Algorithms to Maximize the Availability of Flying ad-hoc Networks

Catarro, Miguel do Couto Pereira

http://hdl.handle.net/10400.5/102283

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Authors

Catarro, Miguel do Couto Pereira

Advisor(s)

Sá, Alan Oliveira de

Pinto, Luís Ramos

Abstract(s)

Flying ad-hoc networks (FANETs) are networks comprised of highly mobile nodes called unmanned aerial vehicles (UAVs). Due to the ease of deployment of the UAVs, FANETs are very advantageous in scenarios where there is no functioning infrastructure, either because it has not been built yet or because natural or man-made disasters have destroyed it. However, these networks have the disadvantage of being energy-constrained - i.e., UAVs are battery-powered - meaning that they have a limited time of operation. Beyond that, to aggravate the situation, UAVs handling heavy traffic will become inoperative sooner than those handling less (hot-spot problem), limiting the operation time even more, as the network can’t operate without heavy traffic UAVs. To address the hot-spot problem, this dissertation proposes a UAV swapping strategy to maximize network Availability, prioritizing user-perceived Quality of Service (QoS) over network lifetime. For example, in a network of three UAVs, if UAV1 serves more users than the others and its battery runs low, exchanging positions with UAV2 (or UAV3) ensures UAV1’s area remains covered, minimizing disruption for users. In this work, Particle Swarm Optimization (PSO) and a novel algorithm, called RAMNA, are presented as viable options - delivering good solutions with low computational cost - to determine when and which UAVs should swap. Lastly, PSO and RAMNA performance were evaluated, with both algorithms showing an average Availability improvement, relative to doing nothing, of 62.5% and 61%, respectively, for all topologies tested. Furthermore, this work showed that by using RAMNA instead of a state-of-the-art algorithm - named SwapLevel - the network Availability is improved, on average, by 14.8 percentage points (pp), achieving an improvement up to 31.8 pp. Additionally, every result shown during this work was supported by energy models developed using real-world data collected from UAVs in the field.