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Multi-metric optimization with a new metaheuristic approach developed for 3D deployment of multiple drone-BSs

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Abstract

The use of Unmanned Aerial Vehicles (UAVs) as mobile Base Stations (BSs) in wireless communication has been claimed to be an effective technique for services planned in next generation cellular networks (5G and 5G+). Effective 3D deployments of BS mounted on drone (drone-BS), in the Area of Interest (AoI) may increase the Quality-of-Service (QoS) of wireless communication in the Internet of Things (IoT). In this article, we solve a dynamic deployment problem (location optimization) of multiple drone-BSs that is NP-hard according to the Air-to-Ground (ATG) model using optimization algorithms with the aim of ensuring optimal rates of coverage of the ground users (user equipments) assumed to be located at certain distance intervals in a defined urban environment. In this regard, we have developed Optimal Fitness-value Search Approaches at Continuous-data range (OFSAC-PSO and OFSAC-EML) and Optimal Fitness-value Search Approaches at Discrete-data range (OFSAD-PSO and OFSAD-EML), which are both based on Particle Swarm Optimization (PSO) and Electromagnetism-Like (EML) algorithm. In the study where Monte-Carlo simulations are run, we have created different scenarios for uniform and non-uniform distributions of user equipments (UEs). We have made comparisons according to the following metrics for the approaches that we have developed: the fitness function values of drone-BSs, AoI coverage rates, the number of iteration of simulations, drone-BS altitudes, optimal 2D coverage map, and the 3D optimal locations of drone-BSs. Our simulation results show that, based on the compared metrics, OFSAC developed with PSO-based is optimal compared to other approaches.

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  1. Department of Computer Engineering, Faculty of Engineering, Van Yuzuncu Yil University, Van, 65080, Turkey

    Recep Ozdag

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Ozdag, R. Multi-metric optimization with a new metaheuristic approach developed for 3D deployment of multiple drone-BSs. Peer-to-Peer Netw. Appl. 15, 1535–1561 (2022). https://doi.org/10.1007/s12083-022-01298-4

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