This paper proposes a comprehensive framework for optimizing the deployment of Unmanned Aerial Vehicles (UAVs) in a communication system, aiming to minimize the transmission power of UAVs while ensuring the required data rates for ground users. The proposed model accounts for both large- and small-scale fading effects. To accurately capture the characteristics of the wireless channel, the Rician Shadowed fading model is adopted, as it incorporates both line-of-sight (LoS) and nonline-of-sight (NLoS) components. A mathematical formulation of the problem is presented, and the impact of the Rician shadowed fading parameters, the severity of the fluctuations in the LoS component, and the ratio between the powers of the LoS and NLoS components is considered. The simulation results demonstrate that increasing the parameters improves channel conditions, thus reducing the transmission power required to meet the minimum data rate constraints. To address the UAV 3D placement problem, which results in a non-convex and analytically intractable optimization due to the nonlinearities introduced by the channel model. Therefore, we employ a heuristic algorithm, Particle Swarm Optimization Evolutionary (PSO-E).
Electric vehicles are a major step toward sustainable urban mobility. Vehicle-to-Grid (V2G) systems allow energy to be exchanged in both directions between the vehicle and the grid, which makes bi-directional charging possible. For such systems to work reliably and securely, strong communication and protection mechanisms are necessary.Even though several solutions have been suggested in literature, some limitations still exist — such as relying on a trusted third party, using heavy authentication mechanisms, or ignoring the limited capabilities of certain devices. In this work, we propose a solution that follows the ISO 15118 standard, combining blockchain with a lightweight authentication scheme.We used Tamarin Prover for security modeling and RiseV2G for simulation. Our solution meets key security needs — including confidentiality, data integrity, anonymity, and non-repudiation. Results show that it resists many attacks and works well with limited-resource devices, improving performance in terms of computing time, energy use, and network reliability. We also plan to test our system later using IBM Hyperledger Fabric. [1]
The escalating demand for multimedia content requires efficient and scalable distribution solutions for adaptive streaming, particularly MPEG-DASH, in web environments. This paper presents SwarmLayer, a novel hybrid peer-to-peer (P2P) assisted streaming solution designed to enhance web content distribution. SwarmLayer integrates a traditional client-server model with a P2P paradigm, where active users concurrently serve as content distribution nodes by sharing acquired video segments. This approach aims to significantly reduce the load of the origin server, minimize redundant content transmission, and optimize network bandwidth utilization. Implemented as a transparent and non-intrusive intermediate layer between the media player and the content distribution system, SwarmLayer requires no modifications to existing application logic or infrastructure. Its design leverages standard web technologies, including WebRTC for peer connectivity, Service Workers for request interception, and Protocol Buffers for efficient data serialization, along with the MPEG-DASH standard. Experimental evaluations demonstrate substantial server offload, achieving up to a 96% reduction in server traffic, while maintaining seamless playback fluidity across diverse network conditions, including geographically distributed nodes and NAT environments.
This work presents the foundations of a tool created for the simulation and management of airspaces according to the U-space regulations proposed by the European Union for UAV traffic. The tool will provide a realistic simulation of the flight of numerous UAVs in an urban scenario, with the aim of simplifying and reducing the cost of the development and verification process of all the systems involved, as well as anticipating possible conflicts before deploying a solution in the real world. The tool has been built on one of the most advanced physics simulators currently available, Isaac Sim, which offers numerous tools for experiment analysis, allowing multiple tasks to be executed in parallel in a hyper-realistic environment.
The design of effective mobility-aware systems requires a deep understanding of how different user groups navigate and interact within urban environments, enabling the development of services and infrastructures that adapt to dynamic movement patterns. Tourist mobility patterns, in particular, offer valuable insights into the spatial and social dynamics that influence system design decisions for smart cities, recommendation engines, and urban planning applications. This work presents a comprehensive approach to modeling urban mobility networks by examining tourist behavior through network analysis techniques. Using Rome as a case study, we leverage a large dataset of Foursquare check-ins collected over 18 months to construct a co-visitation graph where nodes represent points of interest and edges reflect shared visits by the same users. Through network analysis of Rome’s tourist mobility, our work identifies clear structural differences between visitor types: long-term visitors form cohesive, highly clustered mobility networks, while short-term visitors create more fragmented interaction patterns. Key points of interest (especially transportation hubs and cultural landmarks) serve as essential connectors shaping network flow, following preferential attachment dynamics. Mobility-aware systems play a crucial role in leveraging such insights to design adaptive, data-driven services that respond to varying mobility behaviors, ultimately enhancing urban efficiency and user experience.
