals into account and, therefore, is still susceptible to collisions caused by them. Recently, a spectral clustering algorithm showed potential in finding more efficient RAW groups to reduce hidden terminals, increasing DWN performance. However, because its execution time is very high, in this paper, we propose a faster approach. Our proposal uses a recursive divide and conquer strategy, replacing the full spectral clustering with several executions of a simpler binary split. Simulation results indicate that the proposed recursive clustering algorithm still significantly outperforms the standard IEEE 802.11ah grouping in terms of pairs of hidden terminals, while performing close to the original spectral clustering, but with much lower execution time.
An essential approach for evaluating the performance of applications designed for control and diagnosis of Radio Access Networks (RANs) is simulation. Assessing proposed solutions in simulated environments that closely approximate real-world network behavior is crucial to mitigate potential risks during deployment. This work aims to extend the capabilities of the “ns3-oran” module in the “ns-3” network simulator. The new functionalities remove restrictions on the type of data that can be exchanged with the Near-RT RIC (Near-Real-Time RAN Intelligent Controller), thereby increasing flexibility for researchers and developers. The ability to collect, store, and analyze heterogeneous data is a key enabler for the design and integration of machine learning-based solutions into the RAN. With these enhancements, the simulator can support the creation of scenarios tailored to reinforcement learning (RL) training, leveraging performance metrics that were previously inaccessible or non-configurable within ns-3. As a result, the proposed “ns3-oran-customizable-db” provides a more powerful simulation environment for advancing AI-driven RAN control and optimization approaches.
The proliferation of IoT devices has significantly expanded the attack surface for cyber threats, as demonstrated by the Mirai malware's ability to form botnets and launch devastating distributed denial-of-service attacks using vulnerable IoT devices. This article proposes and validates a flow-based Intrusion Detection System (IDS) specifically designed for IoT networks connected via 5G, with a focus on detecting Mirai attacks. Leveraging Machine Learning algorithms (Random Forest and LightGBM) trained on the CICIoT2023 dataset and utilizing Pcap2csv to process live traffic, the proposed system ensures seamless consistency between training and real-time inference. The IDS was thoroughly validated in a functional 5G environment, utilizing Open5GS and UERANSIM, within a NWDAF-inspired Edge-Fog-Cloud architecture. The solution achieved a remarkable recall rate of 97.35%, demonstrating its practical feasibility in detecting anomalies in IoT networks under 5G.
This paper investigates the impact of key coexistence parameters on achieving fairness between 5 G New Radio in unlicensed spectrum (NR-U) and Wi-Fi networks, according to the 3GPP Release 16 criterion. Through an analytical model and numerical simulations, the study evaluates how varying the initial backoff window size, sensing slots, and slot duration affects channel access fairness. Results demonstrate that increasing backoff values and sensing periods, as well as adopting higher numerologies, enhance fair coexistence and mitigate NRU aggressiveness in spectrum sharing. The findings support the dynamic tuning of NR-U parameters to guarantee 3GPP fairness without compromising performance.
With the rising popularity of decentralized wireless networks, their vulnerability to cyber attacks has become a pressing concern. The availability of Mobile Ad Hoc Networks (MANETs) is critical for applications like disaster recovery and emergency response, making them especially vulnerable to physical-layer threats such as jamming, where adversaries transmit high-power noise to disrupt communication. We consider critical scenarios in which MANET nodes serve as a backbone for mobile users. Existing jamming defenses often rely on non-adaptive optimization, assume simplified environments, target secondary metrics, or ignore mobility. We propose Proactive Data-driven Relocation (PADRE), a proactive decentralized Multi-Agent Reinforcement Learning-based node relocation scheme aimed at maximizing throughput during jamming. Simulations show that PADRE outperforms our proposed heuristic baseline by up to 278 % in static and 585 % in dynamic scenarios with realistic mobility, varying node/jammer counts and strengths. It also shows quick recovery and maintains high performance under severe signaling disruption, with nearly unimpaired results even beyond 75 % signaling packet loss.
