Improvement of Emergency Communication Systems Using Drones in 5G and Beyond for Safety Applications

Proposed Solution for damaged Macro BS with FANETs

Throughput v Number of Drones - PSO uses more drones effectively

Packet Dropping Rate v. number of Drones - PSO falters with >45 drones

Improvement of Emergency Communication Systems Using  Drones in 5G and Beyond for Safety Applications

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posted on 2023-11-20, 14:18 authored by A F M Shahen Shah

, Muhammet Ali Karabulut, Khaled Rabie

Drones are used for public safety missions because of their communication capabilities, unmanned mission, flexible deployment, and low cost. Recently, drone-assisted emergency communication systems in disasters have been developed where instead of a single large drone, flying ad hoc networks (FANETs) are proposed through clustering. Although cluster size has an impact on the proposed system's performance, no method is provided to effectively regulate cluster size. 

In this paper, optimum cluster size is obtained through two distinct meta-heuristic optimization algorithms - the Cuckoo Search Algorithm (CUCO) and the Particle Swarm Algorithm (PSO). Flowcharts and algorithms of CUCO and PSO are provided. A presentation of an analytical investigation based on the Markov chain model is provided. To further validate the analytical study, simulation results are presented. Simulation shows the improvement in terms of throughput and packet dropping rate (PDR).

The contributions of this study are summarized as follows:

drone-assisted emergency communication systemsin disasters are presented where clustering isused.

• The performance of the proposed system is improved through two meta-heuristic algorithms–CUCO and PSO. The cluster size is optimized by algorithms.
• The Markov chain model isused forananalyticalinvestigation.
• The analytical analyses are backedup by simulation results,whicharealsopresented.
•  A comparison with the previous study (1), CUCO, and PSO is presented.
• Simulation showstheimprovementintermsof throughputandpacketdroppingrate(PDR).

The remainder of the paper is structured as follows:

Section I introduction

Section II outlines the meta-heuristic optimization algorithms.
Section III presents the performance analysis.
Section IV discusses Simulation results. Lastly,
Section V provides the conclusions.
 

Email Address of Submitting Author

k.rabie@mmu.ac.uk

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