Drone Fleet Survivability Evaluation Based on Lanchester’s Modified Deterministic Model

Fesenko, Herman; Kharchenko,; Bardis, Nikos; Kor, Ah-Lian; Brezhniev, Eugene

doi:10.46300/9106.2020.14.99

Cited by 3 publications

(3 citation statements)

References 25 publications

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“…One of them is use of a Binary Decision Diagram (BDD), which has been developed in Boolean Algebra [73] and is effective for the processing of the function of large dimensions [74][75][76]. The drone fleet is not a large dimensional system [32,77]. Therefore, the structure function can be an effective mathematical model for drone fleet reliability analysis.…”

Section: Structure Functionmentioning

confidence: 99%

Review of Reliability Assessment Methods of Drone Swarm (Fleet) and a New Importance Evaluation Based Method of Drone Swarm Structure Analysis

et al. 2023

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Drones, or UAVs, are developed very intensively. There are many effective applications of drones for problems of monitoring, searching, detection, communication, delivery, and transportation of cargo in various sectors of the economy. The reliability of drones in the resolution of these problems should play a principal role. Therefore, studies encompassing reliability analysis of drones and swarms (fleets) of drones are important. As shown in this paper, the analysis of drone reliability and its components is considered in studies often. Reliability analysis of drone swarms is investigated less often, despite the fact that many applications cannot be performed by a single drone and require the involvement of several drones. In this paper, a systematic review of the reliability analysis of drone swarms is proposed. Based on this review, a new method for the analysis and quantification of the topological aspects of drone swarms is considered. In particular, this method allows for the computing of swarm availability and importance measures. Importance measures in reliability analysis are used for system maintenance and to indicate the components (drones) whose fault has the most impact on the system failure. Structural and Birnbaum importance measures are introduced for drone swarms’ components. These indices are defined for the following topologies: a homogenous irredundant drone fleet, a homogenous hot stable redundant drone fleet, a heterogeneous irredundant drone fleet, and a heterogeneous hot stable redundant drone fleet.

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Section: Structure Functionmentioning

confidence: 99%

Review of Reliability Assessment Methods of Drone Swarm (Fleet) and a New Importance Evaluation Based Method of Drone Swarm Structure Analysis

et al. 2023

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“…Development of the concept of UAVaaS requires a transition to the more efficient and systematic use of UAVs in smart cities considering many circumstances and tendencies such as [10][11][12][13]:…”

Section: Introduction 1motivationmentioning

confidence: 99%

“…• Structure redundancy of UAV fleets by use of additional drones, stations, and other system components [12,28];…”

mentioning

confidence: 99%

UAV Fleet as a Dependable Service for Smart Cities: Model-Based Assessment and Application

et al. 2022

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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.

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UAV and Soft Computing Methodology for Monitoring Landslide Areas (Susceptibility to Landslides and Early Warning)

Bernardo

Palamara

Boima

2021

Wseas Transactions on Environment and Development

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In this work, we created a map of the susceptibility to landslides in GIS environment using neural network, Analytical Hierarchy Process (AHP) multicriteria analysis method and fuzzy methodology, producing five categories (levels) of risk. Subsequently, starting from this map, we identified (fuzzy methodology) the areas of the road’s network most exposed to landslide risk also using remote sensing techniques (classification and segmentation techniques) overlapped on the street map. This system therefore provides us the level of attention that affects the transport infrastructure investigated (a higher level of attention corresponds to a higher level of landslide risk). Once the risk map for a large area was identified, we focused on local monitoring of a part of it automatically selected by the GIS. The monitoring of this area was carried out through an innovative system (made by us) that allows to monitor landslide risk areas and to study landslide phenomena through the use of Unmanned Aerial Vehicles (UAVs). Specifically, with this innovative solution, data are acquired thanks to an automated system of UAVs and wireless charging platforms (capable to acquired, to transmit and to store data); subsequently, the acquired data are stored automatically in a special platform that allows us to create the point cloud and 3D models of the investigated area (which in turn they are superimposed on the digital models created in previous monitoring), also allowing the creation of the land mass displacement’s sequence in a video. Finally, in relation to early warning, the system allows civil protection to be warned in the event of a landslide risk (start of new landslides or continuation of landslides that have already begun) which in this way will be able to warn the population also through social media.

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Drone Fleet Survivability Evaluation Based on Lanchester’s Modified Deterministic Model

Cited by 3 publications

References 25 publications

Review of Reliability Assessment Methods of Drone Swarm (Fleet) and a New Importance Evaluation Based Method of Drone Swarm Structure Analysis

Review of Reliability Assessment Methods of Drone Swarm (Fleet) and a New Importance Evaluation Based Method of Drone Swarm Structure Analysis

UAV Fleet as a Dependable Service for Smart Cities: Model-Based Assessment and Application

UAV and Soft Computing Methodology for Monitoring Landslide Areas (Susceptibility to Landslides and Early Warning)

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