Smart City Gnosys
Smart city article details
| Title | Multi-Objective Simulated Annealing For Efficient Task Allocation In Uav-Assisted Edge Computing For Smart City Traffic Management |
|---|---|
| ID_Doc | 38338 |
| Authors | Mustafa A.S.; Yussof S.; Asyikin Mohamed Radzi N. |
| Year | 2025 |
| Published | IEEE Access, 13 |
| DOI | http://dx.doi.org/10.1109/ACCESS.2025.3538676 |
| Abstract | 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. © 2013 IEEE. |
| Author Keywords | energy efficiency; multi-objective optimization; reinforcement learning; resource optimization; simulated annealing; smart city; task allocation; traffic management; UAV-assisted edge computing |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
16445  | 0.949 | Mustafa A.S.; Yussof S.; Radzi N.A.M. | Cpft-Mosa: A Comprehensive Parallel Fault-Tolerant Multi-Objective Simulated Annealing Framework For Uav-Assisted Edge Computing In Smart City Traffic Management | IEEE Access, 13 (2025) |
| 54447 | 0.889 | 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) |
| 21858 | 0.883 | 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) |
| 27955 | 0.882 | Chen Y.; Ding Y.; Hu Z.-Z.; Ren Z. | Geometrized Task Scheduling And Adaptive Resource Allocation For Large-Scale Edge Computing In Smart Cities | IEEE Internet of Things Journal (2025) |
| 38226 | 0.878 | 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) |
| 2802 | 0.877 | Liu Z.R. | A Multi-Joint Optimisation Method For Distributed Edge Computing Resources In Iot-Based Smart Cities | Journal of Grid Computing, 21, 4 (2023) |
| 37279 | 0.876 | Chanu A.D.; Shelar S.; Nath S.B. | Mobile Edge Computing For Efficient Vehicle Management In Smart City | 2025 IEEE 14th International Conference on Communication Systems and Network Technologies, CSNT 2025 (2025) |
| 21086 | 0.876 | 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) |
| 21768 | 0.876 | Rajagopal S.; Tripathi P.K.; Deshmukh M.; Choudari S.; Kumar A.; Long C.S. | Edge Computing- Smart Cities: Optimizing Data Processing & Resource Management In Urban Environments | Journal of Information Systems Engineering and Management, 10 (2025) |
| 40897 | 0.875 | Alhaizaey Y.; Singer J.; Michala A.L. | Optimizing Task Allocation For Edge Micro-Clusters In Smart Cities | Proceedings - 2021 IEEE 22nd International Symposium on a World of Wireless, Mobile and Multimedia Networks, WoWMoM 2021 (2021) |