Unmanned aerial vehicles (UAVs) are emerging as key enablers for extending coverage and reliability in next-generation wireless networks using millimeter-wave (mmWave) and terahertz (THz) links. However, their narrow directional beams make initial cell search and alignment challenging under stringent latency and reliability demands. We study a risk-aware beam alignment problem where both the expected access delay and its variability are minimized under strict reliability constraints. To tackle this, we develop the Levy Self-Renewable Flow Direction Algorithm (LSRFDA), designed to balance convergence speed and computational efficiency. Simulations confirm that LSRFDA achieves faster alignment, lower latency, and higher reliability compared to Particle Swarm Optimization (PSO) and random search, making it suitable for UAV-assisted mmWave/THz URLLC and HRLLC scenarios.
This paper presents the application of the improved Self-Organizing Migrating Algorithm (iSOMA) to the synthesis and optimization of quantum circuits. We develop a comprehensive method to evolve candidate quantum circuits with minimal cost by integrating iSOMA with circuit evaluators in Qiskit. Experimental evaluation across 100 independent runs demonstrates a 90% success rate in synthesizing Toffoli gates, with an 80% circuit uniqueness rate indicating diverse solution exploration. The algorithm achieves a median cost of 0.000 and determinism = 8/8 for successful runs, confirming its effectiveness for quantum circuit synthesis. This research is important for future 6G networks and beyond, as quantum computing has the potential to be more efficient, especially in the physical layer of the Radio Access Network (RAN), where quantum-supported optimization mechanisms are able to process a large number of tasks faster. The proposed synthesis of Toffoli gates controlled by iSOMA technology contributes to quantum computing by supporting the efficient design of quantum circuits, which is a prerequisite for the deployment of quantum-native functions, such as the Quantum Fourier Transform, in future communications and wireless infrastructures.
WiFi 7 IEEE (802.11be) introduced Multi-Link Operation (MLO) that enables its devices to communicate over multiple links to support evolving latency-sensitive and high-throughput applications. However, MLO requires advanced scheduling algorithms to optimize the operation. This paper models WiFi 7 scheduling as a constrained Markov Decision Process that optimizes throughput, delay, latency and fairness while capturing access constraints and traffic dynamics. We also develop an attention-enhanced Rainbow Deep Q-Network (DQN) scheduling framework that combines multi-head attention, distributional Q-learning, and prioritized experience replay. Simulation results show up to 2.3x throughput improvement, 6x delay reduction, and marked gains in packet drop rate and spectral efficiency over baseline Round Robin scheduling.
Fluid Computing has emerged as a promising paradigm for enhancing massive and heterogeneous resource management across the Cloud-to-Edge continuum for Internet of Things (IoT) and artificial intelligence (AI) applications. Despite its advantages, research into the optimal deployment of distributed applications across fluid scenarios remains scarce, and existing centralized frameworks cannot exploit emerging AI-native features nor model realistic multi-domain deployment scenarios. This paper presents an innovative provider-based architecture for the optimal orchestration of distributed AI services in fluid environments under 6G network capabilities. The proposed hybrid solution includes robust and scalable decentralized orchestration for the placement of cross-provider workloads without a central broker, as well as leveraging autonomous decision-making devices through distributed task offloading techniques. The proposal was tailored to a Decentralized Federated Learning deployment, serving as a use case in settings with strict privacy and security requirements. This approach was adopted to illustrate the viability of the proposal by deploying large distributed AI services in 6G-like networks.
Social Virtual Reality (VR) applications enable real-time interaction among users in shared immersive environments, but pose stringent requirements on latency, bandwidth, and computational resources. In this poster, we consider the placement of rendering Virtual Network Functions (VNFs) on edge servers in a 5G backhaul network, with the goal of minimizing energy consumption while satisfying strict Motion-to-Photon (MTP) latency constraints. We formulate the problem as a non-linear programming model that accounts for multicast transmission of avatars, path delays, and computing constraints. Preliminary results show that our approach outperforms a baseline strategy, achieving lower energy usage while satisfying latency limits.
