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.

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.

Software development in critical airspace cyber-physical systems is challenging, mainly because of its safety-critical nature. Safety standards and regulations, such as DO-178C, provide guidelines for the development of software to ensure they adhere to the essential safety requirements in the certification processes. The requirements process proposed in the standard, which is responsible for developing the high-level requirements, is one of the most crucial steps in the life cycle since it serves as the basis for the subsequent processes. Having safety as a major concern, specifying safety requirements is of fundamental importance, allowing engineers to evaluate them and propose measures to mitigate the impact of a system failure, which can be catastrophic. In this paper, we present ReDaML, a domain-specific modelling language designed to support the development of safety-critical software systems, focused on the specification of high-level requirements in accordance with the DO-178C guidelines. Finally, a scenario of applying the approach to an UAS collision avoidance system is demonstrated.

Smart home gateway has to process different types of network traffic generated from several devices in an optimal way to meet their Quality-of-Service (QoS) requirements. However, the fluctuation of network traffic distributions results in packets concurrency. Current QoS-aware scheduling methods in smart home networks do not consider concurrent traffic in their scheduling solutions. This article presents an analytic model for a QoS-aware scheduling optimization of concurrent smart home network traffic with mixed arrival distributions and using probabilistic queuing disciplines. We formulate a hybrid QoS-aware scheduling problem for concurrent traffic in smart home network, propose an innovative queuing design based on the auction economic model of the game theory to provide a fair multiple access over different communication channels/ports, and design an applicable model to implement auction game on both sides; traffic sources and the home gateway, without changing the structure of the IEEE 802.11 standard. Our experiments show the proposed solution achieves an improvement of 14% of packets that meet their required delay and 57% of delay for different number of concurrent flows in the system.

Smart home gateway has to process different types of network traffic generated from several devices in an optimal way to meet their QoS requirements. However, the fluctuation of network traffic distributions results in packets concurrency. Current QoS-aware scheduling methods in the smart home networks do not consider concurrent traffic in their scheduling solutions. This paper presents an analytic model for a QoS-aware scheduling optimization of concurrent smart home network traffic with mixed arrival distributions and using probabilistic queuing disciplines. We formulate a hybrid QoS-aware scheduling problem for concurrent traffics in smart home network, and propose an innovative queuing design based on the auction economic model of game theory to provide a fair multiple access over different communication channels/ports. Our experiments show the proposed solution achieves an improvement of 14% of packets that meet their required delay and 57% of delay for different number of concurrent flows in the system.

Smart home gateways have to forward multi-sourced network traffic generated with different distributions and with different Quality of Service (QoS) requirements. Most of the current QoS-aware scheduling methods consider only the conventional priority metrics based on the IP Type of Service (ToS) field to make decision for bandwidth allocation. Such priority-based scheduling methods are not optimal to provide both QoS and QoE (quality of experience) since higher-priority traffic do not necessary require higher stringent delay than lower-priority traffic. To solve the gaps between QoS and QoE, we propose a new queuing model for QoS-level Pair traffic with mixed arrival distributions in Smart Home network (QP-SH) to make a dynamic QoS-aware scheduling decision which meets delay requirements of all traffic while preserves their degrees of criticality. A new metric which combines, the ToS field and the maximum number of packets that can be processed by the system's service during the maximum required delay, is defined. Our experiments show the proposed solution provides an improvement regarding the number of packets that meet their priorities and their maximum delays as well as the mean number of packets in the system.