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Smart city article details
| Title | Multi-Joint Symmetric Optimization Approach For Unmanned Aerial Vehicle Assisted Edge Computing Resources In Internet Of Things-Based Smart Cities |
|---|---|
| ID_Doc | 38226 |
| Authors | Chelladurai A.; Deepak M.D.; Falkowski-Gilski P.; Bidare Divakarachari P. |
| Year | 2025 |
| Published | Symmetry, 17, 4 |
| DOI | http://dx.doi.org/10.3390/sym17040574 |
| Abstract | Smart cities are equipped with a vast number of IoT devices, which help to collect and analyze data to improve the quality of life for urban people by offering a sustainable and connected environment. However, the rapid growth of IoT systems has issues related to the Quality of Service (QoS) and allocation of limited resources in IoT-based smart cities. The cloud in the IoT system also faces issues related to higher consumption of energy and extended latency. This research presents an effort to overcome these challenges by introducing opposition-based learning incorporated into Golden Jackal Optimization (OL-GJO) to assign distributed edge capabilities to diminish the energy consumption and delay in IoT-based smart cities. In the context of IoT-based smart cities, a three-layered architecture is developed, comprising the IoT system, the Unmanned Aerial Vehicle (UAV)-assisted edge layer, and the cloud layer. Moreover, the controller positioned at the edge of UAV helps determine the number of tasks. The proposed approach, based on opposition-based learning, is put forth to offer effective computing resources for delay-sensitive tasks. The multi-joint symmetric optimization uses OL-GJO, where opposition-based learning confirms a symmetric search process is employed, improving the task scheduling process in UAV-assisted edge computing. The experimental findings exhibit that OL-GJO performs in an effective manner while offloading resources. For 200 tasks, the delay experienced by OL-GJO is 2.95 ms, whereas Multi Particle Swarm Optimization (M-PSO) and the auction-based approach experience delays of 7.19 ms and 3.78 ms, respectively. |
| Author Keywords | edge computing; golden jackal optimization; internet of things; opposition-based learning; smart cities; task offloading |
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