2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 16 , Issue 1 , PP: 189-198, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Mohammed Ahmed Jubair 1 , Shaima Miqdad Mohamed Najeeb 2 , Kifaa Hadi Thanoon 3 , Mujahid Hamood Hilal Alzakwani 4 * , Fatima Hashim Abbas 5 , Rabei Raad Ali 6
Doi: https://doi.org/10.54216/JISIoT.160116
Nowadays, Vehicular communication is used in intelligent transmission applications. The number of vehicles used in a particular region has numerously increased energy consumption, computation delay, and computation overhead. In this paper, Multi-Objective Optimization in Satellite Assisted UAVs (MO-SAUVs) is proposed under an improved Ant Colony Optimization (IACO) algorithm. The procedures that are considered for the process of MO are optimal logistics distribution, path prediction-based pheromone deposition, and evaporation. Using this method, effective region selection for the UAVs is performed which leads to improving the network energy efficiency by decreasing energy consumption and delay. The simulation is performed in NS2 and the proposed MO-SAUAVs method is compared with the TA-SAUAVs method and PL-SAUAVs method according to different parameters. The results show that the proposed MO-SAUAVs method achieves lower computation delay (70ms to 110ms), higher energy efficiency (6% to 16%), lower energy consumption (7% to 14%), and packets lower computation overhead (500 packets to 700) when we were compared with TA-SAUAVs and PL-SAUAVs.
Unmanned Aerial Vehicles (UAVs) , Vehicular communication , Ant Colony Optimization (ACO) , Multi-objective Optimization
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