The development of software for Unmanned Aerial Vehicles (UAVs) is governed by stringent safety-critical regulations, with DO-178C serving as the primary standard for airborne systems. Ensuring compliance requires extensive verification, validation, and traceability across the software lifecycle, which becomes increasingly complex for autonomous and adaptive UAV functions. This paper proposes an integrated methodology for regulatory compliance checking that combines formal methods with automated verification tools to generate certification-ready evidence under DO-178C. Formal methods are applied at multiple levels: Alloy is used for requirements consistency checking, the SPIN model checker for architectural interaction properties, and bounded model checking for code-level analysis. These techniques are integrated with automated toolchains that provide continuous bidirectional traceability, structural coverage analysis, and automated test execution across the software lifecycle. The approach is evaluated on a Design Assurance Level (DAL) B UAV Collision Avoidance System. The case study demonstrates the production of certification-ready evidence bundles, including closed bidirectional traceability from system requirements through high and low-level software requirements to source code and tests, formal proof summaries linked to requirements, and decision coverage reports on mission-critical logic. Results indicate that tightly integrating formal analysis with automated verification improves early defect detection, reduces manual evidence assembly, and strengthens the auditability of DO-178C compliance. The combined use of formal methods and automation offers a scalable pathway for UAVs and other autonomous systems to achieve compliance with evolving safety regulations. The findings highlight that integrating regulatory compliance checking into development processes can simultaneously enhance rigour and efficiency, providing a model for certifiable autonomy software in civil airspace. • Engineered a workflow that combines formal methods with automation for DO-178C UAV compliance. • End-to-end methodology validated on a UAV Collision Avoidance System case study. • Produced certification-ready evidence: traceability, proofs, and coverage. • Improved early defect detection and reduced manual certification effort. • Illustrates a scalable path for certifiable autonomy in safety-critical UAVs.

Translating natural language (NL) requirements into formal specifications such as Computation Tree Logic (CTL) is essential for improving the efficiency and scalability of formal verification, especially in safety-critical systems. This study evaluates the ability of Large Language Models (LLMs) to automate this process. We compare three approaches: fine-tuning the Mistral model, using GPT-4 in a few-shot learning setup, and a hybrid that feeds a BERT pattern classifier’s prediction to GPT-4. Using the Natural2CTL dataset, we assess strict logical accuracy and an ambiguity-tolerant accuracy, complemented by auxiliary semantic and structural operator similarity measures. Fine-tuning yields the strongest strict correctness and operator-structure fidelity, while the hybrid narrows the gap to fine-tuning and substantially improves over few-shot prompting alone. Residual errors across methods concentrate in path-quantifier selection, temporal granularity, and scoping in multi-clause requirements. Overall, LLMs can draft CTL candidates that are usable after lightweight normalisation, but they should be integrated into human-in-the-loop workflows with basic automated checks before use in high-assurance settings. • Benchmarks three LLM-based methods for NL-to-CTL translation. • Fine-tuned Mistral achieves 47.6% strict logical accuracy and 71.4% ambiguity-tolerant accuracy for CTL specification generation. • GPT-4 few-shot learning offers rapid prototyping but lower syntactic precision. • BERT-GPT hybrid balances pattern recognition and generative translation. • LLM automation reduces expert effort, but expert review remains critical for safety.

Virtual Network Embedding (VNE) approaches typically assume static or slowly-changing network topologies, but emerging applications require deployment in mobile environments where traditional methods become insufficient. This work extends VNE to constrained mesh networks of mobile edge devices, addressing the unique challenges of rapid topology changes and limited resources. We develop models incorporating device capabilities, connectivity, mobility and energy constraints to evaluate optimal deployment strategies for mobile edge environments. Our approach handles the dynamic nature of mobile networks through three allocation strategies: an integer linear program for optimal allocation, a greedy heuristic for immediate deployment, and a multi-objective genetic algorithm for balanced optimization. Our initial evaluation analyzes application acceptance rates, resource utilization, and latency performance under resource limitations. Results demonstrate improvements over traditional approaches, providing a foundation for VNE deployment in highly mobile environments.

In the domain of formal verification, translating natural language (NL) requirements into Computation Tree Logic (CTL) specifications presents a notable challenge due to the disparity between human-readable documents and formal specifications. This paper introduces a novel approach that leverages Large Language Models (LLMs) to automate this translation process, thereby enhancing the accuracy and efficiency of formal verification practices. We fine-tune three state-of-the-art LLMs—LLAMA3, Mistral, and Qwen2—with a particular focus on optimizing the Mistral model due to its superior performance. Our methodology is supported by the Natural2CTL dataset, consisting of 2,095 NL requirements and their corresponding CTL specifications. We employ evaluation metrics such as validation loss, accuracy, semantic similarity, and Structural Operator Jaccard Similarity (SOJS) for a comprehensive assessment of model performance. Additionally, a comparative analysis with human translators, trained in CTL logic, underscores the LLMs’ potential to match or even surpass human accuracy in translating NL requirements into formal specifications. Our findings reveal that the fine-tuned Mistral model significantly outperforms the other LLMs and human participants, demonstrating superior accuracy in generating CTL specifications. This study advances the field of formal verification by proposing a scalable solution to the NL-to-CTL translation challenge, setting a new benchmark for the integration of AI tools in complex specification tasks.

Recently, IoT, SDN and NFV have emerged as significant technological enablers for telemedicine. Because of specific characteristics of telemedicine services, reliability is one of the critical elements to guarantee the quality of services. To maintain high availability of services, existing backup solutions focus on resources constraints where backup instances are placed at the same node (vertical backup) or distributively deployed at different nodes (horizontal backup). While they put more effort to satisfy resource requirements, routing issues are often neglected such as end-to-end latency, multi-path routing, and synchronization in a multi-path scenario. Such aspects are key requirements to deploy high reliability telemedicine services. Therefore, we investigate the dynamic backup mechanism for a telemedicine system. We aim to optimize the reliability of telemedicine service function chains (TSFCs) where a joint horizontal/vertical backup (JHVB) optimization problem is first formulated. Since JHVB is a combinatorial optimization problem, which is NP-Hard, we then solve this problem in both offline and online fashions using Block Successive Upper Bound Minimization (BSUM) and Multi-Armed Bandit (MAB) frameworks. We compare our methods to the benchmarks via intensive simulations based on the Nano Datacenter solution that is used for enabling telemedicine services. The results demonstrates an outstanding performance in terms of failure awareness and service reliability.

Swarm robotics carries the potential of solving complex tasks using simple devices. To do so, however, one must define distributed control algorithms capable of producing globally coordinated behaviours. We propose a methodology to address the problem of the spatial coverage of multiple tasks with a swarm of robots that must not lose global connectivity. Our methodology comprises two layers: (i) a distributed Robot Navigation Controller (RNC) is responsible for simultaneously guaranteeing connectivity and pursuit of multiple tasks; and (ii) a global Task Scheduling Controller approximates the optimal strategy for the RNC with minimal computational load. Our contributions include: (i) a qualitative analysis of the literature on connectivity assessment, (ii) our proposed methodology, (iii) simulations in a multi-physics environment, (iv) real-life robot experiments, and (v) the experimental validation of connectivity, coverage optimality, and fault-tolerance.