Energy efficiency is a critical concern in the deployment and operation of 5G networks, particularly due to the low utilization of 4G and 5G carriers during off-peak hours. While considerable research has focused on designing energy-efficient cell on/off switching strategies that avoid disrupting user connectivity, the integration of operator-specific policies to guarantee particular Quality of Service (QoS) levels has received limited attention. This paper presents a machine learning (ML)-based energy saving strategy, trained using a real-world dataset from a European mobile operator, that enforces operator-defined policies that jointly consider strong throughput requirements and maximum outage tolerance constraints. By tuning the model’s class ratios during training, the proposed solution enables operators to manage the trade-off between energy savings and QoS policy compliance prior to deployment in live networks. Evaluation results show that the method provides substantial energy savings while maintaining policy-compliant service levels under realistic 5G operating conditions.
A digital twin (DT) is a virtual representation of a physical object or system created using data obtained through numerical simulation or sensor measurements from Internet of Things (IoT) technologies, among others. These measurements allow the virtual model to be specific to a particular object or system, enabling diagnosis, predictive, and prescriptive maintenance. In this paper, we present the development of a DT for an IoT monitoring network with applications in Industry 5.0. To achieve this, we employ reduced-order models (ROM) based on the proper orthogonality decomposition (POD) technique to reconstruct a field of interest, e.g., temperature in a cold room or on a pig livestock farming, from a reduced set of optimally placed sensors. The results demonstrate that optimal placement can achieve low reconstruction errors even when only a few sensors are deployed - for example, three or four.
Recently, the use of Internet of Things (IoT) technologies has been widely adopted, enabling large-scale monitoring and data acquisition. Together with the so-called artificial intelligence of things (AIoT), data-driven models are to be executed close to the data source. Nevertheless, one potential problem is the quality of the data used to feed these models. Data loss is a common issue in these systems and can hinder the use of monitoring data and the application of artificial intelligence (AI) models. This paper evaluates a set of missing value imputation techniques, emphasizing their application in an edge setting. Specifically, offline and sliding window variants are evaluated to meet data availability and computational complexity criteria. The benchmarking has been performed using the data of a real IoT air quality monitoring platform. The results demonstrate that the windowed variants can approach the offline performance using reduced sliding windows of 25 to 100 samples per sensor.
The proliferation of mobile devices and permanent Internet connectivity generates massive data flows that carry personal information and can compromise user privacy. This doctoral research focuses on analyzing privacy vulnerabilities in the Domain Name System (DNS), a fundamental protocol for Internet communications, and its encrypted variants. The research aims to develop novel methodologies that enhance DNS privacy protection while optimizing energy consumption. Initial contributions include the proposal of DNS query forgery techniques as privacy-enhancing mechanisms and the development of PARROT, a reproducible traffic capture system for mobile app analysis. The work demonstrates protocol evolution trends and validates privacy protection strategies through experimental evaluation using synthetic datasets. Future research will focus on energy-aware optimization of DNS privacy solutions and the development of practical implementations for mobile environments.
Low Earth Orbit (LEO) satellite constellations are poised to become key enablers of global connectivity. However, their dynamic topologies, high latency, and intermittent links pose significant challenges for traditional IP routing protocols. This paper evaluates the applicability of OSPFv3 in LEO networks using a novel emulation framework that integrates accurate orbital modeling with full-stack network simulation. Our framework decouples topology generation from network emulation, enabling protocol-independent testing in realistic conditions. We analyze OSPFv3 performance using the Iridium constellation as a case study, assessing protocol convergence, stability, and routing efficiency under worst-case scenarios. Results reveal the limitations of default OSPFv3 configurations in LEO environments and offer insights into potential optimizations for future integration of satellite networks into global IP infrastructures.
