Flying Sensor and Edge Network-Based Advanced Air Mobility Systems: Reliability Analysis and Applications for Urban Monitoring
Abstract
:1. Introduction and Related Works
1.1. Motivation
- First Generation FENets: The first generation of FENets was focused on developing the underlying technology for UAVs and edge computing. The primary focus was developing the necessary hardware and software to enable UAVs to provide edge computing services.
- Second Generation FENets: The second generation of FENets was characterized by developing more advanced hardware and software to support more sophisticated edge computing services. These FENets were expected to be smaller, faster, and more reliable than first-generation FENets.
- Third Generation FENets: The third generation of FENets is expected to be more autonomous and capable of performing complex tasks without human intervention. These FENets are expected to be equipped with advanced AI and machine learning capabilities, thus enabling them to adapt to changing conditions and make real-time decisions.
- Fourth Generation FENets: The fourth generation of FENets is expected to be even more advanced, with the ability to operate in more challenging environments and perform more complex tasks. These FENets are expected to be equipped with advanced sensors, communication systems, and propulsion technologies, thus enabling them to operate in a broader range of environments.
- Improved data collection: FSNets can collect real-time data from various sensors, whereas FENets can process and analyze this data at the edge of the network. This can enable more efficient and accurate decision-making.
- Enhanced situational awareness: FSNets can provide real-time data on the environment, whereas FENets can process and analyze this data to provide actionable insights. This can improve situational awareness and enable faster responses to changing conditions.
- Increased flexibility: The combination of FSNets and FENets can provide increased data collection, processing, and analysis flexibility. This can enable new applications and services to be developed cost-effectively and reliably.
- Reduced latency: FENets can provide low-latency services. At the same time, FSNets can collect and transmit data in real-time, reduce the time it takes to respond to changing conditions and improve overall MS performance.
- Improved reliability: The combination of FSNets and FENets can provide an increased level of reliability by distributing computing and communication capabilities across a fleet of UAVs. If one UAV fails, others in the network can take over its tasks, thus ensuring continuity of service.
- The classification and development of the typical structures of FSNets and FENets for MSs for use in UCOs; these tasks require a combination of mobile (flying) sensor network, edge and cloud computing, and artificial intelligence technologies. Such solutions can form a structural basis for developing and involving monitoring AAM systems.
- The analysis of the reliability and survivability issues of these systems considers various operation conditions, a set of centers for crisis management, and the control of FSNets and FENets applications. An assessment of reliability and survivability indicators is a critical task in the context of meeting operation requirements.
- The development of model-based recommendations for choosing structures and their parameters considers features of the monitoring tasks and UCOs. This is a vital task because many urban services and applications exist, and the choice of structure should be well-considered.
1.2. State of the Art
- scalability, reliability, stability, and security of the proposed architectures;
- offloading tasks;
- minimization of energy consumption;
- allocation of resources;
- communication and coverage organization;
- delays during real-time operations;
- features of management of UAVs, their interaction, trajectories, and route optimization;
- information processing and storage in the cloud environment.
1.3. Objectives
- to define the methodology of the investigation;
- to classify and describe variants of the FSNets and FENets-based system structures;
- to develop and describe the general scheme of the FSNets and FENets-based monitoring system;
- to develop and explore reliability and survivability models for the monitoring system;
- to develop a tool to support research on MS reliability, survivability, and the choice of parameters.
2. The Methodology and Researched Structures
2.1. Methodology, Research Questions, and Stages
- a systematic analysis of classification attributes so that the MS can suggest a set of structures that consider various levels of the system, applied technologies of sensors, ground/flying fog, edge and cloud computing, and the distribution of processing resources (stage 1);
- the development of the generalized MS structure for reliability and survivability research, considering component failures using the known techniques of reliability block diagrams (RBDs) and degradation diagrams (DDs) (stage 2);
- the development of a tool to support the research on MS reliability and choice of parameters (stage 3).