The rapid growth of wireless devices highlights the competition for limited radio frequency spectrum. Optical Wireless Communication (OWC) emerges as a promising complementary technology, but it requires specialized routing solutions. Existing protocols, primarily designed for radio frequency, often struggle with the unique challenges of OWC. This paper introduces and evaluates PARC (Proactive and Adaptive Routing Control), a novel routing protocol that combines proactive control with adaptive mechanisms tailored to dynamic optical channels. Through extensive simulations in ultra-high-density and high-mobility scenarios, we compare PARC to the reference protocol OLSR. Our results demonstrate that PARC significantly outperforms OLSR, with an 85 % higher data throughput for PARC (139.88 kbps vs. 75.64 kbps). PARC's architecture offers a robust, efficient, and scalable solution for future wireless optical networks, effectively overcoming the limitations of traditional proactive protocols in dynamic environments.
The fifth generation of cellular networks (5G) established a service-oriented paradigm in mobile networks. In this context, the 5G Core (5GC) and Network Functions (NFs) have become extremely flexible. In addition to serving mobile networks, the 5GC may also support connecting devices from non-3GPP networks (e.g., Wi-Fi). Given these characteristics and the potential for 5G and Wi-Fi convergence, carefully selecting the authentication methods used by non-3GPP user equipment is necessary to ensure secure authentication. This paper presents free5GC Auto Deploy (FAD), a Free/Libre and Open Source Software (FLOSS) tool that automates the deployment of 5G simulation projects such as free5GC and UERANSIM. It also contains an architectural study focused on 5G and Wi-Fi convergence. In light of the above, it highlights key findings from an analysis conducted in a simulated testing environment deployed via FAD, which includes Wi-Fi non-3GPP access that could be utilized within the Eduroam federation.
The evolution of mobile networks has faced the persistent challenge of extending coverage in remote and underserved areas. The advent of fifth-generation (5 G) systems with the 3GPP Release 15 in 2018 enabled new services and applications, yet terrestrial networks remain limited in both capacity and coverage. To address this limitation, Non-terrestrial Networks (NTNs) have emerged as a promising solution, lever-aging Geostationary Earth Orbit (GEO), Medium Earth Orbit (MEO), and Low Earth Orbit (LEO) satellites, as well as High-Altitude Platform Systems (HAPS), to extended connectivity beyond terrestrial infrastructures. Standardized by the 3rd Generation Partnership Project (3GPP) in Release 17, NB-IoT and eMTC technologies were adapted to support NTN operation, ensuring seamless integration with terrestrial networks while still undergoing validation and optimization before large-scale deployment. This paper evaluates the performance of Short Message Service (SMS) over Narrowband NTN (NB-NTN) using smartphones connected exclusively via satellite links. Experiments were carried out in a laboratory-based testbed replicating real-world conditions, comparing transmissions with and without Hybrid Automatic Repeat Request (HARQ). Results indicate that HARQ does not significantly impact device energy consumption, while message delivery latency remains mainly dominated by satellite propagation delay and SMS size. These outcomes confirm the feasibility of NB-NTN for reliable SMS delivery in remote areas, particularly for emergency communications.
Maintaining cohesive and stable flight formations in UAV swarms under adverse communication conditions remains a major challenge. This study evaluates the control of swarm formation based on a modified Raft consensus protocol (Raft-lite) through 625 simulated scenarios in the GrADyS-SIM NextGen framework, varying communication delay, range, failure rate, and formation size. Three performance metrics were analyzed: shape error, presence error, and time without a leader. Pearson and partial correlations revealed that while the three metrics capture complementary aspects of swarm behavior, the shape error consistently integrates the geometric effects of membership and coordination losses. Multiple regression analysis ($R^{2}=0.876$) further showed that delay and failure rate dominate formation degradation, confirming that shape error encapsulates the cumulative impact of communication constraints. Hence, despite non-redundant statistical relationships, shape error remains an operationally sufficient indicator of swarm stability, simplifying performance evaluation in large-scale or real-time swarm operations.