With the increasing deployment of 5G and emerging 6G network functions (NF) in cloud-native environments, persistent storage management has become a critical concern, particularly regarding data sovereignty, integrity, and transparency. This paper presents a novel approach to volume claim management leveraging Verifiable Credentials (VCs) as a mechanism to ensure trusted and tamper-resistant storage provisioning across decentralized infrastructure providers. Our framework empowers service creators—rather than cloud providers—to retain control over their data, enabling granular, selective disclosure and policy enforcement in multi-stakeholder ecosystems typical of 6G architectures. By integrating VCs into the persistent storage lifecycle, we introduce cryptographic assurances around volume ownership, access rights, and lifecycle events. This mitigates the risks of unauthorized data modification, supports data residency requirements, and enhances interoperability across federated environments. The proposed model not only strengthens transparency but also aligns with the principles of decentralized trust essential for the next generation of cloud-native network deployments.
Indoor Localization Systems (ILS) are critical for applications requiring high positioning accuracy, such as emergency response. However, traditional fingerprinting-based methods face challenges including multipath interference and the need for labor-intensive site surveys using Received Signal Strength Indicators (RSSIs). To address these limitations, this paper proposes two key contributions: (1) A data augmentation framework using Autoencoders (AE) and Variational Autoencoders (VAE) to expand RSSI datasets and reduce manual survey efforts; and (2) a hybrid localization model, Zonal-Weighted K-Nearest Neighbours (Zx-WKNN), which first classifies the target's probable sub-area (zone) and then refines the location using a regression model. Each zone is defined around a reference point and contains RSSI fingerprints with corresponding coordinates. Unlike traditional WKNN, which considers all reference points, Zx-WKNN focuses on a limited number of zones (x = 1, 2, or 3) to enhance accuracy. Experimental results demonstrate that incorporating synthetic data from our generative models reduces Root Mean Squared Error (RMSE) by 6-25% and improves R-squared (R-2) by 11-400%. Compared to baseline models such as WKNN and Random Forest, Zx-WKNN achieves 2.1-18.8% lower RMSE and 3.2-29% higher R-2. Overall, our approach significantly improves localization accuracy while addressing the scalability limitations of traditional fingerprinting methods.
This paper presents a machine learning-based approach for network intrusion detection. It relies on a fast binary classifier to quickly distinguish between benign and malicious traffic, being the actual type of attack detected in an independent second stage. The model was trained and evaluated using a benchmark dataset that combines the well known CIC-IDS2017, CIC-IDS2018, and UNSW-NB15 datasets, following extensive preprocessing and exploratory data analysis. We compared multiple algorithms (Neural Networks, XGBoost, Random Forest, and Logistic Regression) based on standard metrics like precision, F1 score, ROC-AUC, and inference time, among others. Experimental results show that XGBoost consistently achieves the best balance between classification performance and deployment efficiency.
Computer vision is becoming a building block in many intelligent systems. Accordingly, video frames are timely analyzed, and computationally generated information can be overlaid on users’ visual perception of the physical world to augment their experiences or produce commands to control autonomous mobile robots. Traditionally, computer vision is performed at cloud and edge servers, where deep learning models are deployed to process video frames. However, accelerators for embedded devices have been proposed, and video frame inference at embedded devices might reduce latency, overhead, and costs in computer vision-based systems. This paper proposes a novel architecture to enable distributed and collaborative vision analytics in networked embedded devices. The proposed architecture encompasses the different building blocks to enable video frame offloading among the network-embedded devices, video frame inference, and devices’ telemetry for data collection. A preliminary analysis of the proposed architecture is conducted, and its potential for enabling distributed video analytics at networked embedded devices is highlighted.
Smart agriculture increasingly depends on advanced sensing and data analysis to optimize crop yields and support sustainable pest management. This work introduces an innovative approach to characterizing insect chemical communication, focusing on the Spodoptera genus, through the application of Information Theory Quantifiers (ITQs) and causality planes (Complexity-Entropy and Fisher-Shannon). Data were obtained from Electroantennography (GC-EAG) experiments, which record bioelectrical insect responses to volatile compounds via integrated sensors and microelectrodes. The methodology incorporates signal processing techniques such as ordinal patterns, sliding windows, and pre-processing stages to address the complexity and noise of time-series data. Results reveal that ITQs effectively capture distinct transitions from stochastic resting states to more organized active responses, highlighting their ability to detect and characterize complex chemical stimuli. These findings underscore the potential of ITQs as robust tools for identifying bioactive compounds, with direct applications in pest management and intelligent sensing within smart agriculture.