2.2. Classification and Description of Variations in Structures with Flying/Ground Sensors and Cloud/Edge/Fog Computing
- A main crisis center (MCC) designed to provide solutions aimed at preventing and eliminating the consequences of accidents at the UCO, as well as forecasting the occurrence of such accidents and assessing their impact.
- A virtual crisis center (VCC) that is formed by a group of external experts who remotely convene with the relevant staff of the MCC to provide solutions aimed at preventing and eliminating the consequences of accidents at the UCO.
- A ground monitoring subsystem (SubGM) comprising EDs (sensors) responsible for measuring critical technological process parameters or parameters that characterize the degree of environmental pollution and the meteorological conditions.
- A fleet of UAVs (FoU) that can perform one or a combination of the following functions: collect information from EDs (sensors), partially process this information, transmit it to the MCC.
- A ground control station (GCS) that manages the FoU using external pilots (operators).
2.3. Artificial Intelligence to Service the Monitoring System
3. Reliability and Survivability Models
3.1. Stages of Assessment
- developing and describing a general scheme of the MS;
- defining UAVs so that they can be used as an FSen/FEN;
- developing an RBD for the MS;
- developing and exploring the reliability and survivability models of the MS;
- developing and describing a tool for calculating reliability/survivability indicators.
3.2. General Scheme of the Monitoring System
- a fleet of flying sensors (FoFSen) that are deployed to measure the parameters characterizing the degree of environmental pollution and meteorological conditions (such sensors can be deployed instead of damaged stationary monitoring stations);
- a ground control station (GCS), which manages the UAV using external pilots (operators);
- a fleet of flying edge nodes (FoFEN) that are deployed to collect information from flying sensors, partially process this information, and transmit it to the main crisis center (MCC);
- a main crisis center (MCC) that is designed to provide solutions aimed at preventing and eliminating the consequences of accidents at the UCO, as well as forecasting the occurrence of such accidents and assessing their impact;
- a virtual crisis center (VCC) that is formed by a group of external experts who remotely convene with the relevant staff of the MCC to work out solutions to prevent and eliminate the consequences of accidents at the UCO.
- the MCC allows for the possibility of partially taking over control of the UAV in case the GCS fails;
- the MCC has enough authorized and qualified specialists to make decisions concerning responding to an accident in the event when information is received from the VCC.
3.3. Reliability Models
3.3.1. Development
- elements of the MS have exponential time to failure (TTF);
- during the operating time, the MS is considered to be an unrecoverable system;
- the FoFEN has a structure of type “k-out-of-n”. The FoFEN with such a structure consists of flying edge nodes and remains in an operable state until flying edge nodes have failed, where is the number of redundant flying edge nodes.
3.3.2. Choice of Parameter Values
3.3.3. Simulation and Analysis
- The increase in operating time from 0 to 12, led to a reduction in the values of the reliability functions the values were reduced by 1.05 (from 1 to 0.95358), 1.08 (from 1 to 0.92985), and 1.1 (from 1 to 0.90671), at times of 1/h, 1/h, and 1/h, respectively (Figure 6).
- The increase in operating time from 0 to 12, led to a reduction in the values of the reliability functions the values were reduced by 1.05 (from 1 to 0.95358), 1.05 (from 1 to 0.95069), and 1.1 (from 1 to 0.94158) at times of 1/h, 1/h, and 1/h, respectively (Figure 7).
- The utilization of more reliable (with a lower failure rate) FSen/FEN for the MS makes it possible to increase its reliability. For example, at , the reduction in the failure rate , from to led to an increase in the reliability functions the functions increased by 1.05 (from 0.90671 to 0.95358) (Figure 6). As for FEN, at , the reduction in the failure rate , from to led to an increase in the reliability functions the functions increased by 1.01 (from 0.94158 to 0.95358) (Figure 7).
- The increased number of redundant flying edge nodes also improved MS reliability. For example, at 1/h, this increase led to an increase in reliability functions the functions increased by 1.05 (from 0.92124 to 0.96446). It is important to note that the most significant effect was achieved by increasing the number of redundant flying edge nodes from 1 to 2 (Figure 8).
