Smart City Gnosys
Smart city article details
| Title | Cpft-Mosa: A Comprehensive Parallel Fault-Tolerant Multi-Objective Simulated Annealing Framework For Uav-Assisted Edge Computing In Smart City Traffic Management |
|---|---|
| ID_Doc | 16445 |
| Authors | Mustafa A.S.; Yussof S.; Radzi N.A.M. |
| Year | 2025 |
| Published | IEEE Access, 13 |
| DOI | http://dx.doi.org/10.1109/ACCESS.2025.3558851 |
| Abstract | UAV-assisted edge computing has emerged as a critical technology for real-time monitoring and control in smart city traffic management systems. A key challenge in these systems is efficiently distributing computational tasks across available edge nodes while ensuring system reliability and performance, particularly for resource-intensive applications like object detection. This paper aims to address these challenges by proposing CPFT-MOSA (Comprehensive Parallel Fault-Tolerant Multi-Objective Simulated Annealing), a novel task allocation optimization framework specifically designed for UAV-assisted edge computing in traffic management scenarios. Our approach makes three primary contributions: 1) a multi-objective optimization framework that minimizes active nodes, optimizes energy distribution, and reduces execution time while ensuring fault tolerance through parallel task execution; 2) an integrated YOLO-based real-time task prioritization system that dynamically adapts to traffic conditions under resource and bandwidth constraints; and 3) a feasibility-driven task assignment strategy that maintains computational balance while meeting system constraints. The proposed method decouples YOLO-based object detection tasks into several parallel modules within each UAV processing block and implements a dynamic resource management mechanism to balance processing efficacy and reliability. An experimental evaluation conducted across 17 nodes managing 45 full layers (comprising 270 total layer assignments) demonstrates that CPFT-MOSA performs superiorly to traditional methods. Our results show quantitative improvements in three key metrics: efficient resource utilization with an average of 16.8 active nodes; a 21% improvement in energy distribution efficiency with a value of 0.063; and an 8.5% reduction in execution time to 1.6 units compared to conventional approaches. The framework’s ability to balance multiple competing objectives while maintaining fault tolerance makes it particularly suitable for real-world traffic management applications where reliability and performance are crucial for time-sensitive decision-making in dynamic urban environments. © 2013 IEEE. |
| Author Keywords | fault-tolerant; multi-objectives; simulated annealing; smart cities; Task allocation; traffic management; UAV-assisted edge computing; YOLO |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
38338  | 0.949 | Mustafa A.S.; Yussof S.; Asyikin Mohamed Radzi N. | Multi-Objective Simulated Annealing For Efficient Task Allocation In Uav-Assisted Edge Computing For Smart City Traffic Management | IEEE Access, 13 (2025) |
| 21858 | 0.873 | Awada U.; Zhang J.; Chen S.; Li S.; Yang S. | Edgedrones: Co-Scheduling Of Drones For Multi-Location Aerial Computing Missions | Journal of Network and Computer Applications, 215 (2023) |
| 21086 | 0.861 | Ren X.; Vashisht S.; Aujla G.S.; Zhang P. | Drone-Edge Coalesce For Energy-Aware And Sustainable Service Delivery For Smart City Applications | Sustainable Cities and Society, 77 (2022) |
| 38226 | 0.857 | Chelladurai A.; Deepak M.D.; Falkowski-Gilski P.; Bidare Divakarachari P. | Multi-Joint Symmetric Optimization Approach For Unmanned Aerial Vehicle Assisted Edge Computing Resources In Internet Of Things-Based Smart Cities | Symmetry, 17, 4 (2025) |
| 7185 | 0.856 | Hayawi K.; Anwar Z.; Malik A.W.; Trabelsi Z. | Airborne Computing: A Toolkit For Uav-Assisted Federated Computing For Sustainable Smart Cities | IEEE Internet of Things Journal, 10, 21 (2023) |
| 54447 | 0.852 | Deng, YQ; Chen, ZG; Yao, X; Hassan, S; Wu, J | Task Scheduling For Smart City Applications Based On Multi-Server Mobile Edge Computing | IEEE ACCESS, 7 (2019) |
| 21100 | 0.852 | 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) |