Smart City Gnosys
Smart city article details
| Title | Advanced Path Planning For Uav Swarms In Smart City Disaster Scenarios Using Hybrid Metaheuristic Algorithms |
|---|---|
| ID_Doc | 6534 |
| Authors | Adam M.S.; Abdullah N.F.; Abu-Samah A.; Amodu O.A.; Nordin R. |
| Year | 2025 |
| Published | Drones, 9, 1 |
| DOI | http://dx.doi.org/10.3390/drones9010064 |
| Abstract | In disaster-stricken areas, rapid restoration of communication infrastructure is critical to ensuring effective emergency response and recovery. Swarm UAVs, operating as mobile aerial base stations (MABS), offer a transformative solution for bridging connectivity gaps in environments where the traditional infrastructure has been compromised. This paper presents a novel hybrid path planning approach combining affinity propagation clustering (APC) with genetic algorithms (GA), aimed at maximizing coverage, and ensuring quality of service (QoS) compliance across diverse environmental conditions. Comprehensive simulations conducted in suburban, urban, dense urban, and high-rise urban environments demonstrated the efficacy of the APC-GA approach. The proposed method achieved up to 100% coverage in suburban settings with only eight unmanned aerial vehicle (UAV) swarms, and maintained superior performance in dense and high-rise urban environments, achieving 97% and 93% coverage, respectively, with 10 UAV swarms. The QoS compliance reached 98%, outperforming benchmarks such as GA (94%), PSO (90%), and ACO (88%). The solution exhibited significant stability, maintaining consistently high performance, highlighting its robustness under dynamic disaster scenarios. Mobility model analysis further underscores the adaptability of the proposed approach. The reference point group mobility (RPGM) model consistently achieved higher coverage rates (95%) than the random waypoint model (RWPM) (90%), thereby demonstrating the importance of group-based mobility patterns in enhancing UAV deployment efficiency. The findings reveal that the APC-GA adaptive clustering and path planning mechanisms effectively navigate propagation challenges, interference, and non-line-of-sight (NLOS) conditions, ensuring reliable connectivity in the most demanding environments. This research establishes the APC-GA hybrid as a scalable and QoS-compliant solution for UAV deployment in disaster response scenarios. By dynamically adapting to environmental complexities and user mobility patterns, it advances state-of-the-art emergency communication systems, offering a robust framework for real-world applications in disaster resilience and recovery. © 2025 by the authors. |
| Author Keywords | Affinity Propagation Clustering; disaster response; Genetic Algorithm; hybrid algorithms; path optimization; UAV swarms |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
8990  | 0.905 | Alawad W.; Halima N.B.; Aziz L. | An Unmanned Aerial Vehicle (Uav) System For Disaster And Crisis Management In Smart Cities | Electronics (Switzerland), 12, 4 (2023) |
| 6398 | 0.895 | Qadir, Z; Ullah, F; Munawar, HS; Al-Turjman, F | Addressing Disasters In Smart Cities Through Uavs Path Planning And 5G Communications: A Systematic Review | COMPUTER COMMUNICATIONS, 168 (2021) |
| 46706 | 0.876 | Tariq Z.U.A.; Baccour E.; Erbad A.; Hamdi M.; Guizani M. | Rl-Based Adaptive Uav Swarm Formation And Clustering For Secure 6G Wireless Communications In Dynamic Dense Environments | IEEE Access, 12 (2024) |
| 34952 | 0.876 | Gao T.; Goins D.; Ballotti C.; Liu J.; Qu C. | Learning-Based Uav Swarm Video Analytics Orchestration In Disaster Response Management | SN Computer Science, 6, 5 (2025) |
| 40927 | 0.874 | Qadir Z.; Zafar M.H.; Moosavi S.K.R.; Le K.N.; Tam V.W.Y. | Optimizing Uav Path For Disaster Management In Smart Cities Using Metaheuristic Algorithms | Studies in Computational Intelligence, 1033 (2022) |
| 59686 | 0.867 | Arshi O.; Khan I.U.; Kaushik K.; Iqbal N.; Ullah I.; Slimani K. | Unmanned Aerial Vehicles Swarm For Protecting Smart Cities: Future Trends And Challenges | Unmanned Aerial Vehicles Swarm for Protecting Smart Cities: Future Trends and Challenges (2025) |
| 21100 | 0.863 | Amarcha F.A.; Chehri A.; Jakimi A.; Bouya M.; Ahl Laamara R.; Saadane R. | Drones Optimization For Public Transportation Safety: Enhancing Surveillance And Efficiency In Smart Cities | Proceedings - 2024 IEEE World Forum on Public Safety Technology, WFPST 2024 (2024) |
| 7011 | 0.857 | Du P.; Xiao T.; Cao H.; Zhai D.; Gadekallu T.R.; Piran M.J. | Ai-Based Uavs 3D Coverage Deployment In 6G-Enabled Iov Networks For Industry 5.0 | IEEE Transactions on Consumer Electronics (2024) |
| 2202 | 0.856 | Ulaş M.; Sezgin A.; Boyacı A. | A Hybrid Optimization Framework For Dynamic Drone Networks: Integrating Genetic Algorithms With Reinforcement Learning | Applied Sciences (Switzerland), 15, 9 (2025) |
| 32085 | 0.852 | Eskandari M.; Savkin A.V. | Integrating Uavs And Riss In Future Wireless Networks: A Review And Tutorial On Iots And Vehicular Communications | Future Internet, 16, 12 (2024) |