3.4. Models of the MS as Multi-State Systems
3.4.1. Development
- in the event of a VCC/GCS failure, its functions can be partially performed by the MCC;
- the failure of FoFSen/FoFEN occurs only after the failure of more than one FSen/FEN.
- is the number of non-operable flying sensors needed for the FoFSen to transition from a fully operable state to a partially operable state;
- is the number of main non-operable flying edge nodes needed for the FoFEN to transition from a fully operable state to a partially operable state;
- is the number of non-operable flying edge nodes required for the fleet of the flying edge nodes to transition from a fully operable state to a partially operable state, where ;
- is the GCS in the non-operable state;
- is the VCC in the non-operable state;
- flying sensors represent the non-operable state (FoFSen is in a partially operable state FoFSen_L1);
- flying edge nodes represent the non-operable state (FoFSen is in a partially operable state FoFEN_L1);
- is the probability of the MS being in a fully operable state, where
- is the probability of the MS being in a partially operable state , where is the degradation level number and is the state number at Level .
3.4.2. Simulation and Analysis
- The increase in the operating time , from 8 to 12, led to an increase in the probability of the MS being in partially operable state 12 the probability increased by 1.88 (from 0.00142 to 0.00268), 1.68 (from 0.00199 to 0.00335), and 1.5 (from 0.00247 to 0.00369), at times of 1/h, 1/h, and 1/h, respectively (Figure 10).
- The increase in the operating time , from 0 to 12, led to an increase in the probability of the MS being in partially operable state 12 ; the probability increased by 1.96 (from 0.00154 to 0.00302), 1.88 (from 0.00142 to 0.00268), and 1.81 (from 0.00131 to 0.00238), at times of , , and , respectively (Figure 11).
- The utilization of more reliable (with a lower failure rate) FEN for the MS makes it possible to reduce the probability of the MS being in partially operable state 12 . For example, at , the reduction in the failure rate , from to , led to a reduction in the probability of the MS being in partially operable state 12 the probability decreased by 1.38 (from 0.00268 to 0.00369) (Figure 10).
- An increase in the number of flying sensors also decreases the probability of the MS being in partially operable state 12 . For example, at , this increase led to a reduced probability of the MS being in partially operable state 12 the probability decreased by 1.27 (from 0.00302 to 0.00238) (Figure 11).
4. Case study
4.1. Tool for Calculating Reliability/Survivability Indicators
- the total number of flying sensors;
- the total number of flying edge nodes;
- the total number of main flying edge nodes;
- the failure rate of the ground control station (1/h);
- the failure rate of a flying sensor (1/h);
- the failure rate of a flying edge node (1/h);
- the failure rate of the main crisis center;
- the failure rate of the virtual crisis center;
- the number of non-operable flying sensors needed for the fleet of flying sensors to transition from a fully operable state to a partially operable state;
- the number of main non-operable flying edge nodes needed for the fleet of flying edge nodes to transition from a fully operable state to a partially operable state;
- the number of non-operable flying edge nodes needed for the fleet of flying edge nodes to transition from a fully operable state to a partially operable state;
- the operating time (h).
- “Show the general scheme of the monitoring system” button. After clicking this button, the user can see a window containing the general scheme of the MS.
- “Show the reliability block diagram of the monitoring system” button. After clicking on this button, the user can see a window containing the reliability block diagram of the MS.
- “Show the degradation levels of the monitoring system and failed elements that correspond to them” button. After clicking on this button, the user can see a window containing the degradation levels of the MS, the failed elements that correspond with them, and the probability of the MS being in a partially operable state , where is the degradation level number and is the state number at Level .
