Cellular Phone Loaded Network Field Test Using Swarming Drones: Replacing Drive Test and Particle Swarm Optimization

Mirzaeinia, Amir; Hassanalian, Mostafa; Mirzaeinia, Mehdi

doi:10.2514/6.2019-3353

Cited by 8 publications

(1 citation statement)

References 16 publications

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“…Unmanned aerial vehicles (UAVs) or drones are the main mobile means to perform different functionalities [10][11][12][13][14][15]. For a decade, autonomous systems have received more attention than before, which has led to the invention of a wide range of mobile vehicles with various configurations [16].…”

Section: Introductionmentioning

confidence: 99%

Minimum-Cost Drone–Nest Matching through the Kuhn–Munkres Algorithm in Smart Cities: Energy Management and Efficiency Enhancement

Mirzaeinia

Hassanalian

2019

Aerospace

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The development of new concepts for smart cities and the application of drones in this area requires different architecture for the drones’ stations (nests) and their placement. Drones’ stations are designed to protect drones from hazards and utilize charging mechanisms such as solar cells to recharge them. Increasing the number of drones in smart cities makes it harder to find the optimum station for each drone to go to after performing its mission. In classic ordered technique, each drone returns to its preassigned station, which is shown to be not very efficient. Greedy and Kuhn‒Munkres (Hungarian) algorithms are used to match the drone to the best nesting station. Three different scenarios are investigated in this study; (1) drones with the same level of energy, (2) drones with different levels of energy, and (3) drones and stations with different levels of energy. The results show that an energy consumption reduction of 25‒80% can be achieved by applying the Kuhn‒Munkres and greedy algorithms in drone‒nest matching compared to preassigned stations. A graphical user interface is also designed to demonstrate drone‒station matching through the Kuhn‒Munkres and greedy algorithms.

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