Multi-Objective Simulated Annealing for Efficient Task Allocation in UAV-Assisted Edge Computing for Smart City Traffic Management
Smart city traffic management relies increasingly on UAV-assisted edge computing systems to process real-time data and make informed decisions. A critical challenge in these systems is the efficient allocation of computational tasks across available edge computing resources. While existing technolog...
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2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10870050/ |
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| author | Ahmed Shamil Mustafa Salman Yussof Nurul Asyikin Mohamed Radzi |
| author_facet | Ahmed Shamil Mustafa Salman Yussof Nurul Asyikin Mohamed Radzi |
| author_sort | Ahmed Shamil Mustafa |
| collection | DOAJ |
| description | Smart city traffic management relies increasingly on UAV-assisted edge computing systems to process real-time data and make informed decisions. A critical challenge in these systems is the efficient allocation of computational tasks across available edge computing resources. While existing technologies provide solutions for data collection (UAVs), processing (computer vision), and control (reinforcement learning), the integration and resource optimization of these components remains a significant challenge. We propose a multi-objective simulated annealing (MOSA) algorithm for optimizing task allocation in edge computing systems, focusing on three key objectives: minimizing active computational nodes, optimizing energy distribution, and reducing execution time. We compared the MOSA algorithm with uniform random allocation, greedy algorithm, and single-objective simulated annealing (SOSA) methods under both standard and peak load conditions. The peak load scenario tested system performance under significantly increased computational demands and reduced resource availability. Our evaluation focused on three key metrics: the number of active nodes, energy distribution efficiency, and task execution time. The proposed MOSA algorithm demonstrated superior resource utilization under standard conditions and maintained robust performance during peak loads, showing significant improvements over baseline methods in all metrics. Results show that MOSA effectively balances multiple objectives while adapting to varying operational demands. It consistently outperformed comparison methods in minimizing active nodes while maintaining competitive performance in energy distribution and execution time. The framework demonstrated particular strength in maintaining efficiency under significantly increased computational loads, offering a robust solution for task allocation in edge computing systems. While some limitations exist in real-world applications, this work provides a strong foundation for optimizing resource utilization in smart city systems that integrate multiple computational tasks. |
| format | Article |
| id | doaj-art-b6bd2e58d7a14132b2d97f7e6dbd72a6 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| spelling | doaj-art-b6bd2e58d7a14132b2d97f7e6dbd72a62025-02-11T00:00:41ZengIEEEIEEE Access2169-35362025-01-0113242512427510.1109/ACCESS.2025.353867610870050Multi-Objective Simulated Annealing for Efficient Task Allocation in UAV-Assisted Edge Computing for Smart City Traffic ManagementAhmed Shamil Mustafa0https://orcid.org/0000-0001-7379-2940Salman Yussof1https://orcid.org/0000-0002-2040-4454Nurul Asyikin Mohamed Radzi2https://orcid.org/0000-0003-0481-7686Department of Electrical and Electronics Engineering, College of Engineering, Universiti Tenaga Nasional, Kajang, Selangor, MalaysiaInstitute of Informatics and Computing in Energy, Universiti Tenaga Nasional, Kajang, Selangor, MalaysiaInstitute of Power Engineering, Universiti Tenaga Nasional, Kajang, Selangor, MalaysiaSmart city traffic management relies increasingly on UAV-assisted edge computing systems to process real-time data and make informed decisions. A critical challenge in these systems is the efficient allocation of computational tasks across available edge computing resources. While existing technologies provide solutions for data collection (UAVs), processing (computer vision), and control (reinforcement learning), the integration and resource optimization of these components remains a significant challenge. We propose a multi-objective simulated annealing (MOSA) algorithm for optimizing task allocation in edge computing systems, focusing on three key objectives: minimizing active computational nodes, optimizing energy distribution, and reducing execution time. We compared the MOSA algorithm with uniform random allocation, greedy algorithm, and single-objective simulated annealing (SOSA) methods under both standard and peak load conditions. The peak load scenario tested system performance under significantly increased computational demands and reduced resource availability. Our evaluation focused on three key metrics: the number of active nodes, energy distribution efficiency, and task execution time. The proposed MOSA algorithm demonstrated superior resource utilization under standard conditions and maintained robust performance during peak loads, showing significant improvements over baseline methods in all metrics. Results show that MOSA effectively balances multiple objectives while adapting to varying operational demands. It consistently outperformed comparison methods in minimizing active nodes while maintaining competitive performance in energy distribution and execution time. The framework demonstrated particular strength in maintaining efficiency under significantly increased computational loads, offering a robust solution for task allocation in edge computing systems. While some limitations exist in real-world applications, this work provides a strong foundation for optimizing resource utilization in smart city systems that integrate multiple computational tasks.https://ieeexplore.ieee.org/document/10870050/UAV-assisted edge computingsmart citytraffic managementmulti-objective optimizationsimulated annealingtask allocation |
| spellingShingle | Ahmed Shamil Mustafa Salman Yussof Nurul Asyikin Mohamed Radzi Multi-Objective Simulated Annealing for Efficient Task Allocation in UAV-Assisted Edge Computing for Smart City Traffic Management IEEE Access UAV-assisted edge computing smart city traffic management multi-objective optimization simulated annealing task allocation |
| title | Multi-Objective Simulated Annealing for Efficient Task Allocation in UAV-Assisted Edge Computing for Smart City Traffic Management |
| title_full | Multi-Objective Simulated Annealing for Efficient Task Allocation in UAV-Assisted Edge Computing for Smart City Traffic Management |
| title_fullStr | Multi-Objective Simulated Annealing for Efficient Task Allocation in UAV-Assisted Edge Computing for Smart City Traffic Management |
| title_full_unstemmed | Multi-Objective Simulated Annealing for Efficient Task Allocation in UAV-Assisted Edge Computing for Smart City Traffic Management |
| title_short | Multi-Objective Simulated Annealing for Efficient Task Allocation in UAV-Assisted Edge Computing for Smart City Traffic Management |
| title_sort | multi objective simulated annealing for efficient task allocation in uav assisted edge computing for smart city traffic management |
| topic | UAV-assisted edge computing smart city traffic management multi-objective optimization simulated annealing task allocation |
| url | https://ieeexplore.ieee.org/document/10870050/ |
| work_keys_str_mv | AT ahmedshamilmustafa multiobjectivesimulatedannealingforefficienttaskallocationinuavassistededgecomputingforsmartcitytrafficmanagement AT salmanyussof multiobjectivesimulatedannealingforefficienttaskallocationinuavassistededgecomputingforsmartcitytrafficmanagement AT nurulasyikinmohamedradzi multiobjectivesimulatedannealingforefficienttaskallocationinuavassistededgecomputingforsmartcitytrafficmanagement |