Continual Safety, Security and Trustworthiness for Dynamic Connected and Intelligent Ultra-Large Scale Systems

Dear Colleagues,  

The increasing convergence of the Internet of Things (IoT), cyber–physical systems, edge/cloud computing, artificial intelligence, autonomous systems, unmanned aerial vehicles (UAVs), wireless sensing technologies, embedded communication systems and next-generation communication networks is giving rise to highly interconnected and increasingly complex digital infrastructures. Such infrastructures are dynamic in nature, with devices, vehicles, UAVs, sensors, medical devices, services, data sources and dependencies continuously joining, leaving or changing their operational behaviour. These characteristics are particularly evident in smart cities, connected and autonomous transportation, UAV-assisted and aerial networks, integrated aerial–terrestrial systems, intelligent healthcare, connected medical systems, intelligent energy systems, industrial IoT, autonomous systems and other safety- and security-critical environments.  Advances in radio-frequency (RF) sensing, ultra-wideband (UWB), millimetre-wave (mmWave) radar, embedded wireless communication, signal processing and intelligent sensing are further expanding the capabilities of connected systems. In healthcare and assisted-living environments, RF-based and contactless sensing technologies can support applications such as human activity recognition, vital-sign monitoring, localisation, fall detection, behavioural monitoring and continuous health assessment. However, the increasing use of wireless sensing, multimodal data, connected medical devices and AI-based decision-making also introduces important challenges related to data integrity, privacy, cybersecurity, trustworthiness, uncertainty, robustness and the reliability of machine-learning models. Traditional security and safety assessment approaches are often performed at design time or assume relatively stable system configurations. However, in dynamic and ultra-large-scale environments, assurance must increasingly be established and maintained during operation. Changes in system composition, communication quality, sensing reliability, data integrity, trustworthiness, cyber threats, uncertainty, system visibility, mobility and network topology can affect whether previously established security and safety claims remain valid. These challenges are especially significant in highly mobile autonomous environments, including UAV networks and drone swarms, as well as safety-critical healthcare and IoT environments where sensing errors, communication failures, compromised devices, adversarial manipulation or unreliable AI predictions may directly influence system decisions. Digital twins, intelligent monitoring, multi-source and multimodal data fusion, signal processing, predictive analytics, runtime verification, deep learning and AI-assisted security mechanisms provide promising means of maintaining the awareness of the evolving system state and supporting assurance-informed decision-making. In UAV and aerial-network environments, such approaches can support continuous assessment of communication reliability, navigation integrity, cooperative behaviour, mission safety, cyberattack likelihood and resilience to failures, spoofing, jamming, compromised nodes and uncertain operating conditions. In healthcare and connected sensing environments, similar approaches can support trustworthy contactless monitoring, secure medical-device communication, robust multimodal sensing, reliable health-state estimation and early detection of anomalous or compromised behaviour. This Special Issue aims to present and disseminate recent advances in continual security and safety assurance for dynamic, interconnected, autonomous, sensing-enabled and ultra-large-scale digital infrastructures. Particular emphasis is placed on approaches that integrate digital twins, IoT and cyber–physical systems, connected and autonomous vehicles, UAVs and aerial networks, RF and wireless sensing, connected healthcare systems, runtime monitoring, heterogeneous and multimodal evidence fusion, cybersecurity analysis, predictive modelling, signal processing, deep learning and intelligent decision-making. We also encourage research investigating how systems can dynamically reassess their assurance level and adapt their operation when evidence indicates increasing risk, uncertainty, degraded communication or sensing, cyberattacks, compromised information, unreliable AI predictions or potential safety violations.  Topics of interest include, but are not limited to, the following:

• Digital twins for security, safety, resilience and runtime assurance;
• Continual, dynamic and runtime security assurance;
• Security and safety assurance for ultra-large-scale systems and networks;
• IoT, Industrial IoT, cyber–physical systems and connected infrastructures;
• Security–safety interactions in autonomous and intelligent systems;
• Runtime monitoring, verification and predictive safety assessment;
• Multi-source evidence aggregation and data fusion for assurance;
• Multimodal sensing, multimodal learning and heterogeneous data fusion;
• Trust, uncertainty, confidence and risk-aware decision-making;
• AI-, deep-learning- and machine-learning-based cyber threat and anomaly detection;
• Explainable, robust and trustworthy machine learning for safety- and security-critical systems;
• Communication reliability, data integrity, freshness and trustworthiness;
• Adaptive cybersecurity and assurance-informed system reconfiguration;
• Graceful degradation, resilient operation and recovery mechanisms;
• Dynamic attack surfaces and evolving cyber threats;
• Edge–cloud intelligence for distributed security monitoring;
• Digital-twin-assisted intrusion detection and threat modelling;
• Explainable and trustworthy AI for security-critical systems;
• Probabilistic and uncertainty-aware assurance models;
• Event-triggered and adaptive security monitoring;
• RF sensing and wireless sensing for intelligent and connected systems;
• Ultra-wideband (UWB) sensing, localisation, tracking and communication;
• Millimetre-wave (mmWave) radar sensing and intelligent radar signal processing;
• Contactless sensing and monitoring for healthcare and assisted-living applications;
• RF-based human activity recognition, localisation, vital-sign sensing and behavioural monitoring;
• Deep learning and signal processing for RF, radar and wireless sensing;
• Multimodal learning combining RF, radar, vision, wearable, physiological and IoT sensing modalities;
• Embedded wireless communication systems and intelligent edge sensing;
• IoT-enabled healthcare, smart healthcare and connected medical systems;
• Cybersecurity of connected and wireless medical devices;
• Security, privacy and trust management in healthcare IoT and Internet of Medical Things (IoMT);
• Trustworthy and explainable machine learning for healthcare sensing and decision support;
• Adversarial robustness and security of AI-enabled healthcare and sensing systems;
• Secure transmission, processing and fusion of medical and physiological sensing data;
• Security, safety and runtime assurance for UAVs and autonomous aerial systems;
• Cybersecurity and resilience of UAV networks, drone swarms and multi-UAV systems;
• Security assurance for UAV-assisted and integrated aerial–terrestrial networks;
• Secure and trustworthy UAV communication, coordination and cooperative decision-making;
• Detection and mitigation of spoofing, jamming, false-data injection and compromised UAVs;
• Digital twins for UAV security, mission assurance and autonomous aerial systems;
• Runtime monitoring and predictive safety assessment for highly mobile autonomous systems;
• Trust management and anomaly detection in UAV swarms and cooperative autonomous systems;
• Resilient navigation, localisation, communication and control for UAV-based CPSs;
• Assurance of AI-enabled UAVs and multi-agent autonomous systems;
• Resilience of smart cities, intelligent transportation, healthcare, energy and industrial infrastructures;
• Testbeds, simulation platforms, RF/radar datasets, cybersecurity datasets, healthcare sensing datasets and experimental validation for runtime assurance. 

We welcome original research articles, review papers, methodological contributions, datasets, experimental studies and practical applications that advance the understanding, modelling, sensing, monitoring, prediction, security and maintenance of safety and security assurance in complex and evolving digital infrastructures. 

Dr. Muhammad Awais
Dr. Sheraz Aslam
Guest Editors

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