This paper suggests a methodology (conception and principles) for building two-mode monitoring systems (SMs) for industrial facilities and their adjacent territories based on the application of unmanned aerial vehicle (UAV), Internet of Things (IoT), and digital twin (DT) technologies, and a set of SM reliability models considering the parameters of the channels and components. The concept of building a reliable and resilient SM is proposed. For this purpose, the von Neumann paradigm for the synthesis of reliable systems from unreliable components is developed. For complex SMs of industrial facilities, the concept covers the application of various types of redundancy (structural, version, time, and space) for basic components—sensors, means of communication, processing, and presentation—in the form of DTs for decision support systems. The research results include: the methodology for the building and general structures of UAV-, IoT-, and DT-based SMs in industrial facilities as multi-level systems; reliability models for SMs considering the applied technologies and operation modes (normal and emergency); and industrial cases of SMs for manufacture and nuclear power plants. The results obtained are the basis for further development of the theory and for practical applications of SMs in industrial facilities within the framework of the implementation and improvement of Industry 4.0 principles.

The subject of this study is systems for detection and identification (D&I) of explosive ordnance (EO). The aim of this study is to develop a concept, general structure, and models of a robotic-biological system for D&I of EO (RBS-D&I). The objectives are as follows: 1) to classify mobile systems for D&I of EO and suggest a concept of RBS-D&I; 2) to develop the general structure of RBS-D&I consisting of robotic (flying and ground) and biological subsystems; 3) to develop models of RBS-D&I including automaton, hierarchical, and operational ones; 4) to describe tasks and planned results of the article-related scientific project; and 5) to discuss research results. The following results were obtained. 1) The general structure of the RBS-D&I. The structure comprises the following levels: control and processing centres (mobile ground control and processing centre (MGCPC) and virtual control and processing centre); forces for detection and identification (fleet of unmanned aerial vehicles (FoU), biological detection information subsystem (BDIS), and robotic detection information subsystem (RDIS)); interference; natural covers and a bedding surface; and target objects (all munitions containing explosives, nuclear fission or fusion materials and biological and chemical agents). 2) A concept of RBS-D&I. The concept is based on RBS-D&I description, analysis, development, and operation as an integrated complex cyber-physical and cyber-biological system running in changing physical and information environments. 3) The RBS-D&I automata model. The model describes RBS-D&I operating in two modes. In mode 1, FoU and BDIS operate separately and interact through the MGCPC only. In mode 2, depending on the specifics of the tasks performed, FoU and RDIS can directly interact among themselves or through the MGCPC. 4) hierarchical model. The model has two sets of vertices: EO detection and platforms equipped with the necessary sensors. 5) An operational cycle model. The model describes land release operations via a methodology of functional modeling and graphic description of IDEF0 processes. Conclusions. The proposed concept and RBS-D&I solutions can provide high-performance and guaranteed EO detection in designated areas by the implementation of an intelligent platform and tools for planning the use of multifunctional fleets of UAVs and other RBS-D&I subsystems.

The paper suggests a model-based approach to assessment and choice of parameters of unmanned aerial vehicle (UAV) fleets applied as one of the main services for Smart Cities and recommendations to assure their dependability. The principles of building and modeling a UAV Fleet as a Dependable Service (UAVFaaDS) for Smart Cities are formulated. Dependability issues for UAVFaaDS including a taxonomy of UAVF failures caused by equipment faults and attacks on assets were specified. The main results cover methodology, classification of UAVFaaDS models as models of queuing systems, and a set of queueing theory-based models for assessment of UAVFaaDS performance, and availability allowing for analysis and choice of fleet parameters. The efficiency of UAVFaaDS is assessed by the probability of successful delivery of services. The proposed modeling base and algorithms provide a choice of appropriate models for analysis and synthesis of UAVFaaDS, grounding of parameters of UAV fleets considering operation modes, and maintenance policy. The application of the developed models and algorithms during the synthesis of UAVFaaDS allows choosing the appropriate parameters of the fleet and ensuring the dependability of services, as well as service of orders with a probability of 0.9–0.99 depending on the requirements. Two cases of UAVFaaDS application for delivery of medicines in normal and emergence modes, models’ development, and recommendations for their utilization are discussed.

Typical structures of monitoring systems (MSs) that are used in urban complex objects (UCOs) (such as large industrial facilities, power facilities, and others) during the post-accident period are combined with the technologies of flying sensor networks (FSNets) and flying edge networks (FENets) (FSNets and FENets); cloud/fog computing and artificial intelligence are also developed. An FSNets and FENets-based MS, composed of one of the Advanced Air Mobility (AAM) systems classes, which comprise main and virtual crisis centers, fleets of flying sensors, edge nodes, and a ground control station, is presented and discussed. Reliability and survivability models of the MS for the UCOs, considering various operation conditions and options of redundancy, are developed and explored. A tool to support the research on MS reliability, survivability, and the choice of parameters is developed and described. Crucially, this paper enhances the technique for assessing systems using the multi-parametrical deterioration of characteristics as a class of multi-state systems. Problems that may arise when using FSNets/FENet-based AAM systems are discussed. The main research results comprise a structural basis, a set of models, and a tool for calculating the reliability and survivability of FSNets/FENet-based AAM systems, with various options for distributing the processing and control resources between components, their failure rates, and degradation scenarios.

The paper is devoted to routing an unmanned aerial vehicle (UAV) during the Zaporizhzhia nuclear power plant monitoring without returning to the depot due to the deployment of an automatic battery replacement aerial system in specified places. The monitoring includes the overflight of the 11 monitoring stations to collect radiation monitoring data from them in case of damage of wired networks between the monitoring stations and the crisis centre. The shortest possible UAV route that visits every monitoring station (route point) exactly once and returns to the UAV depot is found by solving the Travelling Salesman Problem. An algorithm for determining the places for deployment of the automatic battery replacement aerial system is presented. A flight schedule both for the UAV and the automatic battery replacement aerial system is developed. The DJI Mavic 2 Enterprise Dual quadcopter is considered as a UAV for monitoring missions. The SL-231 Scout helicopter capable to operate in an unmanned mode is considered as a platform for the automatic battery replacement system.

Sustainable development of the Smart Cities and Smart Regions concept is impossible without the development of a modern transport infrastructure, which must be maintained in proper condition. Inspections are required to assess the condition of objects in the transport infrastructure (OTI). Moreover, the efficiency of these inspections can be enhanced with unmanned aerial vehicles (UAVs), whose application areas are continuously expanding. When inspecting OTI (bridges, highways, etc.) the problem of improving the quality of image processing, and analysis of data collected by UAV, for example, is particularly relevant. The application of advanced methods for assessing the quantity of information and making decisions to reduce information uncertainty and redundancy for such systems is often complicated by the presence of noise there. To harmonize the characteristics of certain procedures in such conditions, authors propose conducting data processing using wavelet transform clustering in three main phases: determining the number of clusters, defining the coordinates of cluster centres, and assessing the quality and efficiency of clustering. We compared the efficiency and quality of existing clustering methods with one using wavelet transform. The research has shown that UAVs can be used for OTI inspecting; moreover, the clustering method with wavelet transform is characterised by an improved quality and efficiency of data processing. In addition, the quality assessment enables us to assess the degree of approximation of the clustering result to the ideal one. In addition, authors examined the specific challenges associated with planning UAV flights during inspections to obtain data that will enhance the accuracy of clustering and recognition. This is especially important for a comprehensive quantitative assessment of adaptation degree for image processing procedures to the tasks of inspecting OTI “Smart Cities/Regions” based on a pragmatic measure of informativeness.