UAV Ad Hoc Network Routing Algorithms in Space–Air–Ground Integrated Networks: Challenges and Directions
Abstract
:1. Introduction
- High dynamics: The stability of the routing path is greatly affected by the rapid speed of the drone’s movement through the air [10].
- High loss of connection rate: Due to the flight height, terrain, and weather of the UAV, there will be situations where the UAV loses connection with ground nodes or other UAVs [11].
- Low bandwidth and high latency: Due to the long communication distance between UAVs, the communication signal is affected by factors such as atmosphere and terrain. The communication bandwidth is low, and the communication latency is high [12].
- In this paper, we review 48 relevant articles, which were published in the past five years and represent the latest research advancements in routing algorithms. We conduct an in-depth analysis of the limitations of these routing algorithms.
- We categorize existing routing algorithms into eight major classes and discuss the optimization directions for each class. Additionally, we provide insights into the research directions for FANET routing algorithms in the context of future SAGINs.
- To provide foundational experimental ideas for future researchers, we summarize the existing research’s experimental methodologies from multiple aspects, including network scale, UAV types, and simulation platforms. Moreover, we present the trends and shortcomings of the experimental designs in the form of statistical charts to provide a visual analysis of experimental methodologies.
2. FANET Architecture and Routing Protocol
2.1. Communication and Application Architecture
2.2. Routing Protocols
2.2.1. Topology-Based
2.2.2. Security-Based
2.2.3. Swarm-Based
2.2.4. Hierarchical-Based
2.2.5. Energy-Based
2.2.6. Heterogeneous-Based
2.2.7. Position-Based
2.2.8. DTN-Based
3. FANET Routing Algorithms
3.1. Exact-Based Routing Algorithms
3.2. Heuristic-Based Routing Algorithms
3.3. Reinforcement-Learning-Based Routing Algorithms
4. Analysis and Perspectives
4.1. Analysis of Experiments
4.2. Analysis of Other Issues
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Ref. | Network Scale (Sorties) | UAV Type | Method | Dataset Type | Simulation Platform |
---|---|---|---|---|---|
[82] | 76 | Small drones | GA+SA | Real dataset | Win10+Python |
[102] | 20–70 | Small drones | MADDGP+LSTM | Self-generated | NS-3 |
[103] | 10 | Small drones | MARL | Self-generated | Ubuntu16.04 |
[81] | 100 | Small drones | MA+GA | Real dataset | Win10+Python |
[111] | 15 | Small drones | Fuzzy Logic+Q-learning | Self-generated | MATLAB |
[112] | 30 | Small drones | FSR+Dijkstra | Self-generated | NS-2 |
[88] | 12 | Medium to large drones | GA+BM | Real dataset | MATLAB |
[99] | 50 | N/A | Q-learning | Self-generated | NS-3 |
[101] | 12 | N/A | MARL+CNN | Self-generated | MATLAB |
[77] | 10–30 | Small drones | B&C | Self-generated | Python+Gurobi |
[113] | 5–10 | Medium to large drones | Fuzzy Logic+RL | Self-generated | MATLAB |
[100] | 25 | Medium to large drones | Fuzzy Logic+Q-learning+DFS | Self-generated | NS-3 |
[114] | 14–18 | Medium to large drones | Minimize network power+K-means | Self-generated | MATLAB |
[95] | 3 | Medium to large drones | Q-learning | Self-generated | MATLAB |
[98] | 100–1000 | Medium to large drones | RL | Self-generated | MATLAB |
[96] | 25 | Small drones | Q-learning | Self-generated | WSNet |
[104] | 20, 40 | Small drones | Q-learning | Self-generated | NS-3 |
