Preprint Article Version 1 This version is not peer-reviewed

Centralized Unmanned Aerial Vehicle (UAV) Mesh Networks Placement Scheme: A Multi-Objective Evolutionary Algorithm Approach

Version 1 : Received: 15 October 2018 / Approved: 16 October 2018 / Online: 16 October 2018 (06:16:42 CEST)

A peer-reviewed article of this Preprint also exists.

Sabino, S.; Horta, N.; Grilo, A. Centralized Unmanned Aerial Vehicle Mesh Network Placement Scheme: A Multi-Objective Evolutionary Algorithm Approach. Sensors 2018, 18, 4387. Sabino, S.; Horta, N.; Grilo, A. Centralized Unmanned Aerial Vehicle Mesh Network Placement Scheme: A Multi-Objective Evolutionary Algorithm Approach. Sensors 2018, 18, 4387.

Journal reference: Sensors 2018, 18, 4387
DOI: 10.3390/s18124387

Abstract

In the past, Unmanned Aerial Vehicles (UAVs) were mostly used in the military operations to prevent pilot losses. Nowadays, the fast technological evolution enables the production of a class of cost-effective UAVs which can service a plethora of public and civilian applications, specially when configured to work cooperatively to accomplish a task. However, designing a communication network among the UAVs is challenging task. In this article, we propose a centralized UAV placement strategy, where UAVs are used as flying access points forming a mesh network, providing connectivity to ground nodes deployed in a target area. The geographical placement of UAVs is optimized based on a Multi-Objective Evolutionary Algorithm (MOEA). The goal of the proposed scheme is to cover all ground nodes using a minimum number of UAVs, while maximizing the fulfillment of their data rate requirements. The UAVs can employ different data rates depending on the channel conditions, which are expressed by the Signal-to-Noise-Ratio (SNR). In this work, elitist Non-Dominated Sorting Genetic Algorithm II (NSGA-II) is used to find a set of optimal positions to place UAVs, given the positions of the ground nodes. We evaluate the trade-off between the number of UAVs used to cover the target area and the data rate requirement of the ground nodes. Simulation results show that the proposed algorithm can optimize the UAV placement given the requirement and the positions of the ground nodes in the geographical area.

Subject Areas

unmanned aerial vehicles; genetic algorithm; mesh networks; optimization; MOEA; NSGA-II

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