4.2. Example of the Developed Tool Being Utilized
- the total number of flying sensors = 21;
- the total number of flying edge nodes = 10;
- the total number of main flying edge nodes = 7;
- the failure rate of the ground control station = 0.00025 1/h;
- the failure rate of a flying sensor = 0.001 1/h;
- the failure rate of a flying edge node = 0.005 1/h;
- the failure rate of the main crisis center = 0.00085 1/h;
- the failure rate of the virtual crisis center = 0.00075 1/h;
- the number of non-operable flying sensors needed for the fleet of flying sensors to transition from a fully operable state to a partially operable state = 1 1/h;
- the number of the main non-operable flying edge nodes needed for the fleet of flying edge nodes to transition from a fully operable state to a partially operable state = 1 1/h;
- the number of non-operable flying edge nodes needed for the fleet of the flying edge nodes to transition from a fully operable state to a partially operable state = 4;
- the operating time = 9 h;
- the panel after entering the initial data is shown in Figure 13.
5. Discussion
6. Conclusions
- number of FSen/FEN;
- redundancy scheme for FSen/FEN;
- number of redundant FSen/FEN;
- failure rate of FSen/FEN.
- the reliability block diagrams for the MS, depending on the features of the FoFSen and FoFEN structures, to be obtained;
- the reliability function of the MS and its elements to be calculated;
- the degradation levels, partially operable states, and failed elements that correspond to the levels to be defined;
- the probability of the MS being in partially operable states to be calculated.
Author Contributions
Funding
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations and Acronyms
Abbreviation/Acronym | Meaning |
AAM | Advanced Air Mobility |
AI | artificial intelligence |
DD | degradation diagram |
DL | deep learning |
DRL | deep reinforcement learning |
DSL | deep supervised learning |
ED | end device |
FANET | Flying Ad hoc Network |
FCC | flying cloud computing |
FCN | flying cloud node |
FEC | flying edge computing |
FEN | Flying edge node |
FENet | Flying Edge Network |
FFC | flying fog computing |
FFN | flying fog nodes |
FI | fuzzy inference |
FL | federated learning |
FoFEN | fleet of flying edge nodes |
FoFSen | fleet of flying sensors |
FoU | fleet of UAVs |
FSen | flying sensor |
FSNet | Flying Sensor Network |
GA | genetic algorithm |
GCC | ground cloud computing |
GCS | ground control station |
GEC | ground edge computing |
GEN | ground edge node |
GFC | ground fog computing |
GFN | ground fog node |
IoD | Internet of drones |
IoFT | Internet of Flying Things |
IoT | Internet of Things |
MCC | main crisis center |
MIMO | Multiple Input Multiple Output |
MS | monitoring system |
RBD | reliability block diagram |
RL | reinforcement learning |
RL-ACO | reinforcement learning based on ant colony optimization |
RQ | Research Question |
SubFCC | subsystem of flying cloud computing |
SubFEC | subsystem of flying edge computing |
SubFFC | subsystem of flying fog computing |
SubGM | ground monitoring subsystem |
TTF | time to failure |
UAV | unmanned aerial vehicle |
UCO | urban complex object |
VCC | virtual crisis center |
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Type of Computing | Elements of the MS | |||||
---|---|---|---|---|---|---|
MCC | VCC | SubGM | FoU | GCS | ||
Cloud computing | flying | – | – | – | + | – |
ground | + | + | + | – | + | |
Edge computing | flying | – | – | – | + | – |
