Membrane Systems (or P Systems) are a computational paradigm, inspired by the biological cell, that focuses on information processing through the use and formalization of distributed and parallel structures separated by membranes. Within these systems, a set of objects evolves through rules that modify and communicate them among regions. The work aims to formalize a Membrane System for the analysis of network traffic to detect volumetric anomalies within a packet-switched computer network, and classify them. We focus on two volumetric anomalies, i.e., Elephant Flow and DDoS , and on a high-cardinality anomaly, i.e., Super-Spreader , which is volumetric-by-effect. All these anomalies are typically related to well-known network attacks. After formalizing the model, an implementation on GPU using the CUDA platform is proposed, and compared to a CPU implementation. The reported results provide deep insights into the proposed model, highlighting its scalability and showing its ability in identifying and adapting to different scenarios of attacks, especially when they do not occur simultaneously, provided a correct tuning of the parameters. Experiments on a real trace show a very good detection performance for Elephant Flow and Super-Spreader anomalies (up to 0.95 F1 score), while the model struggles more in the detection of DDoS traffic (0.71 F1 score). The use of GPU for computation has proven to be well-suited for parallel processing tasks, such as those naturally enabled by Membrane Computing, strongly outperforming the use of CPU, with a time speedup of about 50 times versus the CPU. Understanding the characteristics and performance of this approach, as well as the alternatives proposed within the context of Membrane Computing, could open pathways to new strategies for networking system protection and guide future development of Network-based Intrusion Detection Systems based on P Systems.
Multi-agent UAV navigation in cluttered environments is challenged by deadlock, starvation-like persistent yielding, oscillation, and unsafe congestion, particularly in narrow passages and dynamically emerging bottlenecks. Existing approaches often rely on static planning assumptions, reciprocal compliance, or learning-based policies in which temporal ordering and fairness are not explicitly represented. This paper introduces T-CARE (Temporal Coordination-Aware Reinforcement Learning), a hybrid learning-heuristic multi-UAV coordination framework that integrates zero-shot constrained action selection with priority-aware temporal reservations. T-CARE extends the prior ZSE-CRL action-selection backbone by adding: (i) runtime spatiotemporal reservations, (ii) priority aging and wait-time accounting designed to reduce persistent yielding, (iii) temporary-goal reassignment for blocked or congested motion, (iv) stagnation detection with escape and recovery, and (v) geometry-agnostic bottleneck discovery with shared-corridor reuse. We evaluate T-CARE in simulation using three-swarm adversarial congestion stress tests, and ten-swarm reconstructed 3D urban/suburban environments, with each ten-swarm scenario containing one leader and three followers per swarm for a total of 40 UAVs, under strict zero-shot deployment. Across the tested scenarios, T-CARE achieved 100% success, 0% collision rate, and no observed persistent starvation or deadlock, while learning-only, reactive, and coordination-reduced baselines exhibited failures under the same evaluation protocol. These results support T-CARE as an empirically effective coordination architecture for simulated multi-UAV traffic, while formal convergence-time guarantees, communication-delay robustness, and real-world flight validation remain future work.
The proliferation of unmanned aerial vehicles (UAVs) in both civilian and military domains has intensified the need for autonomous counter-drone systems capable of operating without reliance on ground infrastructure. Existing ground-based and hybrid approaches suffer from high latency and complete failure under communication jamming or denial. This paper proposes a fully onboard (OB) architecture for autonomous drone-to-drone detection in both the visible (RGB) and thermal (IR) domains, where all perception and decision-making tasks are executed exclusively using the embedded computational resources of the unmanned aerial vehicle. In particular, this work analyzes the features of the physical components of the architecture (i.e., the Single-Board Computers or SBCs for short, the available sensors, and the UAV platforms) and their performances in the experimental settings. Various computational platforms are tested to assess their impact on the performance of the detection pipeline, evaluating specific parameters such as inference speed (fps), inference time (ms), power consumption (W) and operational autonomy. In order to enable a comprehensive evaluation, a ground-based (GB) counterpart was also implemented, where real-time video streams are transmitted from the drone to a ground station for processing and control commands are subsequently sent back. The onboard architecture offers significantly lower latency and complete independence from radio links and controllers, making it particularly suitable for applications requiring high robustness in communication-denied or contested environments. In particular, the findings highlight the advantages of the Jetson Orin Nano platform in achieving inference speeds up to 80.93 fps at 12.36 ms on YOLO v8n quantized models, overcoming state-of-the-art performances. According to our knowledge, this is the first fully onboard RGB-IR drone-to-drone visual detection architecture in the literature.
The increasing frequency and intensity of extreme meteorological events attributed to climate change have necessitated a paradigm shift in power grid resilience. Initiatives such as the RESISTO project address this challenge by bolstering critical infrastructure through the integration of advanced technologies, specifically artificial intelligence (AI) and thermal imaging. By prioritising proactive prediction, prevention, and detection, these interventions seek to ensure grid stability and service continuity under evolving environmental stressors. This study proposes a real-time novelty detection architecture for the thermal monitoring of power transformers, leveraging AI to provide early-warning signals for thermal anomalies. The experimental methodology involved the deployment of a distributed network of thermal cameras within Donana National Park (Spain), supported by a robust backend architecture for data ingestion, analysis, and automated alerting. The analytical system's long-term performance was validated using a thermodynamically simulated synthetic dataset spanning the full 2023 year, with performance evaluated through an event-based confusion matrix. Short-term efficacy was assessed using high-fidelity real thermal imaging. The root mean square error (RMSE) was used to quantify the predictive accuracy across both approaches, which produced values below 2.65% and 5.32% for synthetic data and real-world applications, respectively. These results confirm the system's robustness in anomaly detection and underscore its significant potential for enhancing the resilience of electrical distribution networks.