Low-Density Parity-Check (LDPC) codes are a key component of modern wireless standards. Most FPGA-based implementations emphasize decoding performance while over-looking system-level concerns such as network integration and autonomous operation. This paper introduces a configurable FPGA-based LDPC decoder with an integrated UDP/Ethernet stack, enabling direct offloading in real 5G New Radio (NR) deployments for 5G access networks equipped with FPGA-based smart-network interface cards (Smart-NICs). The architecture supports multiple base graphs, variable lifting sizes, and diverse code rates through parameter-driven synthesis, while a dual-clock design ensures reliable timing across heterogeneous interfaces. Implemented on the NetFPGA-SUME platform, the proposed decoder achieves efficient resource utilization (15.09% of FPGA resources), a sustained throughput of 181.51 Mbps, and an end-to-end decoding latency of 423.1 microseconds. Experimental evaluation further reports a bit error rate of 4.688 x10(-5) at 6 dB of E-b/N-0, confirming good performance and deployment readiness.
The increasing volume of network traffic and the growing sophistication of cyber-attacks pose challenges for the scalability and accuracy of Intrusion Detection Systems (IDS). A specific limitation is the difficulty of detecting intrusions and estimating the intensity of attacks in resource-constrained environments, such as access networks with IoT devices. This article proposes a modular framework that encodes network flows into fixed-size Bloom Filter matrices, enabling scalable and efficient learning through different modeling strategies. The methodology was validated using real traffic collected from a Brazilian broadband network, and three approaches were tested: a baseline linear predictor, an ensemble based on XGBoost, and a Convolutional Neural Network (CNN). The linear model showed limited performance, with an R-squared of 0.0233, while the ensemble improved results, reaching an F1-score of 0.9790 and an R-squared of 0.3897. CNN achieved the highest overall performance, with near-perfect classification metrics, including an accuracy of 0.9965 and an F1-score of 0.9972, as well as a strong regression accuracy with an R-squared of 0.8260 and a mean absolute error of 0.0789. These findings confirm the effectiveness of Bloom-Filter-based summarization for low-atency intrusion detection.
This work proposes two collision-aware control request prioritization algorithms for Cellular IoT (CIoT) networks: the Two-Tier Collision Response Algorithm (TTCRA) and the Single-Tier Collision Response Algorithm (STCRA), that improve Random Access (RA) performance by classifying detected preambles according to collision events and processing them accordingly. Simulation results show that both algorithms significantly improve key RA metrics: access success rate, latency, and MSG2 blocking compared to a baseline without prioritization. In high-density scenarios, STCRA stands out by achieving the lowest blocking and delay, demonstrating the benefits of selective resource allocation under critical load. These results validate the proposed prioritization schemes as practical strategies to enhance the scalability and robustness of cellular networks targeting massive Internet of Things (IoT) traffic.
The growth of the Internet of Things (IoT) has highlighted the limitations of centralized data platforms, particularly in terms of security and scalability. Distributed Ledger Technologies (DLT) offer a solution, but traditional DLT architectures, such as blockchain, are often incompatible with low-power wireless IoT networks (LPWAN) due to their latency and operational cost. This paper presents a comparative and empirical performance analysis of two end-to-end data oracle systems designed to record data from a LoRaWAN sensor network. The first implementation uses IOTA Tangle, while the second is based on a blockchain compatible with the Ethereum Virtual Machine (EVM). By evaluating key indicators such as latency and transaction costs, our results demonstrate that the IOTA system offers significant superiority, with a predictable median latency of 4.47 seconds and transaction costs that are economically negligible. In contrast, the blockchain implementation incurred measurable gas costs and demonstrated an architecture with inherently higher and extremely unpredictable latency, with a median of 11.52 seconds and outliers exceeding 200 seconds. We conclude that the IOTA Tangle architecture is technologically and economically better prepared to support scalable and sustainable IoT applications over wireless infrastructures.
Wi-Fi has existed for almost 30 years now. In that period, it has evolved by means of several versions, the most recent one being Wi-Fi 7. However, the technology has good backwards compatibility, which allows coexistence between newer and older equipment. That, in turn, allows for a gradual migration to newer versions of Wi-Fi, which brings up several questions, such as: how prevalent are the most recent versions of Wi-Fi? how common are deployments which still use legacy versions? Surprisingly, information on those issues is scarce and the available data is based on indirect measurements or is biased. In this work, we take a more direct approach to measuring WiFi versions adoption. We report on our methodology as well as results from a case study conducted in Lisbon, Portugal. Results suggest that the adoption time for newer Wi-Fi versions is rather slow, indicating that older versions remain relevant for significant periods of time.