[87] | 1–7 | Medium to large drones | SA+MILP | Real dataset | MATLAB+ILOG |
[115] | 150 | Small drones | KF+Shortest-circuit algorithm | Self-generated | MATLAB |
[75] | 50–100 | Medium to large drones | OPAR | Self-generated | NS-3 |
[116] | 10–100 | Small drones | Greedy algorithm+HA+RL | Self-generated | OPNET |
[117] | 5–100 | Medium to large drones | MILP+HA | Self-generated | Python+AWS c5n.4xlarge |
[97] | 10–20 | Different types of drones | SA+Q-learning | Self-generated | N/A |
[118] | 100 | Small drones | ACO | Real dataset | Raspberry Pi+Python |
[119] | 25 | Different types of drones | GA | Real dataset | MATLAB |
[120] | 200 | Small drones | DNN+Greedy algorithm | Self-generated | NS3 |
[121] | 10-100 | Small drones | HA+Fuzzy clustering | Self-generated | MATLAB |
[122] | 10–40 | Small drones | Multicast routing protocol algorithm | Self-generated | NS-2 |
[123] | 40–100 | Small drones | DDQN | Self-generated | Python |
[124] | 100 | Small drones | SA | Self-generated | Python+F-SDN |
[85] | 20–200 | Small drones | PSO | Self-generated | MATLAB |
[125] | 1–4 | Medium to large drones | LS+HA | Real dataset | MATLAB |
[83] | 30 | N/A | ABC | Self-generated | N/A |
[74] | 137 | Small drones | Online learning algorithm | Real dataset | MATLAB |
[86] | 15 | Small drones | PSO | Real dataset | N/A |
[126] | 25 | Small drones | Q-learning | Self-generated | WSNet |
[127] | 1–6 | Small drones | Agent-based algorithms | Self-generated | Python+Windows |
[89] | 5–30 | Small drones | ALNS+MBATA | Real dataset | Python+Gurobi |
[128] | 10–100 | N/A | Routing conversion+Shortest path algorithms | Self-generated | Python+Gurobi +Windows |
[129] | 20–40 | Small drones | L&F (HA) | Self-generated | Java+Python +CLPEX+ILOG |
[130] | >100 | N/A | Hybrid algorithms | Self-generated | MATLAB+C++ |
[131] | 10–40 | Small drones | Multicast routing algorithms | Self-generated | NS-2 |
[76] | 100 | Small drones | Fuzzy logic | Self-generated | NS-2 |
[108] | N/A | Small drones | Load-balancing dynamic routing algorithm | Self-generated | Windows+OPNET +C++ |
[105] | 2–35 | Medium to large drones | GNN+RL | Self-generated | N/A |
[132] | 10–200 | Medium to large drones | DPSO | Self-generated | N/A |
[133] | 50 | Small drones | Digital signature algorithms | Self-generated | NS-2 |
[134] | 60–100 | Small drones | RL | Self-generated | Python |
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Lu, Y.; Wen, W.; Igorevich, K.K.; Ren, P.; Zhang, H.; Duan, Y.; Zhu, H.; Zhang, P. UAV Ad Hoc Network Routing Algorithms in Space–Air–Ground Integrated Networks: Challenges and Directions. Drones 2023, 7, 448. https://doi.org/10.3390/drones7070448
Lu Y, Wen W, Igorevich KK, Ren P, Zhang H, Duan Y, Zhu H, Zhang P. UAV Ad Hoc Network Routing Algorithms in Space–Air–Ground Integrated Networks: Challenges and Directions. Drones. 2023; 7(7):448. https://doi.org/10.3390/drones7070448
Chicago/Turabian StyleLu, Yuxi, Wu Wen, Kostromitin Konstantin Igorevich, Peng Ren, Hongxia Zhang, Youxiang Duan, Hailong Zhu, and Peiying Zhang. 2023. "UAV Ad Hoc Network Routing Algorithms in Space–Air–Ground Integrated Networks: Challenges and Directions" Drones 7, no. 7: 448. https://doi.org/10.3390/drones7070448
APA StyleLu, Y., Wen, W., Igorevich, K. K., Ren, P., Zhang, H., Duan, Y., Zhu, H., & Zhang, P. (2023). UAV Ad Hoc Network Routing Algorithms in Space–Air–Ground Integrated Networks: Challenges and Directions. Drones, 7(7), 448. https://doi.org/10.3390/drones7070448