Selection of Drone Locations to Act as Mobile Road-Side Units in An Intelligent Transportation System
DOI:
https://doi.org/10.31272/jeasd.2508Keywords:
Drone, Metaheuristic Algorithms, Skellam Distribution, Throughput, Vehicular Ad-Hoc NetworkAbstract
Vehicular ad hoc networks have been used successfully in intelligent transportation systems for smart cities in recent years. However, the main obstacles to the widespread use of automotive ad hoc networks are high packet collision rates, inflexible communication infrastructure, high routing overhead, unscalable networks, and inconsistent connectivity. Integrating vehicular ad hoc networks with uncrewed aerial vehicles (UAVs) offers a workable way around these obstacles. This paper presents a vehicular ad hoc network communication architecture supported by UAVs that serve as mobile roadside units (mRSUs), flying above the deployed region and providing communication services to the underlying coverage area. To determine the optimal locations for these drones to ensure continuous communication covering vehicles on intelligent transportation roads, three different optimization algorithms were used: Artificial Bee Colony (ABC), ant colony optimization (ACO), and particle swarm optimization (PSO). Skellam distribution has been used in optimization algorithms as a cost function. The impact of these new locations on throughput, packet delay, and packet loss was calculated. Among these three algorithms, the PSO algorithm showed higher throughput, lower packet delay and loss rates, and the best distribution of drone locations.
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Copyright (c) 2026 Huda Adel Talib, Mohammed Ali Tawfeeq (Author)
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