ground | – | – | + | – | – | |
Fog computing | flying | – | – | – | + | – |
ground | – | – | + | – | – |
Task | AI Method | Elements of the MS | ||||
---|---|---|---|---|---|---|
MCC | VCC | SubGM | FoU | GCS | ||
Computation offloading | RL | + | + | + | + | + |
DRL | + | + | + | + | + | |
GA | + | + | + | + | + | |
DL | + | + | + | + | + | |
FI | + | + | + | + | + | |
Resource allocation | RL | + | – | – | + | + |
DRL | + | – | – | + | + | |
GA | + | – | – | + | + | |
RL-ACO | + | – | – | + | + | |
Decision-making support | RL | + | + | – | – | – |
DRL | + | + | – | – | – | |
GA | + | + | – | – | – | |
DL | + | + | – | – | – | |
FI | + | + | – | – | – | |
FL | + | + | – | – | – | |
Ensuring safety | RL | + | + | + | + | + |
DRL | + | + | + | + | + | |
GA | + | + | + | + | + | |
DL | + | + | + | + | + | |
FL | + | + | + | + | + | |
UAV path planning | RL | + | – | – | + | + |
DL | + | – | – | + | + | |
FL | + | – | – | + | + |
AI Methods (for All Tasks) | Elements of the MS | Number of AI Methods Used | ||||
---|---|---|---|---|---|---|
MCC | VCC | SubGM | FoU | GCS | ||
RL | 5 | 3 | 2 | 4 | 4 | 18 |
DL | 4 | 3 | 2 | 3 | 3 | 15 |
DRL | 4 | 3 | 2 | 3 | 3 | 15 |
GA | 4 | 3 | 2 | 3 | 3 | 15 |
FL | 3 | 2 | 1 | 2 | 2 | 10 |
FI | 2 | 2 | 1 | 1 | 1 | 7 |
RL-ACO | 1 | 0 | 0 | 1 | 1 | 3 |
No | Name | Manufacturer | Engine Type | Wing Span/ Rotor Dia (m) | Max Range (Km) | Endurance (max, h) | MTOW (Kg) |
---|---|---|---|---|---|---|---|
1 | Bird-Eye 650D | Israel Aerospace Industries | internal combustion | 4.0 | 150 | 15 | 30 |
2 | Bayraktar TB2 | Baykar Makina | internal combustion | 12.0 | 150 | 20 | 650 |
3 | PD-1 FW VTOL | Ukrspec Systems | internal combustion | 4.7 | 100 | 12 | 45 |
4 | PD-1 | Ukrspec Systems | internal combustion | 3 | 85 | 10 | 40 |
5 | Scan Eagle | Boeing | internal combustion | 3.1 | 100 | 22 | 18 |
6 | Raybird 3 | Scaeton | internal combustion | 2.93 | 80 | 28 | 23 |
7 | FT-200 FH | FT Sistemas | gas turbine | 2.8 | 100 | 12 | 80 |
8 | Camcopter S-100 | Schiebel | internal combustion | 3.4 | 200 | 10 | 200 |
No | Name | Manufacturer | Engine Type | Max Range (Km) | Endurance (max, h) | MTOW (Kg) |
---|---|---|---|---|---|---|
1 | DJI Matrice 300 RTK | DJI | electric | 15 | 55 min | 3.6 |
2 | DJI Mavic 3 | DJI | electric | 15 | 55 min | 3.6 |
3 | T-hawk | FCS DARPA | internal combustion | 11 | 40 min | 6.6 |
4 | Draganfly | Draganfly Drones | electric | 30 | 50 min | 30.4 |
5 | KWT-X6L-Q | ALLTECH | electric | 50 | 150 min | 2.5 |
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Share and Cite
Fesenko, H.; Illiashenko, O.; Kharchenko, V.; Kliushnikov, I.; Morozova, O.; Sachenko, A.; Skorobohatko, S. Flying Sensor and Edge Network-Based Advanced Air Mobility Systems: Reliability Analysis and Applications for Urban Monitoring. Drones 2023, 7, 409. https://doi.org/10.3390/drones7070409
Fesenko H, Illiashenko O, Kharchenko V, Kliushnikov I, Morozova O, Sachenko A, Skorobohatko S. Flying Sensor and Edge Network-Based Advanced Air Mobility Systems: Reliability Analysis and Applications for Urban Monitoring. Drones. 2023; 7(7):409. https://doi.org/10.3390/drones7070409
Chicago/Turabian StyleFesenko, Herman, Oleg Illiashenko, Vyacheslav Kharchenko, Ihor Kliushnikov, Olga Morozova, Anatoliy Sachenko, and Stanislav Skorobohatko. 2023. "Flying Sensor and Edge Network-Based Advanced Air Mobility Systems: Reliability Analysis and Applications for Urban Monitoring" Drones 7, no. 7: 409. https://doi.org/10.3390/drones7070409